mirror of https://github.com/FlowiseAI/Flowise.git
Compare commits
No commits in common. "main" and "flowise-ui@1.7.2" have entirely different histories.
main
...
flowise-ui
|
|
@ -5,6 +5,3 @@ build
|
||||||
**/node_modules
|
**/node_modules
|
||||||
**/build
|
**/build
|
||||||
**/dist
|
**/dist
|
||||||
|
|
||||||
packages/server/.env
|
|
||||||
packages/ui/.env
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,2 @@
|
||||||
|
node_modules
|
||||||
|
dist
|
||||||
|
|
@ -11,14 +11,14 @@ jobs:
|
||||||
permissions:
|
permissions:
|
||||||
contents: write
|
contents: write
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v3
|
||||||
- name: Show PR info
|
- name: Show PR info
|
||||||
env:
|
env:
|
||||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||||
run: |
|
run: |
|
||||||
echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
|
echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
|
||||||
- name: Repository Dispatch
|
- name: Repository Dispatch
|
||||||
uses: peter-evans/repository-dispatch@v3
|
uses: peter-evans/repository-dispatch@v2
|
||||||
with:
|
with:
|
||||||
token: ${{ secrets.AUTOSYNC_TOKEN }}
|
token: ${{ secrets.AUTOSYNC_TOKEN }}
|
||||||
repository: ${{ secrets.AUTOSYNC_CH_URL }}
|
repository: ${{ secrets.AUTOSYNC_CH_URL }}
|
||||||
|
|
@ -28,6 +28,6 @@ jobs:
|
||||||
"ref": "${{ github.ref }}",
|
"ref": "${{ github.ref }}",
|
||||||
"prNumber": "${{ github.event.pull_request.number }}",
|
"prNumber": "${{ github.event.pull_request.number }}",
|
||||||
"prTitle": "${{ github.event.pull_request.title }}",
|
"prTitle": "${{ github.event.pull_request.title }}",
|
||||||
"prDescription": "",
|
"prDescription": "${{ toJSON(github.event.pull_request.description) }}",
|
||||||
"sha": "${{ github.sha }}"
|
"sha": "${{ github.sha }}"
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -27,7 +27,6 @@ jobs:
|
||||||
with:
|
with:
|
||||||
node-version: ${{ matrix.node-version }}
|
node-version: ${{ matrix.node-version }}
|
||||||
check-latest: false
|
check-latest: false
|
||||||
cache: 'pnpm'
|
|
||||||
- run: npm i -g pnpm
|
- run: npm i -g pnpm
|
||||||
- run: pnpm install
|
- run: pnpm install
|
||||||
- run: ./node_modules/.bin/cypress install
|
- run: ./node_modules/.bin/cypress install
|
||||||
|
|
|
||||||
|
|
@ -11,9 +11,6 @@
|
||||||
**/logs
|
**/logs
|
||||||
**/*.log
|
**/*.log
|
||||||
|
|
||||||
## pnpm
|
|
||||||
.pnpm-store/
|
|
||||||
|
|
||||||
## build
|
## build
|
||||||
**/dist
|
**/dist
|
||||||
**/build
|
**/build
|
||||||
|
|
@ -47,60 +44,3 @@
|
||||||
|
|
||||||
## compressed
|
## compressed
|
||||||
**/*.tgz
|
**/*.tgz
|
||||||
|
|
||||||
## vscode
|
|
||||||
.vscode/*
|
|
||||||
!.vscode/settings.json
|
|
||||||
!.vscode/tasks.json
|
|
||||||
!.vscode/launch.json
|
|
||||||
!.vscode/extensions.json
|
|
||||||
!.vscode/*.code-snippets
|
|
||||||
|
|
||||||
# Local History for Visual Studio Code
|
|
||||||
.history/
|
|
||||||
|
|
||||||
## other keys
|
|
||||||
*.key
|
|
||||||
*.keys
|
|
||||||
*.priv
|
|
||||||
*.rsa
|
|
||||||
*.key.json
|
|
||||||
|
|
||||||
## ssh keys
|
|
||||||
*.ssh
|
|
||||||
*.ssh-key
|
|
||||||
.key-mrc
|
|
||||||
|
|
||||||
## Certificate Authority
|
|
||||||
*.ca
|
|
||||||
|
|
||||||
## Certificate
|
|
||||||
*.crt
|
|
||||||
|
|
||||||
## Certificate Sign Request
|
|
||||||
*.csr
|
|
||||||
|
|
||||||
## Certificate
|
|
||||||
*.der
|
|
||||||
|
|
||||||
## Key database file
|
|
||||||
*.kdb
|
|
||||||
|
|
||||||
## OSCP request data
|
|
||||||
*.org
|
|
||||||
|
|
||||||
## PKCS #12
|
|
||||||
*.p12
|
|
||||||
|
|
||||||
## PEM-encoded certificate data
|
|
||||||
*.pem
|
|
||||||
|
|
||||||
## Random number seed
|
|
||||||
*.rnd
|
|
||||||
|
|
||||||
## SSLeay data
|
|
||||||
*.ssleay
|
|
||||||
|
|
||||||
## S/MIME message
|
|
||||||
*.smime
|
|
||||||
*.vsix
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
**/node_modules
|
||||||
|
**/dist
|
||||||
|
**/build
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
module.exports = {
|
||||||
|
printWidth: 140,
|
||||||
|
singleQuote: true,
|
||||||
|
jsxSingleQuote: true,
|
||||||
|
trailingComma: 'none',
|
||||||
|
tabWidth: 4,
|
||||||
|
semi: false,
|
||||||
|
endOfLine: 'auto'
|
||||||
|
}
|
||||||
|
|
@ -2,7 +2,7 @@
|
||||||
|
|
||||||
# 贡献者公约行为准则
|
# 贡献者公约行为准则
|
||||||
|
|
||||||
[English](../CODE_OF_CONDUCT.md) | 中文
|
[English](<./CODE_OF_CONDUCT.md>) | 中文
|
||||||
|
|
||||||
## 我们的承诺
|
## 我们的承诺
|
||||||
|
|
||||||
|
|
@ -44,6 +44,6 @@
|
||||||
|
|
||||||
## 归属
|
## 归属
|
||||||
|
|
||||||
该行为准则的内容来自于[贡献者公约](http://contributor-covenant.org/)1.4 版，可在[http://contributor-covenant.org/version/1/4](http://contributor-covenant.org/version/1/4)上获取。
|
该行为准则的内容来自于[贡献者公约](http://contributor-covenant.org/)1.4版，可在[http://contributor-covenant.org/version/1/4](http://contributor-covenant.org/version/1/4)上获取。
|
||||||
|
|
||||||
[主页]: http://contributor-covenant.org
|
[主页]: http://contributor-covenant.org
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
# Contributor Covenant Code of Conduct
|
# Contributor Covenant Code of Conduct
|
||||||
|
|
||||||
English | [中文](./i18n/CODE_OF_CONDUCT-ZH.md)
|
English | [中文](<./CODE_OF_CONDUCT-ZH.md>)
|
||||||
|
|
||||||
## Our Pledge
|
## Our Pledge
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -2,7 +2,7 @@
|
||||||
|
|
||||||
# 贡献给 Flowise
|
# 贡献给 Flowise
|
||||||
|
|
||||||
[English](../CONTRIBUTING.md) | 中文
|
[English](./CONTRIBUTING.md) | 中文
|
||||||
|
|
||||||
我们欢迎任何形式的贡献。
|
我们欢迎任何形式的贡献。
|
||||||
|
|
||||||
|
|
@ -118,36 +118,35 @@ Flowise 在一个单一的单体存储库中有 3 个不同的模块。
|
||||||
|
|
||||||
Flowise 支持不同的环境变量来配置您的实例。您可以在 `packages/server` 文件夹中的 `.env` 文件中指定以下变量。阅读[更多信息](https://docs.flowiseai.com/environment-variables)
|
Flowise 支持不同的环境变量来配置您的实例。您可以在 `packages/server` 文件夹中的 `.env` 文件中指定以下变量。阅读[更多信息](https://docs.flowiseai.com/environment-variables)
|
||||||
|
|
||||||
| 变量名 | 描述 | 类型 | 默认值 |
|
| 变量名 | 描述 | 类型 | 默认值 |
|
||||||
| ---------------------------- | -------------------------------------------------------------------- | ----------------------------------------------- | ----------------------------------- |
|
| ---------------------------- | ------------------------------------------------------- | ----------------------------------------------- | ----------------------------------- |
|
||||||
| PORT | Flowise 运行的 HTTP 端口 | 数字 | 3000 |
|
| PORT | Flowise 运行的 HTTP 端口 | 数字 | 3000 |
|
||||||
| FLOWISE_USERNAME | 登录用户名 | 字符串 | |
|
| FLOWISE_USERNAME | 登录用户名 | 字符串 | |
|
||||||
| FLOWISE_PASSWORD | 登录密码 | 字符串 | |
|
| FLOWISE_PASSWORD | 登录密码 | 字符串 | |
|
||||||
| FLOWISE_FILE_SIZE_LIMIT | 上传文件大小限制 | 字符串 | 50mb |
|
| FLOWISE_FILE_SIZE_LIMIT | 上传文件大小限制 | 字符串 | 50mb |
|
||||||
| DISABLE_CHATFLOW_REUSE | 强制为每次调用创建一个新的 ChatFlow，而不是重用缓存中的现有 ChatFlow | 布尔值 | |
|
| DEBUG | 打印组件的日志 | 布尔值 | |
|
||||||
| DEBUG | 打印组件的日志 | 布尔值 | |
|
| LOG_PATH | 存储日志文件的位置 | 字符串 | `your-path/Flowise/logs` |
|
||||||
| LOG_PATH | 存储日志文件的位置 | 字符串 | `your-path/Flowise/logs` |
|
| LOG_LEVEL | 日志的不同级别 | 枚举字符串: `error`, `info`, `verbose`, `debug` | `info` |
|
||||||
| LOG_LEVEL | 日志的不同级别 | 枚举字符串: `error`, `info`, `verbose`, `debug` | `info` |
|
| APIKEY_PATH | 存储 API 密钥的位置 | 字符串 | `your-path/Flowise/packages/server` |
|
||||||
| APIKEY_PATH | 存储 API 密钥的位置 | 字符串 | `your-path/Flowise/packages/server` |
|
| TOOL_FUNCTION_BUILTIN_DEP | 用于工具函数的 NodeJS 内置模块 | 字符串 | |
|
||||||
| TOOL_FUNCTION_BUILTIN_DEP | 用于工具函数的 NodeJS 内置模块 | 字符串 | |
|
| TOOL_FUNCTION_EXTERNAL_DEP | 用于工具函数的外部模块 | 字符串 | |
|
||||||
| TOOL_FUNCTION_EXTERNAL_DEP | 用于工具函数的外部模块 | 字符串 | |
|
| DATABASE_TYPE | 存储 flowise 数据的数据库类型 | 枚举字符串: `sqlite`, `mysql`, `postgres` | `sqlite` |
|
||||||
| DATABASE_TYPE | 存储 flowise 数据的数据库类型 | 枚举字符串: `sqlite`, `mysql`, `postgres` | `sqlite` |
|
| DATABASE_PATH | 数据库保存的位置（当 DATABASE_TYPE 是 sqlite 时） | 字符串 | `your-home-dir/.flowise` |
|
||||||
| DATABASE_PATH | 数据库保存的位置（当 DATABASE_TYPE 是 sqlite 时） | 字符串 | `your-home-dir/.flowise` |
|
| DATABASE_HOST | 主机 URL 或 IP 地址（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
||||||
| DATABASE_HOST | 主机 URL 或 IP 地址（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
| DATABASE_PORT | 数据库端口（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
||||||
| DATABASE_PORT | 数据库端口（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
| DATABASE_USERNAME | 数据库用户名（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
||||||
| DATABASE_USERNAME | 数据库用户名（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
| DATABASE_PASSWORD | 数据库密码（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
||||||
| DATABASE_PASSWORD | 数据库密码（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
| DATABASE_NAME | 数据库名称（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
||||||
| DATABASE_NAME | 数据库名称（当 DATABASE_TYPE 不是 sqlite 时） | 字符串 | |
|
| SECRETKEY_PATH | 保存加密密钥（用于加密/解密凭据）的位置 | 字符串 | `your-path/Flowise/packages/server` |
|
||||||
| SECRETKEY_PATH | 保存加密密钥（用于加密/解密凭据）的位置 | 字符串 | `your-path/Flowise/packages/server` |
|
| FLOWISE_SECRETKEY_OVERWRITE | 加密密钥用于替代存储在 SECRETKEY_PATH 中的密钥 | 字符串 |
|
||||||
| FLOWISE_SECRETKEY_OVERWRITE | 加密密钥用于替代存储在 SECRETKEY_PATH 中的密钥 | 字符串 |
|
| DISABLE_FLOWISE_TELEMETRY | 关闭遥测 | 字符串 |
|
||||||
| DISABLE_FLOWISE_TELEMETRY | 关闭遥测 | 字符串 |
|
| MODEL_LIST_CONFIG_JSON | 加载模型的位置 | 字符 | `/your_model_list_config_file_path` |
|
||||||
| MODEL_LIST_CONFIG_JSON | 加载模型的位置 | 字符 | `/your_model_list_config_file_path` |
|
| STORAGE_TYPE | 上传文件的存储类型 | 枚举字符串: `local`, `s3` | `local` |
|
||||||
| STORAGE_TYPE | 上传文件的存储类型 | 枚举字符串: `local`, `s3` | `local` |
|
| BLOB_STORAGE_PATH | 上传文件存储的本地文件夹路径, 当`STORAGE_TYPE`是`local` | 字符串 | `your-home-dir/.flowise/storage` |
|
||||||
| BLOB_STORAGE_PATH | 上传文件存储的本地文件夹路径, 当`STORAGE_TYPE`是`local` | 字符串 | `your-home-dir/.flowise/storage` |
|
| S3_STORAGE_BUCKET_NAME | S3 存储文件夹路径, 当`STORAGE_TYPE`是`s3` | 字符串 | |
|
||||||
| S3_STORAGE_BUCKET_NAME | S3 存储文件夹路径, 当`STORAGE_TYPE`是`s3` | 字符串 | |
|
| S3_STORAGE_ACCESS_KEY_ID | AWS 访问密钥 (Access Key) | 字符串 | |
|
||||||
| S3_STORAGE_ACCESS_KEY_ID | AWS 访问密钥 (Access Key) | 字符串 | |
|
| S3_STORAGE_SECRET_ACCESS_KEY | AWS 密钥 (Secret Key) | 字符串 | |
|
||||||
| S3_STORAGE_SECRET_ACCESS_KEY | AWS 密钥 (Secret Key) | 字符串 | |
|
| S3_STORAGE_REGION | S3 存储地区 | 字符串 | |
|
||||||
| S3_STORAGE_REGION | S3 存储地区 | 字符串 | |
|
|
||||||
|
|
||||||
您也可以在使用 `npx` 时指定环境变量。例如：
|
您也可以在使用 `npx` 时指定环境变量。例如：
|
||||||
|
|
||||||
|
|
@ -2,7 +2,7 @@
|
||||||
|
|
||||||
# Contributing to Flowise
|
# Contributing to Flowise
|
||||||
|
|
||||||
English | [中文](./i18n/CONTRIBUTING-ZH.md)
|
English | [中文](./CONTRIBUTING-ZH.md)
|
||||||
|
|
||||||
We appreciate any form of contributions.
|
We appreciate any form of contributions.
|
||||||
|
|
||||||
|
|
@ -44,7 +44,7 @@ Flowise has 3 different modules in a single mono repository.
|
||||||
|
|
||||||
#### Prerequisite
|
#### Prerequisite
|
||||||
|
|
||||||
- Install [PNPM](https://pnpm.io/installation). The project is configured to use pnpm v9.
|
- Install [PNPM](https://pnpm.io/installation)
|
||||||
```bash
|
```bash
|
||||||
npm i -g pnpm
|
npm i -g pnpm
|
||||||
```
|
```
|
||||||
|
|
@ -120,41 +120,39 @@ Flowise has 3 different modules in a single mono repository.
|
||||||
|
|
||||||
Flowise support different environment variables to configure your instance. You can specify the following variables in the `.env` file inside `packages/server` folder. Read [more](https://docs.flowiseai.com/environment-variables)
|
Flowise support different environment variables to configure your instance. You can specify the following variables in the `.env` file inside `packages/server` folder. Read [more](https://docs.flowiseai.com/environment-variables)
|
||||||
|
|
||||||
| Variable | Description | Type | Default |
|
| Variable | Description | Type | Default |
|
||||||
| ---------------------------- | ----------------------------------------------------------------------------------------------- | ------------------------------------------------ | ----------------------------------- |
|
| ---------------------------- | -------------------------------------------------------------------------------- | ------------------------------------------------ | ----------------------------------- |
|
||||||
| PORT | The HTTP port Flowise runs on | Number | 3000 |
|
| PORT | The HTTP port Flowise runs on | Number | 3000 |
|
||||||
| CORS_ORIGINS | The allowed origins for all cross-origin HTTP calls | String | |
|
| CORS_ORIGINS | The allowed origins for all cross-origin HTTP calls | String | |
|
||||||
| IFRAME_ORIGINS | The allowed origins for iframe src embedding | String | |
|
| IFRAME_ORIGINS | The allowed origins for iframe src embedding | String | |
|
||||||
| FLOWISE_USERNAME | Username to login | String | |
|
| FLOWISE_USERNAME | Username to login | String | |
|
||||||
| FLOWISE_PASSWORD | Password to login | String | |
|
| FLOWISE_PASSWORD | Password to login | String | |
|
||||||
| FLOWISE_FILE_SIZE_LIMIT | Upload File Size Limit | String | 50mb |
|
| FLOWISE_FILE_SIZE_LIMIT | Upload File Size Limit | String | 50mb |
|
||||||
| DISABLE_CHATFLOW_REUSE | Forces the creation of a new ChatFlow for each call instead of reusing existing ones from cache | Boolean | |
|
| DEBUG | Print logs from components | Boolean | |
|
||||||
| DEBUG | Print logs from components | Boolean | |
|
| LOG_PATH | Location where log files are stored | String | `your-path/Flowise/logs` |
|
||||||
| LOG_PATH | Location where log files are stored | String | `your-path/Flowise/logs` |
|
| LOG_LEVEL | Different levels of logs | Enum String: `error`, `info`, `verbose`, `debug` | `info` |
|
||||||
| LOG_LEVEL | Different levels of logs | Enum String: `error`, `info`, `verbose`, `debug` | `info` |
|
| APIKEY_PATH | Location where api keys are saved | String | `your-path/Flowise/packages/server` |
|
||||||
| LOG_JSON_SPACES | Spaces to beautify JSON logs | | 2 |
|
| TOOL_FUNCTION_BUILTIN_DEP | NodeJS built-in modules to be used for Tool Function | String | |
|
||||||
| APIKEY_PATH | Location where api keys are saved | String | `your-path/Flowise/packages/server` |
|
| TOOL_FUNCTION_EXTERNAL_DEP | External modules to be used for Tool Function | String | |
|
||||||
| TOOL_FUNCTION_BUILTIN_DEP | NodeJS built-in modules to be used for Tool Function | String | |
|
| DATABASE_TYPE | Type of database to store the flowise data | Enum String: `sqlite`, `mysql`, `postgres` | `sqlite` |
|
||||||
| TOOL_FUNCTION_EXTERNAL_DEP | External modules to be used for Tool Function | String | |
|
| DATABASE_PATH | Location where database is saved (When DATABASE_TYPE is sqlite) | String | `your-home-dir/.flowise` |
|
||||||
| DATABASE_TYPE | Type of database to store the flowise data | Enum String: `sqlite`, `mysql`, `postgres` | `sqlite` |
|
| DATABASE_HOST | Host URL or IP address (When DATABASE_TYPE is not sqlite) | String | |
|
||||||
| DATABASE_PATH | Location where database is saved (When DATABASE_TYPE is sqlite) | String | `your-home-dir/.flowise` |
|
| DATABASE_PORT | Database port (When DATABASE_TYPE is not sqlite) | String | |
|
||||||
| DATABASE_HOST | Host URL or IP address (When DATABASE_TYPE is not sqlite) | String | |
|
| DATABASE_USER | Database username (When DATABASE_TYPE is not sqlite) | String | |
|
||||||
| DATABASE_PORT | Database port (When DATABASE_TYPE is not sqlite) | String | |
|
| DATABASE_PASSWORD | Database password (When DATABASE_TYPE is not sqlite) | String | |
|
||||||
| DATABASE_USER | Database username (When DATABASE_TYPE is not sqlite) | String | |
|
| DATABASE_NAME | Database name (When DATABASE_TYPE is not sqlite) | String | |
|
||||||
| DATABASE_PASSWORD | Database password (When DATABASE_TYPE is not sqlite) | String | |
|
| DATABASE_SSL_KEY_BASE64 | Database SSL client cert in base64 (takes priority over DATABASE_SSL) | Boolean | false |
|
||||||
| DATABASE_NAME | Database name (When DATABASE_TYPE is not sqlite) | String | |
|
| DATABASE_SSL | Database connection overssl (When DATABASE_TYPE is postgre) | Boolean | false |
|
||||||
| DATABASE_SSL_KEY_BASE64 | Database SSL client cert in base64 (takes priority over DATABASE_SSL) | Boolean | false |
|
| SECRETKEY_PATH | Location where encryption key (used to encrypt/decrypt credentials) is saved | String | `your-path/Flowise/packages/server` |
|
||||||
| DATABASE_SSL | Database connection overssl (When DATABASE_TYPE is postgre) | Boolean | false |
|
| FLOWISE_SECRETKEY_OVERWRITE | Encryption key to be used instead of the key stored in SECRETKEY_PATH | String |
|
||||||
| SECRETKEY_PATH | Location where encryption key (used to encrypt/decrypt credentials) is saved | String | `your-path/Flowise/packages/server` |
|
| DISABLE_FLOWISE_TELEMETRY | Turn off telemetry | Boolean |
|
||||||
| FLOWISE_SECRETKEY_OVERWRITE | Encryption key to be used instead of the key stored in SECRETKEY_PATH | String |
|
| MODEL_LIST_CONFIG_JSON | File path to load list of models from your local config file | String | `/your_model_list_config_file_path` |
|
||||||
| DISABLE_FLOWISE_TELEMETRY | Turn off telemetry | Boolean |
|
| STORAGE_TYPE | Type of storage for uploaded files. default is `local` | Enum String: `s3`, `local` | `local` |
|
||||||
| MODEL_LIST_CONFIG_JSON | File path to load list of models from your local config file | String | `/your_model_list_config_file_path` |
|
| BLOB_STORAGE_PATH | Local folder path where uploaded files are stored when `STORAGE_TYPE` is `local` | String | `your-home-dir/.flowise/storage` |
|
||||||
| STORAGE_TYPE | Type of storage for uploaded files. default is `local` | Enum String: `s3`, `local` | `local` |
|
| S3_STORAGE_BUCKET_NAME | Bucket name to hold the uploaded files when `STORAGE_TYPE` is `s3` | String | |
|
||||||
| BLOB_STORAGE_PATH | Local folder path where uploaded files are stored when `STORAGE_TYPE` is `local` | String | `your-home-dir/.flowise/storage` |
|
| S3_STORAGE_ACCESS_KEY_ID | AWS Access Key | String | |
|
||||||
| S3_STORAGE_BUCKET_NAME | Bucket name to hold the uploaded files when `STORAGE_TYPE` is `s3` | String | |
|
| S3_STORAGE_SECRET_ACCESS_KEY | AWS Secret Key | String | |
|
||||||
| S3_STORAGE_ACCESS_KEY_ID | AWS Access Key | String | |
|
| S3_STORAGE_REGION | Region for S3 bucket | String | |
|
||||||
| S3_STORAGE_SECRET_ACCESS_KEY | AWS Secret Key | String | |
|
|
||||||
| S3_STORAGE_REGION | Region for S3 bucket | String | |
|
|
||||||
|
|
||||||
You can also specify the env variables when using `npx`. For example:
|
You can also specify the env variables when using `npx`. For example:
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -10,7 +10,7 @@
|
||||||
[](https://star-history.com/#FlowiseAI/Flowise)
|
[](https://star-history.com/#FlowiseAI/Flowise)
|
||||||
[](https://github.com/FlowiseAI/Flowise/fork)
|
[](https://github.com/FlowiseAI/Flowise/fork)
|
||||||
|
|
||||||
[English](../README.md) | [中文](./README-ZH.md) | 日本語 | [한국어](./README-KR.md)
|
[English](./README.md) | [中文](./README-ZH.md) | 日本語 | [한국어](./README-KR.md)
|
||||||
|
|
||||||
<h3>ドラッグ＆ドロップでカスタマイズした LLM フローを構築できる UI</h3>
|
<h3>ドラッグ＆ドロップでカスタマイズした LLM フローを構築できる UI</h3>
|
||||||
<a href="https://github.com/FlowiseAI/Flowise">
|
<a href="https://github.com/FlowiseAI/Flowise">
|
||||||
|
|
@ -44,9 +44,9 @@
|
||||||
|
|
||||||
1. プロジェクトのルートにある `docker` フォルダに移動する
|
1. プロジェクトのルートにある `docker` フォルダに移動する
|
||||||
2. `.env.example` ファイルをコピーして同じ場所に貼り付け、名前を `.env` に変更する
|
2. `.env.example` ファイルをコピーして同じ場所に貼り付け、名前を `.env` に変更する
|
||||||
3. `docker compose up -d`
|
3. `docker-compose up -d`
|
||||||
4. [http://localhost:3000](http://localhost:3000) を開く
|
4. [http://localhost:3000](http://localhost:3000) を開く
|
||||||
5. コンテナを停止するには、`docker compose stop` を使用します
|
5. コンテナを停止するには、`docker-compose stop` を使用します
|
||||||
|
|
||||||
### Docker Image
|
### Docker Image
|
||||||
|
|
||||||
|
|
@ -10,7 +10,7 @@
|
||||||
[](https://star-history.com/#FlowiseAI/Flowise)
|
[](https://star-history.com/#FlowiseAI/Flowise)
|
||||||
[](https://github.com/FlowiseAI/Flowise/fork)
|
[](https://github.com/FlowiseAI/Flowise/fork)
|
||||||
|
|
||||||
[English](../README.md) | [中文](./README-ZH.md) | [日本語](./README-JA.md) | 한국어
|
English | [中文](./README-ZH.md) | [日本語](./README-JA.md) | 한국어
|
||||||
|
|
||||||
<h3>드래그 앤 드롭 UI로 맞춤형 LLM 플로우 구축하기</h3>
|
<h3>드래그 앤 드롭 UI로 맞춤형 LLM 플로우 구축하기</h3>
|
||||||
<a href="https://github.com/FlowiseAI/Flowise">
|
<a href="https://github.com/FlowiseAI/Flowise">
|
||||||
|
|
@ -44,9 +44,9 @@
|
||||||
|
|
||||||
1. 프로젝트의 최상위(root) 디렉토리에 있는 `docker` 폴더로 이동하세요.
|
1. 프로젝트의 최상위(root) 디렉토리에 있는 `docker` 폴더로 이동하세요.
|
||||||
2. `.env.example` 파일을 복사한 후, 같은 경로에 붙여넣기 한 다음, `.env`로 이름을 변경합니다.
|
2. `.env.example` 파일을 복사한 후, 같은 경로에 붙여넣기 한 다음, `.env`로 이름을 변경합니다.
|
||||||
3. `docker compose up -d` 실행
|
3. `docker-compose up -d` 실행
|
||||||
4. [http://localhost:3000](http://localhost:3000) URL 열기
|
4. [http://localhost:3000](http://localhost:3000) URL 열기
|
||||||
5. `docker compose stop` 명령어를 통해 컨테이너를 종료시킬 수 있습니다.
|
5. `docker-compose stop` 명령어를 통해 컨테이너를 종료시킬 수 있습니다.
