mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-22 07:01:07 +03:00
Fix merge conflicts
This commit is contained in:
Ilango
@@ -0,0 +1,33 @@
|
||||
name: autoSyncMergedPullRequest
|
||||
on:
|
||||
pull_request_target:
|
||||
types:
|
||||
- closed
|
||||
branches: ['main']
|
||||
jobs:
|
||||
autoSyncMergedPullRequest:
|
||||
if: github.event.pull_request.merged == true
|
||||
runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: write
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Show PR info
|
||||
env:
|
||||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
|
||||
run: |
|
||||
echo The PR #${{ github.event.pull_request.number }} was merged on main branch!
|
||||
- name: Repository Dispatch
|
||||
uses: peter-evans/repository-dispatch@v2
|
||||
with:
|
||||
token: ${{ secrets.AUTOSYNC_TOKEN }}
|
||||
repository: ${{ secrets.AUTOSYNC_CH_URL }}
|
||||
event-type: ${{ secrets.AUTOSYNC_PR_EVENT_TYPE }}
|
||||
client-payload: >-
|
||||
{
|
||||
"ref": "${{ github.ref }}",
|
||||
"prNumber": "${{ github.event.pull_request.number }}",
|
||||
"prTitle": "${{ github.event.pull_request.title }}",
|
||||
"prDescription": "${{ github.event.pull_request.description }}",
|
||||
"sha": "${{ github.sha }}"
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
name: autoSyncSingleCommit
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
jobs:
|
||||
doNotAutoSyncSingleCommit:
|
||||
if: github.event.commits[1] != null
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: IGNORE autoSyncSingleCommit
|
||||
run: |
|
||||
echo This single commit has came from a merged commit. We will ignore it. This case is handled in autoSyncMergedPullRequest workflow for merge commits comming from merged pull requests only! Beware, the regular merge commits are not handled by any workflow for the moment.
|
||||
autoSyncSingleCommit:
|
||||
if: github.event.commits[1] == null
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: autoSyncSingleCommit
|
||||
env:
|
||||
GITHUB_CONTEXT: ${{ toJSON(github) }}
|
||||
run: |
|
||||
echo Autosync a single commit with id: ${{ github.sha }} from openSource main branch towards cloud hosted version.
|
||||
- name: Repository Dispatch
|
||||
uses: peter-evans/repository-dispatch@v2
|
||||
with:
|
||||
token: ${{ secrets.AUTOSYNC_TOKEN }}
|
||||
repository: ${{ secrets.AUTOSYNC_CH_URL }}
|
||||
event-type: ${{ secrets.AUTOSYNC_SC_EVENT_TYPE }}
|
||||
client-payload: >-
|
||||
{
|
||||
"ref": "${{ github.ref }}",
|
||||
"sha": "${{ github.sha }}",
|
||||
"commitMessage": "${{ github.event.commits[0].message }}"
|
||||
}
|
||||
+5
-2
@@ -30,7 +30,7 @@
|
||||
|
||||
不确定要贡献什么？一些想法：
|
||||
|
||||
- 从 Langchain 创建新组件
|
||||
- 从 `packages/components` 创建新组件
|
||||
- 更新现有组件，如扩展功能、修复错误
|
||||
- 添加新的 Chatflow 想法
|
||||
|
||||
@@ -40,7 +40,7 @@ Flowise 在一个单一的单体存储库中有 3 个不同的模块。
|
||||
|
||||
- `server`：用于提供 API 逻辑的 Node 后端
|
||||
- `ui`：React 前端
|
||||
- `components`：Langchain 组件
|
||||
- `components`：Langchain/LlamaIndex 组件
|
||||
|
||||
#### 先决条件
|
||||
|
||||
@@ -123,7 +123,9 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
|
||||
| PORT | Flowise 运行的 HTTP 端口 | 数字 | 3000 |
|
||||
| FLOWISE_USERNAME | 登录用户名 | 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 |
|
||||
| DEBUG | 打印组件的日志 | 布尔值 | |
|
||||
| BLOB_STORAGE_PATH | 存储位置 | 字符串 | `your-home-dir/.flowise/storage` |
|
||||
| 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` |
|
||||
| APIKEY_PATH | 存储 API 密钥的位置 | 字符串 | `your-path/Flowise/packages/server` |
|
||||
@@ -138,6 +140,7 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
|
||||
| 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` |
|
||||
| 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 |
|
||||
|
||||
您也可以在使用 `npx` 时指定环境变量。例如：
|
||||
|
||||
|
||||
+9
-2
@@ -30,7 +30,7 @@ Found an issue? [Report it](https://github.com/FlowiseAI/Flowise/issues/new/choo
|
||||
|
||||
Not sure what to contribute? Some ideas:
|
||||
|
||||
- Create new components from Langchain
|
||||
- Create new components from `packages/components`
|
||||
- Update existing components such as extending functionality, fixing bugs
|
||||
- Add new chatflow ideas
|
||||
|
||||
@@ -40,7 +40,7 @@ Flowise has 3 different modules in a single mono repository.
|
||||
|
||||
- `server`: Node backend to serve API logics
|
||||
- `ui`: React frontend
|
||||
- `components`: Langchain components
|
||||
- `components`: Third-party nodes integrations
|
||||
|
||||
#### Prerequisite
|
||||
|
||||
@@ -123,9 +123,13 @@ Flowise support different environment variables to configure your instance. You
|
||||
| Variable | Description | Type | Default |
|
||||
| --------------------------- | ---------------------------------------------------------------------------- | ------------------------------------------------ | ----------------------------------- |
|
||||
| PORT | The HTTP port Flowise runs on | Number | 3000 |
|
||||
| CORS_ORIGINS | The allowed origins for all cross-origin HTTP calls | String | |
|
||||
| IFRAME_ORIGINS | The allowed origins for iframe src embedding | String | |
|
||||
| FLOWISE_USERNAME | Username to login | String | |
|
||||
| FLOWISE_PASSWORD | Password to login | String | |
|
||||
| FLOWISE_FILE_SIZE_LIMIT | Upload File Size Limit | String | 50mb |
|
||||
| DEBUG | Print logs from components | Boolean | |
|
||||
| BLOB_STORAGE_PATH | Location where uploaded files are stored | String | `your-home-dir/.flowise/storage` |
|
||||
| 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` |
|
||||
| APIKEY_PATH | Location where api keys are saved | String | `your-path/Flowise/packages/server` |
|
||||
@@ -138,8 +142,11 @@ Flowise support different environment variables to configure your instance. You
|
||||
| DATABASE_USER | Database username (When DATABASE_TYPE is not sqlite) | String | |
|
||||
| DATABASE_PASSWORD | Database password (When DATABASE_TYPE is not sqlite) | String | |
|
||||
| DATABASE_NAME | Database name (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_SSL | Database connection overssl (When DATABASE_TYPE is postgre) | Boolean | false |
|
||||
| SECRETKEY_PATH | Location where encryption key (used to encrypt/decrypt credentials) is saved | String | `your-path/Flowise/packages/server` |
|
||||
| FLOWISE_SECRETKEY_OVERWRITE | Encryption key to be used instead of the key stored in SECRETKEY_PATH | String |
|
||||
| DISABLE_FLOWISE_TELEMETRY | Turn off telemetry | Boolean |
|
||||
|
||||
You can also specify the env variables when using `npx`. For example:
|
||||
|
||||
|
||||
-16
@@ -2,22 +2,6 @@
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
Flowise is governed by the Apache License 2.0, with additional terms and conditions outlined below:
|
||||
|
||||
Flowise can be used for commercial purposes for "backend-as-a-service" for your applications or as a development platform for enterprises. However, under specific conditions, you must reach out to the project's administrators to secure a commercial license:
|
||||
|
||||
a. Multi-tenant SaaS service: Unless you have explicit written authorization from Flowise, you may not utilize the Flowise source code to operate a multi-tenant SaaS service that closely resembles the Flowise cloud-based services.
|
||||
b. Logo and copyright information: While using Flowise in commercial application, you are prohibited from removing or altering the LOGO or copyright information displayed in the Flowise console and UI.
|
||||
|
||||
For inquiries regarding licensing matters, please contact hello@flowiseai.com via email.
|
||||
|
||||
Contributors are required to consent to the following terms related to their contributed code:
|
||||
|
||||
a. The project maintainers have the authority to modify the open-source agreement to be more stringent or lenient.
|
||||
b. Contributed code can be used for commercial purposes, including Flowise's cloud-based services.
|
||||
|
||||
All other rights and restrictions are in accordance with the Apache License 2.0.
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
+26
-11
@@ -71,7 +71,7 @@ Flowise 在一个单一的代码库中有 3 个不同的模块。
|
||||
|
||||
- `server`：用于提供 API 逻辑的 Node 后端
|
||||
- `ui`：React 前端
|
||||
- `components`：Langchain 组件
|
||||
- `components`：第三方节点集成
|
||||
|
||||
### 先决条件
|
||||
|
||||
@@ -145,25 +145,40 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
|
||||
|
||||
## 🌐 自托管
|
||||
|
||||
### [Railway](https://docs.flowiseai.com/deployment/railway)
|
||||
在您现有的基础设施中部署自托管的 Flowise，我们支持各种[部署](https://docs.flowiseai.com/configuration/deployment)
|
||||
|
||||
[](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
|
||||
- [AWS](https://docs.flowiseai.com/deployment/aws)
|
||||
- [Azure](https://docs.flowiseai.com/deployment/azure)
|
||||
- [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean)
|
||||
- [GCP](https://docs.flowiseai.com/deployment/gcp)
|
||||
- <details>
|
||||
<summary>其他</summary>
|
||||
|
||||
### [Render](https://docs.flowiseai.com/deployment/render)
|
||||
- [Railway](https://docs.flowiseai.com/deployment/railway)
|
||||
|
||||
[](https://docs.flowiseai.com/deployment/render)
|
||||
[](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
|
||||
|
||||
### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
|
||||
- [Render](https://docs.flowiseai.com/deployment/render)
|
||||
|
||||
<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
[](https://docs.flowiseai.com/deployment/render)
|
||||
|
||||
### [AWS](https://docs.flowiseai.com/deployment/aws)
|
||||
- [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
|
||||
|
||||
### [Azure](https://docs.flowiseai.com/deployment/azure)
|
||||
<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
|
||||
### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean)
|
||||
- [Elestio](https://elest.io/open-source/flowiseai)
|
||||
|
||||
### [GCP](https://docs.flowiseai.com/deployment/gcp)
|
||||
[](https://elest.io/open-source/flowiseai)
|
||||
|
||||
- [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
|
||||
|
||||
[](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
|
||||
|
||||
- [RepoCloud](https://repocloud.io/details/?app_id=29)
|
||||
|
||||
[](https://repocloud.io/details/?app_id=29)
|
||||
|
||||
</details>
|
||||
|
||||
## 💻 云托管
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ Flowise has 3 different modules in a single mono repository.