|
||||||
|
|
||||||
### 도커 이미지 활용
|
### 도커 이미지 활용
|
||||||
|
|
||||||
|
|
@ -10,7 +10,7 @@
|
||||||
[](https://star-history.com/#FlowiseAI/Flowise)
|
[](https://star-history.com/#FlowiseAI/Flowise)
|
||||||
[](https://github.com/FlowiseAI/Flowise/fork)
|
[](https://github.com/FlowiseAI/Flowise/fork)
|
||||||
|
|
||||||
[English](../README.md) | 中文 | [日本語](./README-JA.md) | [한국어](./README-KR.md)
|
[English](./README.md) | 中文 | [日本語](./README-JA.md) | [한국어](./README-KR.md)
|
||||||
|
|
||||||
<h3>拖放界面构建定制化的LLM流程</h3>
|
<h3>拖放界面构建定制化的LLM流程</h3>
|
||||||
<a href="https://github.com/FlowiseAI/Flowise">
|
<a href="https://github.com/FlowiseAI/Flowise">
|
||||||
|
|
@ -44,9 +44,9 @@
|
||||||
|
|
||||||
1. 进入项目根目录下的 `docker` 文件夹
|
1. 进入项目根目录下的 `docker` 文件夹
|
||||||
2. 创建 `.env` 文件并指定 `PORT`（参考 `.env.example`）
|
2. 创建 `.env` 文件并指定 `PORT`（参考 `.env.example`）
|
||||||
3. 运行 `docker compose up -d`
|
3. 运行 `docker-compose up -d`
|
||||||
4. 打开 [http://localhost:3000](http://localhost:3000)
|
4. 打开 [http://localhost:3000](http://localhost:3000)
|
||||||
5. 可以通过 `docker compose stop` 停止容器
|
5. 可以通过 `docker-compose stop` 停止容器
|
||||||
|
|
||||||
### Docker 镜像
|
### Docker 镜像
|
||||||
|
|
||||||
|
|
@ -10,7 +10,7 @@
|
||||||
[](https://star-history.com/#FlowiseAI/Flowise)
|
[](https://star-history.com/#FlowiseAI/Flowise)
|
||||||
[](https://github.com/FlowiseAI/Flowise/fork)
|
[](https://github.com/FlowiseAI/Flowise/fork)
|
||||||
|
|
||||||
English | [中文](./i18n/README-ZH.md) | [日本語](./i18n/README-JA.md) | [한국어](./i18n/README-KR.md)
|
English | [中文](./README-ZH.md) | [日本語](./README-JA.md) | [한국어](./README-KR.md)
|
||||||
|
|
||||||
<h3>Drag & drop UI to build your customized LLM flow</h3>
|
<h3>Drag & drop UI to build your customized LLM flow</h3>
|
||||||
<a href="https://github.com/FlowiseAI/Flowise">
|
<a href="https://github.com/FlowiseAI/Flowise">
|
||||||
|
|
@ -44,9 +44,9 @@ Download and Install [NodeJS](https://nodejs.org/en/download) >= 18.15.0
|
||||||
|
|
||||||
1. Go to `docker` folder at the root of the project
|
1. Go to `docker` folder at the root of the project
|
||||||
2. Copy `.env.example` file, paste it into the same location, and rename to `.env`
|
2. Copy `.env.example` file, paste it into the same location, and rename to `.env`
|
||||||
3. `docker compose up -d`
|
3. `docker-compose up -d`
|
||||||
4. Open [http://localhost:3000](http://localhost:3000)
|
4. Open [http://localhost:3000](http://localhost:3000)
|
||||||
5. You can bring the containers down by `docker compose stop`
|
5. You can bring the containers down by `docker-compose stop`
|
||||||
|
|
||||||
### Docker Image
|
### Docker Image
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,13 @@
|
||||||
|
module.exports = {
|
||||||
|
presets: [
|
||||||
|
'@babel/preset-typescript',
|
||||||
|
[
|
||||||
|
'@babel/preset-env',
|
||||||
|
{
|
||||||
|
targets: {
|
||||||
|
node: 'current'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
]
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
@ -6,8 +6,8 @@ LOG_PATH=/root/.flowise/logs
|
||||||
BLOB_STORAGE_PATH=/root/.flowise/storage
|
BLOB_STORAGE_PATH=/root/.flowise/storage
|
||||||
|
|
||||||
# NUMBER_OF_PROXIES= 1
|
# NUMBER_OF_PROXIES= 1
|
||||||
# CORS_ORIGINS=*
|
# CORS_ORIGINS="*"
|
||||||
# IFRAME_ORIGINS=*
|
# IFRAME_ORIGINS="*"
|
||||||
|
|
||||||
# DATABASE_TYPE=postgres
|
# DATABASE_TYPE=postgres
|
||||||
# DATABASE_PORT=5432
|
# DATABASE_PORT=5432
|
||||||
|
|
@ -23,10 +23,8 @@ BLOB_STORAGE_PATH=/root/.flowise/storage
|
||||||
# FLOWISE_SECRETKEY_OVERWRITE=myencryptionkey
|
# FLOWISE_SECRETKEY_OVERWRITE=myencryptionkey
|
||||||
# FLOWISE_FILE_SIZE_LIMIT=50mb
|
# FLOWISE_FILE_SIZE_LIMIT=50mb
|
||||||
|
|
||||||
# DISABLE_CHATFLOW_REUSE=true
|
|
||||||
|
|
||||||
# DEBUG=true
|
# DEBUG=true
|
||||||
# LOG_LEVEL=info (error | warn | info | verbose | debug)
|
# LOG_LEVEL=debug (error | warn | info | verbose | debug)
|
||||||
# TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
|
# TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
|
||||||
# TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
|
# TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,25 +1,22 @@
|
||||||
# Stage 1: Build stage
|
FROM node:20-alpine
|
||||||
FROM node:20-alpine as build
|
|
||||||
|
|
||||||
USER root
|
USER root
|
||||||
|
|
||||||
# Skip downloading Chrome for Puppeteer (saves build time)
|
RUN apk add --no-cache git
|
||||||
|
RUN apk add --no-cache python3 py3-pip make g++
|
||||||
|
RUN apk add --no-cache cmake
|
||||||
|
# needed for pdfjs-dist
|
||||||
|
RUN apk add --no-cache build-base cairo-dev pango-dev
|
||||||
|
|
||||||
|
# Install Chromium
|
||||||
|
RUN apk add --no-cache chromium
|
||||||
|
|
||||||
ENV PUPPETEER_SKIP_DOWNLOAD=true
|
ENV PUPPETEER_SKIP_DOWNLOAD=true
|
||||||
|
|
||||||
# Install latest Flowise globally (specific version can be set: flowise@1.0.0)
|
|
||||||
RUN npm install -g flowise
|
|
||||||
|
|
||||||
# Stage 2: Runtime stage
|
|
||||||
FROM node:20-alpine
|
|
||||||
|
|
||||||
# Install runtime dependencies
|
|
||||||
RUN apk add --no-cache chromium git python3 py3-pip make g++ build-base cairo-dev pango-dev
|
|
||||||
|
|
||||||
# Set the environment variable for Puppeteer to find Chromium
|
|
||||||
ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser
|
ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser
|
||||||
|
|
||||||
# Copy Flowise from the build stage
|
# You can install a specific version like: flowise@1.0.0
|
||||||
COPY --from=build /usr/local/lib/node_modules /usr/local/lib/node_modules
|
RUN npm install -g flowise
|
||||||
COPY --from=build /usr/local/bin /usr/local/bin
|
|
||||||
|
|
||||||
ENTRYPOINT ["flowise", "start"]
|
WORKDIR /data
|
||||||
|
|
||||||
|
CMD "flowise"
|
||||||
|
|
@ -5,9 +5,9 @@ Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise
|
||||||
## Usage
|
## Usage
|
||||||
|
|
||||||
1. Create `.env` file and specify the `PORT` (refer to `.env.example`)
|
1. Create `.env` file and specify the `PORT` (refer to `.env.example`)
|
||||||
2. `docker compose up -d`
|
2. `docker-compose up -d`
|
||||||
3. Open [http://localhost:3000](http://localhost:3000)
|
3. Open [http://localhost:3000](http://localhost:3000)
|
||||||
4. You can bring the containers down by `docker compose stop`
|
4. You can bring the containers down by `docker-compose stop`
|
||||||
|
|
||||||
## 🔒 Authentication
|
## 🔒 Authentication
|
||||||
|
|
||||||
|
|
@ -19,9 +19,9 @@ Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise
|
||||||
- FLOWISE_USERNAME=${FLOWISE_USERNAME}
|
- FLOWISE_USERNAME=${FLOWISE_USERNAME}
|
||||||
- FLOWISE_PASSWORD=${FLOWISE_PASSWORD}
|
- FLOWISE_PASSWORD=${FLOWISE_PASSWORD}
|
||||||
```
|
```
|
||||||
3. `docker compose up -d`
|
3. `docker-compose up -d`
|
||||||
4. Open [http://localhost:3000](http://localhost:3000)
|
4. Open [http://localhost:3000](http://localhost:3000)
|
||||||
5. You can bring the containers down by `docker compose stop`
|
5. You can bring the containers down by `docker-compose stop`
|
||||||
|
|
||||||
## 🌱 Env Variables
|
## 🌱 Env Variables
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -33,4 +33,4 @@ services:
|
||||||
- '${PORT}:${PORT}'
|
- '${PORT}:${PORT}'
|
||||||
volumes:
|
volumes:
|
||||||
- ~/.flowise:/root/.flowise
|
- ~/.flowise:/root/.flowise
|
||||||
entrypoint: /bin/sh -c "sleep 3; flowise start"
|
command: /bin/sh -c "sleep 3; flowise start"
|
||||||
|
|
|
||||||
32
package.json
32
package.json
|
|
@ -1,6 +1,6 @@
|
||||||
{
|
{
|
||||||
"name": "flowise",
|
"name": "flowise",
|
||||||
"version": "1.8.4",
|
"version": "1.7.1",
|
||||||
"private": true,
|
"private": true,
|
||||||
"homepage": "https://flowiseai.com",
|
"homepage": "https://flowiseai.com",
|
||||||
"workspaces": [
|
"workspaces": [
|
||||||
|
|
@ -65,34 +65,6 @@
|
||||||
"resolutions": {
|
"resolutions": {
|
||||||
"@qdrant/openapi-typescript-fetch": "1.2.1",
|
"@qdrant/openapi-typescript-fetch": "1.2.1",
|
||||||
"@google/generative-ai": "^0.7.0",
|
"@google/generative-ai": "^0.7.0",
|
||||||
"openai": "4.51.0"
|
"openai": "4.38.3"
|
||||||
},
|
|
||||||
"eslintIgnore": [
|
|
||||||
"**/dist",
|
|
||||||
"**/node_modules",
|
|
||||||
"**/build",
|
|
||||||
"**/package-lock.json"
|
|
||||||
],
|
|
||||||
"prettier": {
|
|
||||||
"printWidth": 140,
|
|
||||||
"singleQuote": true,
|
|
||||||
"jsxSingleQuote": true,
|
|
||||||
"trailingComma": "none",
|
|
||||||
"tabWidth": 4,
|
|
||||||
"semi": false,
|
|
||||||
"endOfLine": "auto"
|
|
||||||
},
|
|
||||||
"babel": {
|
|
||||||
"presets": [
|
|
||||||
"@babel/preset-typescript",
|
|
||||||
[
|
|
||||||
"@babel/preset-env",
|
|
||||||
{
|
|
||||||
"targets": {
|
|
||||||
"node": "current"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
]
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -1,28 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class BaiduApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Baidu API'
|
|
||||||
this.name = 'baiduApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Baidu Api Key',
|
|
||||||
name: 'baiduApiKey',
|
|
||||||
type: 'password'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Baidu Secret Key',
|
|
||||||
name: 'baiduSecretKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: BaiduApi }
|
|
||||||
|
|
@ -1,23 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class ChatflowApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Chatflow API'
|
|
||||||
this.name = 'chatflowApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Chatflow Api Key',
|
|
||||||
name: 'chatflowApiKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: ChatflowApi }
|
|
||||||
|
|
@ -1,26 +0,0 @@
|
||||||
/*
|
|
||||||
* TODO: Implement codeInterpreter column to chat_message table
|
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class E2BApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'E2B API'
|
|
||||||
this.name = 'E2BApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'E2B Api Key',
|
|
||||||
name: 'e2bApiKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: E2BApi }
|
|
||||||
*/
|
|
||||||
|
|
@ -1,26 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class ExaSearchApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Exa Search API'
|
|
||||||
this.name = 'exaSearchApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.description =
|
|
||||||
'Refer to <a target="_blank" href="https://docs.exa.ai/reference/getting-started#getting-access">official guide</a> on how to get an API Key from Exa'
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'ExaSearch Api Key',
|
|
||||||
name: 'exaSearchApiKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: ExaSearchApi }
|
|
||||||
|
|
@ -1,26 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class FireCrawlApiCredential implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'FireCrawl API'
|
|
||||||
this.name = 'fireCrawlApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.description =
|
|
||||||
'You can find the FireCrawl API token on your <a target="_blank" href="https://www.firecrawl.dev/">FireCrawl account</a> page.'
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'FireCrawl API',
|
|
||||||
name: 'firecrawlApiToken',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: FireCrawlApiCredential }
|
|
||||||
|
|
@ -1,23 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class FireworksApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Fireworks API'
|
|
||||||
this.name = 'fireworksApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Fireworks Api Key',
|
|
||||||
name: 'fireworksApiKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: FireworksApi }
|
|
||||||
|
|
@ -1,33 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class LangWatchApi implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'LangWatch API'
|
|
||||||
this.name = 'langwatchApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.description =
|
|
||||||
'Refer to <a target="_blank" href="https://docs.langwatch.ai/integration/python/guide">integration guide</a> on how to get API keys on LangWatch'
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'API Key',
|
|
||||||
name: 'langWatchApiKey',
|
|
||||||
type: 'password',
|
|
||||||
placeholder: '<LANGWATCH_API_KEY>'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Endpoint',
|
|
||||||
name: 'langWatchEndpoint',
|
|
||||||
type: 'string',
|
|
||||||
default: 'https://app.langwatch.ai'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: LangWatchApi }
|
|
||||||
|
|
@ -1,38 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class OpenSearchUrl implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'OpenSearch'
|
|
||||||
this.name = 'openSearchUrl'
|
|
||||||
this.version = 2.0
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'OpenSearch Url',
|
|
||||||
name: 'openSearchUrl',
|
|
||||||
type: 'string'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'User',
|
|
||||||
name: 'user',
|
|
||||||
type: 'string',
|
|
||||||
placeholder: '<OPENSEARCH_USERNAME>',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Password',
|
|
||||||
name: 'password',
|
|
||||||
type: 'password',
|
|
||||||
placeholder: '<OPENSEARCH_PASSWORD>',
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: OpenSearchUrl }
|
|
||||||
|
|
@ -1,25 +0,0 @@
|
||||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
|
||||||
|
|
||||||
class SpiderApiCredential implements INodeCredential {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Spider API'
|
|
||||||
this.name = 'spiderApi'
|
|
||||||
this.version = 1.0
|
|
||||||
this.description = 'Get your API key from the <a target="_blank" href="https://spider.cloud">Spider</a> dashboard.'
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Spider API Key',
|
|
||||||
name: 'spiderApiKey',
|
|
||||||
type: 'password'
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { credClass: SpiderApiCredential }
|
|
||||||
|
|
@ -5,73 +5,47 @@
|
||||||
"models": [
|
"models": [
|
||||||
{
|
{
|
||||||
"label": "anthropic.claude-3-haiku",
|
"label": "anthropic.claude-3-haiku",
|
||||||
"name": "anthropic.claude-3-haiku-20240307-v1:0",
|
"name": "anthropic.claude-3-haiku-20240307-v1:0"
|
||||||
"description": "Image to text, conversation, chat optimized"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "anthropic.claude-3.5-sonnet",
|
|
||||||
"name": "anthropic.claude-3-5-sonnet-20240620-v1:0",
|
|
||||||
"description": "3.5 version of Claude Sonnet model"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "anthropic.claude-3-sonnet",
|
"label": "anthropic.claude-3-sonnet",
|
||||||
"name": "anthropic.claude-3-sonnet-20240229-v1:0",
|
"name": "anthropic.claude-3-sonnet-20240229-v1:0"
|
||||||
"description": "Image to text and code, multilingual conversation, complex reasoning and analysis"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "anthropic.claude-3-opus",
|
|
||||||
"name": "anthropic.claude-3-opus-20240229-v1:0",
|
|
||||||
"description": "Image to text and code, multilingual conversation, complex reasoning and analysis"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "anthropic.claude-instant-v1",
|
"label": "anthropic.claude-instant-v1",
|
||||||
"name": "anthropic.claude-instant-v1",
|
"name": "anthropic.claude-instant-v1"
|
||||||
"description": "Text generation, conversation"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "anthropic.claude-v2:1",
|
"label": "anthropic.claude-v2:1",
|
||||||
"name": "anthropic.claude-v2:1",
|
"name": "anthropic.claude-v2:1"
|
||||||
"description": "Text generation, conversation, complex reasoning and analysis"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "anthropic.claude-v2",
|
"label": "anthropic.claude-v2",
|
||||||
"name": "anthropic.claude-v2",
|
"name": "anthropic.claude-v2"
|
||||||
"description": "Text generation, conversation, complex reasoning and analysis"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "meta.llama2-13b-chat-v1",
|
"label": "meta.llama2-13b-chat-v1",
|
||||||
"name": "meta.llama2-13b-chat-v1",
|
"name": "meta.llama2-13b-chat-v1"
|
||||||
"description": "Text generation, conversation"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "meta.llama2-70b-chat-v1",
|
"label": "meta.llama2-70b-chat-v1",
|
||||||
"name": "meta.llama2-70b-chat-v1",
|
"name": "meta.llama2-70b-chat-v1"
|
||||||
"description": "Text generation, conversation"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "meta.llama3-8b-instruct-v1:0",
|
"label": "meta.llama3-8b-instruct-v1:0",
|
||||||
"name": "meta.llama3-8b-instruct-v1:0",
|
"name": "meta.llama3-8b-instruct-v1:0"
|
||||||
"description": "Text summarization, text classification, sentiment analysis"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "meta.llama3-70b-instruct-v1:0",
|
"label": "meta.llama3-70b-instruct-v1:0",
|
||||||
"name": "meta.llama3-70b-instruct-v1:0",
|
"name": "meta.llama3-70b-instruct-v1:0"
|
||||||
"description": "Language modeling, dialog systems, code generation, text summarization, text classification, sentiment analysis"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "mistral.mistral-7b-instruct-v0:2",
|
"label": "mistral.mistral-7b-instruct-v0:2",
|
||||||
"name": "mistral.mistral-7b-instruct-v0:2",
|
"name": "mistral.mistral-7b-instruct-v0:2"
|
||||||
"description": "Classification, text generation, code generation"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "mistral.mixtral-8x7b-instruct-v0:1",
|
"label": "mistral.mixtral-8x7b-instruct-v0:1",
|
||||||
"name": "mistral.mixtral-8x7b-instruct-v0:1",
|
"name": "mistral.mixtral-8x7b-instruct-v0:1"
|
||||||
"description": "Complex reasoning and analysis, text generation, code generation"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "mistral.mistral-large-2402-v1:0",
|
|
||||||
"name": "mistral.mistral-large-2402-v1:0",
|
|
||||||
"description": "Complex reasoning and analysis, text generation, code generation, RAG, agents"
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"regions": [
|
"regions": [
|
||||||
|
|
@ -220,10 +194,6 @@
|
||||||
{
|
{
|
||||||
"name": "azureChatOpenAI",
|
"name": "azureChatOpenAI",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gpt-4o",
|
|
||||||
"name": "gpt-4o"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gpt-4",
|
"label": "gpt-4",
|
||||||
"name": "gpt-4"
|
"name": "gpt-4"
|
||||||
|
|
@ -249,37 +219,25 @@
|
||||||
{
|
{
|
||||||
"name": "azureChatOpenAI_LlamaIndex",
|
"name": "azureChatOpenAI_LlamaIndex",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gpt-4o",
|
|
||||||
"name": "gpt-4o"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gpt-4",
|
"label": "gpt-4",
|
||||||
"name": "gpt-4"
|
"name": "gpt-4"
|
||||||
},
|
},
|
||||||
{
|
|
||||||
"label": "gpt-4-turbo",
|
|
||||||
"name": "gpt-4-turbo"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gpt-4-32k",
|
"label": "gpt-4-32k",
|
||||||
"name": "gpt-4-32k"
|
"name": "gpt-4-32k"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "gpt-3.5-turbo",
|
"label": "gpt-35-turbo",
|
||||||
"name": "gpt-3.5-turbo"
|
"name": "gpt-35-turbo"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "gpt-3.5-turbo-16k",
|
"label": "gpt-35-turbo-16k",
|
||||||
"name": "gpt-3.5-turbo-16k"
|
"name": "gpt-35-turbo-16k"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "gpt-4-vision-preview",
|
"label": "gpt-4-vision-preview",
|
||||||
"name": "gpt-4-vision-preview"
|
"name": "gpt-4-vision-preview"
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "gpt-4-1106-preview",
|
|
||||||
"name": "gpt-4-1106-preview"
|
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
|
@ -296,11 +254,6 @@
|
||||||
"name": "claude-3-opus-20240229",
|
"name": "claude-3-opus-20240229",
|
||||||
"description": "Most powerful model for highly complex tasks"
|
"description": "Most powerful model for highly complex tasks"
|
||||||
},
|
},
|
||||||
{
|
|
||||||
"label": "claude-3.5-sonnet",
|
|
||||||
"name": "claude-3-5-sonnet-20240620",
|
|
||||||
"description": "3.5 version of Claude Sonnet model"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "claude-3-sonnet",
|
"label": "claude-3-sonnet",
|
||||||
"name": "claude-3-sonnet-20240229",
|
"name": "claude-3-sonnet-20240229",
|
||||||
|
|
@ -356,10 +309,6 @@
|
||||||
{
|
{
|
||||||
"name": "chatGoogleGenerativeAI",
|
"name": "chatGoogleGenerativeAI",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gemini-1.5-flash-latest",
|
|
||||||
"name": "gemini-1.5-flash-latest"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gemini-1.5-pro-latest",
|
"label": "gemini-1.5-pro-latest",
|
||||||
"name": "gemini-1.5-pro-latest"
|
"name": "gemini-1.5-pro-latest"
|
||||||
|
|
@ -386,10 +335,6 @@
|
||||||
{
|
{
|
||||||
"name": "chatGoogleVertexAI",
|
"name": "chatGoogleVertexAI",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gemini-1.5-flash",
|
|
||||||
"name": "gemini-1.5-flash-preview-0514"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gemini-1.5-pro",
|
"label": "gemini-1.5-pro",
|
||||||
"name": "gemini-1.5-pro-preview-0409"
|
"name": "gemini-1.5-pro-preview-0409"
|
||||||
|
|
@ -457,10 +402,6 @@
|
||||||
{
|
{
|
||||||
"name": "chatOpenAI",
|
"name": "chatOpenAI",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gpt-4o",
|
|
||||||
"name": "gpt-4o"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gpt-4",
|
"label": "gpt-4",
|
||||||
"name": "gpt-4"
|
"name": "gpt-4"
|
||||||
|
|
@ -530,10 +471,6 @@
|
||||||
{
|
{
|
||||||
"name": "chatOpenAI_LlamaIndex",
|
"name": "chatOpenAI_LlamaIndex",
|
||||||
"models": [
|
"models": [
|
||||||
{
|
|
||||||
"label": "gpt-4o",
|
|
||||||
"name": "gpt-4o"
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
"label": "gpt-4",
|
"label": "gpt-4",
|
||||||
"name": "gpt-4"
|
"name": "gpt-4"
|
||||||
|
|
@ -652,23 +589,6 @@
|
||||||
"name": "mistral-large-2402"
|
"name": "mistral-large-2402"
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
},
|
|
||||||
{
|
|
||||||
"name": "chatMistral_LlamaIndex",
|
|
||||||
"models": [
|
|
||||||
{
|
|
||||||
"label": "mistral-tiny",
|
|
||||||
"name": "mistral-tiny"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "mistral-small",
|
|
||||||
"name": "mistral-small"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "mistral-medium",
|
|
||||||
"name": "mistral-medium"
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"llm": [
|
"llm": [
|
||||||
|
|
@ -1047,42 +967,22 @@
|
||||||
{
|
{
|
||||||
"label": "voyage-2",
|
"label": "voyage-2",
|
||||||
"name": "voyage-2",
|
"name": "voyage-2",
|
||||||
"description": "General-purpose embedding model optimized for a balance between cost, latency, and retrieval quality."
|
"description": "Base generalist embedding model optimized for both latency and quality"
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "voyage-code-2",
|
"label": "voyage-code-2",
|
||||||
"name": "voyage-code-2",
|
"name": "voyage-code-2",
|
||||||
"description": "Optimized for code retrieval."
|
"description": "Optimized for code retrieval"
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "voyage-finance-2",
|
|
||||||
"name": "voyage-finance-2",
|
|
||||||
"description": "Optimized for finance retrieval and RAG."
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "voyage-large-2",
|
"label": "voyage-large-2",
|
||||||
"name": "voyage-large-2",
|
"name": "voyage-large-2",
|
||||||
"description": "General-purpose embedding model that is optimized for retrieval quality."
|
"description": "Powerful generalist embedding model"
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "voyage-large-2-instruct",
|
|
||||||
"name": "voyage-large-2-instruct",
|
|
||||||
"description": "Instruction-tuned general-purpose embedding model optimized for clustering, classification, and retrieval."
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "voyage-law-2",
|
|
||||||
"name": "voyage-law-2",
|
|
||||||
"description": "Optimized for legal and long-context retrieval and RAG. Also improved performance across all domains."
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "voyage-lite-02-instruct",
|
"label": "voyage-lite-02-instruct",
|
||||||
"name": "voyage-lite-02-instruct",
|
"name": "voyage-lite-02-instruct",
|
||||||
"description": "Instruction-tuned for classification, clustering, and sentence textual similarity tasks"
|
"description": "Instruction-tuned for classification, clustering, and sentence textual similarity tasks"
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "voyage-multilingual-2",
|
|
||||||
"name": "voyage-multilingual-2",
|
|
||||||
"description": "Optimized for multilingual retrieval and RAG."
|
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
|
@ -1170,28 +1070,19 @@
|
||||||
"models": [
|
"models": [
|
||||||
{
|
{
|
||||||
"label": "amazon.titan-embed-text-v1",
|
"label": "amazon.titan-embed-text-v1",
|
||||||
"name": "amazon.titan-embed-text-v1",
|
"name": "amazon.titan-embed-text-v1"
|
||||||
"description": "Embedding Dimensions: 1536"
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"label": "amazon.titan-embed-text-v2",
|
|
||||||
"name": "amazon.titan-embed-text-v2:0",
|
|
||||||
"description": "Embedding Dimensions: 1024"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "amazon.titan-embed-g1-text-02",
|
"label": "amazon.titan-embed-g1-text-02",
|
||||||
"name": "amazon.titan-embed-g1-text-02",
|
"name": "amazon.titan-embed-g1-text-02"
|
||||||
"description": "Embedding Dimensions: 1536"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "cohere.embed-english-v3",
|
"label": "cohere.embed-english-v3",
|
||||||
"name": "cohere.embed-english-v3",
|
"name": "cohere.embed-english-v3"
|
||||||
"description": "Embedding Dimensions: 1024"
|
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"label": "cohere.embed-multilingual-v3",
|
"label": "cohere.embed-multilingual-v3",
|
||||||
"name": "cohere.embed-multilingual-v3",
|
"name": "cohere.embed-multilingual-v3"
|
||||||
"description": "Embedding Dimensions: 1024"
|
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"regions": [
|
"regions": [
|
||||||
|
|
|
||||||
|
|
@ -190,7 +190,6 @@ const prepareAgent = async (
|
||||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
const outputParser = ChatConversationalAgent.getDefaultOutputParser({
|
const outputParser = ChatConversationalAgent.getDefaultOutputParser({
|
||||||
llm: model,
|
llm: model,
|
||||||
|
|
@ -241,7 +240,7 @@ const prepareAgent = async (
|
||||||
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||||
agent_scratchpad: async (i: { input: string; steps: AgentStep[] }) => await constructScratchPad(i.steps),
|
agent_scratchpad: async (i: { input: string; steps: AgentStep[] }) => await constructScratchPad(i.steps),
|
||||||
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
return messages ?? []
|
return messages ?? []
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,186 @@
|
||||||
|
import { flatten } from 'lodash'
|
||||||
|
import { BaseMessage } from '@langchain/core/messages'
|
||||||
|
import { ChainValues } from '@langchain/core/utils/types'
|
||||||
|
import { AgentStep } from '@langchain/core/agents'
|
||||||
|
import { RunnableSequence } from '@langchain/core/runnables'
|
||||||
|
import { ChatOpenAI, formatToOpenAIFunction } from '@langchain/openai'
|
||||||
|
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||||
|
import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||||
|
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||||
|
import { getBaseClasses } from '../../../src/utils'
|
||||||
|
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||||
|
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||||
|
import { checkInputs, Moderation } from '../../moderation/Moderation'
|
||||||
|
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||||
|
|
||||||
|
const defaultMessage = `Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`
|
||||||
|
|
||||||
|
class ConversationalRetrievalAgent_Agents implements INode {
|
||||||
|
label: string
|
||||||
|
name: string
|
||||||
|
version: number
|
||||||
|
description: string
|
||||||
|
type: string
|
||||||
|
icon: string
|
||||||
|
category: string
|
||||||
|
baseClasses: string[]
|
||||||
|
inputs: INodeParams[]
|
||||||
|
badge?: string
|
||||||
|
sessionId?: string
|
||||||
|
|
||||||
|
constructor(fields?: { sessionId?: string }) {
|
||||||
|
this.label = 'Conversational Retrieval Agent'
|
||||||
|
this.name = 'conversationalRetrievalAgent'
|
||||||
|
this.version = 4.0
|
||||||
|
this.type = 'AgentExecutor'
|
||||||
|
this.category = 'Agents'
|
||||||
|
this.badge = 'DEPRECATING'
|
||||||
|
this.icon = 'agent.svg'
|
||||||
|
this.description = `An agent optimized for retrieval during conversation, answering questions based on past dialogue, all using OpenAI's Function Calling`
|
||||||
|
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||||
|
this.inputs = [
|
||||||
|
{
|
||||||
|
label: 'Allowed Tools',
|
||||||
|
name: 'tools',
|
||||||
|
type: 'Tool',
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Memory',
|
||||||
|
name: 'memory',
|
||||||
|
type: 'BaseChatMemory'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'OpenAI/Azure Chat Model',
|
||||||
|
name: 'model',
|
||||||
|
type: 'BaseChatModel'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'System Message',
|
||||||
|
name: 'systemMessage',
|
||||||
|
type: 'string',
|
||||||
|
default: defaultMessage,
|
||||||
|
rows: 4,
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Input Moderation',
|
||||||
|
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
|
||||||
|
name: 'inputModeration',
|
||||||
|
type: 'Moderation',
|
||||||
|
optional: true,
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Max Iterations',
|
||||||
|
name: 'maxIterations',
|
||||||
|
type: 'number',
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
this.sessionId = fields?.sessionId
|
||||||
|
}
|
||||||
|
|
||||||
|
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||||
|
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
}
|
||||||
|
|
||||||
|
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
|
|
||||||
|
if (moderations && moderations.length > 0) {
|
||||||
|
try {
|
||||||
|
// Use the output of the moderation chain as input for the BabyAGI agent
|
||||||
|
input = await checkInputs(moderations, input)
|
||||||
|
} catch (e) {
|
||||||
|
await new Promise((resolve) => setTimeout(resolve, 500))
|
||||||
|
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
|
||||||
|
return formatResponse(e.message)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
|
||||||
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
|
let res: ChainValues = {}
|
||||||
|
|
||||||
|
if (options.socketIO && options.socketIOClientId) {
|
||||||
|
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||||
|
} else {
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||||
|
}
|
||||||
|
|
||||||
|
await memory.addChatMessages(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
text: input,
|
||||||
|
type: 'userMessage'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
text: res?.output,
|
||||||
|
type: 'apiMessage'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
this.sessionId
|
||||||
|
)
|
||||||
|
|
||||||
|
return res?.output
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const prepareAgent = (nodeData: INodeData, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||||
|
const model = nodeData.inputs?.model as ChatOpenAI
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
|
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||||
|
let tools = nodeData.inputs?.tools
|
||||||
|
tools = flatten(tools)
|
||||||
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
|
|
||||||
|
const prompt = ChatPromptTemplate.fromMessages([
|
||||||
|
['ai', systemMessage ? systemMessage : defaultMessage],
|
||||||
|
new MessagesPlaceholder(memoryKey),
|
||||||
|
['human', `{${inputKey}}`],
|
||||||
|
new MessagesPlaceholder('agent_scratchpad')
|
||||||
|
])
|
||||||
|
|
||||||
|
const modelWithFunctions = model.bind({
|
||||||
|
functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
|
||||||
|
})
|
||||||
|
|
||||||
|
const runnableAgent = RunnableSequence.from([
|
||||||
|
{
|
||||||
|
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||||
|
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||||
|
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||||
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
|
return messages ?? []
|
||||||
|
}
|
||||||
|
},
|
||||||
|
prompt,
|
||||||
|
modelWithFunctions,
|
||||||
|
new OpenAIFunctionsAgentOutputParser()
|
||||||
|
])
|
||||||
|
|
||||||
|
const executor = AgentExecutor.fromAgentAndTools({
|
||||||
|
agent: runnableAgent,
|
||||||
|
tools,
|
||||||
|
sessionId: flowObj?.sessionId,
|
||||||
|
chatId: flowObj?.chatId,
|
||||||
|
input: flowObj?.input,
|
||||||
|
returnIntermediateSteps: true,
|
||||||
|
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||||
|
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||||
|
})
|
||||||
|
|
||||||
|
return executor
|
||||||
|
}
|
||||||
|
|
||||||
|
module.exports = { nodeClass: ConversationalRetrievalAgent_Agents }
|
||||||
|
|
@ -0,0 +1,7 @@
|
||||||
|
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||||
|
<path d="M10 6C10 5.44772 10.4477 5 11 5H21C21.5523 5 22 5.44772 22 6V11C22 13.2091 20.2091 15 18 15H14C11.7909 15 10 13.2091 10 11V6Z" stroke="black" stroke-width="2" stroke-linejoin="round"/>
|
||||||
|
<path d="M16 5V3" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<circle cx="14" cy="9" r="1.5" fill="black"/>
|
||||||
|
<circle cx="18" cy="9" r="1.5" fill="black"/>
|
||||||
|
<path d="M26 27C26 22.0294 21.5228 18 16 18C10.4772 18 6 22.0294 6 27" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||||
|
</svg>
|
||||||
|
After Width: | Height: | Size: 616 B |
|
|
@ -1 +0,0 @@
|
||||||
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><circle cx="16" cy="16" r="14" fill="#CC9B7A"/><path d="m10 21 4.5-10L19 21m-7.2-2.857h5.4M18.5 11 23 21" stroke="#1F1F1E" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>
|
|
||||||
|
Before Width: | Height: | Size: 269 B |
|
|
@ -1,167 +0,0 @@
|
||||||
import { flatten } from 'lodash'
|
|
||||||
import { ChatMessage, OpenAI, OpenAIAgent } from 'llamaindex'
|
|
||||||
import { getBaseClasses } from '../../../../src/utils'
|
|
||||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, IUsedTool } from '../../../../src/Interface'
|
|
||||||
|
|
||||||
class OpenAIFunctionAgent_LlamaIndex_Agents implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
baseClasses: string[]
|
|
||||||
tags: string[]
|
|
||||||
inputs: INodeParams[]
|
|
||||||
sessionId?: string
|
|
||||||
badge?: string
|
|
||||||
|
|
||||||
constructor(fields?: { sessionId?: string }) {
|
|
||||||
this.label = 'OpenAI Tool Agent'
|
|
||||||
this.name = 'openAIToolAgentLlamaIndex'
|
|
||||||
this.version = 2.0
|
|
||||||
this.type = 'OpenAIToolAgent'
|
|
||||||
this.category = 'Agents'
|
|
||||||
this.icon = 'function.svg'
|
|
||||||
this.description = `Agent that uses OpenAI Function Calling to pick the tools and args to call using LlamaIndex`
|
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(OpenAIAgent)]
|
|
||||||
this.tags = ['LlamaIndex']
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Tools',
|
|
||||||
name: 'tools',
|
|
||||||
type: 'Tool_LlamaIndex',
|
|
||||||
list: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Memory',
|
|
||||||
name: 'memory',
|
|
||||||
type: 'BaseChatMemory'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'OpenAI/Azure Chat Model',
|
|
||||||
name: 'model',
|
|
||||||
type: 'BaseChatModel_LlamaIndex'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'System Message',
|
|
||||||
name: 'systemMessage',
|
|
||||||
type: 'string',
|
|
||||||
rows: 4,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
this.sessionId = fields?.sessionId
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(): Promise<any> {
|
|
||||||
return null
|
|
||||||
}
|
|
||||||
|
|
||||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
|
||||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
|
||||||
const model = nodeData.inputs?.model as OpenAI
|
|
||||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
|
||||||
let tools = nodeData.inputs?.tools
|
|
||||||
tools = flatten(tools)
|
|
||||||
|
|
||||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
|
||||||
|
|
||||||
const chatHistory = [] as ChatMessage[]
|
|
||||||
|
|
||||||
if (systemMessage) {
|
|
||||||
chatHistory.push({
|
|
||||||
content: systemMessage,
|
|
||||||
role: 'system'
|
|
||||||
})
|
|
||||||
}
|
|
||||||
|
|
||||||
const msgs = (await memory.getChatMessages(this.sessionId, false)) as IMessage[]
|
|
||||||
for (const message of msgs) {
|
|
||||||
if (message.type === 'apiMessage') {
|
|
||||||
chatHistory.push({
|
|
||||||
content: message.message,
|
|
||||||
role: 'assistant'
|
|
||||||
})
|
|
||||||
} else if (message.type === 'userMessage') {
|
|
||||||
chatHistory.push({
|
|
||||||
content: message.message,
|
|
||||||
role: 'user'
|
|
||||||
})
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
const agent = new OpenAIAgent({
|
|
||||||
tools,
|
|
||||||
llm: model,
|
|
||||||
chatHistory: chatHistory,
|
|
||||||
verbose: process.env.DEBUG === 'true' ? true : false
|
|
||||||
})
|
|
||||||
|
|
||||||
let text = ''
|
|
||||||
let isStreamingStarted = false
|
|
||||||
const usedTools: IUsedTool[] = []
|
|
||||||
|
|
||||||
if (isStreamingEnabled) {
|
|
||||||
const stream = await agent.chat({
|
|
||||||
message: input,
|
|
||||||
chatHistory,
|
|
||||||
stream: true,
|
|
||||||
verbose: process.env.DEBUG === 'true' ? true : false
|
|
||||||
})
|
|
||||||
for await (const chunk of stream) {
|
|
||||||
//console.log('chunk', chunk)
|
|
||||||
text += chunk.response.delta
|
|
||||||
if (!isStreamingStarted) {
|
|
||||||
isStreamingStarted = true
|
|
||||||
options.socketIO.to(options.socketIOClientId).emit('start', chunk.response.delta)
|
|
||||||
if (chunk.sources.length) {
|
|
||||||
for (const sourceTool of chunk.sources) {
|
|
||||||
usedTools.push({
|
|
||||||
tool: sourceTool.tool?.metadata.name ?? '',
|
|
||||||
toolInput: sourceTool.input,
|
|
||||||
toolOutput: sourceTool.output as any
|
|
||||||
})
|
|
||||||
}
|
|
||||||
options.socketIO.to(options.socketIOClientId).emit('usedTools', usedTools)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
options.socketIO.to(options.socketIOClientId).emit('token', chunk.response.delta)
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
const response = await agent.chat({ message: input, chatHistory, verbose: process.env.DEBUG === 'true' ? true : false })
|
|
||||||
if (response.sources.length) {
|
|
||||||
for (const sourceTool of response.sources) {
|
|
||||||
usedTools.push({
|
|
||||||
tool: sourceTool.tool?.metadata.name ?? '',
|
|
||||||
toolInput: sourceTool.input,
|
|
||||||
toolOutput: sourceTool.output as any
|
|
||||||
})
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
text = response.response.message.content as string
|
|
||||||
}
|
|
||||||
|
|
||||||
await memory.addChatMessages(
|
|
||||||
[
|
|
||||||
{
|
|
||||||
text: input,
|
|
||||||
type: 'userMessage'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
text: text,
|
|
||||||
type: 'apiMessage'
|
|
||||||
}
|
|
||||||
],
|
|
||||||
this.sessionId
|
|
||||||
)
|
|
||||||
|
|
||||||
return usedTools.length ? { text: text, usedTools } : text
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: OpenAIFunctionAgent_LlamaIndex_Agents }
|
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M5 6H4v19.5h1m8-7.5v3h1m7-11.5V6h1m-5 7.5V10h1" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><mask id="MistralAI__a" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__a)"><path fill="#FFCD00" d="M4 6h25v4H4z"/></g><mask id="MistralAI__b" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__b)"><path fill="#FFA200" d="M4 10h25v4H4z"/></g><mask id="MistralAI__c" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__c)"><path fill="#FF6E00" d="M4 14h25v4H4z"/></g><mask id="MistralAI__d" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__d)"><path fill="#FF4A09" d="M4 18h25v4H4z"/></g><mask id="MistralAI__e" style="mask-type:alpha" maskUnits="userSpaceOnUse" x="5" y="6" width="22" height="20"><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" fill="#FD7000"/></mask><g mask="url(#MistralAI__e)"><path fill="#FE060F" d="M4 22h25v4H4z"/></g><path d="M21 18v7h1" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><path d="M5 6v19.5h5v-8h4V21h4v-3.5h4V25h5V6h-4.5v4H18v3.5h-4v-4h-4V6H5Z" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>
|
||||||
|
After Width: | Height: | Size: 1.8 KiB |
|
|
@ -0,0 +1,212 @@
|
||||||
|
import { flatten } from 'lodash'
|
||||||
|
import { BaseMessage } from '@langchain/core/messages'
|
||||||
|
import { ChainValues } from '@langchain/core/utils/types'
|
||||||
|
import { AgentStep } from '@langchain/core/agents'
|
||||||
|
import { RunnableSequence } from '@langchain/core/runnables'
|
||||||
|
import { ChatOpenAI } from '@langchain/openai'
|
||||||
|
import { convertToOpenAITool } from '@langchain/core/utils/function_calling'
|
||||||
|
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||||
|
import { OpenAIToolsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||||
|
import { getBaseClasses } from '../../../src/utils'
|
||||||
|
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||||
|
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||||
|
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||||
|
import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
|
||||||
|
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||||
|
|
||||||
|
class MistralAIToolAgent_Agents implements INode {
|
||||||
|
label: string
|
||||||
|
name: string
|
||||||
|
version: number
|
||||||
|
description: string
|
||||||
|
type: string
|
||||||
|
icon: string
|
||||||
|
category: string
|
||||||
|
baseClasses: string[]
|
||||||
|
inputs: INodeParams[]
|
||||||
|
sessionId?: string
|
||||||
|
badge?: string
|
||||||
|
|
||||||
|
constructor(fields?: { sessionId?: string }) {
|
||||||
|
this.label = 'MistralAI Tool Agent'
|
||||||
|
this.name = 'mistralAIToolAgent'
|
||||||
|
this.version = 1.0
|
||||||
|
this.type = 'AgentExecutor'
|
||||||
|
this.category = 'Agents'
|
||||||
|
this.icon = 'MistralAI.svg'
|
||||||
|
this.badge = 'DEPRECATING'
|
||||||
|
this.description = `Agent that uses MistralAI Function Calling to pick the tools and args to call`
|
||||||
|
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||||
|
this.inputs = [
|
||||||
|
{
|
||||||
|
label: 'Tools',
|
||||||
|
name: 'tools',
|
||||||
|
type: 'Tool',
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Memory',
|
||||||
|
name: 'memory',
|
||||||
|
type: 'BaseChatMemory'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'MistralAI Chat Model',
|
||||||
|
name: 'model',
|
||||||
|
type: 'BaseChatModel'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'System Message',
|
||||||
|
name: 'systemMessage',
|
||||||
|
type: 'string',
|
||||||
|
rows: 4,
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Input Moderation',
|
||||||
|
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
|
||||||
|
name: 'inputModeration',
|
||||||
|
type: 'Moderation',
|
||||||
|
optional: true,
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Max Iterations',
|
||||||
|
name: 'maxIterations',
|
||||||
|
type: 'number',
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
this.sessionId = fields?.sessionId
|
||||||
|
}
|
||||||
|
|
||||||
|
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||||
|
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
}
|
||||||
|
|
||||||
|
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
|
|
||||||
|
if (moderations && moderations.length > 0) {
|
||||||
|
try {
|
||||||
|
// Use the output of the moderation chain as input for the OpenAI Function Agent
|
||||||
|
input = await checkInputs(moderations, input)
|
||||||
|
} catch (e) {
|
||||||
|
await new Promise((resolve) => setTimeout(resolve, 500))
|
||||||
|
streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
|
||||||
|
return formatResponse(e.message)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
|
||||||
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
|
let res: ChainValues = {}
|
||||||
|
let sourceDocuments: ICommonObject[] = []
|
||||||
|
let usedTools: IUsedTool[] = []
|
||||||
|
|
||||||
|
if (options.socketIO && options.socketIOClientId) {
|
||||||
|
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await memory.addChatMessages(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
text: input,
|
||||||
|
type: 'userMessage'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
text: res?.output,
|
||||||
|
type: 'apiMessage'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
this.sessionId
|
||||||
|
)
|
||||||
|
|
||||||
|
let finalRes = res?.output
|
||||||
|
|
||||||
|
if (sourceDocuments.length || usedTools.length) {
|
||||||
|
finalRes = { text: res?.output }
|
||||||
|
if (sourceDocuments.length) {
|
||||||
|
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||||
|
}
|
||||||
|
if (usedTools.length) {
|
||||||
|
finalRes.usedTools = usedTools
|
||||||
|
}
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const prepareAgent = (nodeData: INodeData, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||||
|
const model = nodeData.inputs?.model as ChatOpenAI
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||||
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
|
let tools = nodeData.inputs?.tools
|
||||||
|
tools = flatten(tools)
|
||||||
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
|
|
||||||
|
const prompt = ChatPromptTemplate.fromMessages([
|
||||||
|
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||||
|
new MessagesPlaceholder(memoryKey),
|
||||||
|
['human', `{${inputKey}}`],
|
||||||
|
new MessagesPlaceholder('agent_scratchpad')
|
||||||
|
])
|
||||||
|
|
||||||
|
const llmWithTools = model.bind({
|
||||||
|
tools: tools.map(convertToOpenAITool)
|
||||||
|
})
|
||||||
|
|
||||||
|
const runnableAgent = RunnableSequence.from([
|
||||||
|
{
|
||||||
|
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||||
|
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||||
|
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||||
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
|
return messages ?? []
|
||||||
|
}
|
||||||
|
},
|
||||||
|
prompt,
|
||||||
|
llmWithTools,
|
||||||
|
new OpenAIToolsAgentOutputParser()
|
||||||
|
])
|
||||||
|
|
||||||
|
const executor = AgentExecutor.fromAgentAndTools({
|
||||||
|
agent: runnableAgent,
|
||||||
|
tools,
|
||||||
|
sessionId: flowObj?.sessionId,
|
||||||
|
chatId: flowObj?.chatId,
|
||||||
|
input: flowObj?.input,
|
||||||
|
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||||
|
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||||
|
})
|
||||||
|
|
||||||
|
return executor
|
||||||
|
}
|
||||||
|
|
||||||
|
module.exports = { nodeClass: MistralAIToolAgent_Agents }
|
||||||
|
|
@ -8,7 +8,7 @@ import { zodToJsonSchema } from 'zod-to-json-schema'
|
||||||
import { AnalyticHandler } from '../../../src/handler'
|
import { AnalyticHandler } from '../../../src/handler'
|
||||||
import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
|
import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
|
||||||
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||||
import { addSingleFileToStorage } from '../../../src/storageUtils'
|
import { addFileToStorage } from '../../../src/storageUtils'
|
||||||
|
|
||||||
const lenticularBracketRegex = /【[^】]*】/g
|
const lenticularBracketRegex = /【[^】]*】/g
|
||||||
const imageRegex = /<img[^>]*\/>/g
|
const imageRegex = /<img[^>]*\/>/g
|
||||||
|
|
@ -27,7 +27,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
constructor() {
|
constructor() {
|
||||||
this.label = 'OpenAI Assistant'
|
this.label = 'OpenAI Assistant'
|
||||||
this.name = 'openAIAssistant'
|
this.name = 'openAIAssistant'
|
||||||
this.version = 4.0
|
this.version = 3.0
|
||||||
this.type = 'OpenAIAssistant'
|
this.type = 'OpenAIAssistant'
|
||||||
this.category = 'Agents'
|
this.category = 'Agents'
|
||||||
this.icon = 'assistant.svg'
|
this.icon = 'assistant.svg'
|
||||||
|
|
@ -54,25 +54,6 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
optional: true,
|
optional: true,
|
||||||
list: true
|
list: true
|
||||||
},
|
},
|
||||||
{
|
|
||||||
label: 'Tool Choice',
|
|
||||||
name: 'toolChoice',
|
|
||||||
type: 'string',
|
|
||||||
description:
|
|
||||||
'Controls which (if any) tool is called by the model. Can be "none", "auto", "required", or the name of a tool. Refer <a href="https://platform.openai.com/docs/api-reference/runs/createRun#runs-createrun-tool_choice" target="_blank">here</a> for more information',
|
|
||||||
placeholder: 'file_search',
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Parallel Tool Calls',
|
|
||||||
name: 'parallelToolCalls',
|
|
||||||
type: 'boolean',
|
|
||||||
description: 'Whether to enable parallel function calling during tool use. Defaults to true',
|
|
||||||
default: true,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
label: 'Disable File Download',
|
label: 'Disable File Download',
|
||||||
name: 'disableFileDownload',
|
name: 'disableFileDownload',
|
||||||
|
|
@ -157,14 +138,10 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
const openai = new OpenAI({ apiKey: openAIApiKey })
|
const openai = new OpenAI({ apiKey: openAIApiKey })
|
||||||
options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
|
options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
|
||||||
try {
|
try {
|
||||||
if (sessionId && sessionId.startsWith('thread_')) {
|
if (sessionId) await openai.beta.threads.del(sessionId)
|
||||||
await openai.beta.threads.del(sessionId)
|
options.logger.info(`Successfully cleared OpenAI Thread ${sessionId}`)
|
||||||
options.logger.info(`Successfully cleared OpenAI Thread ${sessionId}`)
|
|
||||||
} else {
|
|
||||||
options.logger.error(`Error clearing OpenAI Thread ${sessionId}`)
|
|
||||||
}
|
|
||||||
} catch (e) {
|
} catch (e) {
|
||||||
options.logger.error(`Error clearing OpenAI Thread ${sessionId}`)
|
throw new Error(e)
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -174,8 +151,6 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||||
const disableFileDownload = nodeData.inputs?.disableFileDownload as boolean
|
const disableFileDownload = nodeData.inputs?.disableFileDownload as boolean
|
||||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
const _toolChoice = nodeData.inputs?.toolChoice as string
|
|
||||||
const parallelToolCalls = nodeData.inputs?.parallelToolCalls as boolean
|
|
||||||
const isStreaming = options.socketIO && options.socketIOClientId
|
const isStreaming = options.socketIO && options.socketIOClientId
|
||||||
const socketIO = isStreaming ? options.socketIO : undefined
|
const socketIO = isStreaming ? options.socketIO : undefined
|
||||||
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
||||||
|
|
@ -294,25 +269,10 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
let runThreadId = ''
|
let runThreadId = ''
|
||||||
let isStreamingStarted = false
|
let isStreamingStarted = false
|
||||||
|
|
||||||
let toolChoice: any
|
|
||||||
if (_toolChoice) {
|
|
||||||
if (_toolChoice === 'file_search') {
|
|
||||||
toolChoice = { type: 'file_search' }
|
|
||||||
} else if (_toolChoice === 'code_interpreter') {
|
|
||||||
toolChoice = { type: 'code_interpreter' }
|
|
||||||
} else if (_toolChoice === 'none' || _toolChoice === 'auto' || _toolChoice === 'required') {
|
|
||||||
toolChoice = _toolChoice
|
|
||||||
} else {
|
|
||||||
toolChoice = { type: 'function', function: { name: _toolChoice } }
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
if (isStreaming) {
|
if (isStreaming) {
|
||||||
const streamThread = await openai.beta.threads.runs.create(threadId, {
|
const streamThread = await openai.beta.threads.runs.create(threadId, {
|
||||||
assistant_id: retrievedAssistant.id,
|
assistant_id: retrievedAssistant.id,
|
||||||
stream: true,
|
stream: true
|
||||||
tool_choice: toolChoice,
|
|
||||||
parallel_tool_calls: parallelToolCalls
|
|
||||||
})
|
})
|
||||||
|
|
||||||
for await (const event of streamThread) {
|
for await (const event of streamThread) {
|
||||||
|
|
@ -635,9 +595,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
|
|
||||||
// Polling run status
|
// Polling run status
|
||||||
const runThread = await openai.beta.threads.runs.create(threadId, {
|
const runThread = await openai.beta.threads.runs.create(threadId, {
|
||||||
assistant_id: retrievedAssistant.id,
|
assistant_id: retrievedAssistant.id
|
||||||
tool_choice: toolChoice,
|
|
||||||
parallel_tool_calls: parallelToolCalls
|
|
||||||
})
|
})
|
||||||
runThreadId = runThread.id
|
runThreadId = runThread.id
|
||||||
let state = await promise(threadId, runThread.id)
|
let state = await promise(threadId, runThread.id)
|
||||||
|
|
@ -650,9 +608,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||||
if (retries > 0) {
|
if (retries > 0) {
|
||||||
retries -= 1
|
retries -= 1
|
||||||
const newRunThread = await openai.beta.threads.runs.create(threadId, {
|
const newRunThread = await openai.beta.threads.runs.create(threadId, {
|
||||||
assistant_id: retrievedAssistant.id,
|
assistant_id: retrievedAssistant.id
|
||||||
tool_choice: toolChoice,
|
|
||||||
parallel_tool_calls: parallelToolCalls
|
|
||||||
})
|
})
|
||||||
runThreadId = newRunThread.id
|
runThreadId = newRunThread.id
|
||||||
state = await promise(threadId, newRunThread.id)
|
state = await promise(threadId, newRunThread.id)
|
||||||
|
|
@ -775,7 +731,7 @@ const downloadImg = async (openai: OpenAI, fileId: string, fileName: string, ...