|
||||
|
||||
- `server`: Node backend to serve API logics
|
||||
- `ui`: React frontend
|
||||
- `components`: Langchain components
|
||||
- `components`: Third-party nodes integrations
|
||||
|
||||
### Prerequisite
|
||||
|
||||
@@ -145,29 +145,40 @@ Flowise support different environment variables to configure your instance. You
|
||||
|
||||
## 🌐 Self Host
|
||||
|
||||
### [Railway](https://docs.flowiseai.com/deployment/railway)
|
||||
Deploy Flowise self-hosted in your existing infrastructure, we support various [deployments](https://docs.flowiseai.com/configuration/deployment)
|
||||
|
||||
[](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
|
||||
- [AWS](https://docs.flowiseai.com/deployment/aws)
|
||||
- [Azure](https://docs.flowiseai.com/deployment/azure)
|
||||
- [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean)
|
||||
- [GCP](https://docs.flowiseai.com/deployment/gcp)
|
||||
- <details>
|
||||
<summary>Others</summary>
|
||||
|
||||
### [Render](https://docs.flowiseai.com/deployment/render)
|
||||
- [Railway](https://docs.flowiseai.com/deployment/railway)
|
||||
|
||||
[](https://docs.flowiseai.com/deployment/render)
|
||||
[](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
|
||||
|
||||
### [Elestio](https://elest.io/open-source/flowiseai)
|
||||
- [Render](https://docs.flowiseai.com/deployment/render)
|
||||
|
||||
[](https://elest.io/open-source/flowiseai)
|
||||
[](https://docs.flowiseai.com/deployment/render)
|
||||
|
||||
### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
|
||||
- [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
|
||||
|
||||
<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
|
||||
### [AWS](https://docs.flowiseai.com/deployment/aws)
|
||||
- [Elestio](https://elest.io/open-source/flowiseai)
|
||||
|
||||
### [Azure](https://docs.flowiseai.com/deployment/azure)
|
||||
[](https://elest.io/open-source/flowiseai)
|
||||
|
||||
### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean)
|
||||
- [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
|
||||
|
||||
### [GCP](https://docs.flowiseai.com/deployment/gcp)
|
||||
[](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
|
||||
|
||||
- [RepoCloud](https://repocloud.io/details/?app_id=29)
|
||||
|
||||
[](https://repocloud.io/details/?app_id=29)
|
||||
|
||||
</details>
|
||||
|
||||
## 💻 Cloud Hosted
|
||||
|
||||
|
||||
@@ -33,4 +33,4 @@ scenarios:
|
||||
# Seconds
|
||||
# Total Users = 2 + 3 + 3 = 8
|
||||
# Each making 1 HTTP call
|
||||
# Over a duration of 3 seconds
|
||||
# Over a durations of 3 seconds
|
||||
|
||||
+10
-1
@@ -3,6 +3,10 @@ DATABASE_PATH=/root/.flowise
|
||||
APIKEY_PATH=/root/.flowise
|
||||
SECRETKEY_PATH=/root/.flowise
|
||||
LOG_PATH=/root/.flowise/logs
|
||||
BLOB_STORAGE_PATH=/root/.flowise/storage
|
||||
|
||||
# CORS_ORIGINS="*"
|
||||
# IFRAME_ORIGINS="*"
|
||||
|
||||
# NUMBER_OF_PROXIES= 1
|
||||
|
||||
@@ -12,10 +16,13 @@ LOG_PATH=/root/.flowise/logs
|
||||
# DATABASE_NAME="flowise"
|
||||
# DATABASE_USER=""
|
||||
# DATABASE_PASSWORD=""
|
||||
# DATABASE_SSL=true
|
||||
# DATABASE_SSL_KEY_BASE64=<Self signed certificate in BASE64>
|
||||
|
||||
# FLOWISE_USERNAME=user
|
||||
# FLOWISE_PASSWORD=1234
|
||||
# FLOWISE_SECRETKEY_OVERWRITE=myencryptionkey
|
||||
# FLOWISE_FILE_SIZE_LIMIT=50mb
|
||||
# DEBUG=true
|
||||
# LOG_LEVEL=debug (error | warn | info | verbose | debug)
|
||||
# TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
|
||||
@@ -24,4 +31,6 @@ LOG_PATH=/root/.flowise/logs
|
||||
# LANGCHAIN_TRACING_V2=true
|
||||
# LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
|
||||
# LANGCHAIN_API_KEY=your_api_key
|
||||
# LANGCHAIN_PROJECT=your_project
|
||||
# LANGCHAIN_PROJECT=your_project
|
||||
|
||||
# DISABLE_FLOWISE_TELEMETRY=true
|
||||
+2
-1
@@ -1,6 +1,6 @@
|
||||
# Flowise Docker Hub Image
|
||||
|
||||
Starts Flowise from [DockerHub Image](https://hub.docker.com/repository/docker/flowiseai/flowise/general)
|
||||
Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise)
|
||||
|
||||
## Usage
|
||||
|
||||
@@ -31,5 +31,6 @@ If you like to persist your data (flows, logs, apikeys, credentials), set these
|
||||
- APIKEY_PATH=/root/.flowise
|
||||
- LOG_PATH=/root/.flowise/logs
|
||||
- SECRETKEY_PATH=/root/.flowise
|
||||
- BLOB_STORAGE_PATH=/root/.flowise/storage
|
||||
|
||||
Flowise also support different environment variables to configure your instance. Read [more](https://docs.flowiseai.com/environment-variables)
|
||||
|
||||
@@ -6,8 +6,11 @@ services:
|
||||
restart: always
|
||||
environment:
|
||||
- PORT=${PORT}
|
||||
- CORS_ORIGINS=${CORS_ORIGINS}
|
||||
- IFRAME_ORIGINS=${IFRAME_ORIGINS}
|
||||
- FLOWISE_USERNAME=${FLOWISE_USERNAME}
|
||||
- FLOWISE_PASSWORD=${FLOWISE_PASSWORD}
|
||||
- FLOWISE_FILE_SIZE_LIMIT=${FLOWISE_FILE_SIZE_LIMIT}
|
||||
- DEBUG=${DEBUG}
|
||||
- DATABASE_PATH=${DATABASE_PATH}
|
||||
- DATABASE_TYPE=${DATABASE_TYPE}
|
||||
@@ -16,11 +19,15 @@ services:
|
||||
- DATABASE_NAME=${DATABASE_NAME}
|
||||
- DATABASE_USER=${DATABASE_USER}
|
||||
- DATABASE_PASSWORD=${DATABASE_PASSWORD}
|
||||
- DATABASE_SSL=${DATABASE_SSL}
|
||||
- DATABASE_SSL_KEY_BASE64=${DATABASE_SSL_KEY_BASE64}
|
||||
- APIKEY_PATH=${APIKEY_PATH}
|
||||
- SECRETKEY_PATH=${SECRETKEY_PATH}
|
||||
- FLOWISE_SECRETKEY_OVERWRITE=${FLOWISE_SECRETKEY_OVERWRITE}
|
||||
- LOG_LEVEL=${LOG_LEVEL}
|
||||
- LOG_PATH=${LOG_PATH}
|
||||
- BLOB_STORAGE_PATH=${BLOB_STORAGE_PATH}
|
||||
- DISABLE_FLOWISE_TELEMETRY=${DISABLE_FLOWISE_TELEMETRY}
|
||||
ports:
|
||||
- '${PORT}:${PORT}'
|
||||
volumes:
|
||||
|
||||
+4
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "flowise",
|
||||
"version": "1.4.3",
|
||||
"version": "1.6.0",
|
||||
"private": true,
|
||||
"homepage": "https://flowiseai.com",
|
||||
"workspaces": [
|
||||
@@ -60,5 +60,8 @@
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=18.15.0 <19.0.0 || ^20"
|
||||
},
|
||||
"resolutions": {
|
||||
"@qdrant/openapi-typescript-fetch": "1.2.1"
|
||||
}
|
||||
}
|
||||
|
||||
+6
-7
@@ -1,24 +1,23 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class ZapierNLAApi implements INodeCredential {
|
||||
class AssemblyAIApi implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Zapier NLA API'
|
||||
this.name = 'zapierNLAApi'
|
||||
this.label = 'AssemblyAI API'
|
||||
this.name = 'assemblyAIApi'
|
||||
this.version = 1.0
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Zapier NLA Api Key',
|
||||
name: 'zapierNLAApiKey',
|
||||
label: 'AssemblyAI Api Key',
|
||||
name: 'assemblyAIApiKey',
|
||||
type: 'password'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: ZapierNLAApi }
|
||||
module.exports = { credClass: AssemblyAIApi }
|
||||
@@ -0,0 +1,34 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class AstraDBApi implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Astra DB API'
|
||||
this.name = 'AstraDBApi'
|
||||
this.version = 1.0
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Astra DB Collection Name',
|
||||
name: 'collectionName',
|
||||
type: 'string'
|
||||
},
|
||||
{
|
||||
label: 'Astra DB Application Token',
|
||||
name: 'applicationToken',
|
||||
type: 'password'
|
||||
},
|
||||
{
|
||||
label: 'Astra DB Api Endpoint',
|
||||
name: 'dbEndPoint',
|
||||
type: 'string'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: AstraDBApi }
|
||||
@@ -0,0 +1,26 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class GoogleGenerativeAICredential implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Google Generative AI'
|
||||
this.name = 'googleGenerativeAI'
|
||||
this.version = 1.0
|
||||
this.description =
|
||||
'You can get your API key from official <a target="_blank" href="https://ai.google.dev/tutorials/setup">page</a> here.'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Google AI API Key',
|
||||
name: 'googleGenerativeAPIKey',
|
||||
type: 'password'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: GoogleGenerativeAICredential }
|
||||
@@ -0,0 +1,23 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class GroqApi implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'Groq API'
|
||||
this.name = 'groqApi'
|
||||
this.version = 1.0
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Groq Api Key',
|
||||
name: 'groqApiKey',
|
||||
type: 'password'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: GroqApi }
|
||||
@@ -0,0 +1,23 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class LocalAIApi implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'LocalAI API'
|
||||
this.name = 'localAIApi'
|
||||
this.version = 1.0
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'LocalAI Api Key',
|
||||
name: 'localAIApiKey',
|
||||
type: 'password'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: LocalAIApi }
|
||||
+9
-9
@@ -1,6 +1,6 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class LLMonitorApi implements INodeCredential {
|
||||
class LunaryApi implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
@@ -8,25 +8,25 @@ class LLMonitorApi implements INodeCredential {
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'LLMonitor API'
|
||||
this.name = 'llmonitorApi'
|
||||
this.label = 'Lunary API'
|
||||
this.name = 'lunaryApi'
|
||||
this.version = 1.0
|
||||
this.description = 'Refer to <a target="_blank" href="https://llmonitor.com/docs">official guide</a> to get APP ID'
|
||||
this.description = 'Refer to <a target="_blank" href="https://lunary.ai/docs">official guide</a> to get APP ID'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'APP ID',
|
||||
name: 'llmonitorAppId',
|
||||
name: 'lunaryAppId',
|
||||
type: 'password',
|
||||
placeholder: '<LLMonitor_APP_ID>'
|
||||
placeholder: '<Lunary_APP_ID>'
|
||||
},
|
||||
{
|
||||
label: 'Endpoint',
|
||||
name: 'llmonitorEndpoint',
|
||||
name: 'lunaryEndpoint',
|
||||
type: 'string',
|
||||
default: 'https://app.llmonitor.com'
|
||||
default: 'https://app.lunary.ai'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: LLMonitorApi }
|
||||
module.exports = { credClass: LunaryApi }
|
||||
@@ -0,0 +1,25 @@
|
||||
import { INodeParams, INodeCredential } from '../src/Interface'
|
||||
|
||||
class MistralAICredential implements INodeCredential {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
|
||||
constructor() {
|
||||
this.label = 'MistralAI API'
|
||||
this.name = 'mistralAIApi'
|
||||
this.version = 1.0
|
||||
this.description = 'You can get your API key from official <a target="_blank" href="https://console.mistral.ai/">console</a> here.'