|
||||||
const image_data_buffer = Buffer.from(image_data)
|
const image_data_buffer = Buffer.from(image_data)
|
||||||
const mime = 'image/png'
|
const mime = 'image/png'
|
||||||
|
|
||||||
await addSingleFileToStorage(mime, image_data_buffer, fileName, ...paths)
|
await addFileToStorage(mime, image_data_buffer, fileName, ...paths)
|
||||||
|
|
||||||
return image_data_buffer
|
return image_data_buffer
|
||||||
}
|
}
|
||||||
|
|
@ -798,7 +754,7 @@ const downloadFile = async (openAIApiKey: string, fileObj: any, fileName: string
|
||||||
const data_buffer = Buffer.from(data)
|
const data_buffer = Buffer.from(data)
|
||||||
const mime = 'application/octet-stream'
|
const mime = 'application/octet-stream'
|
||||||
|
|
||||||
return await addSingleFileToStorage(mime, data_buffer, fileName, ...paths)
|
return await addFileToStorage(mime, data_buffer, fileName, ...paths)
|
||||||
} catch (error) {
|
} catch (error) {
|
||||||
console.error('Error downloading or writing the file:', error)
|
console.error('Error downloading or writing the file:', error)
|
||||||
return ''
|
return ''
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,211 @@
|
||||||
|
import { flatten } from 'lodash'
|
||||||
|
import { BaseMessage } from '@langchain/core/messages'
|
||||||
|
import { ChainValues } from '@langchain/core/utils/types'
|
||||||
|
import { AgentStep } from '@langchain/core/agents'
|
||||||
|
import { RunnableSequence } from '@langchain/core/runnables'
|
||||||
|
import { ChatOpenAI, formatToOpenAIFunction } from '@langchain/openai'
|
||||||
|
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||||
|
import { OpenAIFunctionsAgentOutputParser } from 'langchain/agents/openai/output_parser'
|
||||||
|
import { getBaseClasses } from '../../../src/utils'
|
||||||
|
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||||
|
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||||
|
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||||
|
import { Moderation, checkInputs } from '../../moderation/Moderation'
|
||||||
|
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||||
|
|
||||||
|
class OpenAIFunctionAgent_Agents implements INode {
|
||||||
|
label: string
|
||||||
|
name: string
|
||||||
|
version: number
|
||||||
|
description: string
|
||||||
|
type: string
|
||||||
|
icon: string
|
||||||
|
category: string
|
||||||
|
baseClasses: string[]
|
||||||
|
inputs: INodeParams[]
|
||||||
|
badge?: string
|
||||||
|
sessionId?: string
|
||||||
|
|
||||||
|
constructor(fields?: { sessionId?: string }) {
|
||||||
|
this.label = 'OpenAI Function Agent'
|
||||||
|
this.name = 'openAIFunctionAgent'
|
||||||
|
this.version = 4.0
|
||||||
|
this.type = 'AgentExecutor'
|
||||||
|
this.category = 'Agents'
|
||||||
|
this.icon = 'function.svg'
|
||||||
|
this.description = `An agent that uses OpenAI Function Calling to pick the tool and args to call`
|
||||||
|
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||||
|
this.badge = 'DEPRECATING'
|
||||||
|
this.inputs = [
|
||||||
|
{
|
||||||
|
label: 'Allowed Tools',
|
||||||
|
name: 'tools',
|
||||||
|
type: 'Tool',
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Memory',
|
||||||
|
name: 'memory',
|
||||||
|
type: 'BaseChatMemory'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'OpenAI/Azure Chat Model',
|
||||||
|
name: 'model',
|
||||||
|
type: 'BaseChatModel'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'System Message',
|
||||||
|
name: 'systemMessage',
|
||||||
|
type: 'string',
|
||||||
|
rows: 4,
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Input Moderation',
|
||||||
|
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
|
||||||
|
name: 'inputModeration',
|
||||||
|
type: 'Moderation',
|
||||||
|
optional: true,
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Max Iterations',
|
||||||
|
name: 'maxIterations',
|
||||||
|
type: 'number',
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
this.sessionId = fields?.sessionId
|
||||||
|
}
|
||||||
|
|
||||||
|
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||||
|
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
}
|
||||||
|
|
||||||
|
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
|
|
||||||
|
if (moderations && moderations.length > 0) {
|
||||||
|
try {
|
||||||
|
// Use the output of the moderation chain as input for the OpenAI Function Agent
|
||||||
|
input = await checkInputs(moderations, input)
|
||||||
|
} catch (e) {
|
||||||
|
await new Promise((resolve) => setTimeout(resolve, 500))
|
||||||
|
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
|
||||||
|
return formatResponse(e.message)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
|
||||||
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
|
let res: ChainValues = {}
|
||||||
|
let sourceDocuments: ICommonObject[] = []
|
||||||
|
let usedTools: IUsedTool[] = []
|
||||||
|
|
||||||
|
if (options.socketIO && options.socketIOClientId) {
|
||||||
|
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await memory.addChatMessages(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
text: input,
|
||||||
|
type: 'userMessage'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
text: res?.output,
|
||||||
|
type: 'apiMessage'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
this.sessionId
|
||||||
|
)
|
||||||
|
|
||||||
|
let finalRes = res?.output
|
||||||
|
|
||||||
|
if (sourceDocuments.length || usedTools.length) {
|
||||||
|
finalRes = { text: res?.output }
|
||||||
|
if (sourceDocuments.length) {
|
||||||
|
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||||
|
}
|
||||||
|
if (usedTools.length) {
|
||||||
|
finalRes.usedTools = usedTools
|
||||||
|
}
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const prepareAgent = (nodeData: INodeData, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||||
|
const model = nodeData.inputs?.model as ChatOpenAI
|
||||||
|
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
|
let tools = nodeData.inputs?.tools
|
||||||
|
tools = flatten(tools)
|
||||||
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
|
|
||||||
|
const prompt = ChatPromptTemplate.fromMessages([
|
||||||
|
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||||
|
new MessagesPlaceholder(memoryKey),
|
||||||
|
['human', `{${inputKey}}`],
|
||||||
|
new MessagesPlaceholder('agent_scratchpad')
|
||||||
|
])
|
||||||
|
|
||||||
|
const modelWithFunctions = model.bind({
|
||||||
|
functions: [...tools.map((tool: any) => formatToOpenAIFunction(tool))]
|
||||||
|
})
|
||||||
|
|
||||||
|
const runnableAgent = RunnableSequence.from([
|
||||||
|
{
|
||||||
|
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||||
|
agent_scratchpad: (i: { input: string; steps: AgentStep[] }) => formatAgentSteps(i.steps),
|
||||||
|
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||||
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
|
return messages ?? []
|
||||||
|
}
|
||||||
|
},
|
||||||
|
prompt,
|
||||||
|
modelWithFunctions,
|
||||||
|
new OpenAIFunctionsAgentOutputParser()
|
||||||
|
])
|
||||||
|
|
||||||
|
const executor = AgentExecutor.fromAgentAndTools({
|
||||||
|
agent: runnableAgent,
|
||||||
|
tools,
|
||||||
|
sessionId: flowObj?.sessionId,
|
||||||
|
chatId: flowObj?.chatId,
|
||||||
|
input: flowObj?.input,
|
||||||
|
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||||
|
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||||
|
})
|
||||||
|
|
||||||
|
return executor
|
||||||
|
}
|
||||||
|
|
||||||
|
module.exports = { nodeClass: OpenAIFunctionAgent_Agents }
|
||||||
|
Before Width: | Height: | Size: 2.3 KiB After Width: | Height: | Size: 2.3 KiB |
|
|
@ -0,0 +1,210 @@
|
||||||
|
import { flatten } from 'lodash'
|
||||||
|
import { BaseMessage } from '@langchain/core/messages'
|
||||||
|
import { ChainValues } from '@langchain/core/utils/types'
|
||||||
|
import { RunnableSequence } from '@langchain/core/runnables'
|
||||||
|
import { ChatOpenAI } from '@langchain/openai'
|
||||||
|
import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||||
|
import { convertToOpenAITool } from '@langchain/core/utils/function_calling'
|
||||||
|
import { formatToOpenAIToolMessages } from 'langchain/agents/format_scratchpad/openai_tools'
|
||||||
|
import { OpenAIToolsAgentOutputParser, type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
|
||||||
|
import { getBaseClasses } from '../../../src/utils'
|
||||||
|
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||||
|
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||||
|
import { AgentExecutor } from '../../../src/agents'
|
||||||
|
import { Moderation, checkInputs } from '../../moderation/Moderation'
|
||||||
|
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||||
|
|
||||||
|
class OpenAIToolAgent_Agents implements INode {
|
||||||
|
label: string
|
||||||
|
name: string
|
||||||
|
version: number
|
||||||
|
description: string
|
||||||
|
type: string
|
||||||
|
icon: string
|
||||||
|
category: string
|
||||||
|
baseClasses: string[]
|
||||||
|
inputs: INodeParams[]
|
||||||
|
sessionId?: string
|
||||||
|
badge?: string
|
||||||
|
|
||||||
|
constructor(fields?: { sessionId?: string }) {
|
||||||
|
this.label = 'OpenAI Tool Agent'
|
||||||
|
this.name = 'openAIToolAgent'
|
||||||
|
this.version = 1.0
|
||||||
|
this.type = 'AgentExecutor'
|
||||||
|
this.category = 'Agents'
|
||||||
|
this.icon = 'function.svg'
|
||||||
|
this.description = `Agent that uses OpenAI Function Calling to pick the tools and args to call`
|
||||||
|
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||||
|
this.badge = 'DEPRECATING'
|
||||||
|
this.inputs = [
|
||||||
|
{
|
||||||
|
label: 'Tools',
|
||||||
|
name: 'tools',
|
||||||
|
type: 'Tool',
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Memory',
|
||||||
|
name: 'memory',
|
||||||
|
type: 'BaseChatMemory'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'OpenAI/Azure Chat Model',
|
||||||
|
name: 'model',
|
||||||
|
type: 'BaseChatModel'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'System Message',
|
||||||
|
name: 'systemMessage',
|
||||||
|
type: 'string',
|
||||||
|
rows: 4,
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Input Moderation',
|
||||||
|
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
|
||||||
|
name: 'inputModeration',
|
||||||
|
type: 'Moderation',
|
||||||
|
optional: true,
|
||||||
|
list: true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
label: 'Max Iterations',
|
||||||
|
name: 'maxIterations',
|
||||||
|
type: 'number',
|
||||||
|
optional: true,
|
||||||
|
additionalParams: true
|
||||||
|
}
|
||||||
|
]
|
||||||
|
this.sessionId = fields?.sessionId
|
||||||
|
}
|
||||||
|
|
||||||
|
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||||
|
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
}
|
||||||
|
|
||||||
|
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
|
|
||||||
|
if (moderations && moderations.length > 0) {
|
||||||
|
try {
|
||||||
|
// Use the output of the moderation chain as input for the OpenAI Function Agent
|
||||||
|
input = await checkInputs(moderations, input)
|
||||||
|
} catch (e) {
|
||||||
|
await new Promise((resolve) => setTimeout(resolve, 500))
|
||||||
|
//streamResponse(options.socketIO && options.socketIOClientId, e.message, options.socketIO, options.socketIOClientId)
|
||||||
|
return formatResponse(e.message)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
|
||||||
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
|
let res: ChainValues = {}
|
||||||
|
let sourceDocuments: ICommonObject[] = []
|
||||||
|
let usedTools: IUsedTool[] = []
|
||||||
|
|
||||||
|
if (options.socketIO && options.socketIOClientId) {
|
||||||
|
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', flatten(res.sourceDocuments))
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
options.socketIO.to(options.socketIOClientId).emit('usedTools', res.usedTools)
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
} else {
|
||||||
|
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||||
|
if (res.sourceDocuments) {
|
||||||
|
sourceDocuments = res.sourceDocuments
|
||||||
|
}
|
||||||
|
if (res.usedTools) {
|
||||||
|
usedTools = res.usedTools
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
await memory.addChatMessages(
|
||||||
|
[
|
||||||
|
{
|
||||||
|
text: input,
|
||||||
|
type: 'userMessage'
|
||||||
|
},
|
||||||
|
{
|
||||||
|
text: res?.output,
|
||||||
|
type: 'apiMessage'
|
||||||
|
}
|
||||||
|
],
|
||||||
|
this.sessionId
|
||||||
|
)
|
||||||
|
|
||||||
|
let finalRes = res?.output
|
||||||
|
|
||||||
|
if (sourceDocuments.length || usedTools.length) {
|
||||||
|
finalRes = { text: res?.output }
|
||||||
|
if (sourceDocuments.length) {
|
||||||
|
finalRes.sourceDocuments = flatten(sourceDocuments)
|
||||||
|
}
|
||||||
|
if (usedTools.length) {
|
||||||
|
finalRes.usedTools = usedTools
|
||||||
|
}
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
|
||||||
|
return finalRes
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const prepareAgent = (nodeData: INodeData, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||||
|
const model = nodeData.inputs?.model as ChatOpenAI
|
||||||
|
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||||
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
|
let tools = nodeData.inputs?.tools
|
||||||
|
tools = flatten(tools)
|
||||||
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
|
|
||||||
|
const prompt = ChatPromptTemplate.fromMessages([
|
||||||
|
['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
|
||||||
|
new MessagesPlaceholder(memoryKey),
|
||||||
|
['human', `{${inputKey}}`],
|
||||||
|
new MessagesPlaceholder('agent_scratchpad')
|
||||||
|
])
|
||||||
|
|
||||||
|
const modelWithTools = model.bind({ tools: tools.map(convertToOpenAITool) })
|
||||||
|
|
||||||
|
const runnableAgent = RunnableSequence.from([
|
||||||
|
{
|
||||||
|
[inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
|
||||||
|
agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) => formatToOpenAIToolMessages(i.steps),
|
||||||
|
[memoryKey]: async (_: { input: string; steps: ToolsAgentStep[] }) => {
|
||||||
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
|
return messages ?? []
|
||||||
|
}
|
||||||
|
},
|
||||||
|
prompt,
|
||||||
|
modelWithTools,
|
||||||
|
new OpenAIToolsAgentOutputParser()
|
||||||
|
])
|
||||||
|
|
||||||
|
const executor = AgentExecutor.fromAgentAndTools({
|
||||||
|
agent: runnableAgent,
|
||||||
|
tools,
|
||||||
|
sessionId: flowObj?.sessionId,
|
||||||
|
chatId: flowObj?.chatId,
|
||||||
|
input: flowObj?.input,
|
||||||
|
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||||
|
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
|
||||||
|
})
|
||||||
|
|
||||||
|
return executor
|
||||||
|
}
|
||||||
|
|
||||||
|
module.exports = { nodeClass: OpenAIToolAgent_Agents }
|
||||||
|
|
@ -1,9 +1,9 @@
|
||||||
import { flatten } from 'lodash'
|
import { flatten } from 'lodash'
|
||||||
import { MessageContentTextDetail, ChatMessage, AnthropicAgent, Anthropic } from 'llamaindex'
|
import { ChatMessage, OpenAI, OpenAIAgent } from 'llamaindex'
|
||||||
import { getBaseClasses } from '../../../../src/utils'
|
import { getBaseClasses } from '../../../src/utils'
|
||||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, IUsedTool } from '../../../../src/Interface'
|
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, IUsedTool } from '../../../src/Interface'
|
||||||
|
|
||||||
class AnthropicAgent_LlamaIndex_Agents implements INode {
|
class OpenAIFunctionAgent_LlamaIndex_Agents implements INode {
|
||||||
label: string
|
label: string
|
||||||
name: string
|
name: string
|
||||||
version: number
|
version: number
|
||||||
|
|
@ -18,15 +18,16 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
badge?: string
|
badge?: string
|
||||||
|
|
||||||
constructor(fields?: { sessionId?: string }) {
|
constructor(fields?: { sessionId?: string }) {
|
||||||
this.label = 'Anthropic Agent'
|
this.label = 'OpenAI Tool Agent'
|
||||||
this.name = 'anthropicAgentLlamaIndex'
|
this.name = 'openAIToolAgentLlamaIndex'
|
||||||
this.version = 1.0
|
this.version = 1.0
|
||||||
this.type = 'AnthropicAgent'
|
this.type = 'OpenAIToolAgent'
|
||||||
this.category = 'Agents'
|
this.category = 'Agents'
|
||||||
this.icon = 'Anthropic.svg'
|
this.icon = 'function.svg'
|
||||||
this.description = `Agent that uses Anthropic Claude Function Calling to pick the tools and args to call using LlamaIndex`
|
this.description = `Agent that uses OpenAI Function Calling to pick the tools and args to call using LlamaIndex`
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(AnthropicAgent)]
|
this.baseClasses = [this.type, ...getBaseClasses(OpenAIAgent)]
|
||||||
this.tags = ['LlamaIndex']
|
this.tags = ['LlamaIndex']
|
||||||
|
this.badge = 'NEW'
|
||||||
this.inputs = [
|
this.inputs = [
|
||||||
{
|
{
|
||||||
label: 'Tools',
|
label: 'Tools',
|
||||||
|
|
@ -40,7 +41,7 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
type: 'BaseChatMemory'
|
type: 'BaseChatMemory'
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
label: 'Anthropic Claude Model',
|
label: 'OpenAI/Azure Chat Model',
|
||||||
name: 'model',
|
name: 'model',
|
||||||
type: 'BaseChatModel_LlamaIndex'
|
type: 'BaseChatModel_LlamaIndex'
|
||||||
},
|
},
|
||||||
|
|
@ -60,12 +61,10 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
return null
|
return null
|
||||||
}
|
}
|
||||||
|
|
||||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
async run(nodeData: INodeData, input: string): Promise<string | ICommonObject> {
|
||||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
const model = nodeData.inputs?.model as Anthropic
|
const model = nodeData.inputs?.model as OpenAI
|
||||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
let tools = nodeData.inputs?.tools
|
let tools = nodeData.inputs?.tools
|
||||||
tools = flatten(tools)
|
tools = flatten(tools)
|
||||||
|
|
||||||
|
|
@ -78,7 +77,7 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
})
|
})
|
||||||
}
|
}
|
||||||
|
|
||||||
const msgs = (await memory.getChatMessages(this.sessionId, false, prependMessages)) as IMessage[]
|
const msgs = (await memory.getChatMessages(this.sessionId, false)) as IMessage[]
|
||||||
for (const message of msgs) {
|
for (const message of msgs) {
|
||||||
if (message.type === 'apiMessage') {
|
if (message.type === 'apiMessage') {
|
||||||
chatHistory.push({
|
chatHistory.push({
|
||||||
|
|
@ -93,33 +92,31 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
const agent = new AnthropicAgent({
|
const agent = new OpenAIAgent({
|
||||||
tools,
|
tools,
|
||||||
llm: model,
|
llm: model,
|
||||||
chatHistory: chatHistory,
|
prefixMessages: chatHistory,
|
||||||
verbose: process.env.DEBUG === 'true' ? true : false
|
verbose: process.env.DEBUG === 'true' ? true : false
|
||||||
})
|
})
|
||||||
|
|
||||||
let text = ''
|
let text = ''
|
||||||
const usedTools: IUsedTool[] = []
|
const usedTools: IUsedTool[] = []
|
||||||
|
|
||||||
const response = await agent.chat({ message: input, chatHistory, verbose: process.env.DEBUG === 'true' ? true : false })
|
const response = await agent.chat({
|
||||||
|
message: input
|
||||||
|
})
|
||||||
|
|
||||||
if (response.sources.length) {
|
if (response.sources.length) {
|
||||||
for (const sourceTool of response.sources) {
|
for (const sourceTool of response.sources) {
|
||||||
usedTools.push({
|
usedTools.push({
|
||||||
tool: sourceTool.tool?.metadata.name ?? '',
|
tool: sourceTool.toolName,
|
||||||
toolInput: sourceTool.input,
|
toolInput: sourceTool.rawInput,
|
||||||
toolOutput: sourceTool.output as any
|
toolOutput: sourceTool.rawOutput
|
||||||
})
|
})
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
if (Array.isArray(response.response.message.content) && response.response.message.content.length > 0) {
|
text = String(response)
|
||||||
text = (response.response.message.content[0] as MessageContentTextDetail).text
|
|
||||||
} else {
|
|
||||||
text = response.response.message.content as string
|
|
||||||
}
|
|
||||||
|
|
||||||
await memory.addChatMessages(
|
await memory.addChatMessages(
|
||||||
[
|
[
|
||||||
|
|
@ -139,4 +136,4 @@ class AnthropicAgent_LlamaIndex_Agents implements INode {
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
module.exports = { nodeClass: AnthropicAgent_LlamaIndex_Agents }
|
module.exports = { nodeClass: OpenAIFunctionAgent_LlamaIndex_Agents }
|
||||||
|
|
@ -0,0 +1,9 @@
|
||||||
|
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||||
|
<path d="M16 12.6108L22 15.9608" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M7.17701 19.5848C6.49568 20.4069 6.12505 21.4424 6.12993 22.5101C6.13481 23.5779 6.51489 24.6099 7.2037 25.4258C7.89252 26.2416 8.84622 26.7893 9.89802 26.9732C10.9498 27.157 12.0328 26.9653 12.9575 26.4314L15.4787 24.9657M18.6002 14.106V19.5848" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M8.19877 9.98459C6.39026 9.67775 4.57524 10.4982 3.60403 12.1806C3.00524 13.2178 2.84295 14.4504 3.15284 15.6073C3.46273 16.7642 4.21943 17.7507 5.25652 18.3498L10.3049 21.3269C10.6109 21.5074 10.9898 21.5119 11.3001 21.3388L18.6 17.2655" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M17.0172 6.06585C16.6456 5.06522 15.9342 4.227 15.0072 3.6977C14.0803 3.1684 12.9969 2.98168 11.9462 3.17018C10.8956 3.35869 9.94464 3.91042 9.25954 4.72895C8.57444 5.54747 8.19879 6.58074 8.19824 7.64814V13.6575C8.19824 14.0154 8.38951 14.346 8.69977 14.5244L15.9992 18.7215" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M24.8216 11.7476C25.5029 10.9255 25.8735 9.89004 25.8687 8.8223C25.8638 7.75457 25.4837 6.72253 24.7949 5.90667C24.1061 5.09082 23.1524 4.54308 22.1006 4.35924C21.0488 4.17541 19.9658 4.36718 19.0411 4.90101L13.8942 7.90613C13.5872 8.08539 13.3984 8.41418 13.3984 8.76971V17.2265" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M28.3944 19.0635C28.9932 18.0263 29.1555 16.7937 28.8456 15.6368C28.5357 14.4799 27.779 13.4934 26.7419 12.8943L21.6409 9.91752C21.3316 9.73703 20.9494 9.7357 20.6388 9.91405L13.3984 14.0723" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
<path d="M18 28.9997H18.8071C19.6909 28.9997 20.4526 28.3921 20.6297 27.546L21.991 21.4537C22.1681 20.6076 22.9299 20 23.8136 20H24.6207M20.0929 22.7023H23.8136M24 25.0214H24.5014C24.8438 25.0214 25.1586 25.2052 25.3207 25.5L27.3429 28.5213C27.5051 28.8161 27.8198 29 28.1622 29H28.6997M24.049 29C24.6261 29 25.1609 28.7041 25.4578 28.2205L27.2424 25.8009C27.5393 25.3173 28.0741 25.0214 28.6512 25.0214" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||||
|
</svg>
|
||||||
|
After Width: | Height: | Size: 2.3 KiB |
|
|
@ -80,7 +80,6 @@ class ReActAgentChat_Agents implements INode {
|
||||||
const model = nodeData.inputs?.model as BaseChatModel
|
const model = nodeData.inputs?.model as BaseChatModel
|
||||||
let tools = nodeData.inputs?.tools as Tool[]
|
let tools = nodeData.inputs?.tools as Tool[]
|
||||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
if (moderations && moderations.length > 0) {
|
if (moderations && moderations.length > 0) {
|
||||||
try {
|
try {
|
||||||
|
|
@ -135,7 +134,7 @@ class ReActAgentChat_Agents implements INode {
|
||||||
|
|
||||||
const callbacks = await additionalCallbacks(nodeData, options)
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
const chatHistory = ((await memory.getChatMessages(this.sessionId, false, prependMessages)) as IMessage[]) ?? []
|
const chatHistory = ((await memory.getChatMessages(this.sessionId, false)) as IMessage[]) ?? []
|
||||||
const chatHistoryString = chatHistory.map((hist) => hist.message).join('\\n')
|
const chatHistoryString = chatHistory.map((hist) => hist.message).join('\\n')
|
||||||
|
|
||||||
const result = await executor.invoke({ input, chat_history: chatHistoryString }, { callbacks })
|
const result = await executor.invoke({ input, chat_history: chatHistoryString }, { callbacks })
|
||||||
|
|
|
||||||
|
|
@ -36,6 +36,7 @@ class ToolAgent_Agents implements INode {
|
||||||
this.icon = 'toolAgent.png'
|
this.icon = 'toolAgent.png'
|
||||||
this.description = `Agent that uses Function Calling to pick the tools and args to call`
|
this.description = `Agent that uses Function Calling to pick the tools and args to call`
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||||
|
this.badge = 'NEW'
|
||||||
this.inputs = [
|
this.inputs = [
|
||||||
{
|
{
|
||||||
label: 'Tools',
|
label: 'Tools',
|
||||||
|
|
@ -53,7 +54,7 @@ class ToolAgent_Agents implements INode {
|
||||||
name: 'model',
|
name: 'model',
|
||||||
type: 'BaseChatModel',
|
type: 'BaseChatModel',
|
||||||
description:
|
description:
|
||||||
'Only compatible with models that are capable of function calling: ChatOpenAI, ChatMistral, ChatAnthropic, ChatGoogleGenerativeAI, ChatVertexAI, GroqChat'
|
'Only compatible with models that are capable of function calling. ChatOpenAI, ChatMistral, ChatAnthropic, ChatVertexAI'
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
label: 'System Message',
|
label: 'System Message',
|
||||||
|
|
@ -190,7 +191,6 @@ const prepareAgent = async (
|
||||||
tools = flatten(tools)
|
tools = flatten(tools)
|
||||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
const prompt = ChatPromptTemplate.fromMessages([
|
const prompt = ChatPromptTemplate.fromMessages([
|
||||||
['system', systemMessage],
|
['system', systemMessage],
|
||||||
|
|
@ -239,7 +239,7 @@ const prepareAgent = async (
|
||||||
[inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
|
[inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
|
||||||
agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) => formatToOpenAIToolMessages(i.steps),
|
agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) => formatToOpenAIToolMessages(i.steps),
|
||||||
[memoryKey]: async (_: { input: string; steps: ToolsAgentStep[] }) => {
|
[memoryKey]: async (_: { input: string; steps: ToolsAgentStep[] }) => {
|
||||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, prependMessages)) as BaseMessage[]
|
const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
|
||||||
return messages ?? []
|
return messages ?? []
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
|
|
|
||||||
|
|
@ -122,7 +122,7 @@ class XMLAgent_Agents implements INode {
|
||||||
return formatResponse(e.message)
|
return formatResponse(e.message)
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
const executor = await prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
|
const executor = await prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input })
|
||||||
|
|
||||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
const callbacks = await additionalCallbacks(nodeData, options)
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
@ -183,11 +183,7 @@ class XMLAgent_Agents implements INode {
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
const prepareAgent = async (
|
const prepareAgent = async (nodeData: INodeData, flowObj: { sessionId?: string; chatId?: string; input?: string }) => {
|
||||||
nodeData: INodeData,
|
|
||||||
options: ICommonObject,
|
|
||||||
flowObj: { sessionId?: string; chatId?: string; input?: string }
|
|
||||||
) => {
|
|
||||||
const model = nodeData.inputs?.model as BaseChatModel
|
const model = nodeData.inputs?.model as BaseChatModel
|
||||||
const maxIterations = nodeData.inputs?.maxIterations as string
|
const maxIterations = nodeData.inputs?.maxIterations as string
|
||||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
|
|
@ -196,7 +192,6 @@ const prepareAgent = async (
|
||||||
tools = flatten(tools)
|
tools = flatten(tools)
|
||||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
let promptMessage = systemMessage ? systemMessage : defaultSystemMessage
|
let promptMessage = systemMessage ? systemMessage : defaultSystemMessage
|
||||||
if (memory.memoryKey) promptMessage = promptMessage.replaceAll('{chat_history}', `{${memory.memoryKey}}`)
|
if (memory.memoryKey) promptMessage = promptMessage.replaceAll('{chat_history}', `{${memory.memoryKey}}`)
|
||||||
|
|
@ -215,7 +210,7 @@ const prepareAgent = async (
|
||||||
|
|
||||||
const llmWithStop = model.bind({ stop: ['</tool_input>', '</final_answer>'] })
|
const llmWithStop = model.bind({ stop: ['</tool_input>', '</final_answer>'] })
|
||||||
|
|
||||||