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'MistralAI API Key',
|
||||
name: 'mistralAIAPIKey',
|
||||
type: 'password'
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { credClass: MistralAICredential }
|
||||
@@ -16,11 +16,6 @@ class PineconeApi implements INodeCredential {
|
||||
label: 'Pinecone Api Key',
|
||||
name: 'pineconeApiKey',
|
||||
type: 'password'
|
||||
},
|
||||
{
|
||||
label: 'Pinecone Environment',
|
||||
name: 'pineconeEnv',
|
||||
type: 'string'
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -35,6 +35,11 @@ class RedisCacheApi implements INodeCredential {
|
||||
name: 'redisCachePwd',
|
||||
type: 'password',
|
||||
placeholder: '<REDIS_PASSWORD>'
|
||||
},
|
||||
{
|
||||
label: 'Use SSL',
|
||||
name: 'redisCacheSslEnabled',
|
||||
type: 'boolean'
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
|
||||
import { AgentExecutor } from 'langchain/agents'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||
import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import axios from 'axios'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { AgentExecutor } from 'langchain/agents'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
|
||||
|
||||
class Airtable_Agents implements INode {
|
||||
label: string
|
||||
|
||||
@@ -1,9 +1,5 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg width="256px" height="215px" viewBox="0 0 256 215" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid">
|
||||
<g>
|
||||
<path d="M114.25873,2.70101695 L18.8604023,42.1756384 C13.5552723,44.3711638 13.6102328,51.9065311 18.9486282,54.0225085 L114.746142,92.0117514 C123.163769,95.3498757 132.537419,95.3498757 140.9536,92.0117514 L236.75256,54.0225085 C242.08951,51.9065311 242.145916,44.3711638 236.83934,42.1756384 L141.442459,2.70101695 C132.738459,-0.900338983 122.961284,-0.900338983 114.25873,2.70101695" fill="#FFBF00"></path>
|
||||
<path d="M136.349071,112.756863 L136.349071,207.659101 C136.349071,212.173089 140.900664,215.263892 145.096461,213.600615 L251.844122,172.166219 C254.281184,171.200072 255.879376,168.845451 255.879376,166.224705 L255.879376,71.3224678 C255.879376,66.8084791 251.327783,63.7176768 247.131986,65.3809537 L140.384325,106.815349 C137.94871,107.781496 136.349071,110.136118 136.349071,112.756863" fill="#26B5F8"></path>
|
||||
<path d="M111.422771,117.65355 L79.742409,132.949912 L76.5257763,134.504714 L9.65047684,166.548104 C5.4112904,168.593211 0.000578531073,165.503855 0.000578531073,160.794612 L0.000578531073,71.7210757 C0.000578531073,70.0173017 0.874160452,68.5463864 2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill="#ED3049"></path>
|
||||
<path d="M111.422771,117.65355 L79.742409,132.949912 L2.04568588,67.4384994 C2.53454463,66.9481944 3.08848814,66.5446689 3.66412655,66.2250305 C5.26231864,65.2661153 7.54173107,65.0101153 9.47981017,65.7766689 L110.890522,105.957098 C116.045234,108.002206 116.450206,115.225166 111.422771,117.65355" fill-opacity="0.25" fill="#000000"></path>
|
||||
</g>
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M5 13V23L14 17L5 13Z" fill="#ED304A" stroke="#ED304A" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M17 17.5V27L27 22.8V13L17 17.5Z" fill="#26B5F8" stroke="#26B5F8" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M16 14.5L6 10L16 5L26 10L16 14.5Z" fill="#FFBF00" stroke="#FFBF00" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.9 KiB After Width: | Height: | Size: 514 B |
@@ -1,13 +1,12 @@
|
||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { BaseChatModel } from 'langchain/chat_models/base'
|
||||
import { AutoGPT } from 'langchain/experimental/autogpt'
|
||||
import { Tool } from 'langchain/tools'
|
||||
import { AIMessage, HumanMessage, SystemMessage } from 'langchain/schema'
|
||||
import { VectorStoreRetriever } from 'langchain/vectorstores/base'
|
||||
import { flatten } from 'lodash'
|
||||
import { StructuredTool } from 'langchain/tools'
|
||||
import { Tool, StructuredTool } from '@langchain/core/tools'
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
|
||||
import { AIMessage, HumanMessage, SystemMessage } from '@langchain/core/messages'
|
||||
import { VectorStoreRetriever } from '@langchain/core/vectorstores'
|
||||
import { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { AutoGPT } from 'langchain/experimental/autogpt'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { PromptTemplate } from 'langchain/prompts'
|
||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
|
||||
type ObjectTool = StructuredTool
|
||||
const FINISH_NAME = 'finish'
|
||||
@@ -29,7 +28,7 @@ class AutoGPT_Agents implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'AutoGPT'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'autogpt.png'
|
||||
this.icon = 'autogpt.svg'
|
||||
this.description = 'Autonomous agent with chain of thoughts for self-guided task completion'
|
||||
this.baseClasses = ['AutoGPT']
|
||||
this.inputs = [
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 22 KiB |
@@ -0,0 +1,11 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M16 15V27" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 15V18.4545C19 19.5591 19.8954 20.4545 21 20.4545H22C23.1046 20.4545 24 21.35 24 22.4545V27" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M13 15V18.4545C13 19.5591 12.1046 20.4545 11 20.4545H10C8.89543 20.4545 8 21.35 8 22.4545V27" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M21 14V16C21 17.1046 21.8954 18 23 18H24C25.1046 18 26 17.1046 26 16V12.5C26 11.9477 26.4477 11.5 27 11.5V11.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M11 14V16C11 17.1046 10.1046 18 9 18H8C6.89543 18 6 17.1046 6 16V12C6 11.4477 5.55228 11 5 11V11" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<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="13.5" cy="11" r="1.5" fill="black"/>
|
||||
<circle cx="18.5" cy="11" r="1.5" fill="black"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.3 KiB |
@@ -1,7 +1,7 @@
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
|
||||
import { VectorStore } from '@langchain/core/vectorstores'
|
||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { BabyAGI } from './core'
|
||||
import { BaseChatModel } from 'langchain/chat_models/base'
|
||||
import { VectorStore } from 'langchain/vectorstores/base'
|
||||
|
||||
class BabyAGI_Agents implements INode {
|
||||
label: string
|
||||
@@ -20,7 +20,7 @@ class BabyAGI_Agents implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'BabyAGI'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'babyagi.jpg'
|
||||
this.icon = 'babyagi.svg'
|
||||
this.description = 'Task Driven Autonomous Agent which creates new task and reprioritizes task list based on objective'
|
||||
this.baseClasses = ['BabyAGI']
|
||||
this.inputs = [
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 20 KiB |
@@ -0,0 +1,7 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<circle cx="16" cy="16" r="13" stroke="black" stroke-width="2"/>
|
||||
<path d="M16 3C17 3.12455 19 3.97145 19 6.36272C19 8.75399 17 9.10271 16 8.97817" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<circle cx="11" cy="16" r="1.5" fill="black"/>
|
||||
<circle cx="21" cy="16" r="1.5" fill="black"/>
|
||||
<path d="M13 22C13 22 14 23 16 23C18 23 19 22 19 22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 559 B |
@@ -1,8 +1,8 @@
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
|
||||
import { VectorStore } from '@langchain/core/vectorstores'
|
||||
import { Document } from '@langchain/core/documents'
|
||||
import { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { BaseChatModel } from 'langchain/chat_models/base'
|
||||
import { VectorStore } from 'langchain/dist/vectorstores/base'
|
||||
import { Document } from 'langchain/document'
|
||||
import { PromptTemplate } from 'langchain/prompts'
|
||||
|
||||
class TaskCreationChain extends LLMChain {
|
||||
constructor(prompt: PromptTemplate, llm: BaseChatModel) {
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { AgentExecutor } from 'langchain/agents'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { ICommonObject, INode, INodeData, INodeParams, PromptTemplate } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { LoadPyodide, finalSystemPrompt, systemPrompt } from './core'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
class CSV_Agents implements INode {
|
||||
label: string
|
||||
@@ -23,7 +23,7 @@ class CSV_Agents implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'csvagent.png'
|
||||
this.icon = 'CSVagent.svg'
|
||||
this.description = 'Agent used to to answer queries on CSV data'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.inputs = [
|
||||
|
||||
@@ -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="M21 20L23.5 27L26 20" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M11 21C10.609 20.3986 10 20 9 20C7.067 20 6 21.567 6 23.5C6 25.433 7.067 27 9 27C10 27 10.7037 26.4812 11 26" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M18.01 20.45C17.0115 19.7553 14 19.6248 14 21.8374C14 24.288 18.5 22.6248 18.5 25.2074C18.5 27.1484 15.4962 27.766 14 26.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<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"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 990 B |
Binary file not shown.
|
Before Width: | Height: | Size: 2.0 KiB |
@@ -1,11 +1,18 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { initializeAgentExecutorWithOptions, AgentExecutor, InitializeAgentExecutorOptions } from 'langchain/agents'
|
||||
import { Tool } from 'langchain/tools'
|
||||
import { BaseChatMemory } from 'langchain/memory'
|
||||
import { getBaseClasses, mapChatHistory } from '../../../src/utils'
|
||||
import { BaseChatModel } from 'langchain/chat_models/base'
|
||||
import { flatten } from 'lodash'
|
||||
import { additionalCallbacks } from '../../../src/handler'
|
||||
import { Tool } from '@langchain/core/tools'
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import { AgentStep } from '@langchain/core/agents'
|
||||
import { renderTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { ChatConversationalAgent } from 'langchain/agents'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { AgentExecutor } from '../../../src/agents'
|
||||
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
|
||||
import { addImagesToMessages } from '../../../src/multiModalUtils'
|
||||
|
||||
const DEFAULT_PREFIX = `Assistant is a large language model trained by OpenAI.
|
||||
|
||||
@@ -15,6 +22,15 @@ Assistant is constantly learning and improving, and its capabilities are constan
|
||||
|
||||
Overall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.`
|
||||
|
||||
const TEMPLATE_TOOL_RESPONSE = `TOOL RESPONSE:
|
||||
---------------------
|
||||
{observation}
|
||||
|
||||
USER'S INPUT
|
||||
--------------------
|
||||
|
||||
Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else.`
|
||||
|
||||
class ConversationalAgent_Agents implements INode {
|
||||
label: string
|
||||
name: string
|
||||
@@ -25,8 +41,9 @@ class ConversationalAgent_Agents implements INode {
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Conversational Agent'
|
||||
this.name = 'conversationalAgent'
|
||||
this.version = 2.0
|
||||
@@ -43,7 +60,7 @@ class ConversationalAgent_Agents implements INode {
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Language Model',
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
@@ -62,52 +79,151 @@ class ConversationalAgent_Agents implements INode {
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model as BaseChatModel
|
||||
let tools = nodeData.inputs?.tools as Tool[]
|
||||
tools = flatten(tools)
|
||||
const memory = nodeData.inputs?.memory as BaseChatMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
|
||||
const obj: InitializeAgentExecutorOptions = {
|
||||
agentType: 'chat-conversational-react-description',
|
||||
verbose: process.env.DEBUG === 'true' ? true : false
|
||||
}
|
||||
|
||||
const agentArgs: any = {}
|
||||
if (systemMessage) {
|
||||
agentArgs.systemMessage = systemMessage
|
||||
}
|
||||
|
||||
if (Object.keys(agentArgs).length) obj.agentArgs = agentArgs
|
||||
|
||||
const executor = await initializeAgentExecutorWithOptions(tools, model, obj)
|
||||
executor.memory = memory
|
||||
return executor
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const executor = nodeData.instance as AgentExecutor
|
||||
const memory = nodeData.inputs?.memory as BaseChatMemory
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
|
||||
if (options && options.chatHistory) {
|
||||
const chatHistoryClassName = memory.chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
memory.chatHistory = mapChatHistory(options)
|
||||
executor.memory = memory
|
||||
}
|
||||
}
|
||||
|
||||
;(executor.memory as any).returnMessages = true // Return true for BaseChatModel
|
||||
const executor = await prepareAgent(
|
||||
nodeData,
|
||||
options,
|
||||
{ sessionId: this.sessionId, chatId: options.chatId, input },
|
||||
options.chatHistory
|
||||
)
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
const result = await executor.call({ input }, [...callbacks])
|
||||
return result?.output
|
||||
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 = async (
|
||||
nodeData: INodeData,
|
||||
options: ICommonObject,
|
||||
flowObj: { sessionId?: string; chatId?: string; input?: string },
|
||||
chatHistory: IMessage[] = []
|
||||
) => {
|
||||
const model = nodeData.inputs?.model as BaseChatModel
|
||||
let tools = nodeData.inputs?.tools as Tool[]
|
||||
tools = flatten(tools)
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
|
||||
const outputParser = ChatConversationalAgent.getDefaultOutputParser({
|
||||
llm: model,
|
||||
toolNames: tools.map((tool) => tool.name)
|
||||
})
|
||||
|
||||
const prompt = ChatConversationalAgent.createPrompt(tools, {
|
||||
systemMessage: systemMessage ? systemMessage : DEFAULT_PREFIX,
|
||||
outputParser
|
||||
})
|
||||
|
||||
if (model instanceof ChatOpenAI) {
|
||||
let humanImageMessages: HumanMessage[] = []
|
||||
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
|
||||
|
||||
if (messageContent?.length) {
|
||||
// Change model to gpt-4-vision
|
||||
model.modelName = 'gpt-4-vision-preview'
|
||||
|
||||
// Change default max token to higher when using gpt-4-vision
|
||||
model.maxTokens = 1024
|
||||
|
||||
for (const msg of messageContent) {
|
||||
humanImageMessages.push(new HumanMessage({ content: [msg] }))
|
||||
}
|
||||
|
||||
// Pop the `agent_scratchpad` MessagePlaceHolder
|
||||
let messagePlaceholder = prompt.promptMessages.pop() as MessagesPlaceholder