const messages = (await memory.getChatMessages(flowObj.sessionId, false, prependMessages)) as IMessage[]
|
const messages = (await memory.getChatMessages(flowObj.sessionId, false)) as IMessage[]
|
||||||
let chatHistoryMsgTxt = ''
|
let chatHistoryMsgTxt = ''
|
||||||
for (const message of messages) {
|
for (const message of messages) {
|
||||||
if (message.type === 'apiMessage') {
|
if (message.type === 'apiMessage') {
|
||||||
|
|
|
||||||
|
|
@ -1,3 +0,0 @@
|
||||||
<svg width="38" height="52" viewBox="0 0 38 52" fill="none" xmlns="http://www.w3.org/2000/svg">
|
|
||||||
<path d="M0 12.383V41.035C0 41.392 0.190002 41.723 0.500002 41.901L17.095 51.481C17.25 51.571 17.422 51.616 17.595 51.616C17.768 51.616 17.94 51.571 18.095 51.481L37.279 40.409C37.589 40.23 37.779 39.9 37.779 39.543V10.887C37.779 10.53 37.589 10.199 37.279 10.021L31.168 6.49498C31.014 6.40598 30.841 6.36098 30.669 6.36098C30.496 6.36098 30.323 6.40498 30.169 6.49498L27.295 8.15398V4.83698C27.295 4.47998 27.105 4.14898 26.795 3.97098L20.684 0.441982C20.529 0.352982 20.357 0.307983 20.184 0.307983C20.011 0.307983 19.839 0.352982 19.684 0.441982L13.781 3.85098C13.471 4.02998 13.281 4.35998 13.281 4.71698V12.157L12.921 12.365V11.872C12.921 11.515 12.731 11.185 12.421 11.006L7.405 8.10698C7.25 8.01798 7.077 7.97298 6.905 7.97298C6.733 7.97298 6.56 8.01798 6.405 8.10698L0.501001 11.517C0.191001 11.695 0 12.025 0 12.383ZM1.5 13.248L5.519 15.566V23.294C5.519 23.304 5.524 23.313 5.525 23.323C5.526 23.345 5.529 23.366 5.534 23.388C5.538 23.411 5.544 23.433 5.552 23.455C5.559 23.476 5.567 23.496 5.577 23.516C5.582 23.525 5.581 23.535 5.587 23.544C5.591 23.551 5.6 23.554 5.604 23.561C5.617 23.581 5.63 23.6 5.646 23.618C5.669 23.644 5.695 23.665 5.724 23.686C5.741 23.698 5.751 23.716 5.77 23.727L11.236 26.886C11.243 26.89 11.252 26.888 11.26 26.892C11.328 26.927 11.402 26.952 11.484 26.952C11.566 26.952 11.641 26.928 11.709 26.893C11.728 26.883 11.743 26.87 11.761 26.858C11.812 26.823 11.855 26.781 11.89 26.731C11.898 26.719 11.911 26.715 11.919 26.702C11.924 26.693 11.924 26.682 11.929 26.674C11.944 26.644 11.951 26.613 11.96 26.58C11.969 26.547 11.978 26.515 11.98 26.481C11.98 26.471 11.986 26.462 11.986 26.452V20.138V19.302L17.096 22.251V49.749L1.5 40.747V13.248ZM35.778 10.887L30.879 13.718L25.768 10.766L26.544 10.317L30.668 7.93698L35.778 10.887ZM25.293 4.83598L20.391 7.66498L15.281 4.71598L20.183 1.88398L25.293 4.83598ZM10.92 11.872L6.019 14.701L2.001 12.383L6.904 9.55098L10.92 11.872ZM20.956 16.51L24.268 14.601V18.788C24.268 18.809 24.278 18.827 24.28 18.848C24.284 18.883 24.29 18.917 24.301 18.95C24.311 18.98 24.325 19.007 24.342 19.034C24.358 19.061 24.373 19.088 24.395 19.112C24.417 19.138 24.444 19.159 24.471 19.18C24.489 19.193 24.499 19.21 24.518 19.221L29.878 22.314L23.998 25.708V18.557C23.998 18.547 23.993 18.538 23.992 18.528C23.991 18.506 23.988 18.485 23.984 18.463C23.979 18.44 23.973 18.418 23.965 18.396C23.958 18.375 23.95 18.355 23.941 18.336C23.936 18.327 23.937 18.316 23.931 18.308C23.925 18.299 23.917 18.294 23.911 18.286C23.898 18.267 23.886 18.251 23.871 18.234C23.855 18.216 23.84 18.2 23.822 18.185C23.805 18.17 23.788 18.157 23.769 18.144C23.76 18.138 23.756 18.129 23.747 18.124L20.956 16.51ZM25.268 11.633L30.379 14.585V21.448L25.268 18.499V13.736V11.633ZM12.486 18.437L17.389 15.604L22.498 18.556L17.595 21.385L12.486 18.437ZM10.985 25.587L7.019 23.295L10.985 21.005V25.587ZM12.42 14.385L14.28 13.311L16.822 14.777L12.42 17.32V14.385ZM14.78 5.58198L19.891 8.53098V15.394L14.78 12.445V5.58198Z" fill="#213B41"/>
|
|
||||||
</svg>
|
|
||||||
|
Before Width: | Height: | Size: 3.0 KiB |
|
|
@ -1,33 +0,0 @@
|
||||||
import { INode, INodeParams } from '../../../src/Interface'
|
|
||||||
|
|
||||||
class LangWatch_Analytic implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
description: string
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
baseClasses: string[]
|
|
||||||
inputs?: INodeParams[]
|
|
||||||
credential: INodeParams
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'LangWatch'
|
|
||||||
this.name = 'LangWatch'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'LangWatch'
|
|
||||||
this.icon = 'LangWatch.svg'
|
|
||||||
this.category = 'Analytic'
|
|
||||||
this.baseClasses = [this.type]
|
|
||||||
this.inputs = []
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['langwatchApi']
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: LangWatch_Analytic }
|
|
||||||
|
|
@ -220,7 +220,6 @@ const prepareChain = async (nodeData: INodeData, options: ICommonObject, session
|
||||||
let model = nodeData.inputs?.model as BaseChatModel
|
let model = nodeData.inputs?.model as BaseChatModel
|
||||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||||
const memoryKey = memory.memoryKey ?? 'chat_history'
|
const memoryKey = memory.memoryKey ?? 'chat_history'
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
let messageContent: MessageContentImageUrl[] = []
|
let messageContent: MessageContentImageUrl[] = []
|
||||||
if (llmSupportsVision(model)) {
|
if (llmSupportsVision(model)) {
|
||||||
|
|
@ -253,7 +252,7 @@ const prepareChain = async (nodeData: INodeData, options: ICommonObject, session
|
||||||
{
|
{
|
||||||
[inputKey]: (input: { input: string }) => input.input,
|
[inputKey]: (input: { input: string }) => input.input,
|
||||||
[memoryKey]: async () => {
|
[memoryKey]: async () => {
|
||||||
const history = await memory.getChatMessages(sessionId, true, prependMessages)
|
const history = await memory.getChatMessages(sessionId, true)
|
||||||
return history
|
return history
|
||||||
},
|
},
|
||||||
...promptVariables
|
...promptVariables
|
||||||
|
|
|
||||||
|
|
@ -175,7 +175,6 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||||
const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
|
const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
|
||||||
const responsePrompt = nodeData.inputs?.responsePrompt as string
|
const responsePrompt = nodeData.inputs?.responsePrompt as string
|
||||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||||
const prependMessages = options?.prependMessages
|
|
||||||
|
|
||||||
const appDataSource = options.appDataSource as DataSource
|
const appDataSource = options.appDataSource as DataSource
|
||||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||||
|
|
@ -211,7 +210,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||||
}
|
}
|
||||||
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
|
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
|
||||||
|
|
||||||
const history = ((await memory.getChatMessages(this.sessionId, false, prependMessages)) as IMessage[]) ?? []
|
const history = ((await memory.getChatMessages(this.sessionId, false)) as IMessage[]) ?? []
|
||||||
|
|
||||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
const additionalCallback = await additionalCallbacks(nodeData, options)
|
const additionalCallback = await additionalCallbacks(nodeData, options)
|
||||||
|
|
@ -402,11 +401,7 @@ class BufferMemory extends FlowiseMemory implements MemoryMethods {
|
||||||
this.chatflowid = fields.chatflowid
|
this.chatflowid = fields.chatflowid
|
||||||
}
|
}
|
||||||
|
|
||||||
async getChatMessages(
|
async getChatMessages(overrideSessionId = '', returnBaseMessages = false): Promise<IMessage[] | BaseMessage[]> {
|
||||||
overrideSessionId = '',
|
|
||||||
returnBaseMessages = false,
|
|
||||||
prependMessages?: IMessage[]
|
|
||||||
): Promise<IMessage[] | BaseMessage[]> {
|
|
||||||
if (!overrideSessionId) return []
|
if (!overrideSessionId) return []
|
||||||
|
|
||||||
const chatMessage = await this.appDataSource.getRepository(this.databaseEntities['ChatMessage']).find({
|
const chatMessage = await this.appDataSource.getRepository(this.databaseEntities['ChatMessage']).find({
|
||||||
|
|
@ -419,10 +414,6 @@ class BufferMemory extends FlowiseMemory implements MemoryMethods {
|
||||||
}
|
}
|
||||||
})
|
})
|
||||||
|
|
||||||
if (prependMessages?.length) {
|
|
||||||
chatMessage.unshift(...prependMessages)
|
|
||||||
}
|
|
||||||
|
|
||||||
if (returnBaseMessages) {
|
if (returnBaseMessages) {
|
||||||
return mapChatMessageToBaseMessage(chatMessage)
|
return mapChatMessageToBaseMessage(chatMessage)
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -110,9 +110,7 @@ class LLMChain_Chains implements INode {
|
||||||
})
|
})
|
||||||
const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
|
const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
|
||||||
promptValues = injectOutputParser(this.outputParser, chain, promptValues)
|
promptValues = injectOutputParser(this.outputParser, chain, promptValues)
|
||||||
// Disable streaming because its not final chain
|
const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
|
||||||
const disableStreaming = true
|
|
||||||
const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData, disableStreaming)
|
|
||||||
// eslint-disable-next-line no-console
|
// eslint-disable-next-line no-console
|
||||||
console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
|
console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
|
||||||
// eslint-disable-next-line no-console
|
// eslint-disable-next-line no-console
|
||||||
|
|
@ -156,13 +154,12 @@ const runPrediction = async (
|
||||||
input: string,
|
input: string,
|
||||||
promptValuesRaw: ICommonObject | undefined,
|
promptValuesRaw: ICommonObject | undefined,
|
||||||
options: ICommonObject,
|
options: ICommonObject,
|
||||||
nodeData: INodeData,
|
nodeData: INodeData
|
||||||
disableStreaming?: boolean
|
|
||||||
) => {
|
) => {
|
||||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||||
const callbacks = await additionalCallbacks(nodeData, options)
|
const callbacks = await additionalCallbacks(nodeData, options)
|
||||||
|
|
||||||
const isStreaming = !disableStreaming && options.socketIO && options.socketIOClientId
|
const isStreaming = options.socketIO && options.socketIOClientId
|
||||||
const socketIO = isStreaming ? options.socketIO : undefined
|
const socketIO = isStreaming ? options.socketIO : undefined
|
||||||
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
||||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||||
|
|
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||||
import { OpenAI } from 'llamaindex'
|
import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
|
||||||
import { getModels, MODEL_TYPE } from '../../../src/modelLoader'
|
import { getModels, MODEL_TYPE } from '../../../src/modelLoader'
|
||||||
|
|
||||||
interface AzureOpenAIConfig {
|
interface AzureOpenAIConfig {
|
||||||
|
|
@ -10,28 +10,6 @@ interface AzureOpenAIConfig {
|
||||||
deploymentName?: string
|
deploymentName?: string
|
||||||
}
|
}
|
||||||
|
|
||||||
const ALL_AZURE_OPENAI_CHAT_MODELS = {
|
|
||||||
'gpt-35-turbo': { contextWindow: 4096, openAIModel: 'gpt-3.5-turbo' },
|
|
||||||
'gpt-35-turbo-16k': {
|
|
||||||
contextWindow: 16384,
|
|
||||||
openAIModel: 'gpt-3.5-turbo-16k'
|
|
||||||
},
|
|
||||||
'gpt-4': { contextWindow: 8192, openAIModel: 'gpt-4' },
|
|
||||||
'gpt-4-32k': { contextWindow: 32768, openAIModel: 'gpt-4-32k' },
|
|
||||||
'gpt-4-turbo': {
|
|
||||||
contextWindow: 128000,
|
|
||||||
openAIModel: 'gpt-4-turbo'
|
|
||||||
},
|
|
||||||
'gpt-4-vision-preview': {
|
|
||||||
contextWindow: 128000,
|
|
||||||
openAIModel: 'gpt-4-vision-preview'
|
|
||||||
},
|
|
||||||
'gpt-4-1106-preview': {
|
|
||||||
contextWindow: 128000,
|
|
||||||
openAIModel: 'gpt-4-1106-preview'
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
class AzureChatOpenAI_LlamaIndex_ChatModels implements INode {
|
class AzureChatOpenAI_LlamaIndex_ChatModels implements INode {
|
||||||
label: string
|
label: string
|
||||||
name: string
|
name: string
|
||||||
|
|
@ -112,7 +90,7 @@ class AzureChatOpenAI_LlamaIndex_ChatModels implements INode {
|
||||||
}
|
}
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||||
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AZURE_OPENAI_CHAT_MODELS
|
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_OPENAI_MODELS
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
const temperature = nodeData.inputs?.temperature as string
|
||||||
const maxTokens = nodeData.inputs?.maxTokens as string
|
const maxTokens = nodeData.inputs?.maxTokens as string
|
||||||
const topP = nodeData.inputs?.topP as string
|
const topP = nodeData.inputs?.topP as string
|
||||||
|
|
|
||||||
|
|
@ -36,7 +36,7 @@ class ChatAnthropic_LlamaIndex_ChatModels implements INode {
|
||||||
{
|
{
|
||||||
label: 'Model Name',
|
label: 'Model Name',
|
||||||
name: 'modelName',
|
name: 'modelName',
|
||||||
type: 'asyncOptions',
|
type: 'options',
|
||||||
loadMethod: 'listModels',
|
loadMethod: 'listModels',
|
||||||
default: 'claude-3-haiku'
|
default: 'claude-3-haiku'
|
||||||
},
|
},
|
||||||
|
|
|
||||||
|
|
@ -1,80 +0,0 @@
|
||||||
import { BaseCache } from '@langchain/core/caches'
|
|
||||||
import { ChatBaiduWenxin } from '@langchain/community/chat_models/baiduwenxin'
|
|
||||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
|
|
||||||
class ChatBaiduWenxin_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatBaiduWenxin'
|
|
||||||
this.name = 'chatBaiduWenxin'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatBaiduWenxin'
|
|
||||||
this.icon = 'baiduwenxin.svg'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around BaiduWenxin Chat Endpoints'
|
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(ChatBaiduWenxin)]
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['baiduApi']
|
|
||||||
}
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Cache',
|
|
||||||
name: 'cache',
|
|
||||||
type: 'BaseCache',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Model',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'string',
|
|
||||||
placeholder: 'ERNIE-Bot-turbo'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const cache = nodeData.inputs?.cache as BaseCache
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const baiduApiKey = getCredentialParam('baiduApiKey', credentialData, nodeData)
|
|
||||||
const baiduSecretKey = getCredentialParam('baiduSecretKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
const obj: Partial<ChatBaiduWenxin> = {
|
|
||||||
streaming: true,
|
|
||||||
baiduApiKey,
|
|
||||||
baiduSecretKey,
|
|
||||||
modelName,
|
|
||||||
temperature: temperature ? parseFloat(temperature) : undefined
|
|
||||||
}
|
|
||||||
if (cache) obj.cache = cache
|
|
||||||
|
|
||||||
const model = new ChatBaiduWenxin(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatBaiduWenxin_ChatModels }
|
|
||||||
|
|
@ -1,7 +0,0 @@
|
||||||
<?xml version="1.0" encoding="utf-8"?><!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
|
|
||||||
<svg xmlns="http://www.w3.org/2000/svg"
|
|
||||||
aria-label="Baidu" role="img"
|
|
||||||
viewBox="0 0 512 512"><rect
|
|
||||||
width="512" height="512"
|
|
||||||
rx="15%"
|
|
||||||
fill="#ffffff"/><path d="m131 251c41-9 35-58 34-68-2-17-21-45-48-43-33 3-37 50-37 50-5 22 10 70 51 61m76-82c22 0 40-26 40-58s-18-58-40-58c-23 0-41 26-41 58s18 58 41 58m96 4c31 4 50-28 54-53 4-24-16-52-37-57s-48 29-50 52c-3 27 3 54 33 58m120 41c0-12-10-47-46-47s-41 33-41 57c0 22 2 53 47 52s40-51 40-62m-46 102s-46-36-74-75c-36-57-89-34-106-5-18 29-45 48-49 53-4 4-56 33-44 84 11 52 52 51 52 51s30 3 65-5 65 2 65 2 81 27 104-25c22-53-13-80-13-80" fill="#2319dc"/><path d="m214 266v34h-28s-29 3-39 35c-3 21 4 34 5 36 1 3 10 19 33 23h53v-128zm-1 107h-21s-15-1-19-18c-3-7 0-16 1-20 1-3 6-11 17-14h22zm38-70v68s1 17 24 23h61v-91h-26v68h-25s-8-1-10-7v-61z" fill="#ffffff"/></svg>
|
|
||||||
|
Before Width: | Height: | Size: 924 B |
|
|
@ -1,79 +0,0 @@
|
||||||
import { BaseCache } from '@langchain/core/caches'
|
|
||||||
import { ChatFireworks } from '@langchain/community/chat_models/fireworks'
|
|
||||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
|
|
||||||
class ChatFireworks_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatFireworks'
|
|
||||||
this.name = 'chatFireworks'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatFireworks'
|
|
||||||
this.icon = 'Fireworks.png'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around Fireworks Chat Endpoints'
|
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(ChatFireworks)]
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['fireworksApi']
|
|
||||||
}
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Cache',
|
|
||||||
name: 'cache',
|
|
||||||
type: 'BaseCache',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Model',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'string',
|
|
||||||
default: 'accounts/fireworks/models/llama-v2-13b-chat',
|
|
||||||
placeholder: 'accounts/fireworks/models/llama-v2-13b-chat'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const cache = nodeData.inputs?.cache as BaseCache
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const fireworksApiKey = getCredentialParam('fireworksApiKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
const obj: Partial<ChatFireworks> = {
|
|
||||||
fireworksApiKey,
|
|
||||||
model: modelName,
|
|
||||||
modelName,
|
|
||||||
temperature: temperature ? parseFloat(temperature) : undefined
|
|
||||||
}
|
|
||||||
if (cache) obj.cache = cache
|
|
||||||
|
|
||||||
const model = new ChatFireworks(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatFireworks_ChatModels }
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 6.6 KiB |
|
|
@ -206,8 +206,7 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
|
||||||
options: this['ParsedCallOptions'],
|
options: this['ParsedCallOptions'],
|
||||||
runManager?: CallbackManagerForLLMRun
|
runManager?: CallbackManagerForLLMRun
|
||||||
): Promise<ChatResult> {
|
): Promise<ChatResult> {
|
||||||
let prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel)
|
const prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel)
|
||||||
prompt = checkIfEmptyContentAndSameRole(prompt)
|
|
||||||
|
|
||||||
// Handle streaming
|
// Handle streaming
|
||||||
if (this.streaming) {
|
if (this.streaming) {
|
||||||
|
|
@ -236,9 +235,7 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
|
||||||
options: this['ParsedCallOptions'],
|
options: this['ParsedCallOptions'],
|
||||||
runManager?: CallbackManagerForLLMRun
|
runManager?: CallbackManagerForLLMRun
|
||||||
): AsyncGenerator<ChatGenerationChunk> {
|
): AsyncGenerator<ChatGenerationChunk> {
|
||||||
let prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel)
|
const prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel)
|
||||||
prompt = checkIfEmptyContentAndSameRole(prompt)
|
|
||||||
|
|
||||||
//@ts-ignore
|
//@ts-ignore
|
||||||
if (options.tools !== undefined && options.tools.length > 0) {
|
if (options.tools !== undefined && options.tools.length > 0) {
|
||||||
const result = await this._generateNonStreaming(prompt, options, runManager)
|
const result = await this._generateNonStreaming(prompt, options, runManager)
|
||||||
|
|
@ -336,9 +333,7 @@ function convertAuthorToRole(author: string) {
|
||||||
case 'tool':
|
case 'tool':
|
||||||
return 'function'
|
return 'function'
|
||||||
default:
|
default:
|
||||||
// Instead of throwing, we return model
|
throw new Error(`Unknown / unsupported author: ${author}`)
|
||||||
// throw new Error(`Unknown / unsupported author: ${author}`)
|
|
||||||
return 'model'
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -401,25 +396,6 @@ function convertMessageContentToParts(content: MessageContent, isMultimodalModel
|
||||||
})
|
})
|
||||||
}
|
}
|
||||||
|
|
||||||
/*
|
|
||||||
* This is a dedicated logic for Multi Agent Supervisor to handle the case where the content is empty, and the role is the same
|
|
||||||
*/
|
|
||||||
|
|
||||||
function checkIfEmptyContentAndSameRole(contents: Content[]) {
|
|
||||||
let prevRole = ''
|
|
||||||
const removedContents: Content[] = []
|
|
||||||
for (const content of contents) {
|
|
||||||
const role = content.role
|
|
||||||
if (content.parts.length && content.parts[0].text === '' && role === prevRole) {
|
|
||||||
removedContents.push(content)
|
|
||||||
}
|
|
||||||
|
|
||||||
prevRole = role
|
|
||||||
}
|
|
||||||
|
|
||||||
return contents.filter((content) => !removedContents.includes(content))
|
|
||||||
}
|
|
||||||
|
|
||||||
function convertBaseMessagesToContent(messages: BaseMessage[], isMultimodalModel: boolean) {
|
function convertBaseMessagesToContent(messages: BaseMessage[], isMultimodalModel: boolean) {
|
||||||
return messages.reduce<{
|
return messages.reduce<{
|
||||||
content: Content[]
|
content: Content[]
|
||||||
|
|
@ -552,13 +528,6 @@ function zodToGeminiParameters(zodObj: any) {
|
||||||
const jsonSchema: any = zodToJsonSchema(zodObj)
|
const jsonSchema: any = zodToJsonSchema(zodObj)
|
||||||
// eslint-disable-next-line unused-imports/no-unused-vars
|
// eslint-disable-next-line unused-imports/no-unused-vars
|
||||||
const { $schema, additionalProperties, ...rest } = jsonSchema
|
const { $schema, additionalProperties, ...rest } = jsonSchema
|
||||||
if (rest.properties) {
|
|
||||||
Object.keys(rest.properties).forEach((key) => {
|
|
||||||
if (rest.properties[key].enum?.length) {
|
|
||||||
rest.properties[key] = { type: 'string', format: 'enum', enum: rest.properties[key].enum }
|
|
||||||
}
|
|
||||||
})
|
|
||||||
}
|
|
||||||
return rest
|
return rest
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -18,7 +18,7 @@ class ChatHuggingFace_ChatModels implements INode {
|
||||||
constructor() {
|
constructor() {
|
||||||
this.label = 'ChatHuggingFace'
|
this.label = 'ChatHuggingFace'
|
||||||
this.name = 'chatHuggingFace'
|
this.name = 'chatHuggingFace'
|
||||||
this.version = 3.0
|
this.version = 2.0
|
||||||
this.type = 'ChatHuggingFace'
|
this.type = 'ChatHuggingFace'
|
||||||
this.icon = 'HuggingFace.svg'
|
this.icon = 'HuggingFace.svg'
|
||||||
this.category = 'Chat Models'
|
this.category = 'Chat Models'
|
||||||
|
|
@ -96,16 +96,6 @@ class ChatHuggingFace_ChatModels implements INode {
|
||||||
description: 'Frequency Penalty parameter may not apply to certain model. Please check available model parameters',
|
description: 'Frequency Penalty parameter may not apply to certain model. Please check available model parameters',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Stop Sequence',
|
|
||||||
name: 'stop',
|
|
||||||
type: 'string',
|
|
||||||
rows: 4,
|
|
||||||
placeholder: 'AI assistant:',
|
|
||||||
description: 'Sets the stop sequences to use. Use comma to seperate different sequences.',
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
|
|
@ -119,7 +109,6 @@ class ChatHuggingFace_ChatModels implements INode {
|
||||||
const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
|
const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
|
||||||
const endpoint = nodeData.inputs?.endpoint as string
|
const endpoint = nodeData.inputs?.endpoint as string
|
||||||
const cache = nodeData.inputs?.cache as BaseCache
|
const cache = nodeData.inputs?.cache as BaseCache
|
||||||
const stop = nodeData.inputs?.stop as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
||||||
const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
|
const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
|
||||||
|
|
@ -134,11 +123,7 @@ class ChatHuggingFace_ChatModels implements INode {
|
||||||
if (topP) obj.topP = parseFloat(topP)
|
if (topP) obj.topP = parseFloat(topP)
|
||||||
if (hfTopK) obj.topK = parseFloat(hfTopK)
|
if (hfTopK) obj.topK = parseFloat(hfTopK)
|
||||||
if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
|
if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
|
||||||
if (endpoint) obj.endpointUrl = endpoint
|
if (endpoint) obj.endpoint = endpoint
|
||||||
if (stop) {
|
|
||||||
const stopSequences = stop.split(',')
|
|
||||||
obj.stopSequences = stopSequences
|
|
||||||
}
|
|
||||||
|
|
||||||
const huggingFace = new HuggingFaceInference(obj)
|
const huggingFace = new HuggingFaceInference(obj)
|
||||||
if (cache) huggingFace.cache = cache
|
if (cache) huggingFace.cache = cache
|
||||||
|
|
|
||||||
|
|
@ -1,19 +1,32 @@
|
||||||
import { LLM, BaseLLMParams } from '@langchain/core/language_models/llms'
|
import { LLM, BaseLLMParams } from '@langchain/core/language_models/llms'
|
||||||
import { getEnvironmentVariable } from '../../../src/utils'
|
import { getEnvironmentVariable } from '../../../src/utils'
|
||||||
import { GenerationChunk } from '@langchain/core/outputs'
|
|
||||||
import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager'
|
|
||||||
|
|
||||||
export interface HFInput {
|
export interface HFInput {
|
||||||
|
/** Model to use */
|
||||||
model: string
|
model: string
|
||||||
|
|
||||||
|
/** Sampling temperature to use */
|
||||||
temperature?: number
|
temperature?: number
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Maximum number of tokens to generate in the completion.
|
||||||
|
*/
|
||||||
maxTokens?: number
|
maxTokens?: number
|
||||||
stopSequences?: string[]
|
|
||||||
|
/** Total probability mass of tokens to consider at each step */
|
||||||
topP?: number
|
topP?: number
|
||||||
|
|
||||||
|
/** Integer to define the top tokens considered within the sample operation to create new text. */
|
||||||
topK?: number
|
topK?: number
|
||||||
|
|
||||||
|
/** Penalizes repeated tokens according to frequency */
|
||||||
frequencyPenalty?: number
|
frequencyPenalty?: number
|
||||||
|
|
||||||
|
/** API key to use. */
|
||||||
apiKey?: string
|
apiKey?: string
|
||||||
endpointUrl?: string
|
|
||||||
includeCredentials?: string | boolean
|
/** Private endpoint to use. */
|
||||||
|
endpoint?: string
|
||||||
}
|
}
|
||||||
|
|
||||||
export class HuggingFaceInference extends LLM implements HFInput {
|
export class HuggingFaceInference extends LLM implements HFInput {
|
||||||
|
|
@ -27,8 +40,6 @@ export class HuggingFaceInference extends LLM implements HFInput {
|
||||||
|
|
||||||
temperature: number | undefined = undefined
|
temperature: number | undefined = undefined
|
||||||
|
|
||||||
stopSequences: string[] | undefined = undefined
|
|
||||||
|
|
||||||
maxTokens: number | undefined = undefined
|
maxTokens: number | undefined = undefined
|
||||||
|
|
||||||
topP: number | undefined = undefined
|
topP: number | undefined = undefined
|
||||||
|
|
@ -39,9 +50,7 @@ export class HuggingFaceInference extends LLM implements HFInput {
|
||||||
|
|
||||||
apiKey: string | undefined = undefined
|
apiKey: string | undefined = undefined
|
||||||
|
|
||||||
endpointUrl: string | undefined = undefined
|
endpoint: string | undefined = undefined
|
||||||
|
|
||||||
includeCredentials: string | boolean | undefined = undefined
|
|
||||||
|
|
||||||
constructor(fields?: Partial<HFInput> & BaseLLMParams) {
|
constructor(fields?: Partial<HFInput> & BaseLLMParams) {
|
||||||
super(fields ?? {})
|
super(fields ?? {})
|
||||||
|
|
@ -49,13 +58,11 @@ export class HuggingFaceInference extends LLM implements HFInput {
|
||||||
this.model = fields?.model ?? this.model
|
this.model = fields?.model ?? this.model
|
||||||
this.temperature = fields?.temperature ?? this.temperature
|
this.temperature = fields?.temperature ?? this.temperature
|
||||||
this.maxTokens = fields?.maxTokens ?? this.maxTokens
|
this.maxTokens = fields?.maxTokens ?? this.maxTokens
|
||||||
this.stopSequences = fields?.stopSequences ?? this.stopSequences
|
|
||||||
this.topP = fields?.topP ?? this.topP
|
this.topP = fields?.topP ?? this.topP
|
||||||
this.topK = fields?.topK ?? this.topK
|
this.topK = fields?.topK ?? this.topK
|
||||||
this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty
|
this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty
|
||||||
|
this.endpoint = fields?.endpoint ?? ''
|
||||||
this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
|
this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
|
||||||
this.endpointUrl = fields?.endpointUrl
|
|
||||||
this.includeCredentials = fields?.includeCredentials
|
|
||||||
if (!this.apiKey) {
|
if (!this.apiKey) {
|
||||||
throw new Error(
|
throw new Error(
|
||||||
'Please set an API key for HuggingFace Hub in the environment variable HUGGINGFACEHUB_API_KEY or in the apiKey field of the HuggingFaceInference constructor.'
|
'Please set an API key for HuggingFace Hub in the environment variable HUGGINGFACEHUB_API_KEY or in the apiKey field of the HuggingFaceInference constructor.'