|
||||
|
||||
// Add the HumanMessage for images
|
||||
prompt.promptMessages.push(...humanImageMessages)
|
||||
|
||||
// Add the `agent_scratchpad` MessagePlaceHolder back
|
||||
prompt.promptMessages.push(messagePlaceholder)
|
||||
} else {
|
||||
// revert to previous values if image upload is empty
|
||||
model.modelName = model.configuredModel
|
||||
model.maxTokens = model.configuredMaxToken
|
||||
}
|
||||
}
|
||||
|
||||
/** Bind a stop token to the model */
|
||||
const modelWithStop = model.bind({
|
||||
stop: ['\nObservation']
|
||||
})
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; steps: AgentStep[] }) => i.input,
|
||||
agent_scratchpad: async (i: { input: string; steps: AgentStep[] }) => await constructScratchPad(i.steps),
|
||||
[memoryKey]: async (_: { input: string; steps: AgentStep[] }) => {
|
||||
const messages = (await memory.getChatMessages(flowObj?.sessionId, true, chatHistory)) as BaseMessage[]
|
||||
return messages ?? []
|
||||
}
|
||||
},
|
||||
prompt,
|
||||
modelWithStop,
|
||||
outputParser
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
verbose: process.env.DEBUG === 'true'
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
const constructScratchPad = async (steps: AgentStep[]): Promise<BaseMessage[]> => {
|
||||
const thoughts: BaseMessage[] = []
|
||||
for (const step of steps) {
|
||||
thoughts.push(new AIMessage(step.action.log))
|
||||
thoughts.push(
|
||||
new HumanMessage(
|
||||
renderTemplate(TEMPLATE_TOOL_RESPONSE, 'f-string', {
|
||||
observation: step.observation
|
||||
})
|
||||
)
|
||||
)
|
||||
}
|
||||
return thoughts
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ConversationalAgent_Agents }
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
|
||||
<path d="M10 16h4"></path>
|
||||
<circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<path d="M9 7l-1 -4"></path>
|
||||
<path d="M15 7l1 -4"></path>
|
||||
</svg>
|
||||
<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>
|
||||
|
||||
|
Before Width: | Height: | Size: 650 B After Width: | Height: | Size: 616 B |
+88
-40
@@ -1,9 +1,15 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
|
||||
import { getBaseClasses, mapChatHistory } from '../../../src/utils'
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseChatMemory } from 'langchain/memory'
|
||||
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, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||
|
||||
const defaultMessage = `Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`
|
||||
|
||||
@@ -17,8 +23,9 @@ class ConversationalRetrievalAgent_Agents implements INode {
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Conversational Retrieval Agent'
|
||||
this.name = 'conversationalRetrievalAgent'
|
||||
this.version = 3.0
|
||||
@@ -54,55 +61,96 @@ class ConversationalRetrievalAgent_Agents implements INode {
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model
|
||||
const memory = nodeData.inputs?.memory as BaseChatMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
|
||||
const executor = await initializeAgentExecutorWithOptions(tools, model, {
|
||||
agentType: 'openai-functions',
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
agentArgs: {
|
||||
prefix: systemMessage ?? defaultMessage
|
||||
},
|
||||
returnIntermediateSteps: true
|
||||
})
|
||||
executor.memory = memory
|
||||
return executor
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const executor = nodeData.instance as AgentExecutor
|
||||
|
||||
if (executor.memory) {
|
||||
;(executor.memory as any).memoryKey = 'chat_history'
|
||||
;(executor.memory as any).outputKey = 'output'
|
||||
;(executor.memory as any).returnMessages = true
|
||||
|
||||
const chatHistoryClassName = (executor.memory as any).chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
;(executor.memory as any).chatHistory = mapChatHistory(options)
|
||||
}
|
||||
}
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
|
||||
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)
|
||||
const result = await executor.call({ input }, [loggerHandler, handler, ...callbacks])
|
||||
return result?.output
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, handler, ...callbacks] })
|
||||
} else {
|
||||
const result = await executor.call({ input }, [loggerHandler, ...callbacks])
|
||||
return result?.output
|
||||
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 },
|
||||
chatHistory: IMessage[] = []
|
||||
) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
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([
|
||||
['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, chatHistory)) 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
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ConversationalRetrievalAgent_Agents }
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
|
||||
<path d="M10 16h4"></path>
|
||||
<circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<path d="M9 7l-1 -4"></path>
|
||||
<path d="M15 7l1 -4"></path>
|
||||
</svg>
|
||||
<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>
|
||||
|
||||
|
Before Width: | Height: | Size: 650 B After Width: | Height: | Size: 616 B |
@@ -1,10 +1,17 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { Tool } from 'langchain/tools'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { flatten } from 'lodash'
|
||||
import { AgentExecutor } from 'langchain/agents'
|
||||
import { HumanMessage } from '@langchain/core/messages'
|
||||
import { ChatPromptTemplate, HumanMessagePromptTemplate } from '@langchain/core/prompts'
|
||||
import { Tool } from '@langchain/core/tools'
|
||||
import type { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models'
|
||||
import { pull } from 'langchain/hub'
|
||||
import { additionalCallbacks } from '../../../src/handler'
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { createReactAgent } from '../../../src/agents'
|
||||
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
|
||||
import { addImagesToMessages } from '../../../src/multiModalUtils'
|
||||
|
||||
class MRKLAgentChat_Agents implements INode {
|
||||
label: string
|
||||
@@ -16,11 +23,12 @@ class MRKLAgentChat_Agents implements INode {
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'ReAct Agent for Chat Models'
|
||||
this.name = 'mrklAgentChat'
|
||||
this.version = 1.0
|
||||
this.version = 3.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'agent.svg'
|
||||
@@ -34,30 +42,85 @@ class MRKLAgentChat_Agents implements INode {
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Language Model',
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseLanguageModel'
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
let tools = nodeData.inputs?.tools as Tool[]
|
||||
tools = flatten(tools)
|
||||
const executor = await initializeAgentExecutorWithOptions(tools, model, {
|
||||
agentType: 'chat-zero-shot-react-description',
|
||||
verbose: process.env.DEBUG === 'true' ? true : false
|
||||
})
|
||||
return executor
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const executor = nodeData.instance as AgentExecutor
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const model = nodeData.inputs?.model as BaseChatModel
|
||||
let tools = nodeData.inputs?.tools as Tool[]
|
||||
tools = flatten(tools)
|
||||
|
||||
const prompt = await pull<PromptTemplate>('hwchase17/react-chat')
|
||||
let chatPromptTemplate = undefined
|
||||
|
||||
if (model instanceof ChatOpenAI) {
|
||||
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
|
||||
|
||||
if (messageContent?.length) {
|
||||
// Change model to gpt-4-vision
|
||||
model.modelName = 'gpt-4-vision-preview'
|
||||
|
||||
// Change default max token to higher when using gpt-4-vision
|
||||
model.maxTokens = 1024
|
||||
|
||||
const oldTemplate = prompt.template as string
|
||||
chatPromptTemplate = ChatPromptTemplate.fromMessages([HumanMessagePromptTemplate.fromTemplate(oldTemplate)])
|
||||
chatPromptTemplate.promptMessages.push(new HumanMessage({ content: messageContent }))
|
||||
} else {
|
||||
// revert to previous values if image upload is empty
|
||||
model.modelName = model.configuredModel
|
||||
model.maxTokens = model.configuredMaxToken
|
||||
}
|
||||
}
|
||||
|
||||
const agent = await createReactAgent({
|
||||
llm: model,
|
||||
tools,
|
||||
prompt: chatPromptTemplate ?? prompt
|
||||
})
|
||||
|
||||
const executor = new AgentExecutor({
|
||||
agent,
|
||||
tools,
|
||||
verbose: process.env.DEBUG === 'true'
|
||||
})
|
||||
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
const result = await executor.call({ input }, [...callbacks])
|
||||
const prevChatHistory = options.chatHistory
|
||||
const chatHistory = ((await memory.getChatMessages(this.sessionId, false, prevChatHistory)) as IMessage[]) ?? []
|
||||
const chatHistoryString = chatHistory.map((hist) => hist.message).join('\\n')
|
||||
|
||||
const result = await executor.invoke({ input, chat_history: chatHistoryString }, { callbacks })
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: result?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return result?.output
|
||||
}
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
|
||||
<path d="M10 16h4"></path>
|
||||
<circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<path d="M9 7l-1 -4"></path>
|
||||
<path d="M15 7l1 -4"></path>
|
||||
</svg>
|
||||
<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>
|
||||
|
||||
|
Before Width: | Height: | Size: 650 B After Width: | Height: | Size: 616 B |
@@ -1,10 +1,13 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
|
||||
import { Tool } from 'langchain/tools'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { flatten } from 'lodash'
|
||||
import { AgentExecutor } from 'langchain/agents'
|
||||
import { pull } from 'langchain/hub'
|
||||
import { Tool } from '@langchain/core/tools'
|
||||
import type { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { additionalCallbacks } from '../../../src/handler'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { createReactAgent } from '../../../src/agents'
|
||||
|
||||
class MRKLAgentLLM_Agents implements INode {
|
||||
label: string
|
||||
@@ -41,24 +44,32 @@ class MRKLAgentLLM_Agents implements INode {
|
||||
]
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
async init(): Promise<any> {
|
||||
return null
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
let tools = nodeData.inputs?.tools as Tool[]
|
||||
tools = flatten(tools)
|
||||
|
||||
const executor = await initializeAgentExecutorWithOptions(tools, model, {
|
||||
agentType: 'zero-shot-react-description',
|
||||
const prompt = await pull<PromptTemplate>('hwchase17/react')
|
||||
|
||||
const agent = await createReactAgent({
|
||||
llm: model,
|
||||
tools,
|
||||
prompt
|
||||
})
|
||||
|
||||
const executor = new AgentExecutor({
|
||||
agent,
|
||||
tools,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false
|
||||
})
|
||||
return executor
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const executor = nodeData.instance as AgentExecutor
|
||||
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
const result = await executor.call({ input }, [...callbacks])
|
||||
const result = await executor.invoke({ input }, { callbacks })
|
||||
|
||||
return result?.output
|
||||
}
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-robot" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M7 7h10a2 2 0 0 1 2 2v1l1 1v3l-1 1v3a2 2 0 0 1 -2 2h-10a2 2 0 0 1 -2 -2v-3l-1 -1v-3l1 -1v-1a2 2 0 0 1 2 -2z"></path>
|
||||
<path d="M10 16h4"></path>
|
||||
<circle cx="8.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<circle cx="15.5" cy="11.5" r=".5" fill="currentColor"></circle>
|
||||
<path d="M9 7l-1 -4"></path>
|
||||
<path d="M15 7l1 -4"></path>
|
||||
</svg>
|
||||
<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>
|
||||
|
||||
|
Before Width: | Height: | Size: 650 B After Width: | Height: | Size: 616 B |
@@ -8,6 +8,9 @@ import * as path from 'node:path'
|
||||
import fetch from 'node-fetch'
|
||||
import { flatten, uniqWith, isEqual } from 'lodash'
|
||||
import { zodToJsonSchema } from 'zod-to-json-schema'
|
||||
import { AnalyticHandler } from '../../../src/handler'
|
||||
import { Moderation, checkInputs, streamResponse } from '../../moderation/Moderation'
|
||||
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||
|
||||
class OpenAIAssistant_Agents implements INode {
|
||||
label: string
|
||||
@@ -23,10 +26,10 @@ class OpenAIAssistant_Agents implements INode {
|
||||
constructor() {
|
||||
this.label = 'OpenAI Assistant'
|
||||
this.name = 'openAIAssistant'
|
||||
this.version = 2.0
|
||||
this.version = 3.0
|
||||
this.type = 'OpenAIAssistant'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'openai.png'
|
||||
this.icon = 'assistant.svg'
|
||||
this.description = `An agent that uses OpenAI Assistant API to pick the tool and args to call`
|
||||
this.baseClasses = [this.type]
|
||||
this.inputs = [
|
||||
@@ -42,6 +45,14 @@ class OpenAIAssistant_Agents implements INode {
|
||||
type: 'Tool',
|
||||
list: 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: 'Disable File Download',
|
||||
name: 'disableFileDownload',
|
||||
@@ -85,45 +96,51 @@ class OpenAIAssistant_Agents implements INode {
|
||||
return null
|
||||
}
|
||||
|
||||
//@ts-ignore
|
||||
memoryMethods = {
|
||||
async clearSessionMemory(nodeData: INodeData, options: ICommonObject): Promise<void> {
|
||||
const selectedAssistantId = nodeData.inputs?.selectedAssistant as string
|
||||
const appDataSource = options.appDataSource as DataSource
|
||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||
let sessionId = nodeData.inputs?.sessionId as string
|
||||
async clearChatMessages(nodeData: INodeData, options: ICommonObject, sessionIdObj: { type: string; id: string }): Promise<void> {
|
||||
const selectedAssistantId = nodeData.inputs?.selectedAssistant as string
|
||||
const appDataSource = options.appDataSource as DataSource
|
||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||
|
||||
const assistant = await appDataSource.getRepository(databaseEntities['Assistant']).findOneBy({
|
||||
id: selectedAssistantId
|
||||
const assistant = await appDataSource.getRepository(databaseEntities['Assistant']).findOneBy({
|
||||
id: selectedAssistantId
|
||||
})
|
||||
|
||||
if (!assistant) {
|
||||
options.logger.error(`Assistant ${selectedAssistantId} not found`)
|
||||
return
|
||||
}
|
||||
|
||||
if (!sessionIdObj) return
|
||||
|
||||
let sessionId = ''
|
||||
if (sessionIdObj.type === 'chatId') {
|
||||
const chatId = sessionIdObj.id