|
||||||
|
|
@ -67,65 +74,31 @@ export class HuggingFaceInference extends LLM implements HFInput {
|
||||||
return 'hf'
|
return 'hf'
|
||||||
}
|
}
|
||||||
|
|
||||||
invocationParams(options?: this['ParsedCallOptions']) {
|
/** @ignore */
|
||||||
return {
|
async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
|
||||||
model: this.model,
|
const { HfInference } = await HuggingFaceInference.imports()
|
||||||
|
const hf = new HfInference(this.apiKey)
|
||||||
|
const obj: any = {
|
||||||
parameters: {
|
parameters: {
|
||||||
// make it behave similar to openai, returning only the generated text
|
// make it behave similar to openai, returning only the generated text
|
||||||
return_full_text: false,
|
return_full_text: false,
|
||||||
temperature: this.temperature,
|
temperature: this.temperature,
|
||||||
max_new_tokens: this.maxTokens,
|
max_new_tokens: this.maxTokens,
|
||||||
stop: options?.stop ?? this.stopSequences,
|
|
||||||
top_p: this.topP,
|
top_p: this.topP,
|
||||||
top_k: this.topK,
|
top_k: this.topK,
|
||||||
repetition_penalty: this.frequencyPenalty
|
repetition_penalty: this.frequencyPenalty
|
||||||
}
|
},
|
||||||
|
inputs: prompt
|
||||||
}
|
}
|
||||||
}
|
if (this.endpoint) {
|
||||||
|
hf.endpoint(this.endpoint)
|
||||||
async *_streamResponseChunks(
|
} else {
|
||||||
prompt: string,
|
obj.model = this.model
|
||||||
options: this['ParsedCallOptions'],
|
|
||||||
runManager?: CallbackManagerForLLMRun
|
|
||||||
): AsyncGenerator<GenerationChunk> {
|
|
||||||
const hfi = await this._prepareHFInference()
|
|
||||||
const stream = await this.caller.call(async () =>
|
|
||||||
hfi.textGenerationStream({
|
|
||||||
...this.invocationParams(options),
|
|
||||||
inputs: prompt
|
|
||||||
})
|
|
||||||
)
|
|
||||||
for await (const chunk of stream) {
|
|
||||||
const token = chunk.token.text
|
|
||||||
yield new GenerationChunk({ text: token, generationInfo: chunk })
|
|
||||||
await runManager?.handleLLMNewToken(token ?? '')
|
|
||||||
|
|
||||||
// stream is done
|
|
||||||
if (chunk.generated_text)
|
|
||||||
yield new GenerationChunk({
|
|
||||||
text: '',
|
|
||||||
generationInfo: { finished: true }
|
|
||||||
})
|
|
||||||
}
|
}
|
||||||
}
|
const res = await this.caller.callWithOptions({ signal: options.signal }, hf.textGeneration.bind(hf), obj)
|
||||||
|
|
||||||
/** @ignore */
|
|
||||||
async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
|
|
||||||
const hfi = await this._prepareHFInference()
|
|
||||||
const args = { ...this.invocationParams(options), inputs: prompt }
|
|
||||||
const res = await this.caller.callWithOptions({ signal: options.signal }, hfi.textGeneration.bind(hfi), args)
|
|
||||||
return res.generated_text
|
return res.generated_text
|
||||||
}
|
}
|
||||||
|
|
||||||
/** @ignore */
|
|
||||||
private async _prepareHFInference() {
|
|
||||||
const { HfInference } = await HuggingFaceInference.imports()
|
|
||||||
const hfi = new HfInference(this.apiKey, {
|
|
||||||
includeCredentials: this.includeCredentials
|
|
||||||
})
|
|
||||||
return this.endpointUrl ? hfi.endpoint(this.endpointUrl) : hfi
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @ignore */
|
/** @ignore */
|
||||||
static async imports(): Promise<{
|
static async imports(): Promise<{
|
||||||
HfInference: typeof import('@huggingface/inference').HfInference
|
HfInference: typeof import('@huggingface/inference').HfInference
|
||||||
|
|
|
||||||
|
|
@ -1,100 +0,0 @@
|
||||||
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
|
||||||
import { MODEL_TYPE, getModels } from '../../../src/modelLoader'
|
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
import { ALL_AVAILABLE_MISTRAL_MODELS, MistralAI } from 'llamaindex'
|
|
||||||
|
|
||||||
class ChatMistral_LlamaIndex_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
tags: string[]
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatMistral'
|
|
||||||
this.name = 'chatMistral_LlamaIndex'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatMistral'
|
|
||||||
this.icon = 'MistralAI.svg'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around ChatMistral LLM specific for LlamaIndex'
|
|
||||||
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(MistralAI)]
|
|
||||||
this.tags = ['LlamaIndex']
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['mistralAIApi']
|
|
||||||
}
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Model Name',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'asyncOptions',
|
|
||||||
loadMethod: 'listModels',
|
|
||||||
default: 'mistral-tiny'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Max Tokens',
|
|
||||||
name: 'maxTokensToSample',
|
|
||||||
type: 'number',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Top P',
|
|
||||||
name: 'topP',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
//@ts-ignore
|
|
||||||
loadMethods = {
|
|
||||||
async listModels(): Promise<INodeOptionsValue[]> {
|
|
||||||
return await getModels(MODEL_TYPE.CHAT, 'chatMistral_LlamaIndex')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as keyof typeof ALL_AVAILABLE_MISTRAL_MODELS
|
|
||||||
const maxTokensToSample = nodeData.inputs?.maxTokensToSample as string
|
|
||||||
const topP = nodeData.inputs?.topP as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const apiKey = getCredentialParam('mistralAIAPIKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
const obj: Partial<MistralAI> = {
|
|
||||||
temperature: parseFloat(temperature),
|
|
||||||
model: modelName,
|
|
||||||
apiKey: apiKey
|
|
||||||
}
|
|
||||||
|
|
||||||
if (maxTokensToSample) obj.maxTokens = parseInt(maxTokensToSample, 10)
|
|
||||||
if (topP) obj.topP = parseFloat(topP)
|
|
||||||
|
|
||||||
const model = new MistralAI(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatMistral_LlamaIndex_ChatModels }
|
|
||||||
|
|
@ -1,221 +0,0 @@
|
||||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
|
||||||
import { getBaseClasses } from '../../../src/utils'
|
|
||||||
import { OllamaParams, Ollama } from 'llamaindex'
|
|
||||||
|
|
||||||
class ChatOllama_LlamaIndex_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
tags: string[]
|
|
||||||
baseClasses: string[]
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatOllama'
|
|
||||||
this.name = 'chatOllama_LlamaIndex'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatOllama'
|
|
||||||
this.icon = 'Ollama.svg'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around ChatOllama LLM specific for LlamaIndex'
|
|
||||||
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(Ollama)]
|
|
||||||
this.tags = ['LlamaIndex']
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Base URL',
|
|
||||||
name: 'baseUrl',
|
|
||||||
type: 'string',
|
|
||||||
default: 'http://localhost:11434'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Model Name',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'string',
|
|
||||||
placeholder: 'llama3'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Top P',
|
|
||||||
name: 'topP',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Top K',
|
|
||||||
name: 'topK',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat',
|
|
||||||
name: 'mirostat',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat ETA',
|
|
||||||
name: 'mirostatEta',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat TAU',
|
|
||||||
name: 'mirostatTau',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Context Window Size',
|
|
||||||
name: 'numCtx',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets the size of the context window used to generate the next token. (Default: 2048) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Number of GPU',
|
|
||||||
name: 'numGpu',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Number of Thread',
|
|
||||||
name: 'numThread',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Repeat Last N',
|
|
||||||
name: 'repeatLastN',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Repeat Penalty',
|
|
||||||
name: 'repeatPenalty',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Stop Sequence',
|
|
||||||
name: 'stop',
|
|
||||||
type: 'string',
|
|
||||||
rows: 4,
|
|
||||||
placeholder: 'AI assistant:',
|
|
||||||
description:
|
|
||||||
'Sets the stop sequences to use. Use comma to seperate different sequences. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Tail Free Sampling',
|
|
||||||
name: 'tfsZ',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (Default: 1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData): Promise<any> {
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const baseUrl = nodeData.inputs?.baseUrl as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
const topP = nodeData.inputs?.topP as string
|
|
||||||
const topK = nodeData.inputs?.topK as string
|
|
||||||
const mirostat = nodeData.inputs?.mirostat as string
|
|
||||||
const mirostatEta = nodeData.inputs?.mirostatEta as string
|
|
||||||
const mirostatTau = nodeData.inputs?.mirostatTau as string
|
|
||||||
const numCtx = nodeData.inputs?.numCtx as string
|
|
||||||
const numGpu = nodeData.inputs?.numGpu as string
|
|
||||||
const numThread = nodeData.inputs?.numThread as string
|
|
||||||
const repeatLastN = nodeData.inputs?.repeatLastN as string
|
|
||||||
const repeatPenalty = nodeData.inputs?.repeatPenalty as string
|
|
||||||
const stop = nodeData.inputs?.stop as string
|
|
||||||
const tfsZ = nodeData.inputs?.tfsZ as string
|
|
||||||
|
|
||||||
const obj: OllamaParams = {
|
|
||||||
model: modelName,
|
|
||||||
options: {},
|
|
||||||
config: {
|
|
||||||
host: baseUrl
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
if (temperature) obj.options.temperature = parseFloat(temperature)
|
|
||||||
if (topP) obj.options.top_p = parseFloat(topP)
|
|
||||||
if (topK) obj.options.top_k = parseFloat(topK)
|
|
||||||
if (mirostat) obj.options.mirostat = parseFloat(mirostat)
|
|
||||||
if (mirostatEta) obj.options.mirostat_eta = parseFloat(mirostatEta)
|
|
||||||
if (mirostatTau) obj.options.mirostat_tau = parseFloat(mirostatTau)
|
|
||||||
if (numCtx) obj.options.num_ctx = parseFloat(numCtx)
|
|
||||||
if (numGpu) obj.options.main_gpu = parseFloat(numGpu)
|
|
||||||
if (numThread) obj.options.num_thread = parseFloat(numThread)
|
|
||||||
if (repeatLastN) obj.options.repeat_last_n = parseFloat(repeatLastN)
|
|
||||||
if (repeatPenalty) obj.options.repeat_penalty = parseFloat(repeatPenalty)
|
|
||||||
if (tfsZ) obj.options.tfs_z = parseFloat(tfsZ)
|
|
||||||
if (stop) {
|
|
||||||
const stopSequences = stop.split(',')
|
|
||||||
obj.options.stop = stopSequences
|
|
||||||
}
|
|
||||||
|
|
||||||
const model = new Ollama(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatOllama_LlamaIndex_ChatModels }
|
|
||||||
|
|
@ -1,808 +0,0 @@
|
||||||
import { HumanMessage, AIMessage, BaseMessage, AIMessageChunk, ChatMessage } from '@langchain/core/messages'
|
|
||||||
import { ChatResult } from '@langchain/core/outputs'
|
|
||||||
import { SimpleChatModel, BaseChatModel, BaseChatModelParams } from '@langchain/core/language_models/chat_models'
|
|
||||||
import { SystemMessagePromptTemplate } from '@langchain/core/prompts'
|
|
||||||
import { BaseCache } from '@langchain/core/caches'
|
|
||||||
import { type StructuredToolInterface } from '@langchain/core/tools'
|
|
||||||
import type { BaseFunctionCallOptions, BaseLanguageModelInput } from '@langchain/core/language_models/base'
|
|
||||||
import { convertToOpenAIFunction } from '@langchain/core/utils/function_calling'
|
|
||||||
import { RunnableInterface } from '@langchain/core/runnables'
|
|
||||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
|
||||||
import { getBaseClasses } from '../../../src/utils'
|
|
||||||
import type { BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
|
|
||||||
import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager'
|
|
||||||
import { ChatGenerationChunk } from '@langchain/core/outputs'
|
|
||||||
import type { StringWithAutocomplete } from '@langchain/core/utils/types'
|
|
||||||
import { createOllamaChatStream, createOllamaGenerateStream, type OllamaInput, type OllamaMessage } from './utils'
|
|
||||||
|
|
||||||
const DEFAULT_TOOL_SYSTEM_TEMPLATE = `You have access to the following tools:
|
|
||||||
{tools}
|
|
||||||
You must always select one of the above tools and respond with only a JSON object matching the following schema:
|
|
||||||
{{
|
|
||||||
"tool": <name of the selected tool>,
|
|
||||||
"tool_input": <parameters for the selected tool, matching the tool's JSON schema>
|
|
||||||
}}`
|
|
||||||
|
|
||||||
class ChatOllamaFunction_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
badge?: string
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatOllama Function'
|
|
||||||
this.name = 'chatOllamaFunction'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatOllamaFunction'
|
|
||||||
this.icon = 'Ollama.svg'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Run open-source function-calling compatible LLM on Ollama'
|
|
||||||
this.baseClasses = [this.type, ...getBaseClasses(OllamaFunctions)]
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Cache',
|
|
||||||
name: 'cache',
|
|
||||||
type: 'BaseCache',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Base URL',
|
|
||||||
name: 'baseUrl',
|
|
||||||
type: 'string',
|
|
||||||
default: 'http://localhost:11434'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Model Name',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'string',
|
|
||||||
description: 'Only compatible with function calling model like mistral',
|
|
||||||
placeholder: 'mistral'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Tool System Prompt',
|
|
||||||
name: 'toolSystemPromptTemplate',
|
|
||||||
type: 'string',
|
|
||||||
rows: 4,
|
|
||||||
description: `Under the hood, Ollama's JSON mode is being used to constrain output to JSON. Output JSON will contains two keys: tool and tool_input fields. We then parse it to execute the tool. Because different models have different strengths, it may be helpful to pass in your own system prompt.`,
|
|
||||||
warning: `Prompt must always contains {tools} and instructions to respond with a JSON object with tool and tool_input fields`,
|
|
||||||
default: DEFAULT_TOOL_SYSTEM_TEMPLATE,
|
|
||||||
placeholder: DEFAULT_TOOL_SYSTEM_TEMPLATE,
|
|
||||||
additionalParams: true,
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Top P',
|
|
||||||
name: 'topP',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Top K',
|
|
||||||
name: 'topK',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat',
|
|
||||||
name: 'mirostat',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat ETA',
|
|
||||||
name: 'mirostatEta',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mirostat TAU',
|
|
||||||
name: 'mirostatTau',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Context Window Size',
|
|
||||||
name: 'numCtx',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets the size of the context window used to generate the next token. (Default: 2048) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Number of GQA groups',
|
|
||||||
name: 'numGqa',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Number of GPU',
|
|
||||||
name: 'numGpu',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Number of Thread',
|
|
||||||
name: 'numThread',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Repeat Last N',
|
|
||||||
name: 'repeatLastN',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Repeat Penalty',
|
|
||||||
name: 'repeatPenalty',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Stop Sequence',
|
|
||||||
name: 'stop',
|
|
||||||
type: 'string',
|
|
||||||
rows: 4,
|
|
||||||
placeholder: 'AI assistant:',
|
|
||||||
description:
|
|
||||||
'Sets the stop sequences to use. Use comma to seperate different sequences. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Tail Free Sampling',
|
|
||||||
name: 'tfsZ',
|
|
||||||
type: 'number',
|
|
||||||
description:
|
|
||||||
'Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (Default: 1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
|
|
||||||
step: 0.1,
|
|
||||||
optional: true,
|
|
||||||
additionalParams: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData): Promise<any> {
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const baseUrl = nodeData.inputs?.baseUrl as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
const topP = nodeData.inputs?.topP as string
|
|
||||||
const topK = nodeData.inputs?.topK as string
|
|
||||||
const mirostat = nodeData.inputs?.mirostat as string
|
|
||||||
const mirostatEta = nodeData.inputs?.mirostatEta as string
|
|
||||||
const mirostatTau = nodeData.inputs?.mirostatTau as string
|
|
||||||
const numCtx = nodeData.inputs?.numCtx as string
|
|
||||||
const numGqa = nodeData.inputs?.numGqa as string
|
|
||||||
const numGpu = nodeData.inputs?.numGpu as string
|
|
||||||
const numThread = nodeData.inputs?.numThread as string
|
|
||||||
const repeatLastN = nodeData.inputs?.repeatLastN as string
|
|
||||||
const repeatPenalty = nodeData.inputs?.repeatPenalty as string
|
|
||||||
const stop = nodeData.inputs?.stop as string
|
|
||||||
const tfsZ = nodeData.inputs?.tfsZ as string
|
|
||||||
const toolSystemPromptTemplate = nodeData.inputs?.toolSystemPromptTemplate as string
|
|
||||||
|
|
||||||
const cache = nodeData.inputs?.cache as BaseCache
|
|
||||||
|
|
||||||
const obj: OllamaFunctionsInput = {
|
|
||||||
baseUrl,
|
|
||||||
temperature: parseFloat(temperature),
|
|
||||||
model: modelName,
|
|
||||||
toolSystemPromptTemplate: toolSystemPromptTemplate ? toolSystemPromptTemplate : DEFAULT_TOOL_SYSTEM_TEMPLATE
|
|
||||||
}
|
|
||||||
|
|
||||||
if (topP) obj.topP = parseFloat(topP)
|
|
||||||
if (topK) obj.topK = parseFloat(topK)
|
|
||||||
if (mirostat) obj.mirostat = parseFloat(mirostat)
|
|
||||||
if (mirostatEta) obj.mirostatEta = parseFloat(mirostatEta)
|
|
||||||
if (mirostatTau) obj.mirostatTau = parseFloat(mirostatTau)
|
|
||||||
if (numCtx) obj.numCtx = parseFloat(numCtx)
|
|
||||||
if (numGqa) obj.numGqa = parseFloat(numGqa)
|
|
||||||
if (numGpu) obj.numGpu = parseFloat(numGpu)
|
|
||||||
if (numThread) obj.numThread = parseFloat(numThread)
|
|
||||||
if (repeatLastN) obj.repeatLastN = parseFloat(repeatLastN)
|
|
||||||
if (repeatPenalty) obj.repeatPenalty = parseFloat(repeatPenalty)
|
|
||||||
if (tfsZ) obj.tfsZ = parseFloat(tfsZ)
|
|
||||||
if (stop) {
|
|
||||||
const stopSequences = stop.split(',')
|
|
||||||
obj.stop = stopSequences
|
|
||||||
}
|
|
||||||
if (cache) obj.cache = cache
|
|
||||||
|
|
||||||
const model = new OllamaFunctions(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
interface ChatOllamaFunctionsCallOptions extends BaseFunctionCallOptions {}
|
|
||||||
|
|
||||||
type OllamaFunctionsInput = Partial<ChatOllamaInput> &
|
|
||||||
BaseChatModelParams & {
|
|
||||||
llm?: OllamaChat
|
|
||||||
toolSystemPromptTemplate?: string
|
|
||||||
}
|
|
||||||
|
|
||||||
class OllamaFunctions extends BaseChatModel<ChatOllamaFunctionsCallOptions> {
|
|
||||||
llm: OllamaChat
|
|
||||||
|
|
||||||
fields?: OllamaFunctionsInput
|
|
||||||
|
|
||||||
toolSystemPromptTemplate: string = DEFAULT_TOOL_SYSTEM_TEMPLATE
|
|
||||||
|
|
||||||
protected defaultResponseFunction = {
|
|
||||||
name: '__conversational_response',
|
|
||||||
description: 'Respond conversationally if no other tools should be called for a given query.',
|
|
||||||
parameters: {
|
|
||||||
type: 'object',
|
|
||||||
properties: {
|
|
||||||
response: {
|
|
||||||
type: 'string',
|
|
||||||
description: 'Conversational response to the user.'
|
|
||||||
}
|
|
||||||
},
|
|
||||||
required: ['response']
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
static lc_name(): string {
|
|
||||||
return 'OllamaFunctions'
|
|
||||||
}
|
|
||||||
|
|
||||||
constructor(fields?: OllamaFunctionsInput) {
|
|
||||||
super(fields ?? {})
|
|
||||||
this.fields = fields
|
|
||||||
this.llm = fields?.llm ?? new OllamaChat({ ...fields, format: 'json' })
|
|
||||||
this.toolSystemPromptTemplate = fields?.toolSystemPromptTemplate ?? this.toolSystemPromptTemplate
|
|
||||||
}
|
|
||||||
|
|
||||||
invocationParams() {
|
|
||||||
return this.llm.invocationParams()
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @ignore */
|
|
||||||
_identifyingParams() {
|
|
||||||
return this.llm._identifyingParams()
|
|
||||||
}
|
|
||||||
|
|
||||||
async _generate(
|
|
||||||
messages: BaseMessage[],
|
|
||||||
options: this['ParsedCallOptions'],
|
|
||||||
runManager?: CallbackManagerForLLMRun | undefined
|
|
||||||
): Promise<ChatResult> {
|
|
||||||
let functions = options.functions ?? []
|
|
||||||
if (options.function_call !== undefined) {
|
|
||||||
functions = functions.filter((fn) => fn.name === options.function_call?.name)
|
|
||||||
if (!functions.length) {
|
|
||||||
throw new Error(`If "function_call" is specified, you must also pass a matching function in "functions".`)
|
|
||||||
}
|
|
||||||
} else if (functions.length === 0) {
|
|
||||||
functions.push(this.defaultResponseFunction)
|
|
||||||
}
|
|
||||||
const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(this.toolSystemPromptTemplate)
|
|
||||||
const systemMessage = await systemPromptTemplate.format({
|
|
||||||
tools: JSON.stringify(functions, null, 2)
|
|
||||||
})
|
|
||||||
|
|
||||||
let generatedMessages = [systemMessage, ...messages]
|
|
||||||
let isToolResponse = false
|
|
||||||
if (
|
|
||||||
messages.length > 3 &&
|
|
||||||
messages[messages.length - 1]._getType() === 'tool' &&
|
|
||||||
functions.length &&
|
|
||||||
messages[messages.length - 1].additional_kwargs?.name === functions[0].name
|
|
||||||
) {
|
|
||||||
const lastToolQuestion = messages[messages.length - 3].content
|
|
||||||
const lastToolResp = messages.pop()?.content
|
|
||||||
// Pop the message again to get rid of tool call message
|
|
||||||
messages.pop()?.content
|
|
||||||
const humanMessage = new HumanMessage({
|
|
||||||
content: `Given user question: ${lastToolQuestion} and answer: ${lastToolResp}\n\nWrite a natural language response`
|
|
||||||
})
|
|
||||||
generatedMessages = [...messages, humanMessage]
|
|
||||||
isToolResponse = true
|
|
||||||
this.llm = new OllamaChat({ ...this.fields })
|
|
||||||
}
|
|
||||||
const chatResult = await this.llm._generate(generatedMessages, options, runManager)
|
|
||||||
const chatGenerationContent = chatResult.generations[0].message.content
|
|
||||||
|
|
||||||
if (typeof chatGenerationContent !== 'string') {
|
|
||||||
throw new Error('OllamaFunctions does not support non-string output.')
|
|
||||||
}
|
|
||||||
|
|
||||||
if (isToolResponse) {
|
|
||||||
return {
|
|
||||||
generations: [
|
|
||||||
{
|
|
||||||
message: new AIMessage({
|
|
||||||
content: chatGenerationContent
|
|
||||||
}),
|
|
||||||
text: chatGenerationContent
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
let parsedChatResult
|
|
||||||
try {
|
|
||||||
parsedChatResult = JSON.parse(chatGenerationContent)
|
|
||||||
} catch (e) {
|
|
||||||
throw new Error(`"${this.llm.model}" did not respond with valid JSON. Please try again.`)
|
|
||||||
}
|
|
||||||
|
|
||||||
const calledToolName = parsedChatResult.tool
|
|
||||||
const calledToolArguments = parsedChatResult.tool_input
|
|
||||||
const calledTool = functions.find((fn) => fn.name === calledToolName)
|
|
||||||
if (calledTool === undefined) {
|
|
||||||
throw new Error(`Failed to parse a function call from ${this.llm.model} output: ${chatGenerationContent}`)
|
|
||||||
}
|
|
||||||
|
|
||||||
if (calledTool.name === this.defaultResponseFunction.name) {
|
|
||||||
return {
|
|
||||||
generations: [
|
|
||||||
{
|
|
||||||
message: new AIMessage({
|
|
||||||
content: calledToolArguments.response
|
|
||||||
}),
|
|
||||||
text: calledToolArguments.response
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
const responseMessageWithFunctions = new AIMessage({
|
|
||||||
content: '',
|
|
||||||
tool_calls: [
|
|
||||||
{
|
|
||||||
name: calledToolName,
|
|
||||||
args: calledToolArguments || {}
|
|
||||||
}
|
|
||||||
],
|
|
||||||
invalid_tool_calls: [],
|
|
||||||
additional_kwargs: {
|
|
||||||
function_call: {
|
|
||||||
name: calledToolName,
|
|
||||||
arguments: calledToolArguments ? JSON.stringify(calledToolArguments) : ''
|
|
||||||
},
|
|
||||||
tool_calls: [
|
|
||||||
{
|
|
||||||
id: Date.now().toString(),
|
|
||||||
type: 'function',
|
|
||||||
function: {
|
|
||||||
name: calledToolName,
|
|
||||||
arguments: calledToolArguments ? JSON.stringify(calledToolArguments) : ''
|
|
||||||
}
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
})
|
|
||||||
|
|
||||||
return {
|
|
||||||
generations: [{ message: responseMessageWithFunctions, text: '' }]
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
override bindTools(
|
|
||||||
tools: StructuredToolInterface[],
|
|
||||||
kwargs?: Partial<ICommonObject>
|
|
||||||
): RunnableInterface<BaseLanguageModelInput, AIMessageChunk, ICommonObject> {
|
|
||||||
return this.bind({
|
|
||||||
functions: tools.map((tool) => convertToOpenAIFunction(tool)),
|
|
||||||
...kwargs
|
|
||||||
} as Partial<ICommonObject>)
|
|
||||||
}
|
|
||||||
|
|
||||||
_llmType(): string {
|
|
||||||
return 'ollama_functions'
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @ignore */
|
|
||||||
_combineLLMOutput() {
|
|
||||||
return []
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
export interface ChatOllamaInput extends OllamaInput {}
|
|
||||||
|
|
||||||
interface ChatOllamaCallOptions extends BaseLanguageModelCallOptions {}
|
|
||||||
|
|
||||||
class OllamaChat extends SimpleChatModel<ChatOllamaCallOptions> implements ChatOllamaInput {
|
|
||||||
static lc_name() {
|
|
||||||
return 'ChatOllama'
|
|
||||||
}
|
|
||||||
|
|
||||||
lc_serializable = true
|
|
||||||
|
|
||||||
model = 'llama2'
|
|
||||||
|
|
||||||
baseUrl = 'http://localhost:11434'
|
|
||||||
|
|
||||||
keepAlive = '5m'
|
|
||||||
|
|
||||||
embeddingOnly?: boolean
|
|
||||||
|
|
||||||
f16KV?: boolean
|
|
||||||
|
|
||||||
frequencyPenalty?: number
|
|
||||||
|
|
||||||
headers?: Record<string, string>
|
|
||||||
|
|
||||||
logitsAll?: boolean
|
|
||||||
|
|
||||||
lowVram?: boolean
|
|
||||||
|
|
||||||
mainGpu?: number
|
|
||||||
|
|
||||||
mirostat?: number
|
|
||||||
|
|
||||||
mirostatEta?: number
|
|
||||||
|
|
||||||
mirostatTau?: number
|
|
||||||
|
|
||||||
numBatch?: number
|
|
||||||
|
|
||||||
numCtx?: number
|
|
||||||
|
|
||||||
numGpu?: number
|
|
||||||
|
|
||||||
numGqa?: number
|
|
||||||
|
|
||||||
numKeep?: number
|
|
||||||
|
|
||||||
numPredict?: number
|
|
||||||
|
|
||||||
numThread?: number
|
|
||||||
|
|
||||||
penalizeNewline?: boolean
|
|
||||||
|
|
||||||
presencePenalty?: number
|
|
||||||
|
|
||||||
repeatLastN?: number
|
|
||||||
|
|
||||||
repeatPenalty?: number
|
|
||||||
|
|
||||||
ropeFrequencyBase?: number
|
|
||||||
|
|
||||||
ropeFrequencyScale?: number
|
|
||||||
|
|
||||||
temperature?: number
|
|
||||||
|
|
||||||
stop?: string[]
|
|
||||||
|
|
||||||
tfsZ?: number
|
|
||||||
|
|
||||||
topK?: number
|
|
||||||
|
|
||||||
topP?: number
|
|
||||||
|
|
||||||
typicalP?: number
|
|
||||||
|
|
||||||
useMLock?: boolean
|
|
||||||
|
|
||||||
useMMap?: boolean
|
|
||||||
|
|
||||||
vocabOnly?: boolean
|
|
||||||
|
|
||||||
format?: StringWithAutocomplete<'json'>
|
|
||||||
|
|
||||||
constructor(fields: OllamaInput & BaseChatModelParams) {
|
|
||||||
super(fields)
|
|
||||||
this.model = fields.model ?? this.model
|
|
||||||
this.baseUrl = fields.baseUrl?.endsWith('/') ? fields.baseUrl.slice(0, -1) : fields.baseUrl ?? this.baseUrl
|
|
||||||
this.keepAlive = fields.keepAlive ?? this.keepAlive
|
|
||||||
this.embeddingOnly = fields.embeddingOnly
|
|
||||||
this.f16KV = fields.f16KV
|
|
||||||
this.frequencyPenalty = fields.frequencyPenalty
|
|
||||||
this.headers = fields.headers
|
|
||||||
this.logitsAll = fields.logitsAll
|
|
||||||
this.lowVram = fields.lowVram
|
|
||||||
this.mainGpu = fields.mainGpu
|
|
||||||
this.mirostat = fields.mirostat
|
|
||||||
this.mirostatEta = fields.mirostatEta
|
|
||||||
this.mirostatTau = fields.mirostatTau
|
|
||||||
this.numBatch = fields.numBatch
|
|
||||||
this.numCtx = fields.numCtx
|
|
||||||
this.numGpu = fields.numGpu
|
|
||||||
this.numGqa = fields.numGqa
|
|
||||||
this.numKeep = fields.numKeep
|
|
||||||
this.numPredict = fields.numPredict
|
|
||||||
this.numThread = fields.numThread
|
|
||||||
this.penalizeNewline = fields.penalizeNewline
|
|
||||||
this.presencePenalty = fields.presencePenalty
|
|
||||||
this.repeatLastN = fields.repeatLastN
|
|
||||||
this.repeatPenalty = fields.repeatPenalty
|
|
||||||
this.ropeFrequencyBase = fields.ropeFrequencyBase
|
|
||||||
this.ropeFrequencyScale = fields.ropeFrequencyScale
|
|
||||||
this.temperature = fields.temperature
|
|
||||||
this.stop = fields.stop
|
|
||||||
this.tfsZ = fields.tfsZ
|
|
||||||
this.topK = fields.topK
|
|
||||||
this.topP = fields.topP
|
|
||||||
this.typicalP = fields.typicalP
|
|
||||||
this.useMLock = fields.useMLock
|
|
||||||
this.useMMap = fields.useMMap
|
|
||||||
this.vocabOnly = fields.vocabOnly
|
|
||||||
this.format = fields.format
|
|
||||||
}
|
|
||||||
|
|
||||||
_llmType() {
|
|
||||||
return 'ollama'
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* A method that returns the parameters for an Ollama API call. It
|
|
||||||
* includes model and options parameters.
|
|
||||||
* @param options Optional parsed call options.
|
|
||||||
* @returns An object containing the parameters for an Ollama API call.
|
|
||||||
*/
|
|
||||||
invocationParams(options?: this['ParsedCallOptions']) {
|
|
||||||
return {
|
|
||||||
model: this.model,
|
|
||||||
format: this.format,
|
|
||||||
keep_alive: this.keepAlive,
|
|
||||||
options: {
|
|
||||||
embedding_only: this.embeddingOnly,
|
|
||||||
f16_kv: this.f16KV,
|
|
||||||
frequency_penalty: this.frequencyPenalty,
|
|
||||||
logits_all: this.logitsAll,
|
|
||||||
low_vram: this.lowVram,
|
|
||||||
main_gpu: this.mainGpu,
|
|
||||||
mirostat: this.mirostat,
|
|
||||||
mirostat_eta: this.mirostatEta,
|
|
||||||
mirostat_tau: this.mirostatTau,
|
|
||||||
num_batch: this.numBatch,
|
|
||||||
num_ctx: this.numCtx,
|
|
||||||
num_gpu: this.numGpu,
|
|
||||||
num_gqa: this.numGqa,
|
|
||||||
num_keep: this.numKeep,
|
|
||||||
num_predict: this.numPredict,
|
|
||||||
num_thread: this.numThread,
|
|
||||||
penalize_newline: this.penalizeNewline,
|
|
||||||
presence_penalty: this.presencePenalty,
|
|
||||||
repeat_last_n: this.repeatLastN,
|
|
||||||
repeat_penalty: this.repeatPenalty,
|
|
||||||
rope_frequency_base: this.ropeFrequencyBase,
|
|
||||||
rope_frequency_scale: this.ropeFrequencyScale,
|
|
||||||
temperature: this.temperature,
|
|
||||||
stop: options?.stop ?? this.stop,
|
|
||||||
tfs_z: this.tfsZ,
|
|
||||||
top_k: this.topK,
|
|
||||||
top_p: this.topP,
|
|
||||||
typical_p: this.typicalP,
|
|
||||||
use_mlock: this.useMLock,
|
|
||||||
use_mmap: this.useMMap,
|
|
||||||
vocab_only: this.vocabOnly
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
_combineLLMOutput() {
|
|
||||||
return {}
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @deprecated */
|
|
||||||
async *_streamResponseChunksLegacy(
|
|
||||||
input: BaseMessage[],
|
|
||||||
options: this['ParsedCallOptions'],
|
|
||||||
runManager?: CallbackManagerForLLMRun
|
|
||||||
): AsyncGenerator<ChatGenerationChunk> {
|
|
||||||
const stream = createOllamaGenerateStream(
|
|
||||||
this.baseUrl,
|
|
||||||
{
|
|
||||||
...this.invocationParams(options),
|
|
||||||
prompt: this._formatMessagesAsPrompt(input)
|
|
||||||
},
|
|
||||||
{
|
|
||||||
...options,
|
|
||||||
headers: this.headers
|
|
||||||
}
|
|
||||||
)
|
|
||||||
for await (const chunk of stream) {
|
|
||||||
if (!chunk.done) {
|
|
||||||
yield new ChatGenerationChunk({
|
|
||||||
text: chunk.response,
|
|
||||||
message: new AIMessageChunk({ content: chunk.response })
|
|
||||||
})
|
|
||||||
await runManager?.handleLLMNewToken(chunk.response ?? '')
|
|
||||||
} else {
|
|
||||||
yield new ChatGenerationChunk({
|
|
||||||
text: '',
|
|
||||||
message: new AIMessageChunk({ content: '' }),
|
|
||||||
generationInfo: {
|
|
||||||
model: chunk.model,
|
|
||||||
total_duration: chunk.total_duration,
|
|
||||||
load_duration: chunk.load_duration,
|
|
||||||
prompt_eval_count: chunk.prompt_eval_count,
|
|
||||||
prompt_eval_duration: chunk.prompt_eval_duration,
|
|
||||||
eval_count: chunk.eval_count,
|
|
||||||
eval_duration: chunk.eval_duration
|
|
||||||
}
|
|
||||||
})
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async *_streamResponseChunks(
|
|
||||||
input: BaseMessage[],
|
|
||||||
options: this['ParsedCallOptions'],
|
|
||||||
runManager?: CallbackManagerForLLMRun
|
|
||||||
): AsyncGenerator<ChatGenerationChunk> {
|
|
||||||
try {
|
|
||||||
const stream = await this.caller.call(async () =>
|
|
||||||
createOllamaChatStream(
|
|
||||||
this.baseUrl,
|
|
||||||
{
|
|
||||||
...this.invocationParams(options),
|
|
||||||
messages: this._convertMessagesToOllamaMessages(input)
|
|
||||||
},
|
|
||||||
{
|
|
||||||
...options,
|
|
||||||
headers: this.headers
|
|
||||||
}
|
|
||||||
)
|
|
||||||
)
|
|
||||||
for await (const chunk of stream) {
|
|
||||||
if (!chunk.done) {
|
|
||||||
yield new ChatGenerationChunk({
|
|
||||||
text: chunk.message.content,
|
|
||||||
message: new AIMessageChunk({ content: chunk.message.content })
|
|
||||||
})
|
|
||||||
await runManager?.handleLLMNewToken(chunk.message.content ?? '')
|
|
||||||
} else {
|
|
||||||
yield new ChatGenerationChunk({
|
|
||||||
text: '',
|
|
||||||
message: new AIMessageChunk({ content: '' }),
|
|
||||||
generationInfo: {
|
|
||||||
model: chunk.model,
|
|
||||||
total_duration: chunk.total_duration,
|
|
||||||
load_duration: chunk.load_duration,
|
|
||||||
prompt_eval_count: chunk.prompt_eval_count,
|
|
||||||
prompt_eval_duration: chunk.prompt_eval_duration,
|
|
||||||
eval_count: chunk.eval_count,
|
|
||||||
eval_duration: chunk.eval_duration
|
|
||||||
}
|
|
||||||
})
|
|
||||||
}
|
|
||||||
}
|
|
||||||
} catch (e: any) {
|
|
||||||
if (e.response?.status === 404) {
|
|
||||||
console.warn(
|
|
||||||
'[WARNING]: It seems you are using a legacy version of Ollama. Please upgrade to a newer version for better chat support.'