|
||||
const chatmsg = await appDataSource.getRepository(databaseEntities['ChatMessage']).findOneBy({
|
||||
chatId
|
||||
})
|
||||
|
||||
if (!assistant) {
|
||||
options.logger.error(`Assistant ${selectedAssistantId} not found`)
|
||||
if (!chatmsg) {
|
||||
options.logger.error(`Chat Message with Chat Id: ${chatId} not found`)
|
||||
return
|
||||
}
|
||||
sessionId = chatmsg.sessionId
|
||||
} else if (sessionIdObj.type === 'threadId') {
|
||||
sessionId = sessionIdObj.id
|
||||
}
|
||||
|
||||
if (!sessionId && options.chatId) {
|
||||
const chatmsg = await appDataSource.getRepository(databaseEntities['ChatMessage']).findOneBy({
|
||||
chatId: options.chatId
|
||||
})
|
||||
if (!chatmsg) {
|
||||
options.logger.error(`Chat Message with Chat Id: ${options.chatId} not found`)
|
||||
return
|
||||
}
|
||||
sessionId = chatmsg.sessionId
|
||||
}
|
||||
const credentialData = await getCredentialData(assistant.credential ?? '', options)
|
||||
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
|
||||
if (!openAIApiKey) {
|
||||
options.logger.error(`OpenAI ApiKey not found`)
|
||||
return
|
||||
}
|
||||
|
||||
const credentialData = await getCredentialData(assistant.credential ?? '', options)
|
||||
const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
|
||||
if (!openAIApiKey) {
|
||||
options.logger.error(`OpenAI ApiKey not found`)
|
||||
return
|
||||
}
|
||||
|
||||
const openai = new OpenAI({ apiKey: openAIApiKey })
|
||||
options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
|
||||
const openai = new OpenAI({ apiKey: openAIApiKey })
|
||||
options.logger.info(`Clearing OpenAI Thread ${sessionId}`)
|
||||
try {
|
||||
if (sessionId) await openai.beta.threads.del(sessionId)
|
||||
options.logger.info(`Successfully cleared OpenAI Thread ${sessionId}`)
|
||||
} catch (e) {
|
||||
throw new Error(e)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -132,6 +149,20 @@ class OpenAIAssistant_Agents implements INode {
|
||||
const appDataSource = options.appDataSource as DataSource
|
||||
const databaseEntities = options.databaseEntities as IDatabaseEntity
|
||||
const disableFileDownload = nodeData.inputs?.disableFileDownload as boolean
|
||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||
const isStreaming = options.socketIO && options.socketIOClientId
|
||||
const socketIO = isStreaming ? options.socketIO : undefined
|
||||
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
||||
|
||||
if (moderations && moderations.length > 0) {
|
||||
try {
|
||||
input = await checkInputs(moderations, input)
|
||||
} catch (e) {
|
||||
await new Promise((resolve) => setTimeout(resolve, 500))
|
||||
streamResponse(isStreaming, e.message, socketIO, socketIOClientId)
|
||||
return formatResponse(e.message)
|
||||
}
|
||||
}
|
||||
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
@@ -149,6 +180,11 @@ class OpenAIAssistant_Agents implements INode {
|
||||
|
||||
const openai = new OpenAI({ apiKey: openAIApiKey })
|
||||
|
||||
// Start analytics
|
||||
const analyticHandlers = new AnalyticHandler(nodeData, options)
|
||||
await analyticHandlers.init()
|
||||
const parentIds = await analyticHandlers.onChainStart('OpenAIAssistant', input)
|
||||
|
||||
try {
|
||||
const assistantDetails = JSON.parse(assistant.details)
|
||||
const openAIAssistantId = assistantDetails.id
|
||||
@@ -171,7 +207,8 @@ class OpenAIAssistant_Agents implements INode {
|
||||
}
|
||||
|
||||
const chatmessage = await appDataSource.getRepository(databaseEntities['ChatMessage']).findOneBy({
|
||||
chatId: options.chatId
|
||||
chatId: options.chatId,
|
||||
chatflowid: options.chatflowid
|
||||
})
|
||||
|
||||
let threadId = ''
|
||||
@@ -185,7 +222,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||
threadId = thread.id
|
||||
}
|
||||
|
||||
// List all runs
|
||||
// List all runs, in case existing thread is still running
|
||||
if (!isNewThread) {
|
||||
const promise = (threadId: string) => {
|
||||
return new Promise<void>((resolve) => {
|
||||
@@ -221,6 +258,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||
})
|
||||
|
||||
// Run assistant thread
|
||||
const llmIds = await analyticHandlers.onLLMStart('ChatOpenAI', input, parentIds)
|
||||
const runThread = await openai.beta.threads.runs.create(threadId, {
|
||||
assistant_id: retrievedAssistant.id
|
||||
})
|
||||
@@ -241,7 +279,12 @@ class OpenAIAssistant_Agents implements INode {
|
||||
const actions: ICommonObject[] = []
|
||||
run.required_action.submit_tool_outputs.tool_calls.forEach((item) => {
|
||||
const functionCall = item.function
|
||||
const args = JSON.parse(functionCall.arguments)
|
||||
let args = {}
|
||||
try {
|
||||
args = JSON.parse(functionCall.arguments)
|
||||
} catch (e) {
|
||||
console.error('Error parsing arguments, default to empty object')
|
||||
}
|
||||
actions.push({
|
||||
tool: functionCall.name,
|
||||
toolInput: args,
|
||||
@@ -253,26 +296,57 @@ class OpenAIAssistant_Agents implements INode {
|
||||
for (let i = 0; i < actions.length; i += 1) {
|
||||
const tool = tools.find((tool: any) => tool.name === actions[i].tool)
|
||||
if (!tool) continue
|
||||
const toolOutput = await tool.call(actions[i].toolInput)
|
||||
submitToolOutputs.push({
|
||||
tool_call_id: actions[i].toolCallId,
|
||||
output: toolOutput
|
||||
})
|
||||
usedTools.push({
|
||||
tool: tool.name,
|
||||
toolInput: actions[i].toolInput,
|
||||
toolOutput
|
||||
})
|
||||
|
||||
// Start tool analytics
|
||||
const toolIds = await analyticHandlers.onToolStart(tool.name, actions[i].toolInput, parentIds)
|
||||
if (options.socketIO && options.socketIOClientId)
|
||||
options.socketIO.to(options.socketIOClientId).emit('tool', tool.name)
|
||||
|
||||
try {
|
||||
const toolOutput = await tool.call(actions[i].toolInput, undefined, undefined, {
|
||||
sessionId: threadId,
|
||||
chatId: options.chatId,
|
||||
input
|
||||
})
|
||||
await analyticHandlers.onToolEnd(toolIds, toolOutput)
|
||||
submitToolOutputs.push({
|
||||
tool_call_id: actions[i].toolCallId,
|
||||
output: toolOutput
|
||||
})
|
||||
usedTools.push({
|
||||
tool: tool.name,
|
||||
toolInput: actions[i].toolInput,
|
||||
toolOutput
|
||||
})
|
||||
} catch (e) {
|
||||
await analyticHandlers.onToolEnd(toolIds, e)
|
||||
console.error('Error executing tool', e)
|
||||
clearInterval(timeout)
|
||||
reject(
|
||||
new Error(
|
||||
`Error processing thread: ${state}, Thread ID: ${threadId}, Run ID: ${runId}, Tool: ${tool.name}`
|
||||
)
|
||||
)
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
if (submitToolOutputs.length) {
|
||||
await openai.beta.threads.runs.submitToolOutputs(threadId, runId, {
|
||||
tool_outputs: submitToolOutputs
|
||||
})
|
||||
resolve(state)
|
||||
} else {
|
||||
await openai.beta.threads.runs.cancel(threadId, runId)
|
||||
resolve('requires_action_retry')
|
||||
const newRun = await openai.beta.threads.runs.retrieve(threadId, runId)
|
||||
const newStatus = newRun?.status
|
||||
|
||||
try {
|
||||
if (submitToolOutputs.length && newStatus === 'requires_action') {
|
||||
await openai.beta.threads.runs.submitToolOutputs(threadId, runId, {
|
||||
tool_outputs: submitToolOutputs
|
||||
})
|
||||
resolve(state)
|
||||
} else {
|
||||
await openai.beta.threads.runs.cancel(threadId, runId)
|
||||
resolve('requires_action_retry')
|
||||
}
|
||||
} catch (e) {
|
||||
clearInterval(timeout)
|
||||
reject(new Error(`Error submitting tool outputs: ${state}, Thread ID: ${threadId}, Run ID: ${runId}`))
|
||||
}
|
||||
}
|
||||
} else if (state === 'cancelled' || state === 'expired' || state === 'failed') {
|
||||
@@ -302,7 +376,9 @@ class OpenAIAssistant_Agents implements INode {
|
||||
runThreadId = newRunThread.id
|
||||
state = await promise(threadId, newRunThread.id)
|
||||
} else {
|
||||
throw new Error(`Error processing thread: ${state}, Thread ID: ${threadId}`)
|
||||
const errMsg = `Error processing thread: ${state}, Thread ID: ${threadId}`
|
||||
await analyticHandlers.onChainError(parentIds, errMsg)
|
||||
throw new Error(errMsg)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -387,11 +463,19 @@ class OpenAIAssistant_Agents implements INode {
|
||||
const bitmap = fsDefault.readFileSync(filePath)
|
||||
const base64String = Buffer.from(bitmap).toString('base64')
|
||||
|
||||
// TODO: Use a file path and retrieve image on the fly. Storing as base64 to localStorage and database will easily hit limits
|
||||
const imgHTML = `<img src="data:image/png;base64,${base64String}" width="100%" height="max-content" alt="${fileObj.filename}" /><br/>`
|
||||
returnVal += imgHTML
|
||||
}
|
||||
}
|
||||
|
||||
const imageRegex = /<img[^>]*\/>/g
|
||||
let llmOutput = returnVal.replace(imageRegex, '')
|
||||
llmOutput = llmOutput.replace('<br/>', '')
|
||||
|
||||
await analyticHandlers.onLLMEnd(llmIds, llmOutput)
|
||||
await analyticHandlers.onChainEnd(parentIds, messageData, true)
|
||||
|
||||
return {
|
||||
text: returnVal,
|
||||
usedTools,
|
||||
@@ -399,6 +483,7 @@ class OpenAIAssistant_Agents implements INode {
|
||||
assistant: { assistantId: openAIAssistantId, threadId, runId: runThreadId, messages: messageData }
|
||||
}
|
||||
} catch (error) {
|
||||
await analyticHandlers.onChainError(parentIds, error, true)
|
||||
throw new Error(error)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M20 23C20 22.6319 20.2985 22.3334 20.6667 22.3334H27.3333C27.7015 22.3334 28 22.6319 28 23V26.3334C28 27.8061 26.8061 29 25.3333 29H22.6667C21.1939 29 20 27.8061 20 26.3334V23Z" stroke="black" stroke-width="2" stroke-linejoin="round"/>
|
||||
<path d="M24 22.3333V21" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<circle cx="22.667" cy="25" r="1" fill="black"/>
|
||||
<circle cx="25.333" cy="25" r="1" fill="black"/>
|
||||
<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"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.3 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 3.9 KiB |
@@ -1,10 +1,15 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { initializeAgentExecutorWithOptions, AgentExecutor } from 'langchain/agents'
|
||||
import { getBaseClasses, mapChatHistory } from '../../../src/utils'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { flatten } from 'lodash'
|
||||
import { BaseChatMemory } from 'langchain/memory'
|
||||
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, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { AgentExecutor, formatAgentSteps } from '../../../src/agents'
|
||||
|
||||
class OpenAIFunctionAgent_Agents implements INode {
|
||||
label: string
|
||||
@@ -16,14 +21,15 @@ class OpenAIFunctionAgent_Agents implements INode {
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'OpenAI Function Agent'
|
||||
this.name = 'openAIFunctionAgent'
|
||||
this.version = 3.0
|
||||
this.type = 'AgentExecutor'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'openai.png'
|
||||
this.icon = 'function.svg'
|
||||
this.description = `An agent that uses Function Calling to pick the tool and args to call`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.inputs = [
|
||||
@@ -52,55 +58,103 @@ class OpenAIFunctionAgent_Agents implements INode {
|
||||
additionalParams: true
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
const memory = nodeData.inputs?.memory as BaseChatMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
|
||||
const executor = await initializeAgentExecutorWithOptions(tools, model, {
|
||||
agentType: 'openai-functions',
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
agentArgs: {
|
||||
prefix: systemMessage ?? `You are a helpful AI assistant.`
|
||||
}
|
||||
})
|
||||
if (memory) executor.memory = memory
|
||||
|
||||
return executor
|
||||
async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
|
||||
return prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const executor = nodeData.instance as AgentExecutor
|
||||
const memory = nodeData.inputs?.memory as BaseChatMemory
|
||||
|
||||
if (options && options.chatHistory) {
|
||||
const chatHistoryClassName = memory.chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
memory.chatHistory = mapChatHistory(options)
|
||||
executor.memory = memory
|
||||
}
|
||||
}
|
||||
|
||||
;(executor.memory as any).returnMessages = true // Return true for BaseChatModel
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const executor = prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
const result = await executor.run(input, [loggerHandler, handler, ...callbacks])
|
||||
return result
|
||||
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
|
||||
}
|
||||
} else {
|
||||
const result = await executor.run(input, [loggerHandler, ...callbacks])
|
||||
return result
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return sourceDocuments.length ? { text: res?.output, sourceDocuments: flatten(sourceDocuments) } : res?.output
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = (
|
||||
nodeData: INodeData,
|
||||
flowObj: { sessionId?: string; chatId?: string; input?: string },
|
||||
chatHistory: IMessage[] = []
|
||||
) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
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, chatHistory)) 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
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: OpenAIFunctionAgent_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 |
Binary file not shown.
|
Before Width: | Height: | Size: 3.9 KiB |
@@ -0,0 +1,203 @@
|
||||
import { flatten } from 'lodash'
|
||||
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 { Tool } from '@langchain/core/tools'
|
||||
import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { XMLAgentOutputParser } from 'langchain/agents/xml/output_parser'
|
||||
import { formatLogToMessage } from 'langchain/agents/format_scratchpad/log_to_message'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { AgentExecutor } from '../../../src/agents'
|
||||
//import { AgentExecutor } from "langchain/agents";
|
||||
|
||||
const defaultSystemMessage = `You are a helpful assistant. Help the user answer any questions.
|
||||
|
||||
You have access to the following tools:
|
||||
|
||||
{tools}
|
||||
|
||||
In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
|
||||
For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
|
||||
|
||||
<tool>search</tool><tool_input>weather in SF</tool_input>
|
||||
<observation>64 degrees</observation>
|
||||
|
||||
When you are done, respond with a final answer between <final_answer></final_answer>. For example:
|
||||
|
||||
<final_answer>The weather in SF is 64 degrees</final_answer>
|
||||
|
||||
Begin!