|
|
||||||
)
|
|
||||||
yield* this._streamResponseChunksLegacy(input, options, runManager)
|
|
||||||
} else {
|
|
||||||
throw e
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
protected _convertMessagesToOllamaMessages(messages: BaseMessage[]): OllamaMessage[] {
|
|
||||||
return messages.map((message) => {
|
|
||||||
let role
|
|
||||||
if (message._getType() === 'human') {
|
|
||||||
role = 'user'
|
|
||||||
} else if (message._getType() === 'ai' || message._getType() === 'tool') {
|
|
||||||
role = 'assistant'
|
|
||||||
} else if (message._getType() === 'system') {
|
|
||||||
role = 'system'
|
|
||||||
} else {
|
|
||||||
throw new Error(`Unsupported message type for Ollama: ${message._getType()}`)
|
|
||||||
}
|
|
||||||
let content = ''
|
|
||||||
const images = []
|
|
||||||
if (typeof message.content === 'string') {
|
|
||||||
content = message.content
|
|
||||||
} else {
|
|
||||||
for (const contentPart of message.content) {
|
|
||||||
if (contentPart.type === 'text') {
|
|
||||||
content = `${content}\n${contentPart.text}`
|
|
||||||
} else if (contentPart.type === 'image_url' && typeof contentPart.image_url === 'string') {
|
|
||||||
const imageUrlComponents = contentPart.image_url.split(',')
|
|
||||||
// Support both data:image/jpeg;base64,<image> format as well
|
|
||||||
images.push(imageUrlComponents[1] ?? imageUrlComponents[0])
|
|
||||||
} else {
|
|
||||||
throw new Error(
|
|
||||||
`Unsupported message content type. Must either have type "text" or type "image_url" with a string "image_url" field.`
|
|
||||||
)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
return {
|
|
||||||
role,
|
|
||||||
content,
|
|
||||||
images
|
|
||||||
}
|
|
||||||
})
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @deprecated */
|
|
||||||
protected _formatMessagesAsPrompt(messages: BaseMessage[]): string {
|
|
||||||
const formattedMessages = messages
|
|
||||||
.map((message) => {
|
|
||||||
let messageText
|
|
||||||
if (message._getType() === 'human') {
|
|
||||||
messageText = `[INST] ${message.content} [/INST]`
|
|
||||||
} else if (message._getType() === 'ai') {
|
|
||||||
messageText = message.content
|
|
||||||
} else if (message._getType() === 'system') {
|
|
||||||
messageText = `<<SYS>> ${message.content} <</SYS>>`
|
|
||||||
} else if (ChatMessage.isInstance(message)) {
|
|
||||||
messageText = `\n\n${message.role[0].toUpperCase()}${message.role.slice(1)}: ${message.content}`
|
|
||||||
} else {
|
|
||||||
console.warn(`Unsupported message type passed to Ollama: "${message._getType()}"`)
|
|
||||||
messageText = ''
|
|
||||||
}
|
|
||||||
return messageText
|
|
||||||
})
|
|
||||||
.join('\n')
|
|
||||||
return formattedMessages
|
|
||||||
}
|
|
||||||
|
|
||||||
/** @ignore */
|
|
||||||
async _call(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): Promise<string> {
|
|
||||||
const chunks = []
|
|
||||||
for await (const chunk of this._streamResponseChunks(messages, options, runManager)) {
|
|
||||||
chunks.push(chunk.message.content)
|
|
||||||
}
|
|
||||||
return chunks.join('')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatOllamaFunction_ChatModels }
|
|
||||||
|
|
@ -1 +0,0 @@
|
||||||
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M7 27.5c0-1.273.388-2.388.97-3-.582-.612-.97-1.727-.97-3 0-1.293.4-2.422.996-3.028A4.818 4.818 0 0 1 7 15.5c0-2.485 1.79-4.5 4-4.5l.1.001a5.002 5.002 0 0 1 9.8 0L21 11c2.21 0 4 2.015 4 4.5 0 1.139-.376 2.18-.996 2.972.595.606.996 1.735.996 3.028 0 1.273-.389 2.388-.97 3 .581.612.97 1.727.97 3" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><path d="M9.5 11C9.167 8.5 9 4 11 4c1.5 0 1.667 2.667 2 4m9.5 3c.333-2.5.5-7-1.5-7-1.5 0-1.667 2.667-2 4" stroke="#000" stroke-width="2" stroke-linecap="round"/><circle cx="11" cy="15" r="1" fill="#000"/><circle cx="21" cy="15" r="1" fill="#000"/><path d="M13 17c0-2 2-2.5 3-2.5s3 .5 3 2.5-2 2.5-3 2.5-3-.5-3-2.5Z" stroke="#000" stroke-width="2" stroke-linecap="round"/></svg>
|
|
||||||
|
Before Width: | Height: | Size: 834 B |
|
|
@ -1,185 +0,0 @@
|
||||||
import { IterableReadableStream } from '@langchain/core/utils/stream'
|
|
||||||
import type { StringWithAutocomplete } from '@langchain/core/utils/types'
|
|
||||||
import { BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
|
|
||||||
|
|
||||||
export interface OllamaInput {
|
|
||||||
embeddingOnly?: boolean
|
|
||||||
f16KV?: boolean
|
|
||||||
frequencyPenalty?: number
|
|
||||||
headers?: Record<string, string>
|
|
||||||
keepAlive?: string
|
|
||||||
logitsAll?: boolean
|
|
||||||
lowVram?: boolean
|
|
||||||
mainGpu?: number
|
|
||||||
model?: string
|
|
||||||
baseUrl?: string
|
|
||||||
mirostat?: number
|
|
||||||
mirostatEta?: number
|
|
||||||
mirostatTau?: number
|
|
||||||
numBatch?: number
|
|
||||||
numCtx?: number
|
|
||||||
numGpu?: number
|
|
||||||
numGqa?: number
|
|
||||||
numKeep?: number
|
|
||||||
numPredict?: number
|
|
||||||
numThread?: number
|
|
||||||
penalizeNewline?: boolean
|
|
||||||
presencePenalty?: number
|
|
||||||
repeatLastN?: number
|
|
||||||
repeatPenalty?: number
|
|
||||||
ropeFrequencyBase?: number
|
|
||||||
ropeFrequencyScale?: number
|
|
||||||
temperature?: number
|
|
||||||
stop?: string[]
|
|
||||||
tfsZ?: number
|
|
||||||
topK?: number
|
|
||||||
topP?: number
|
|
||||||
typicalP?: number
|
|
||||||
useMLock?: boolean
|
|
||||||
useMMap?: boolean
|
|
||||||
vocabOnly?: boolean
|
|
||||||
format?: StringWithAutocomplete<'json'>
|
|
||||||
}
|
|
||||||
|
|
||||||
export interface OllamaRequestParams {
|
|
||||||
model: string
|
|
||||||
format?: StringWithAutocomplete<'json'>
|
|
||||||
images?: string[]
|
|
||||||
options: {
|
|
||||||
embedding_only?: boolean
|
|
||||||
f16_kv?: boolean
|
|
||||||
frequency_penalty?: number
|
|
||||||
logits_all?: boolean
|
|
||||||
low_vram?: boolean
|
|
||||||
main_gpu?: number
|
|
||||||
mirostat?: number
|
|
||||||
mirostat_eta?: number
|
|
||||||
mirostat_tau?: number
|
|
||||||
num_batch?: number
|
|
||||||
num_ctx?: number
|
|
||||||
num_gpu?: number
|
|
||||||
num_gqa?: number
|
|
||||||
num_keep?: number
|
|
||||||
num_thread?: number
|
|
||||||
num_predict?: number
|
|
||||||
penalize_newline?: boolean
|
|
||||||
presence_penalty?: number
|
|
||||||
repeat_last_n?: number
|
|
||||||
repeat_penalty?: number
|
|
||||||
rope_frequency_base?: number
|
|
||||||
rope_frequency_scale?: number
|
|
||||||
temperature?: number
|
|
||||||
stop?: string[]
|
|
||||||
tfs_z?: number
|
|
||||||
top_k?: number
|
|
||||||
top_p?: number
|
|
||||||
typical_p?: number
|
|
||||||
use_mlock?: boolean
|
|
||||||
use_mmap?: boolean
|
|
||||||
vocab_only?: boolean
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
export type OllamaMessage = {
|
|
||||||
role: StringWithAutocomplete<'user' | 'assistant' | 'system'>
|
|
||||||
content: string
|
|
||||||
images?: string[]
|
|
||||||
}
|
|
||||||
|
|
||||||
export interface OllamaGenerateRequestParams extends OllamaRequestParams {
|
|
||||||
prompt: string
|
|
||||||
}
|
|
||||||
|
|
||||||
export interface OllamaChatRequestParams extends OllamaRequestParams {
|
|
||||||
messages: OllamaMessage[]
|
|
||||||
}
|
|
||||||
|
|
||||||
export type BaseOllamaGenerationChunk = {
|
|
||||||
model: string
|
|
||||||
created_at: string
|
|
||||||
done: boolean
|
|
||||||
total_duration?: number
|
|
||||||
load_duration?: number
|
|
||||||
prompt_eval_count?: number
|
|
||||||
prompt_eval_duration?: number
|
|
||||||
eval_count?: number
|
|
||||||
eval_duration?: number
|
|
||||||
}
|
|
||||||
|
|
||||||
export type OllamaGenerationChunk = BaseOllamaGenerationChunk & {
|
|
||||||
response: string
|
|
||||||
}
|
|
||||||
|
|
||||||
export type OllamaChatGenerationChunk = BaseOllamaGenerationChunk & {
|
|
||||||
message: OllamaMessage
|
|
||||||
}
|
|
||||||
|
|
||||||
export type OllamaCallOptions = BaseLanguageModelCallOptions & {
|
|
||||||
headers?: Record<string, string>
|
|
||||||
}
|
|
||||||
|
|
||||||
async function* createOllamaStream(url: string, params: OllamaRequestParams, options: OllamaCallOptions) {
|
|
||||||
let formattedUrl = url
|
|
||||||
if (formattedUrl.startsWith('http://localhost:')) {
|
|
||||||
// Node 18 has issues with resolving "localhost"
|
|
||||||
// See https://github.com/node-fetch/node-fetch/issues/1624
|
|
||||||
formattedUrl = formattedUrl.replace('http://localhost:', 'http://127.0.0.1:')
|
|
||||||
}
|
|
||||||
const response = await fetch(formattedUrl, {
|
|
||||||
method: 'POST',
|
|
||||||
body: JSON.stringify(params),
|
|
||||||
headers: {
|
|
||||||
'Content-Type': 'application/json',
|
|
||||||
...options.headers
|
|
||||||
},
|
|
||||||
signal: options.signal
|
|
||||||
})
|
|
||||||
if (!response.ok) {
|
|
||||||
let error
|
|
||||||
const responseText = await response.text()
|
|
||||||
try {
|
|
||||||
const json = JSON.parse(responseText)
|
|
||||||
error = new Error(`Ollama call failed with status code ${response.status}: ${json.error}`)
|
|
||||||
} catch (e) {
|
|
||||||
error = new Error(`Ollama call failed with status code ${response.status}: ${responseText}`)
|
|
||||||
}
|
|
||||||
;(error as any).response = response
|
|
||||||
throw error
|
|
||||||
}
|
|
||||||
if (!response.body) {
|
|
||||||
throw new Error('Could not begin Ollama stream. Please check the given URL and try again.')
|
|
||||||
}
|
|
||||||
|
|
||||||
const stream = IterableReadableStream.fromReadableStream(response.body)
|
|
||||||
|
|
||||||
const decoder = new TextDecoder()
|
|
||||||
let extra = ''
|
|
||||||
for await (const chunk of stream) {
|
|
||||||
const decoded = extra + decoder.decode(chunk)
|
|
||||||
const lines = decoded.split('\n')
|
|
||||||
extra = lines.pop() || ''
|
|
||||||
for (const line of lines) {
|
|
||||||
try {
|
|
||||||
yield JSON.parse(line)
|
|
||||||
} catch (e) {
|
|
||||||
console.warn(`Received a non-JSON parseable chunk: ${line}`)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
export async function* createOllamaGenerateStream(
|
|
||||||
baseUrl: string,
|
|
||||||
params: OllamaGenerateRequestParams,
|
|
||||||
options: OllamaCallOptions
|
|
||||||
): AsyncGenerator<OllamaGenerationChunk> {
|
|
||||||
yield* createOllamaStream(`${baseUrl}/api/generate`, params, options)
|
|
||||||
}
|
|
||||||
|
|
||||||
export async function* createOllamaChatStream(
|
|
||||||
baseUrl: string,
|
|
||||||
params: OllamaChatRequestParams,
|
|
||||||
options: OllamaCallOptions
|
|
||||||
): AsyncGenerator<OllamaChatGenerationChunk> {
|
|
||||||
yield* createOllamaStream(`${baseUrl}/api/chat`, params, options)
|
|
||||||
}
|
|
||||||
|
|
@ -1,6 +1,6 @@
|
||||||
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||||
import { OpenAI, OpenAISession, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
|
import { OpenAI, ALL_AVAILABLE_OPENAI_MODELS } from 'llamaindex'
|
||||||
import { getModels, MODEL_TYPE } from '../../../src/modelLoader'
|
import { getModels, MODEL_TYPE } from '../../../src/modelLoader'
|
||||||
|
|
||||||
class ChatOpenAI_LlamaIndex_LLMs implements INode {
|
class ChatOpenAI_LlamaIndex_LLMs implements INode {
|
||||||
|
|
@ -115,9 +115,8 @@ class ChatOpenAI_LlamaIndex_LLMs implements INode {
|
||||||
if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
|
if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
|
||||||
if (topP) obj.topP = parseFloat(topP)
|
if (topP) obj.topP = parseFloat(topP)
|
||||||
if (timeout) obj.timeout = parseInt(timeout, 10)
|
if (timeout) obj.timeout = parseInt(timeout, 10)
|
||||||
const openai = new OpenAISession(obj)
|
|
||||||
|
|
||||||
const model = new OpenAI({ ...obj, session: openai })
|
const model = new OpenAI(obj)
|
||||||
return model
|
return model
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
|
||||||
|
|
@ -1,71 +0,0 @@
|
||||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
import { TogetherLLM, OpenAI } from 'llamaindex'
|
|
||||||
|
|
||||||
class ChatTogetherAI_LlamaIndex_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
tags: string[]
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatTogetherAI'
|
|
||||||
this.name = 'chatTogetherAI_LlamaIndex'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatTogetherAI'
|
|
||||||
this.icon = 'togetherai.png'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around ChatTogetherAI LLM specific for LlamaIndex'
|
|
||||||
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(TogetherLLM)]
|
|
||||||
this.tags = ['LlamaIndex']
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['togetherAIApi']
|
|
||||||
}
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Model Name',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'string',
|
|
||||||
placeholder: 'mixtral-8x7b-32768',
|
|
||||||
description: 'Refer to <a target="_blank" href="https://docs.together.ai/docs/inference-models">models</a> page'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const togetherAIApiKey = getCredentialParam('togetherAIApiKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
const obj: Partial<OpenAI> = {
|
|
||||||
temperature: parseFloat(temperature),
|
|
||||||
model: modelName,
|
|
||||||
apiKey: togetherAIApiKey
|
|
||||||
}
|
|
||||||
|
|
||||||
const model = new TogetherLLM(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatTogetherAI_LlamaIndex_ChatModels }
|
|
||||||
|
|
@ -1,80 +0,0 @@
|
||||||
import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
|
|
||||||
import { MODEL_TYPE, getModels } from '../../../src/modelLoader'
|
|
||||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
import { Groq, OpenAI } from 'llamaindex'
|
|
||||||
|
|
||||||
class ChatGroq_LlamaIndex_ChatModels implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
version: number
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
category: string
|
|
||||||
description: string
|
|
||||||
tags: string[]
|
|
||||||
baseClasses: string[]
|
|
||||||
credential: INodeParams
|
|
||||||
inputs: INodeParams[]
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'ChatGroq'
|
|
||||||
this.name = 'chatGroq_LlamaIndex'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'ChatGroq'
|
|
||||||
this.icon = 'groq.png'
|
|
||||||
this.category = 'Chat Models'
|
|
||||||
this.description = 'Wrapper around Groq LLM specific for LlamaIndex'
|
|
||||||
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...getBaseClasses(Groq)]
|
|
||||||
this.tags = ['LlamaIndex']
|
|
||||||
this.credential = {
|
|
||||||
label: 'Connect Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['groqApi'],
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Model Name',
|
|
||||||
name: 'modelName',
|
|
||||||
type: 'asyncOptions',
|
|
||||||
loadMethod: 'listModels',
|
|
||||||
placeholder: 'llama3-70b-8192'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Temperature',
|
|
||||||
name: 'temperature',
|
|
||||||
type: 'number',
|
|
||||||
step: 0.1,
|
|
||||||
default: 0.9,
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
|
||||||
|
|
||||||
//@ts-ignore
|
|
||||||
loadMethods = {
|
|
||||||
async listModels(): Promise<INodeOptionsValue[]> {
|
|
||||||
return await getModels(MODEL_TYPE.CHAT, 'groqChat')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const temperature = nodeData.inputs?.temperature as string
|
|
||||||
const modelName = nodeData.inputs?.modelName as string
|
|
||||||
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const groqApiKey = getCredentialParam('groqApiKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
const obj: Partial<OpenAI> = {
|
|
||||||
temperature: parseFloat(temperature),
|
|
||||||
model: modelName,
|
|
||||||
apiKey: groqApiKey
|
|
||||||
}
|
|
||||||
|
|
||||||
const model = new Groq(obj)
|
|
||||||
return model
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: ChatGroq_LlamaIndex_ChatModels }
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 1.7 KiB After Width: | Height: | Size: 1.4 KiB |
|
|
@ -82,7 +82,7 @@ class API_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -132,31 +132,23 @@ class API_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -107,7 +107,7 @@ class Airtable_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -162,31 +162,23 @@ class Airtable_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -106,7 +106,7 @@ class ApifyWebsiteContentCrawler_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -174,31 +174,23 @@ class ApifyWebsiteContentCrawler_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -93,7 +93,7 @@ class Cheerio_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -131,11 +131,7 @@ class Cheerio_DocumentLoaders implements INode {
|
||||||
|
|
||||||
async function cheerioLoader(url: string): Promise<any> {
|
async function cheerioLoader(url: string): Promise<any> {
|
||||||
try {
|
try {
|
||||||
let docs: IDocument[] = []
|
let docs = []
|
||||||
if (url.endsWith('.pdf')) {
|
|
||||||
if (process.env.DEBUG === 'true') options.logger.info(`CheerioWebBaseLoader does not support PDF files: ${url}`)
|
|
||||||
return docs
|
|
||||||
}
|
|
||||||
const loader = new CheerioWebBaseLoader(url, params)
|
const loader = new CheerioWebBaseLoader(url, params)
|
||||||
if (textSplitter) {
|
if (textSplitter) {
|
||||||
docs = await loader.loadAndSplit(textSplitter)
|
docs = await loader.loadAndSplit(textSplitter)
|
||||||
|
|
@ -145,7 +141,6 @@ class Cheerio_DocumentLoaders implements INode {
|
||||||
return docs
|
return docs
|
||||||
} catch (err) {
|
} catch (err) {
|
||||||
if (process.env.DEBUG === 'true') options.logger.error(`error in CheerioWebBaseLoader: ${err.message}, on page: ${url}`)
|
if (process.env.DEBUG === 'true') options.logger.error(`error in CheerioWebBaseLoader: ${err.message}, on page: ${url}`)
|
||||||
return []
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
@ -183,31 +178,23 @@ class Cheerio_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -73,7 +73,7 @@ class Confluence_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -128,31 +128,23 @@ class Confluence_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,8 +1,8 @@
|
||||||
import { omit } from 'lodash'
|
import { omit } from 'lodash'
|
||||||
import { ICommonObject, IDocument, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
import { ICommonObject, IDocument, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||||
import { TextSplitter } from 'langchain/text_splitter'
|
import { TextSplitter } from 'langchain/text_splitter'
|
||||||
import { CSVLoader } from 'langchain/document_loaders/fs/csv'
|
import { CSVLoader } from 'langchain/document_loaders/fs/csv'
|
||||||
import { getFileFromStorage, handleEscapeCharacters } from '../../../src'
|
import { getFileFromStorage } from '../../../src'
|
||||||
|
|
||||||
class Csv_DocumentLoaders implements INode {
|
class Csv_DocumentLoaders implements INode {
|
||||||
label: string
|
label: string
|
||||||
|
|
@ -14,12 +14,11 @@ class Csv_DocumentLoaders implements INode {
|
||||||
category: string
|
category: string
|
||||||
baseClasses: string[]
|
baseClasses: string[]
|
||||||
inputs: INodeParams[]
|
inputs: INodeParams[]
|
||||||
outputs: INodeOutputsValue[]
|
|
||||||
|
|
||||||
constructor() {
|
constructor() {
|
||||||
this.label = 'Csv File'
|
this.label = 'Csv File'
|
||||||
this.name = 'csvFile'
|
this.name = 'csvFile'
|
||||||
this.version = 2.0
|
this.version = 1.0
|
||||||
this.type = 'Document'
|
this.type = 'Document'
|
||||||
this.icon = 'csv.svg'
|
this.icon = 'csv.svg'
|
||||||
this.category = 'Document Loaders'
|
this.category = 'Document Loaders'
|
||||||
|
|
@ -60,26 +59,12 @@ class Csv_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
}
|
}
|
||||||
]
|
]
|
||||||
this.outputs = [
|
|
||||||
{
|
|
||||||
label: 'Document',
|
|
||||||
name: 'document',
|
|
||||||
description: 'Array of document objects containing metadata and pageContent',
|
|
||||||
baseClasses: [...this.baseClasses, 'json']
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Text',
|
|
||||||
name: 'text',
|
|
||||||
description: 'Concatenated string from pageContent of documents',
|
|
||||||
baseClasses: ['string', 'json']
|
|
||||||
}
|
|
||||||
]
|
|
||||||
}
|
}
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||||
|
|
@ -87,7 +72,6 @@ class Csv_DocumentLoaders implements INode {
|
||||||
const csvFileBase64 = nodeData.inputs?.csvFile as string
|
const csvFileBase64 = nodeData.inputs?.csvFile as string
|
||||||
const columnName = nodeData.inputs?.columnName as string
|
const columnName = nodeData.inputs?.columnName as string
|
||||||
const metadata = nodeData.inputs?.metadata
|
const metadata = nodeData.inputs?.metadata
|
||||||
const output = nodeData.outputs?.output as string
|
|
||||||
const _omitMetadataKeys = nodeData.inputs?.omitMetadataKeys as string
|
const _omitMetadataKeys = nodeData.inputs?.omitMetadataKeys as string
|
||||||
|
|
||||||
let omitMetadataKeys: string[] = []
|
let omitMetadataKeys: string[] = []
|
||||||
|
|
@ -144,43 +128,27 @@ class Csv_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
if (output === 'document') {
|
return docs
|
||||||
return docs
|
|
||||||
} else {
|
|
||||||
let finaltext = ''
|
|
||||||
for (const doc of docs) {
|
|
||||||
finaltext += `${doc.pageContent}\n`
|
|
||||||
}
|
|
||||||
return handleEscapeCharacters(finaltext, false)
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -23,6 +23,7 @@ class CustomDocumentLoader_DocumentLoaders implements INode {
|
||||||
this.type = 'Document'
|
this.type = 'Document'
|
||||||
this.icon = 'customDocLoader.svg'
|
this.icon = 'customDocLoader.svg'
|
||||||
this.category = 'Document Loaders'
|
this.category = 'Document Loaders'
|
||||||
|
this.badge = 'NEW'
|
||||||
this.description = `Custom function for loading documents`
|
this.description = `Custom function for loading documents`
|
||||||
this.baseClasses = [this.type]
|
this.baseClasses = [this.type]
|
||||||
this.inputs = [
|
this.inputs = [
|
||||||
|
|
|
||||||
|
|
@ -1,7 +1,6 @@
|
||||||
import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||||
import { DataSource } from 'typeorm'
|
import { DataSource } from 'typeorm'
|
||||||
import { Document } from '@langchain/core/documents'
|
import { Document } from '@langchain/core/documents'
|
||||||
import { handleEscapeCharacters } from '../../../src'
|
|
||||||
|
|
||||||
class DocStore_DocumentLoaders implements INode {
|
class DocStore_DocumentLoaders implements INode {
|
||||||
label: string
|
label: string
|
||||||
|
|
@ -22,6 +21,7 @@ class DocStore_DocumentLoaders implements INode {
|
||||||
this.version = 1.0
|
this.version = 1.0
|
||||||
this.type = 'Document'
|
this.type = 'Document'
|
||||||
this.icon = 'dstore.svg'
|
this.icon = 'dstore.svg'
|
||||||
|
this.badge = 'NEW'
|
||||||
this.category = 'Document Loaders'
|
this.category = 'Document Loaders'
|
||||||
this.description = `Load data from pre-configured document stores`
|
this.description = `Load data from pre-configured document stores`
|
||||||
this.baseClasses = [this.type]
|
this.baseClasses = [this.type]
|
||||||
|
|
@ -83,22 +83,12 @@ class DocStore_DocumentLoaders implements INode {
|
||||||
const chunks = await appDataSource
|
const chunks = await appDataSource
|
||||||
.getRepository(databaseEntities['DocumentStoreFileChunk'])
|
.getRepository(databaseEntities['DocumentStoreFileChunk'])
|
||||||
.find({ where: { storeId: selectedStore } })
|
.find({ where: { storeId: selectedStore } })
|
||||||
const output = nodeData.outputs?.output as string
|
|
||||||
|
|
||||||
const finalDocs = []
|
const finalDocs = []
|
||||||
for (const chunk of chunks) {
|
for (const chunk of chunks) {
|
||||||
finalDocs.push(new Document({ pageContent: chunk.pageContent, metadata: JSON.parse(chunk.metadata) }))
|
finalDocs.push(new Document({ pageContent: chunk.pageContent, metadata: JSON.parse(chunk.metadata) }))
|
||||||
}
|
}
|
||||||
|
return finalDocs
|
||||||
if (output === 'document') {
|
|
||||||
return finalDocs
|
|
||||||
} else {
|
|
||||||
let finaltext = ''
|
|
||||||
for (const doc of finalDocs) {
|
|
||||||
finaltext += `${doc.pageContent}\n`
|
|
||||||
}
|
|
||||||
return handleEscapeCharacters(finaltext, false)
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -51,7 +51,7 @@ class Docx_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -119,31 +119,23 @@ class Docx_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -74,7 +74,7 @@ class Figma_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -111,31 +111,23 @@ class Figma_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,378 +0,0 @@
|
||||||
import { TextSplitter } from 'langchain/text_splitter'
|
|
||||||
import { Document, DocumentInterface } from '@langchain/core/documents'
|
|
||||||
import { BaseDocumentLoader } from 'langchain/document_loaders/base'
|
|
||||||
import { INode, INodeData, INodeParams, ICommonObject } from '../../../src/Interface'
|
|
||||||
import { getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
import axios, { AxiosResponse, AxiosRequestHeaders } from 'axios'
|
|
||||||
import { z } from 'zod'
|
|
||||||
import { zodToJsonSchema } from 'zod-to-json-schema'
|
|
||||||
|
|
||||||
// FirecrawlApp interfaces
|
|
||||||
interface FirecrawlAppConfig {
|
|
||||||
apiKey?: string | null
|
|
||||||
apiUrl?: string | null
|
|
||||||
}
|
|
||||||
|
|
||||||
interface FirecrawlDocumentMetadata {
|
|
||||||
title?: string
|
|
||||||
description?: string
|
|
||||||
language?: string
|
|
||||||
// ... (other metadata fields)
|
|
||||||
[key: string]: any
|
|
||||||
}
|
|
||||||
|
|
||||||
interface FirecrawlDocument {
|
|
||||||
id?: string
|
|
||||||
url?: string
|
|
||||||
content: string
|
|
||||||
markdown?: string
|
|
||||||
html?: string
|
|
||||||
llm_extraction?: Record<string, any>
|
|
||||||
createdAt?: Date
|
|
||||||
updatedAt?: Date
|
|
||||||
type?: string
|
|
||||||
metadata: FirecrawlDocumentMetadata
|
|
||||||
childrenLinks?: string[]
|
|
||||||
provider?: string
|
|
||||||
warning?: string
|
|
||||||
index?: number
|
|
||||||
}
|
|
||||||
|
|
||||||
interface ScrapeResponse {
|
|
||||||
success: boolean
|
|
||||||
data?: FirecrawlDocument
|
|
||||||
error?: string
|
|
||||||
}
|
|
||||||
|
|
||||||
interface CrawlResponse {
|
|
||||||
success: boolean
|
|
||||||
jobId?: string
|
|
||||||
data?: FirecrawlDocument[]
|
|
||||||
error?: string
|
|
||||||
}
|
|
||||||
|
|
||||||
interface Params {
|
|
||||||
[key: string]: any
|
|
||||||
extractorOptions?: {
|
|
||||||
extractionSchema: z.ZodSchema | any
|
|
||||||
mode?: 'llm-extraction'
|
|
||||||
extractionPrompt?: string
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// FirecrawlApp class (not exported)
|
|
||||||
class FirecrawlApp {
|
|
||||||
private apiKey: string
|
|
||||||
private apiUrl: string
|
|
||||||
|
|
||||||
constructor({ apiKey = null, apiUrl = null }: FirecrawlAppConfig) {
|
|
||||||
this.apiKey = apiKey || ''
|
|
||||||
this.apiUrl = apiUrl || 'https://api.firecrawl.dev'
|
|
||||||
if (!this.apiKey) {
|
|
||||||
throw new Error('No API key provided')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async scrapeUrl(url: string, params: Params | null = null): Promise<ScrapeResponse> {
|
|
||||||
const headers = this.prepareHeaders()
|
|
||||||
let jsonData: Params = { url, ...params }
|
|
||||||
if (params?.extractorOptions?.extractionSchema) {
|
|
||||||
let schema = params.extractorOptions.extractionSchema
|
|
||||||
if (schema instanceof z.ZodSchema) {
|
|
||||||
schema = zodToJsonSchema(schema)
|
|
||||||
}
|
|
||||||
jsonData = {
|
|
||||||
...jsonData,
|
|
||||||
extractorOptions: {
|
|
||||||
...params.extractorOptions,
|
|
||||||
extractionSchema: schema,
|
|
||||||
mode: params.extractorOptions.mode || 'llm-extraction'
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
try {
|
|
||||||
const response: AxiosResponse = await this.postRequest(this.apiUrl + '/v0/scrape', jsonData, headers)
|
|
||||||
if (response.status === 200) {
|
|
||||||
const responseData = response.data
|
|
||||||
if (responseData.success) {
|
|
||||||
return responseData
|
|
||||||
} else {
|
|
||||||
throw new Error(`Failed to scrape URL. Error: ${responseData.error}`)
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
this.handleError(response, 'scrape URL')
|
|
||||||
}
|
|
||||||
} catch (error: any) {
|
|
||||||
throw new Error(error.message)
|
|
||||||
}
|
|
||||||
return { success: false, error: 'Internal server error.' }
|
|
||||||
}
|
|
||||||
|
|
||||||
async crawlUrl(
|
|
||||||
url: string,
|
|
||||||
params: Params | null = null,
|
|
||||||
waitUntilDone: boolean = true,
|
|
||||||
pollInterval: number = 2,
|
|
||||||
idempotencyKey?: string
|
|
||||||
): Promise<CrawlResponse | any> {
|
|
||||||
const headers = this.prepareHeaders(idempotencyKey)
|
|
||||||
let jsonData: Params = { url, ...params }
|
|
||||||
try {
|
|
||||||
const response: AxiosResponse = await this.postRequest(this.apiUrl + '/v0/crawl', jsonData, headers)
|
|
||||||
if (response.status === 200) {
|
|
||||||
const jobId: string = response.data.jobId
|
|
||||||
if (waitUntilDone) {
|
|
||||||
return this.monitorJobStatus(jobId, headers, pollInterval)
|
|
||||||
} else {
|
|
||||||
return { success: true, jobId }
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
this.handleError(response, 'start crawl job')
|
|
||||||
}
|
|
||||||
} catch (error: any) {
|
|
||||||
throw new Error(error.message)
|
|
||||||
}
|
|
||||||
return { success: false, error: 'Internal server error.' }
|
|
||||||
}
|
|
||||||
|
|
||||||
private prepareHeaders(idempotencyKey?: string): AxiosRequestHeaders {
|
|
||||||
return {
|
|
||||||
'Content-Type': 'application/json',
|
|
||||||
Authorization: `Bearer ${this.apiKey}`,
|
|
||||||
...(idempotencyKey ? { 'x-idempotency-key': idempotencyKey } : {})
|
|
||||||
} as AxiosRequestHeaders & { 'x-idempotency-key'?: string }
|
|
||||||
}
|
|
||||||
|
|
||||||
private postRequest(url: string, data: Params, headers: AxiosRequestHeaders): Promise<AxiosResponse> {
|
|
||||||
return axios.post(url, data, { headers })
|
|
||||||
}
|
|
||||||
|
|
||||||
private getRequest(url: string, headers: AxiosRequestHeaders): Promise<AxiosResponse> {
|
|
||||||
return axios.get(url, { headers })
|
|
||||||
}
|
|
||||||
|
|
||||||
private async monitorJobStatus(jobId: string, headers: AxiosRequestHeaders, checkInterval: number): Promise<any> {
|
|
||||||
let isJobCompleted = false
|
|
||||||
while (!isJobCompleted) {
|
|
||||||
const statusResponse: AxiosResponse = await this.getRequest(this.apiUrl + `/v0/crawl/status/${jobId}`, headers)
|
|
||||||
if (statusResponse.status === 200) {
|
|
||||||
const statusData = statusResponse.data
|
|
||||||
switch (statusData.status) {
|
|
||||||
case 'completed':
|
|
||||||
isJobCompleted = true
|
|
||||||
if ('data' in statusData) {
|
|
||||||
return statusData.data
|
|
||||||
} else {
|
|
||||||
throw new Error('Crawl job completed but no data was returned')
|
|
||||||
}
|
|
||||||
case 'active':
|
|
||||||
case 'paused':
|
|
||||||
case 'pending':
|
|
||||||
case 'queued':
|
|
||||||
await new Promise((resolve) => setTimeout(resolve, Math.max(checkInterval, 2) * 1000))
|
|
||||||
break
|
|
||||||
default:
|
|
||||||
throw new Error(`Crawl job failed or was stopped. Status: ${statusData.status}`)
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
this.handleError(statusResponse, 'check crawl status')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
private handleError(response: AxiosResponse, action: string): void {
|
|
||||||
if ([402, 408, 409, 500].includes(response.status)) {
|
|
||||||
const errorMessage: string = response.data.error || 'Unknown error occurred'
|
|
||||||
throw new Error(`Failed to ${action}. Status code: ${response.status}. Error: ${errorMessage}`)
|
|
||||||
} else {
|
|
||||||
throw new Error(`Unexpected error occurred while trying to ${action}. Status code: ${response.status}`)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// FireCrawl Loader
|
|
||||||
interface FirecrawlLoaderParameters {
|
|
||||||
url: string
|
|
||||||
apiKey?: string
|
|
||||||
mode?: 'crawl' | 'scrape'
|
|
||||||
params?: Record<string, unknown>
|
|
||||||
}
|
|
||||||
|
|
||||||
class FireCrawlLoader extends BaseDocumentLoader {
|
|
||||||
private apiKey: string
|
|
||||||
private url: string
|
|
||||||
private mode: 'crawl' | 'scrape'
|
|
||||||
private params?: Record<string, unknown>
|
|
||||||
|
|
||||||
constructor(loaderParams: FirecrawlLoaderParameters) {
|
|
||||||
super()
|
|
||||||
const { apiKey, url, mode = 'crawl', params } = loaderParams
|
|
||||||
if (!apiKey) {
|
|
||||||
throw new Error('Firecrawl API key not set. You can set it as FIRECRAWL_API_KEY in your .env file, or pass it to Firecrawl.')
|
|
||||||
}
|
|
||||||
|
|
||||||
this.apiKey = apiKey
|
|
||||||
this.url = url
|
|
||||||
this.mode = mode
|
|
||||||
this.params = params
|
|
||||||
}
|
|
||||||
|
|
||||||
public async load(): Promise<DocumentInterface[]> {
|
|
||||||
const app = new FirecrawlApp({ apiKey: this.apiKey })
|
|
||||||
let firecrawlDocs: FirecrawlDocument[]
|
|
||||||
|
|
||||||
if (this.mode === 'scrape') {
|
|
||||||
const response = await app.scrapeUrl(this.url, this.params)
|
|
||||||
if (!response.success) {
|
|
||||||
throw new Error(`Firecrawl: Failed to scrape URL. Error: ${response.error}`)
|
|
||||||
}
|
|
||||||
firecrawlDocs = [response.data as FirecrawlDocument]
|
|
||||||
} else if (this.mode === 'crawl') {
|
|
||||||
const response = await app.crawlUrl(this.url, this.params, true)
|
|
||||||
firecrawlDocs = response as FirecrawlDocument[]
|
|
||||||
} else {
|
|
||||||
throw new Error(`Unrecognized mode '${this.mode}'. Expected one of 'crawl', 'scrape'.`)
|
|
||||||
}
|
|
||||||
|
|
||||||
return firecrawlDocs.map(
|
|
||||||
(doc) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: doc.markdown || '',
|
|
||||||
metadata: doc.metadata || {}
|
|
||||||
})
|
|
||||||
)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Flowise Node Class
|
|
||||||
class FireCrawl_DocumentLoaders implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
description: string
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
version: number
|
|
||||||
category: string
|
|
||||||
baseClasses: string[]
|
|
||||||
inputs: INodeParams[]
|
|
||||||
credential: INodeParams
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'FireCrawl'
|
|
||||||
this.name = 'fireCrawl'
|
|
||||||
this.type = 'Document'
|
|
||||||
this.icon = 'firecrawl.png'
|
|
||||||
this.version = 1.0
|
|
||||||
this.category = 'Document Loaders'
|
|
||||||
this.description = 'Load data from URL using FireCrawl'
|
|
||||||
this.baseClasses = [this.type]
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Text Splitter',
|
|
||||||
name: 'textSplitter',
|
|
||||||
type: 'TextSplitter',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'URLs',
|
|
||||||
name: 'url',
|
|
||||||
type: 'string',
|
|
||||||
description: 'URL to be crawled/scraped',
|
|
||||||
placeholder: 'https://docs.flowiseai.com'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Crawler type',
|
|
||||||
type: 'options',
|
|
||||||
name: 'crawlerType',
|
|
||||||
options: [
|
|
||||||
{
|
|
||||||
label: 'Crawl',
|
|
||||||
name: 'crawl',
|
|
||||||
description: 'Crawl a URL and all accessible subpages'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Scrape',
|
|
||||||
name: 'scrape',
|
|
||||||
description: 'Scrape a URL and get its content'
|
|
||||||
}
|
|
||||||
],
|
|
||||||
default: 'crawl'
|
|
||||||
}
|
|
||||||
// ... (other input parameters)
|
|
||||||
]
|
|
||||||
this.credential = {
|
|
||||||
label: 'FireCrawl API',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['fireCrawlApi']
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
|
|
||||||
const metadata = nodeData.inputs?.metadata
|
|
||||||
const url = nodeData.inputs?.url as string
|
|
||||||
const crawlerType = nodeData.inputs?.crawlerType as string
|
|
||||||
const maxCrawlPages = nodeData.inputs?.maxCrawlPages as string
|
|
||||||
const generateImgAltText = nodeData.inputs?.generateImgAltText as boolean
|
|
||||||
const returnOnlyUrls = nodeData.inputs?.returnOnlyUrls as boolean
|
|
||||||
const onlyMainContent = nodeData.inputs?.onlyMainContent as boolean
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const firecrawlApiToken = getCredentialParam('firecrawlApiToken', credentialData, nodeData)
|
|
||||||
|
|
||||||
const urlPatternsExcludes = nodeData.inputs?.urlPatternsExcludes
|
|
||||||
? (nodeData.inputs.urlPatternsExcludes.split(',') as string[])
|
|
||||||
: undefined
|
|
||||||
const urlPatternsIncludes = nodeData.inputs?.urlPatternsIncludes
|
|
||||||
? (nodeData.inputs.urlPatternsIncludes.split(',') as string[])
|
|
||||||
: undefined
|
|
||||||
|
|
||||||
const input: FirecrawlLoaderParameters = {
|
|
||||||
url,
|
|
||||||
mode: crawlerType as 'crawl' | 'scrape',
|
|
||||||
apiKey: firecrawlApiToken,
|
|
||||||
params: {
|
|
||||||
crawlerOptions: {
|
|
||||||
includes: urlPatternsIncludes,
|
|
||||||
excludes: urlPatternsExcludes,
|
|
||||||
generateImgAltText,
|
|
||||||
returnOnlyUrls,
|
|
||||||
limit: maxCrawlPages ? parseFloat(maxCrawlPages) : undefined
|
|
||||||
},
|
|
||||||
pageOptions: {
|
|
||||||
onlyMainContent
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
const loader = new FireCrawlLoader(input)
|
|
||||||
|
|
||||||
let docs = []
|
|
||||||
|
|
||||||
if (textSplitter) {
|
|
||||||
docs = await loader.loadAndSplit(textSplitter)
|
|
||||||
} else {
|
|
||||||
docs = await loader.load()
|
|
||||||
}
|
|
||||||
|
|
||||||
if (metadata) {
|
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
|
||||||
let finaldocs = []
|
|
||||||
for (const doc of docs) {
|
|
||||||
const newdoc = {
|
|
||||||
...doc,
|
|
||||||
metadata: {
|
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
}
|
|
||||||
}
|
|
||||||
finaldocs.push(newdoc)
|
|
||||||
}
|
|
||||||
return finaldocs
|
|
||||||
}
|
|
||||||
|
|
||||||
return docs
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: FireCrawl_DocumentLoaders }
|
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
|
|
@ -79,7 +79,7 @@ class Folder_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -162,31 +162,23 @@ class Folder_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -58,7 +58,7 @@ class Gitbook_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -85,31 +85,23 @@ class Gitbook_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -100,7 +100,7 @@ class Github_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -145,31 +145,23 @@ class Github_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -59,7 +59,7 @@ class Json_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -135,31 +135,23 @@ class Json_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -58,7 +58,7 @@ class Jsonlines_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -129,31 +129,23 @@ class Jsonlines_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -58,7 +58,7 @@ class NotionDB_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -104,31 +104,23 @@ class NotionDB_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -51,7 +51,7 @@ class NotionFolder_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -83,31 +83,23 @@ class NotionFolder_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -59,7 +59,7 @@ class NotionPage_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -101,31 +101,23 @@ class NotionPage_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -74,7 +74,7 @@ class Pdf_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -132,31 +132,23 @@ class Pdf_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -54,7 +54,7 @@ class PlainText_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -104,31 +104,23 @@ class PlainText_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -121,7 +121,7 @@ class Playwright_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -217,31 +217,23 @@ class Playwright_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -122,7 +122,7 @@ class Puppeteer_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -218,31 +218,23 @@ class Puppeteer_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -427,7 +427,7 @@ class S3_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -561,33 +561,25 @@ class S3_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata,
|
||||||
}
|
[sourceIdKey]: doc.metadata[sourceIdKey] || sourceIdKey
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata,
|
)
|
||||||
...parsedMetadata,
|
|
||||||
[sourceIdKey]: doc.metadata[sourceIdKey] || sourceIdKey
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata,
|
||||||
: omit(
|
[sourceIdKey]: doc.metadata[sourceIdKey] || sourceIdKey
|
||||||
{
|
},
|
||||||
...doc.metadata,
|
omitMetadataKeys
|
||||||
[sourceIdKey]: doc.metadata[sourceIdKey] || sourceIdKey
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -68,7 +68,7 @@ class SearchAPI_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -112,31 +112,23 @@ class SearchAPI_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -58,7 +58,7 @@ class SerpAPI_DocumentLoaders implements INode {
|
||||||
type: 'string',
|
type: 'string',
|
||||||
rows: 4,
|
rows: 4,
|
||||||
description:
|
description:
|
||||||
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
|
'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma',
|
||||||
placeholder: 'key1, key2, key3.nestedKey1',
|
placeholder: 'key1, key2, key3.nestedKey1',
|
||||||
optional: true,
|
optional: true,
|
||||||
additionalParams: true
|
additionalParams: true
|
||||||
|
|
@ -86,31 +86,23 @@ class SerpAPI_DocumentLoaders implements INode {
|
||||||
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
const parsedMetadata = typeof metadata === 'object' ? metadata : JSON.parse(metadata)
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {
|
...doc.metadata,
|
||||||
...parsedMetadata
|
...parsedMetadata
|
||||||
}
|
},
|
||||||
: omit(
|
omitMetadataKeys
|
||||||
{
|
)
|
||||||
...doc.metadata,
|
|
||||||
...parsedMetadata
|
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
} else {
|
} else {
|
||||||
docs = docs.map((doc) => ({
|
docs = docs.map((doc) => ({
|
||||||
...doc,
|
...doc,
|
||||||
metadata:
|
metadata: omit(
|
||||||
_omitMetadataKeys === '*'
|
{
|
||||||
? {}
|
...doc.metadata
|
||||||
: omit(
|
},
|
||||||
{
|
omitMetadataKeys
|
||||||
...doc.metadata
|
)
|
||||||
},
|
|
||||||
omitMetadataKeys
|
|
||||||
)
|
|
||||||
}))
|
}))
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -1,189 +0,0 @@
|
||||||
import { TextSplitter } from 'langchain/text_splitter'
|
|
||||||
import { Document, DocumentInterface } from '@langchain/core/documents'
|
|
||||||
import { BaseDocumentLoader } from 'langchain/document_loaders/base'
|
|
||||||
import { INode, INodeData, INodeParams, ICommonObject } from '../../../src/Interface'
|
|
||||||
import { getCredentialData, getCredentialParam } from '../../../src/utils'
|
|
||||||
import SpiderApp from './SpiderApp'
|
|
||||||
|
|
||||||
interface SpiderLoaderParameters {
|
|
||||||
url: string
|
|
||||||
apiKey?: string
|
|
||||||
mode?: 'crawl' | 'scrape'
|
|
||||||
limit?: number
|
|
||||||
params?: Record<string, unknown>
|
|
||||||
}
|
|
||||||
|
|
||||||
class SpiderLoader extends BaseDocumentLoader {
|
|
||||||
private apiKey: string
|
|
||||||
private url: string
|
|
||||||
private mode: 'crawl' | 'scrape'
|
|
||||||
private limit?: number
|
|
||||||
private params?: Record<string, unknown>
|
|
||||||
|
|
||||||
constructor(loaderParams: SpiderLoaderParameters) {
|
|
||||||
super()
|
|
||||||
const { apiKey, url, mode = 'crawl', limit, params } = loaderParams
|
|
||||||
if (!apiKey) {
|
|
||||||
throw new Error('Spider API key not set. You can set it as SPIDER_API_KEY in your .env file, or pass it to Spider.')
|
|
||||||
}
|
|
||||||
|
|
||||||
this.apiKey = apiKey
|
|
||||||
this.url = url
|
|
||||||
this.mode = mode
|
|
||||||
this.limit = Number(limit)
|
|
||||||
this.params = params
|
|
||||||
}
|
|
||||||
|
|
||||||
public async load(): Promise<DocumentInterface[]> {
|
|
||||||
const app = new SpiderApp({ apiKey: this.apiKey })
|
|
||||||
let spiderDocs: any[]
|
|
||||||
|
|
||||||
if (this.mode === 'scrape') {
|
|
||||||
const response = await app.scrapeUrl(this.url, this.params)
|
|
||||||
if (!response.success) {
|
|
||||||
throw new Error(`Spider: Failed to scrape URL. Error: ${response.error}`)
|
|
||||||
}
|
|
||||||
spiderDocs = [response.data]
|
|
||||||
} else if (this.mode === 'crawl') {
|
|
||||||
if (this.params) {
|
|
||||||
this.params.limit = this.limit
|
|
||||||
}
|
|
||||||
const response = await app.crawlUrl(this.url, this.params)
|
|
||||||
if (!response.success) {
|
|
||||||
throw new Error(`Spider: Failed to crawl URL. Error: ${response.error}`)
|
|
||||||
}
|
|
||||||
spiderDocs = response.data
|
|
||||||
} else {
|
|
||||||
throw new Error(`Unrecognized mode '${this.mode}'. Expected one of 'crawl', 'scrape'.`)
|
|
||||||
}
|
|
||||||
|
|
||||||
return spiderDocs.map(
|
|
||||||
(doc) =>
|
|
||||||
new Document({
|
|
||||||
pageContent: doc.content || '',
|
|
||||||
metadata: { source: doc.url }
|
|
||||||
})
|
|
||||||
)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
class Spider_DocumentLoaders implements INode {
|
|
||||||
label: string
|
|
||||||
name: string
|
|
||||||
description: string
|
|
||||||
type: string
|
|
||||||
icon: string
|
|
||||||
version: number
|
|
||||||
category: string
|
|
||||||
baseClasses: string[]
|
|
||||||
inputs: INodeParams[]
|
|
||||||
credential: INodeParams
|
|
||||||
|
|
||||||
constructor() {
|
|
||||||
this.label = 'Spider Document Loaders'
|
|
||||||
this.name = 'spiderDocumentLoaders'
|
|
||||||
this.version = 1.0
|
|
||||||
this.type = 'Document'
|
|
||||||
this.icon = 'spider.svg'
|
|
||||||
this.category = 'Document Loaders'
|
|
||||||
this.description = 'Scrape & Crawl the web with Spider'
|
|
||||||
this.baseClasses = [this.type]
|
|
||||||
this.inputs = [
|
|
||||||
{
|
|
||||||
label: 'Text Splitter',
|
|
||||||
name: 'textSplitter',
|
|
||||||
type: 'TextSplitter',
|
|
||||||
optional: true
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Mode',
|
|
||||||
name: 'mode',
|
|
||||||
type: 'options',
|
|
||||||
options: [
|
|
||||||
{
|
|
||||||
label: 'Scrape',
|
|
||||||
name: 'scrape',
|
|
||||||
description: 'Scrape a single page'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Crawl',
|
|
||||||
name: 'crawl',
|
|
||||||
description: 'Crawl a website and extract pages within the same domain'
|
|
||||||
}
|
|
||||||
],
|
|
||||||
default: 'scrape'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Web Page URL',
|
|
||||||
name: 'url',
|
|
||||||
type: 'string',
|
|
||||||
placeholder: 'https://spider.cloud'
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Limit',
|
|
||||||
name: 'limit',
|
|
||||||
type: 'number',
|
|
||||||
default: 25
|
|
||||||
},
|
|
||||||
{
|
|
||||||
label: 'Additional Parameters',
|
|
||||||
name: 'params',
|
|
||||||
description:
|
|
||||||
'Find all the available parameters in the <a _target="blank" href="https://spider.cloud/docs/api">Spider API documentation</a>',
|
|
||||||
additionalParams: true,
|
|
||||||
placeholder: '{ "anti_bot": true }',
|
|
||||||
type: 'json',
|
|
||||||
optional: true
|
|
||||||
}
|
|
||||||
]
|
|
||||||
this.credential = {
|
|
||||||
label: 'Credential',
|
|
||||||
name: 'credential',
|
|
||||||
type: 'credential',
|
|
||||||
credentialNames: ['spiderApi']
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
|
||||||
const textSplitter = nodeData.inputs?.textSplitter as TextSplitter
|
|
||||||
const url = nodeData.inputs?.url as string
|
|
||||||
const mode = nodeData.inputs?.mode as 'crawl' | 'scrape'
|
|
||||||
const limit = nodeData.inputs?.limit as number
|
|
||||||
let params = nodeData.inputs?.params || {}
|
|
||||||
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
|
|
||||||
const spiderApiKey = getCredentialParam('spiderApiKey', credentialData, nodeData)
|
|
||||||
|
|
||||||
if (typeof params === 'string') {
|
|
||||||
try {
|
|
||||||
params = JSON.parse(params)
|
|
||||||
} catch (e) {
|
|
||||||
throw new Error('Invalid JSON string provided for params')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Ensure return_format is set to markdown
|
|
||||||
params.return_format = 'markdown'
|
|
||||||
|
|
||||||
const input: SpiderLoaderParameters = {
|
|
||||||
url,
|
|
||||||
mode: mode as 'crawl' | 'scrape',
|
|
||||||
apiKey: spiderApiKey,
|
|
||||||
limit: limit as number,
|
|
||||||
params: params as Record<string, unknown>
|
|
||||||
}
|
|
||||||
|
|
||||||
const loader = new SpiderLoader(input)
|
|
||||||
|
|
||||||
let docs = []
|
|
||||||
|
|
||||||
if (textSplitter) {
|
|
||||||
docs = await loader.loadAndSplit(textSplitter)
|
|
||||||
} else {
|
|
||||||
docs = await loader.load()
|
|
||||||
}
|
|
||||||
|
|
||||||
return docs
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
module.exports = { nodeClass: Spider_DocumentLoaders }
|
|
||||||
|
|
@ -1,116 +0,0 @@
|
||||||
import axios, { AxiosResponse, AxiosRequestHeaders } from 'axios'
|
|
||||||
|
|
||||||
interface SpiderAppConfig {
|
|
||||||
apiKey?: string | null
|
|
||||||
apiUrl?: string | null
|
|
||||||
}
|
|
||||||
|
|
||||||
interface SpiderDocumentMetadata {
|
|
||||||
title?: string
|
|
||||||
description?: string
|
|
||||||
language?: string
|
|
||||||
[key: string]: any
|
|
||||||
}
|
|
||||||
|
|
||||||
interface SpiderDocument {
|
|
||||||
id?: string
|
|
||||||
url?: string
|
|
||||||
content: string
|
|
||||||
markdown?: string
|
|
||||||
html?: string
|
|
||||||
createdAt?: Date
|
|
||||||
updatedAt?: Date
|
|
||||||
type?: string
|
|
||||||
metadata: SpiderDocumentMetadata
|
|
||||||
}
|
|
||||||
|
|
||||||
interface ScrapeResponse {
|
|
||||||
success: boolean
|
|
||||||
data?: SpiderDocument
|
|
||||||
error?: string
|
|
||||||
}
|
|
||||||
|
|
||||||
interface CrawlResponse {
|
|
||||||
success: boolean
|
|
||||||
data?: SpiderDocument[]
|
|
||||||
error?: string
|
|
||||||
}
|
|
||||||
|
|
||||||
interface Params {
|
|
||||||
[key: string]: any
|
|
||||||
}
|
|
||||||
|
|
||||||
class SpiderApp {
|
|
||||||
private apiKey: string
|
|
||||||
private apiUrl: string
|
|
||||||
|
|
||||||
constructor({ apiKey = null, apiUrl = null }: SpiderAppConfig) {
|
|
||||||
this.apiKey = apiKey || ''
|
|
||||||
this.apiUrl = apiUrl || 'https://api.spider.cloud/v1'
|
|
||||||
if (!this.apiKey) {
|
|
||||||
throw new Error('No API key provided')
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
async scrapeUrl(url: string, params: Params | null = null): Promise<ScrapeResponse> {
|
|
||||||
const headers = this.prepareHeaders()
|
|
||||||
const jsonData: Params = { url, limit: 1, ...params }
|
|
||||||
|
|
||||||
try {
|
|
||||||
const response: AxiosResponse = await this.postRequest('crawl', jsonData, headers)
|
|
||||||
if (response.status === 200) {
|
|
||||||
const responseData = response.data
|
|
||||||
if (responseData[0].status) {
|
|
||||||
return { success: true, data: responseData[0] }
|
|
||||||
} else {
|
|
||||||
throw new Error(`Failed to scrape URL. Error: ${responseData.error}`)
|
|
||||||
}
|
|
||||||
} else {
|
|
||||||
this.handleError(response, 'scrape URL')
|
|
||||||
}
|
|
||||||
} catch (error: any) {
|
|
||||||
throw new Error(error.message)
|
|
||||||
}
|
|
||||||
return { success: false, error: 'Internal server error.' }
|
|
||||||
}
|
|
||||||
|
|
||||||
async crawlUrl(url: string, params: Params | null = null, idempotencyKey?: string): Promise<CrawlResponse | any> {
|
|
||||||
const headers = this.prepareHeaders(idempotencyKey)
|
|
||||||
const jsonData: Params = { url, ...params }
|
|
||||||
|
|
||||||
try {
|
|
||||||
const response: AxiosResponse = await this.postRequest('crawl', jsonData, headers)
|
|
||||||
if (response.status === 200) {
|
|
||||||
return { success: true, data: response.data }
|
|
||||||
} else {
|
|
||||||
this.handleError(response, 'start crawl job')
|
|
||||||
}
|
|
||||||
} catch (error: any) {
|
|
||||||
throw new Error(error.message)
|
|
||||||
}
|
|
||||||
return { success: false, error: 'Internal server error.' }
|
|
||||||
}
|
|
||||||
|
|
||||||
private prepareHeaders(idempotencyKey?: string): AxiosRequestHeaders {
|
|
||||||
return {
|
|
||||||
'Content-Type': 'application/json',
|
|
||||||
Authorization: `Bearer ${this.apiKey}`,
|
|
||||||
...(idempotencyKey ? { 'x-idempotency-key': idempotencyKey } : {})
|
|
||||||
} as AxiosRequestHeaders & { 'x-idempotency-key'?: string }
|
|
||||||
}
|
|
||||||
|
|
||||||
private postRequest(url: string, data: Params, headers: AxiosRequestHeaders): Promise<AxiosResponse> {
|
|
||||||
return axios.post(`${this.apiUrl}/${url}`, data, { headers })
|
|
||||||
}
|
|
||||||
|
|
||||||
private handleError(response: AxiosResponse, action: string): void {
|
|
||||||
if ([402, 408, 409, 500].includes(response.status)) {
|
|
||||||
const errorMessage: string = response.data.error || 'Unknown error occurred'
|
|
||||||
throw new Error(`Failed to ${action}. Status code: ${response.status}. Error: ${errorMessage}`)
|
|
||||||
} else {
|
|
||||||
throw new Error(`Unexpected error occurred while trying to ${action}. Status code: ${response.status}`)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
export default SpiderApp
|
|
||||||
|
|
@ -1 +0,0 @@
|
||||||
<svg height="30" width="30" viewBox="0 0 36 34" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" class="fill-accent-foreground transition-all group-hover:scale-110"><title>Spider v1 Logo</title><path fill-rule="evenodd" clip-rule="evenodd" d="M9.13883 7.06589V0.164429L13.0938 0.164429V6.175L14.5178 7.4346C15.577 6.68656 16.7337 6.27495 17.945 6.27495C19.1731 6.27495 20.3451 6.69807 21.4163 7.46593L22.8757 6.175V0.164429L26.8307 0.164429V7.06589V7.95679L26.1634 8.54706L24.0775 10.3922C24.3436 10.8108 24.5958 11.2563 24.8327 11.7262L26.0467 11.4215L28.6971 8.08749L31.793 10.5487L28.7257 14.407L28.3089 14.9313L27.6592 15.0944L26.2418 15.4502C26.3124 15.7082 26.3793 15.9701 26.4422 16.2355L28.653 16.6566L29.092 16.7402L29.4524 17.0045L35.3849 21.355L33.0461 24.5444L27.474 20.4581L27.0719 20.3816C27.1214 21.0613 27.147 21.7543 27.147 22.4577C27.147 22.5398 27.1466 22.6214 27.1459 22.7024L29.5889 23.7911L30.3219 24.1177L30.62 24.8629L33.6873 32.5312L30.0152 34L27.246 27.0769L26.7298 26.8469C25.5612 32.2432 22.0701 33.8808 17.945 33.8808C13.8382 33.8808 10.3598 32.2577 9.17593 26.9185L8.82034 27.0769L6.05109 34L2.37897 32.5312L5.44629 24.8629L5.74435 24.1177L6.47743 23.7911L8.74487 22.7806C8.74366 22.6739 8.74305 22.5663 8.74305 22.4577C8.74305 21.7616 8.76804 21.0758 8.81654 20.4028L8.52606 20.4581L2.95395 24.5444L0.615112 21.355L6.54761 17.0045L6.908 16.7402L7.34701 16.6566L9.44264 16.2575C9.50917 15.9756 9.5801 15.6978 9.65528 15.4242L8.34123 15.0944L7.69155 14.9313L7.27471 14.407L4.20739 10.5487L7.30328 8.08749L9.95376 11.4215L11.0697 11.7016C11.3115 11.2239 11.5692 10.7716 11.8412 10.3473L9.80612 8.54706L9.13883 7.95679V7.06589Z"></path></svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.6 KiB |
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Reference in New Issue