|
||||
|
||||
Previous Conversation:
|
||||
{chat_history}
|
||||
|
||||
Question: {input}
|
||||
{agent_scratchpad}`
|
||||
|
||||
class XMLAgent_Agents implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
description: string
|
||||
type: string
|
||||
icon: string
|
||||
category: string
|
||||
baseClasses: string[]
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'XML Agent'
|
||||
this.name = 'xmlAgent'
|
||||
this.version = 1.0
|
||||
this.type = 'XMLAgent'
|
||||
this.category = 'Agents'
|
||||
this.icon = 'xmlagent.svg'
|
||||
this.description = `Agent that is designed for LLMs that are good for reasoning/writing XML (e.g: Anthropic Claude)`
|
||||
this.baseClasses = [this.type, ...getBaseClasses(AgentExecutor)]
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Tools',
|
||||
name: 'tools',
|
||||
type: 'Tool',
|
||||
list: true
|
||||
},
|
||||
{
|
||||
label: 'Memory',
|
||||
name: 'memory',
|
||||
type: 'BaseChatMemory'
|
||||
},
|
||||
{
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessage',
|
||||
type: 'string',
|
||||
warning: 'Prompt must include input variables: {tools}, {chat_history}, {input} and {agent_scratchpad}',
|
||||
rows: 4,
|
||||
default: defaultSystemMessage,
|
||||
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 executor = await prepareAgent(nodeData, { sessionId: this.sessionId, chatId: options.chatId, input }, options.chatHistory)
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res: ChainValues = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
|
||||
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
|
||||
}
|
||||
} else {
|
||||
res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
|
||||
if (res.sourceDocuments) {
|
||||
sourceDocuments = res.sourceDocuments
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res?.output,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return sourceDocuments.length ? { text: res?.output, sourceDocuments: flatten(sourceDocuments) } : res?.output
|
||||
}
|
||||
}
|
||||
|
||||
const prepareAgent = async (
|
||||
nodeData: INodeData,
|
||||
flowObj: { sessionId?: string; chatId?: string; input?: string },
|
||||
chatHistory: IMessage[] = []
|
||||
) => {
|
||||
const model = nodeData.inputs?.model as ChatOpenAI
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const systemMessage = nodeData.inputs?.systemMessage as string
|
||||
let tools = nodeData.inputs?.tools
|
||||
tools = flatten(tools)
|
||||
const inputKey = memory.inputKey ? memory.inputKey : 'input'
|
||||
const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
|
||||
|
||||
let promptMessage = systemMessage ? systemMessage : defaultSystemMessage
|
||||
if (memory.memoryKey) promptMessage = promptMessage.replaceAll('{chat_history}', `{${memory.memoryKey}}`)
|
||||
if (memory.inputKey) promptMessage = promptMessage.replaceAll('{input}', `{${memory.inputKey}}`)
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
HumanMessagePromptTemplate.fromTemplate(promptMessage),
|
||||
new MessagesPlaceholder('agent_scratchpad')
|
||||
])
|
||||
|
||||
const missingVariables = ['tools', 'agent_scratchpad'].filter((v) => !prompt.inputVariables.includes(v))
|
||||
|
||||
if (missingVariables.length > 0) {
|
||||
throw new Error(`Provided prompt is missing required input variables: ${JSON.stringify(missingVariables)}`)
|
||||
}
|
||||
|
||||
const llmWithStop = model.bind({ stop: ['</tool_input>', '</final_answer>'] })
|
||||
|
||||
const messages = (await memory.getChatMessages(flowObj.sessionId, false, chatHistory)) as IMessage[]
|
||||
let chatHistoryMsgTxt = ''
|
||||
for (const message of messages) {
|
||||
if (message.type === 'apiMessage') {
|
||||
chatHistoryMsgTxt += `\\nAI:${message.message}`
|
||||
} else if (message.type === 'userMessage') {
|
||||
chatHistoryMsgTxt += `\\nHuman:${message.message}`
|
||||
}
|
||||
}
|
||||
|
||||
const runnableAgent = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (i: { input: string; tools: Tool[]; steps: AgentStep[] }) => i.input,
|
||||
agent_scratchpad: (i: { input: string; tools: Tool[]; steps: AgentStep[] }) => formatLogToMessage(i.steps),
|
||||
tools: (_: { input: string; tools: Tool[]; steps: AgentStep[] }) =>
|
||||
tools.map((tool: Tool) => `${tool.name}: ${tool.description}`),
|
||||
[memoryKey]: (_: { input: string; tools: Tool[]; steps: AgentStep[] }) => chatHistoryMsgTxt
|
||||
},
|
||||
prompt,
|
||||
llmWithStop,
|
||||
new XMLAgentOutputParser()
|
||||
])
|
||||
|
||||
const executor = AgentExecutor.fromAgentAndTools({
|
||||
agent: runnableAgent,
|
||||
tools,
|
||||
sessionId: flowObj?.sessionId,
|
||||
chatId: flowObj?.chatId,
|
||||
input: flowObj?.input,
|
||||
isXML: true,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false
|
||||
})
|
||||
|
||||
return executor
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: XMLAgent_Agents }
|
||||
@@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-file-type-xml" width="24" height="24" viewBox="0 0 24 24" stroke-width="1.5" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M14 3v4a1 1 0 0 0 1 1h4" /><path d="M5 12v-7a2 2 0 0 1 2 -2h7l5 5v4" /><path d="M4 15l4 6" /><path d="M4 21l4 -6" /><path d="M19 15v6h3" /><path d="M11 21v-6l2.5 3l2.5 -3v6" /></svg>
|
||||
|
After Width: | Height: | Size: 476 B |
Binary file not shown.
|
Before Width: | Height: | Size: 1.7 KiB |
@@ -17,7 +17,7 @@ class LangFuse_Analytic implements INode {
|
||||
this.name = 'langFuse'
|
||||
this.version = 1.0
|
||||
this.type = 'LangFuse'
|
||||
this.icon = 'langfuse.png'
|
||||
this.icon = 'Langfuse.svg'
|
||||
this.category = 'Analytic'
|
||||
this.baseClasses = [this.type]
|
||||
this.inputs = []
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M22.7309 12.8968C21.032 12.9395 19.3256 12.5837 17.7555 11.8297L14.6587 10.3427C13.7049 9.88461 12.6797 9.68621 11.6729 9.72974" stroke="#E91212" stroke-width="2.88509" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M22.7309 19.1033C21.0321 19.0606 19.3256 19.4163 17.7555 20.1703L14.6587 21.6574C13.7049 22.1155 12.6797 22.3138 11.6729 22.2703" stroke="#E91212" stroke-width="2.88509" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M3.5 20.8078L4.87684 20.1529C7.84161 18.7428 11.2851 18.7492 14.2446 20.1704L17.3414 21.6575C19.8533 22.8637 22.8598 22.2693 24.7236 20.1979C26.8711 17.8113 26.8711 14.1888 24.7236 11.8022C22.8598 9.73085 19.8533 9.13642 17.3414 10.3427L14.2446 11.8297C11.2851 13.2509 7.84161 13.2573 4.87684 11.8472L3.5 11.1923" stroke="#1363BB" stroke-width="2.88509" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M28.5003 20.8078L27.1234 20.1529C25.7316 19.491 24.2343 19.1412 22.7311 19.1033M28.5003 11.1923L27.1234 11.8472C25.7316 12.5092 24.2343 12.859 22.7311 12.8968M22.7311 12.8968C23.4933 12.8776 24.2538 12.7782 25 12.5986M22.7311 12.8968C21.8155 12.9198 20.8975 12.8271 20 12.6187M22.7311 19.1033C23.4933 19.1225 24.2538 19.2219 25 19.4015M22.7311 19.1033C22.1531 19.0888 21.5741 19.1203 21 19.198M11.673 9.72974C10.0284 9.80083 8.43271 10.5174 7.27662 11.8022C5.12915 14.1888 5.12915 17.8113 7.27662 20.1979C8.43271 21.4827 10.0284 22.1993 11.673 22.2704" stroke="#E91212" stroke-width="2.88509" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.6 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 15 KiB |
@@ -0,0 +1,7 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<ellipse cx="15.9562" cy="16.0002" rx="4.3478" ry="4.34782" stroke="black" stroke-width="2"/>
|
||||
<path d="M23.5651 23.6086C21.603 25.621 18.8688 26.8695 15.8445 26.8695C10.2386 26.8695 5.62933 22.5797 5.08691 17.0869" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M8.34766 8.23751C10.314 6.3155 13.0074 5.13037 15.9785 5.13037C21.6311 5.13037 26.2789 9.42024 26.8258 14.913" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M9.5217 9.47815C9.5217 10.7268 8.50948 11.739 7.26085 11.739C6.01222 11.739 5 10.7268 5 9.47815C5 8.22951 6.01222 7.21729 7.26085 7.21729C8.50948 7.21729 9.5217 8.22951 9.5217 9.47815Z" fill="black" stroke="black" stroke-width="2"/>
|
||||
<path d="M28.0002 21.4347C28.0002 22.6833 26.988 23.6956 25.7394 23.6956C24.4907 23.6956 23.4785 22.6833 23.4785 21.4347C23.4785 20.186 24.4907 19.1738 25.7394 19.1738C26.988 19.1738 28.0002 20.186 28.0002 21.4347Z" fill="black" stroke="black" stroke-width="2"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.0 KiB |
+7
-7
@@ -1,6 +1,6 @@
|
||||
import { INode, INodeParams } from '../../../src/Interface'
|
||||
|
||||
class LLMonitor_Analytic implements INode {
|
||||
class Lunary_Analytic implements INode {
|
||||
label: string
|
||||
name: string
|
||||
version: number
|
||||
@@ -13,11 +13,11 @@ class LLMonitor_Analytic implements INode {
|
||||
credential: INodeParams
|
||||
|
||||
constructor() {
|
||||
this.label = 'LLMonitor'
|
||||
this.name = 'llmonitor'
|
||||
this.label = 'Lunary'
|
||||
this.name = 'lunary'
|
||||
this.version = 1.0
|
||||
this.type = 'LLMonitor'
|
||||
this.icon = 'llmonitor.png'
|
||||
this.type = 'Lunary'
|
||||
this.icon = 'Lunary.svg'
|
||||
this.category = 'Analytic'
|
||||
this.baseClasses = [this.type]
|
||||
this.inputs = []
|
||||
@@ -25,9 +25,9 @@ class LLMonitor_Analytic implements INode {
|
||||
label: 'Connect Credential',
|
||||
name: 'credential',
|
||||
type: 'credential',
|
||||
credentialNames: ['llmonitorApi']
|
||||
credentialNames: ['lunaryApi']
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: LLMonitor_Analytic }
|
||||
module.exports = { nodeClass: Lunary_Analytic }
|
||||
@@ -1,6 +1,6 @@
|
||||
import { getBaseClasses, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { BaseCache } from 'langchain/schema'
|
||||
import { BaseCache } from '@langchain/core/caches'
|
||||
import hash from 'object-hash'
|
||||
import { getBaseClasses, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
|
||||
class InMemoryCache implements INode {
|
||||
label: string
|
||||
@@ -20,7 +20,7 @@ class InMemoryCache implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'InMemoryCache'
|
||||
this.description = 'Cache LLM response in memory, will be cleared once app restarted'
|
||||
this.icon = 'inmemorycache.png'
|
||||
this.icon = 'Memory.svg'
|
||||
this.category = 'Cache'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(InMemoryCacheExtended)]
|
||||
this.inputs = []
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import { getBaseClasses, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { Embeddings } from '@langchain/core/embeddings'
|
||||
import { BaseStore } from '@langchain/core/stores'
|
||||
import { CacheBackedEmbeddings } from 'langchain/embeddings/cache_backed'
|
||||
import { Embeddings } from 'langchain/embeddings/base'
|
||||
import { BaseStore } from 'langchain/schema/storage'
|
||||
import { getBaseClasses, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
|
||||
class InMemoryEmbeddingCache implements INode {
|
||||
label: string
|
||||
@@ -21,7 +21,7 @@ class InMemoryEmbeddingCache implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'InMemoryEmbeddingCache'
|
||||
this.description = 'Cache generated Embeddings in memory to avoid needing to recompute them.'
|
||||
this.icon = 'inmemorycache.png'
|
||||
this.icon = 'Memory.svg'
|
||||
this.category = 'Cache'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(CacheBackedEmbeddings)]
|
||||
this.inputs = [
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<g clip-path="url(#clip0_121_15786)">
|
||||
<path d="M25 10V9C25 7.89543 24.1046 7 23 7H9C7.89543 7 7 7.89543 7 9V23C7 24.1046 7.89543 25 9 25H15" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M20 13.3333V13C20 12.4477 19.5523 12 19 12H13C12.4477 12 12 12.4477 12 13V19C12 19.5523 12.4477 20 13 20H15" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M7 11H5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M7 16H5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M7 21H5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M21 7L21 5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M16 7L16 5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M11 7L11 5" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M11 27L11 25" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M19 23.6429L24 14V19.3571H28.5L23 29V23.6429H19Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</g>
|
||||
<defs>
|
||||
<clipPath id="clip0_121_15786">
|
||||
<rect width="32" height="32" fill="white"/>
|
||||
</clipPath>
|
||||
</defs>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.2 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 12 KiB |
@@ -0,0 +1,4 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<circle cx="19.25" cy="13.25" r="1.25" fill="black"/>
|
||||
<path d="M15.221 7.55121L10.827 24.7611C10.5157 25.9803 8.8952 26.2323 8.2283 25.1653C7.75236 24.4038 7.5 23.5238 7.5 22.6259V20C7.5 17.7909 5.70914 16 3.5 16C3.22697 16 3.00858 15.7732 3.01891 15.5004L3.03784 15C3.54847 8.28755 9.15672 3 16 3C23.1797 3 29 8.8203 29 16C29 19.7112 27.4448 23.0593 24.9506 25.4279C24.5239 25.8332 23.8449 25.725 23.5205 25.234L21.5723 22.2851C21.3795 21.9932 21.2145 21.6792 21.1483 21.3356C20.8998 20.044 21.3021 18.6979 22.2481 17.7519L24 16C25.608 14.392 24.9616 11.6539 22.8043 10.9348L19.518 9.83932C18.8662 9.62206 18.3274 9.15477 18.0201 8.54025L17.4083 7.31663C16.9222 6.34444 15.4899 6.49805 15.221 7.55121Z" fill="black" stroke="black"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 837 B |
@@ -1,6 +1,6 @@
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { MomentoCache as LangchainMomentoCache } from 'langchain/cache/momento'
|
||||
import { CacheClient, Configurations, CredentialProvider } from '@gomomento/sdk'
|
||||
import { MomentoCache as LangchainMomentoCache } from '@langchain/community/caches/momento'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
|
||||
class MomentoCache implements INode {
|
||||
label: string
|
||||
@@ -20,7 +20,7 @@ class MomentoCache implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'MomentoCache'
|
||||
this.description = 'Cache LLM response using Momento, a distributed, serverless cache'
|
||||
this.icon = 'momento.png'
|
||||
this.icon = 'Momento.svg'
|
||||
this.category = 'Cache'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(LangchainMomentoCache)]
|
||||
this.credential = {
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 5.4 KiB |
+51
-9
@@ -1,8 +1,46 @@
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { RedisCache as LangchainRedisCache } from 'langchain/cache/ioredis'
|
||||
import { Redis } from 'ioredis'
|
||||
import { Generation, ChatGeneration, StoredGeneration, mapStoredMessageToChatMessage } from 'langchain/schema'
|
||||
import { Redis, RedisOptions } from 'ioredis'
|
||||
import { isEqual } from 'lodash'
|
||||
import hash from 'object-hash'
|
||||
import { RedisCache as LangchainRedisCache } from '@langchain/community/caches/ioredis'
|
||||
import { StoredGeneration, mapStoredMessageToChatMessage } from '@langchain/core/messages'
|
||||
import { Generation, ChatGeneration } from '@langchain/core/outputs'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
|
||||
let redisClientSingleton: Redis
|
||||
let redisClientOption: RedisOptions
|
||||
let redisClientUrl: string
|
||||
|
||||
const getRedisClientbyOption = (option: RedisOptions) => {
|
||||
if (!redisClientSingleton) {
|
||||
// if client doesn't exists
|
||||
redisClientSingleton = new Redis(option)
|
||||
redisClientOption = option
|
||||
return redisClientSingleton
|
||||
} else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
|
||||
// if client exists but option changed
|
||||
redisClientSingleton.quit()
|
||||
redisClientSingleton = new Redis(option)
|
||||
redisClientOption = option
|
||||
return redisClientSingleton
|
||||
}
|
||||
return redisClientSingleton
|
||||
}
|
||||
|
||||
const getRedisClientbyUrl = (url: string) => {
|
||||
if (!redisClientSingleton) {
|
||||
// if client doesn't exists
|
||||
redisClientSingleton = new Redis(url)
|
||||
redisClientUrl = url
|
||||
return redisClientSingleton
|
||||
} else if (redisClientSingleton && url !== redisClientUrl) {
|
||||
// if client exists but option changed
|
||||
redisClientSingleton.quit()
|
||||
redisClientSingleton = new Redis(url)
|
||||
redisClientUrl = url
|
||||
return redisClientSingleton
|
||||
}
|
||||
return redisClientSingleton
|
||||
}
|
||||
|
||||
class RedisCache implements INode {
|
||||
label: string
|
||||
@@ -56,15 +94,19 @@ class RedisCache implements INode {
|
||||
const password = getCredentialParam('redisCachePwd', credentialData, nodeData)
|
||||
const portStr = getCredentialParam('redisCachePort', credentialData, nodeData)
|
||||
const host = getCredentialParam('redisCacheHost', credentialData, nodeData)
|
||||
const sslEnabled = getCredentialParam('redisCacheSslEnabled', credentialData, nodeData)
|
||||
|
||||
client = new Redis({
|
||||
const tlsOptions = sslEnabled === true ? { tls: { rejectUnauthorized: false } } : {}
|
||||
|
||||
client = getRedisClientbyOption({
|
||||
port: portStr ? parseInt(portStr) : 6379,
|
||||
host,
|
||||
username,
|
||||
password
|
||||
password,
|
||||
...tlsOptions
|
||||
})
|
||||
} else {
|
||||
client = new Redis(redisUrl)
|
||||
client = getRedisClientbyUrl(redisUrl)
|
||||
}
|
||||
|
||||
const redisClient = new LangchainRedisCache(client)
|
||||
@@ -89,8 +131,8 @@ class RedisCache implements INode {
|
||||
redisClient.update = async (prompt: string, llmKey: string, value: Generation[]) => {
|
||||
for (let i = 0; i < value.length; i += 1) {
|
||||
const key = getCacheKey(prompt, llmKey, String(i))
|
||||
if (ttl !== undefined) {
|
||||
await client.set(key, JSON.stringify(serializeGeneration(value[i])), 'EX', parseInt(ttl, 10))
|
||||
if (ttl) {
|
||||
await client.set(key, JSON.stringify(serializeGeneration(value[i])), 'PX', parseInt(ttl, 10))
|
||||
} else {
|
||||
await client.set(key, JSON.stringify(serializeGeneration(value[i])))
|
||||
}
|
||||
|
||||
@@ -1,8 +1,45 @@
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { Redis } from 'ioredis'
|
||||
import { Redis, RedisOptions } from 'ioredis'
|
||||
import { isEqual } from 'lodash'
|
||||
import { RedisByteStore } from '@langchain/community/storage/ioredis'
|
||||
import { Embeddings } from '@langchain/core/embeddings'
|
||||
import { CacheBackedEmbeddings } from 'langchain/embeddings/cache_backed'
|
||||
import { RedisByteStore } from 'langchain/storage/ioredis'
|
||||
import { Embeddings } from 'langchain/embeddings/base'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
|
||||
let redisClientSingleton: Redis
|
||||
let redisClientOption: RedisOptions
|
||||
let redisClientUrl: string
|
||||
|
||||
const getRedisClientbyOption = (option: RedisOptions) => {
|
||||
if (!redisClientSingleton) {
|
||||
// if client doesn't exists
|
||||
redisClientSingleton = new Redis(option)
|
||||
redisClientOption = option
|
||||
return redisClientSingleton
|
||||
} else if (redisClientSingleton && !isEqual(option, redisClientOption)) {
|
||||
// if client exists but option changed
|
||||
redisClientSingleton.quit()
|
||||
redisClientSingleton = new Redis(option)
|
||||
redisClientOption = option
|
||||
return redisClientSingleton
|
||||
}
|
||||
return redisClientSingleton
|
||||
}
|
||||
|
||||
const getRedisClientbyUrl = (url: string) => {
|
||||
if (!redisClientSingleton) {
|
||||
// if client doesn't exists
|
||||
redisClientSingleton = new Redis(url)
|
||||
redisClientUrl = url
|
||||
return redisClientSingleton
|
||||
} else if (redisClientSingleton && url !== redisClientUrl) {
|
||||
// if client exists but option changed
|
||||
redisClientSingleton.quit()
|
||||
redisClientSingleton = new Redis(url)
|
||||
redisClientUrl = url
|
||||
return redisClientSingleton
|
||||
}
|
||||
return redisClientSingleton
|
||||
}
|
||||
|
||||
class RedisEmbeddingsCache implements INode {
|
||||
label: string
|
||||
@@ -71,15 +108,19 @@ class RedisEmbeddingsCache implements INode {
|
||||
const password = getCredentialParam('redisCachePwd', credentialData, nodeData)
|
||||
const portStr = getCredentialParam('redisCachePort', credentialData, nodeData)
|
||||
const host = getCredentialParam('redisCacheHost', credentialData, nodeData)
|
||||
const sslEnabled = getCredentialParam('redisCacheSslEnabled', credentialData, nodeData)
|
||||
|
||||
client = new Redis({
|
||||
const tlsOptions = sslEnabled === true ? { tls: { rejectUnauthorized: false } } : {}
|
||||
|
||||
client = getRedisClientbyOption({
|
||||
port: portStr ? parseInt(portStr) : 6379,
|
||||
host,
|
||||
username,
|
||||
password
|
||||
password,
|
||||
...tlsOptions
|
||||
})
|
||||
} else {
|
||||
client = new Redis(redisUrl)
|
||||
client = getRedisClientbyUrl(redisUrl)
|
||||
}
|
||||
|
||||
ttl ??= '3600'
|
||||
|
||||
+12
-1
@@ -1 +1,12 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 128 128" id="redis"><path fill="#A41E11" d="M121.8 93.1c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.9-11.5 3.8-17.3 1s-42.7-17.6-49.4-20.8c-3.3-1.6-5-2.9-5-4.2v-12.7s48-10.5 55.8-13.2c7.8-2.8 10.4-2.9 17-.5s46.1 9.5 52.6 11.9v12.5c0 1.3-1.5 2.7-4.9 4.4z"></path><path fill="#D82C20" d="M121.8 80.5c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.9-11.5 3.8-17.3 1-5.8-2.8-42.7-17.7-49.4-20.9-6.6-3.2-6.8-5.4-.3-7.9 6.5-2.6 43.2-17 51-19.7 7.8-2.8 10.4-2.9 17-.5s41.1 16.1 47.6 18.5c6.7 2.4 6.9 4.4.2 7.9z"></path><path fill="#A41E11" d="M121.8 72.5c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.8-11.5 3.8-17.3 1-5.8-2.8-42.7-17.7-49.4-20.9-3.3-1.6-5-2.9-5-4.2v-12.7s48-10.5 55.8-13.2c7.8-2.8 10.4-2.9 17-.5s46.1 9.5 52.6 11.9v12.5c0 1.3-1.5 2.7-4.9 4.5z"></path><path fill="#D82C20" d="M121.8 59.8c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.8-11.5 3.8-17.3 1-5.8-2.8-42.7-17.7-49.4-20.9s-6.8-5.4-.3-7.9c6.5-2.6 43.2-17 51-19.7 7.8-2.8 10.4-2.9 17-.5s41.1 16.1 47.6 18.5c6.7 2.4 6.9 4.4.2 7.9z"></path><path fill="#A41E11" d="M121.8 51c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.8-11.5 3.8-17.3 1-5.8-2.7-42.7-17.6-49.4-20.8-3.3-1.6-5.1-2.9-5.1-4.2v-12.7s48-10.5 55.8-13.2c7.8-2.8 10.4-2.9 17-.5s46.1 9.5 52.6 11.9v12.5c.1 1.3-1.4 2.6-4.8 4.4z"></path><path fill="#D82C20" d="M121.8 38.3c-6.7 3.5-41.4 17.7-48.8 21.6-7.4 3.8-11.5 3.8-17.3 1s-42.7-17.6-49.4-20.8-6.8-5.4-.3-7.9c6.5-2.6 43.2-17 51-19.7 7.8-2.8 10.4-2.9 17-.5s41.1 16.1 47.6 18.5c6.7 2.4 6.9 4.4.2 7.8z"></path><path fill="#fff" d="M80.4 26.1l-10.8 1.2-2.5 5.8-3.9-6.5-12.5-1.1 9.3-3.4-2.8-5.2 8.8 3.4 8.2-2.7-2.2 5.4zM66.5 54.5l-20.3-8.4 29.1-4.4z"></path><ellipse cx="38.4" cy="35.4" fill="#fff" rx="15.5" ry="6"></ellipse><path fill="#7A0C00" d="M93.3 27.7l17.2 6.8-17.2 6.8z"></path><path fill="#AD2115" d="M74.3 35.3l19-7.6v13.6l-1.9.8z"></path></svg>
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M5 14L14.3448 18.2476C15.3965 18.7257 16.6035 18.7257 17.6552 18.2476L27 14" stroke="#D82B1F" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M5 18L14.3448 22.2476C15.3965 22.7257 16.6035 22.7257 17.6552 22.2476L27 18" stroke="#D82B1F" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M14.3448 16.2476L5 12L14.3448 7.75233C15.3965 7.27429 16.6035 7.27429 17.6552 7.75233L27 12L17.6552 16.2476C16.6035 16.7256 15.3965 16.7256 14.3448 16.2476Z" fill="#D82B1F" stroke="#D82B1F" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M5 12L14.3448 16.2476C15.3965 16.7257 16.6035 16.7257 17.6552 16.2476L27 12" stroke="#A41E11" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M5 16L14.3448 20.2476C15.3965 20.7257 16.6035 20.7257 17.6552 20.2476L27 16" stroke="#A41E11" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M5 20L14.3448 24.2476C15.3965 24.7257 16.6035 24.7257 17.6552 24.2476L27 20" stroke="#A41E11" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M14.8 10.8257C14.8 11.2675 13.7255 11.7473 12.4 11.7473C11.0745 11.7473 10 11.2675 10 10.8257C10 10.3838 11.0745 9.92261 12.4 9.92261C13.7255 9.92261 14.8 10.3838 14.8 10.8257Z" fill="white"/>
|
||||
<path d="M15.5992 8.82033L14.5843 9.18122C14.5035 9.20994 14.5183 9.32837 14.6037 9.33626L16.1374 9.478C16.163 9.48036 16.1858 9.49484 16.1989 9.51692L16.6877 10.3429C16.7214 10.3998 16.8057 10.3936 16.8306 10.3322L17.1187 9.62285C17.1299 9.59533 17.1554 9.57627 17.185 9.57334L18.4686 9.44639C18.5526 9.43808 18.5681 9.32208 18.4892 9.29202L17.5949 8.95149C17.5526 8.93541 17.5321 8.88756 17.5495 8.84587L17.7896 8.27159C17.816 8.20836 17.7559 8.14334 17.6908 8.16472L16.6275 8.51401C16.6098 8.51981 16.5907 8.51928 16.5734 8.51253L15.4638 8.07934C15.3944 8.05225 15.3289 8.12638 15.3643 8.19192L15.6428 8.7069C15.6661 8.75007 15.6455 8.80389 15.5992 8.82033Z" fill="white"/>
|
||||
<path d="M20.7336 9.65148L18.0589 10.7187C17.9919 10.7455 17.9917 10.8401 18.0585 10.8672L20.4599 11.8397C20.4789 11.8474 20.5002 11.8475 20.5192 11.84L23.2273 10.7728C23.2947 10.7462 23.2949 10.6509 23.2276 10.624L20.793 9.6515C20.7739 9.64389 20.7527 9.64389 20.7336 9.65148Z" fill="#A41E11"/>
|
||||
<path d="M17.855 11.819L13.8573 12.4291C13.7777 12.4412 13.7639 12.5502 13.8381 12.5818L16.7834 13.8355C16.8209 13.8515 16.8644 13.8367 16.8844 13.8012L17.9367 11.9374C17.9695 11.8793 17.921 11.8089 17.855 11.819Z" fill="white"/>
|
||||
</svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.8 KiB After Width: | Height: | Size: 2.6 KiB |
@@ -0,0 +1,6 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M20.9497 11.0503C19.2928 9.39344 16.6066 9.39344 14.9497 11.0503C13.2929 12.7071 13.2929 15.3934 14.9497 17.0503" stroke="#89DFBB" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M7.5149 24.4853C11.4201 28.3905 17.5935 28.5488 21.3035 24.8389C25.0135 21.1289 24.8552 14.9555 20.9499 11.0503" stroke="#04C98D" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M11.0503 20.9497C12.7072 22.6066 15.3934 22.6066 17.0503 20.9497C18.7071 19.2929 18.7071 16.6066 17.0503 14.9497" stroke="#04C98D" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M24.4851 7.5147C20.5799 3.60946 14.4065 3.45117 10.6965 7.16115C6.98654 10.8711 7.14483 17.0445 11.0501 20.9497" stroke="#89DFBB" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 829 B |
@@ -1,5 +1,5 @@
|
||||
import { UpstashRedisCache as LangchainUpstashRedisCache } from '@langchain/community/caches/upstash_redis'
|
||||
import { getBaseClasses, getCredentialData, getCredentialParam, ICommonObject, INode, INodeData, INodeParams } from '../../../src'
|
||||
import { UpstashRedisCache as LangchainUpstashRedisCache } from 'langchain/cache/upstash_redis'
|
||||
|
||||
class UpstashRedisCache implements INode {
|
||||
label: string
|
||||
@@ -19,7 +19,7 @@ class UpstashRedisCache implements INode {
|
||||
this.version = 1.0
|
||||
this.type = 'UpstashRedisCache'
|
||||
this.description = 'Cache LLM response in Upstash Redis, serverless data for Redis and Kafka'
|
||||
this.icon = 'upstash.png'
|
||||
this.icon = 'Upstash.svg'
|
||||
this.category = 'Cache'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(LangchainUpstashRedisCache)]
|
||||
this.credential = {
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 2.6 KiB |
@@ -1,8 +1,8 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { APIChain } from 'langchain/chains'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { PromptTemplate } from 'langchain/prompts'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
export const API_URL_RAW_PROMPT_TEMPLATE = `You are given the below API Documentation:
|
||||
@@ -32,7 +32,7 @@ class GETApiChain_Chains implements INode {
|
||||
this.name = 'getApiChain'
|
||||
this.version = 1.0
|
||||
this.type = 'GETApiChain'
|
||||
this.icon = 'apichain.svg'
|
||||
this.icon = 'get.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chain to run queries against GET API'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ChatOpenAI } from '@langchain/openai'
|
||||
import { APIChain, createOpenAPIChain } from 'langchain/chains'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { ChatOpenAI } from 'langchain/chat_models/openai'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
class OpenApiChain_Chains implements INode {
|
||||
@@ -20,7 +20,7 @@ class OpenApiChain_Chains implements INode {
|
||||
this.name = 'openApiChain'
|
||||
this.version = 1.0
|
||||
this.type = 'OpenAPIChain'
|
||||
this.icon = 'openapi.png'
|
||||
this.icon = 'openapi.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chain that automatically select and call APIs based only on an OpenAPI spec'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { PromptTemplate } from 'langchain/prompts'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { PromptTemplate } from '@langchain/core/prompts'
|
||||
import { API_RESPONSE_RAW_PROMPT_TEMPLATE, API_URL_RAW_PROMPT_TEMPLATE, APIChain } from './postCore'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
|
||||
class POSTApiChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -21,7 +21,7 @@ class POSTApiChain_Chains implements INode {
|
||||
this.name = 'postApiChain'
|
||||
this.version = 1.0
|
||||
this.type = 'POSTApiChain'
|
||||
this.icon = 'apichain.svg'
|
||||
this.icon = 'post.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chain to run queries against POST API'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(APIChain)]
|
||||
|
||||
@@ -1,3 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?><svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 122.88 92.96" style="enable-background:new 0 0 122.88 92.96" xml:space="preserve"><style type="text/css"><![CDATA[
|
||||
.st0{fill-rule:evenodd;clip-rule:evenodd;}
|
||||
]]></style><g><path class="st0" d="M100.09,6.87l2.5,3.3c0.66,0.87,0.49,2.12-0.38,2.77l-2.66,2.02c0.48,1.29,0.79,2.65,0.92,4.06l3.03,0.41 c1.08,0.15,1.84,1.15,1.69,2.23l-0.56,4.1c-0.15,1.08-1.15,1.84-2.23,1.69l-3.3-0.45c-0.59,1.28-1.34,2.46-2.22,3.52l1.85,2.43 c0.66,0.87,0.49,2.12-0.38,2.78l-3.3,2.5c-0.87,0.66-2.12,0.49-2.78-0.38l-2.02-2.66c-1.29,0.48-2.66,0.79-4.06,0.92l-0.41,3.03 c-0.15,1.08-1.15,1.84-2.23,1.69l-4.1-0.56c-1.08-0.15-1.84-1.15-1.69-2.23l0.45-3.3c-1.28-0.59-2.46-1.34-3.52-2.23l-2.43,1.85 c-0.87,0.66-2.12,0.49-2.78-0.38l-2.5-3.3c-0.66-0.87-0.49-2.12,0.38-2.77l2.66-2.02c-0.48-1.29-0.79-2.65-0.92-4.06l-3.03-0.41 c-1.08-0.15-1.84-1.15-1.69-2.23l0.56-4.1c0.15-1.08,1.15-1.84,2.23-1.69l3.3,0.45c0.59-1.28,1.34-2.46,2.23-3.52L70.84,7.9 c-0.66-0.87-0.49-2.12,0.38-2.78l3.3-2.5c0.87-0.66,2.12-0.49,2.78,0.38l2.02,2.66c1.29-0.48,2.66-0.79,4.06-0.92l0.41-3.02 c0.15-1.08,1.15-1.84,2.23-1.69l4.1,0.56c1.08,0.15,1.84,1.15,1.69,2.23l-0.45,3.3c1.28,0.59,2.46,1.34,3.52,2.23l2.43-1.85 C98.19,5.83,99.44,6,100.09,6.87L100.09,6.87L100.09,6.87z M55.71,13.75c-0.23,0.02-0.46,0.04-0.69,0.06 c-5.63,0.54-11.1,2.59-15.62,6.1c-5.23,4.05-9.2,10.11-10.73,18.14l-0.48,2.51L25.69,41c-2.45,0.43-4.64,1.02-6.56,1.77 c-1.86,0.72-3.52,1.61-4.97,2.66c-1.16,0.84-2.16,1.78-3.01,2.8c-2.63,3.15-3.85,7.1-3.82,11.1c0.03,4.06,1.35,8.16,3.79,11.53 c0.91,1.25,1.96,2.4,3.16,3.4l0.03,0.02l-2.68,7.13c-0.71-0.47-1.4-0.98-2.04-1.52c-1.68-1.4-3.15-2.99-4.4-4.72 C1.84,70.57,0.04,64.95,0,59.35c-0.04-5.66,1.72-11.29,5.52-15.85c1.23-1.48,2.68-2.84,4.34-4.04c1.93-1.4,4.14-2.58,6.64-3.55 c1.72-0.67,3.56-1.23,5.5-1.68c2.2-8.74,6.89-15.47,12.92-20.14c5.64-4.37,12.43-6.92,19.42-7.59c2.13-0.21,4.29-0.24,6.43-0.09 c-0.47,0.25-0.91,0.53-1.33,0.85l-0.03,0.02c-1.93,1.47-3.32,3.7-3.68,6.3L55.71,13.75L55.71,13.75z M43.85,87.38H31.99l-1.7,5.58 H19.6l12.75-33.87h11.46l12.7,33.87H45.55L43.85,87.38L43.85,87.38z M41.63,80.04l-3.7-12.17l-3.71,12.17H41.63L41.63,80.04z M59.78,59.09h17.41c3.79,0,6.64,0.9,8.52,2.7c1.88,1.8,2.83,4.38,2.83,7.71c0,3.42-1.04,6.1-3.09,8.03 c-2.06,1.93-5.21,2.89-9.43,2.89h-5.74v12.54h-10.5V59.09L59.78,59.09z M70.28,73.56h2.58c2.03,0,3.46-0.35,4.28-1.06 c0.82-0.7,1.23-1.6,1.23-2.7c0-1.06-0.36-1.96-1.07-2.7c-0.71-0.74-2.05-1.11-4.02-1.11h-3V73.56L70.28,73.56z M92.77,59.09h10.5 v33.87h-10.5V59.09L92.77,59.09z M112.01,31.74c1.07,0.83,2.09,1.77,3.07,2.82c1.07,1.15,2.08,2.45,3.03,3.9 c3.2,4.92,4.84,11.49,4.77,17.92c-0.07,6.31-1.77,12.59-5.25,17.21c-0.84,1.11-1.77,2.15-2.78,3.12V62.15 c0.43-1.87,0.65-3.84,0.67-5.83c0.06-5.07-1.18-10.16-3.59-13.86c-0.69-1.07-1.45-2.03-2.25-2.89c-0.65-0.7-1.34-1.34-2.05-1.9 c0.07-0.3,0.13-0.6,0.17-0.9c0.08-0.62,0.11-1.25,0.07-1.88c0.82-0.32,1.58-0.75,2.26-1.27l0.03-0.02 C110.86,33.06,111.48,32.44,112.01,31.74L112.01,31.74z M85.89,12.37c4.45,0.61,7.57,4.71,6.96,9.17 c-0.61,4.45-4.71,7.57-9.17,6.96c-4.45-0.61-7.57-4.71-6.96-9.17S81.44,11.76,85.89,12.37L85.89,12.37L85.89,12.37z"/></g></svg>
|
||||
|
Before Width: | Height: | Size: 3.2 KiB |
@@ -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="M10.5 20.5C10.5 20.5 10 20 9 20C7.067 20 6 21.567 6 23.5C6 25.433 7.067 27 9 27C10 27 10.7037 26.4812 11 26V24H10" stroke="#110000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M18.5 20H14V27H18.5M14 23.5H17.5" stroke="black" stroke-width="1.8" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M23.5 27V20M21 20H26" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 888 B |
Binary file not shown.
|
Before Width: | Height: | Size: 24 KiB |
@@ -0,0 +1,7 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<circle cx="16" cy="16" r="3" fill="#424143" stroke="#424143" stroke-width="2"/>
|
||||
<circle cx="26" cy="6" r="2" fill="#424143" stroke="#424143" stroke-width="2"/>
|
||||
<path d="M18.5 13.5L24.5 7.5" stroke="#424143" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M16 27C22.0751 27 27 22.0751 27 16C27 14.3968 26.657 12.8736 26.0404 11.5L22.5247 15C22.5743 15.3261 22.6 15.6601 22.6 16C22.6 19.6451 19.6451 22.6 16 22.6C12.3549 22.6 9.4 19.6451 9.4 16C9.4 12.3549 12.3549 9.4 16 9.4C16.3399 9.4 16.6739 9.4257 17 9.47527L20.4094 5.9194C19.0595 5.3281 17.5681 5 16 5C9.92487 5 5 9.92487 5 16C5 22.0751 9.92487 27 16 27Z" fill="#94D609"/>
|
||||
<path d="M17 9.47527L16.8497 10.4639C17.1714 10.5128 17.4967 10.4022 17.7218 10.1674L17 9.47527ZM20.4094 5.9194L21.1312 6.61149C21.3573 6.37567 21.454 6.04428 21.3901 5.72389C21.3262 5.4035 21.1099 5.13451 20.8106 5.00343L20.4094 5.9194ZM22.5247 15L21.8192 14.2913C21.5934 14.5162 21.4882 14.8352 21.5361 15.1503L22.5247 15ZM26.0404 11.5L26.9527 11.0905C26.8182 10.7909 26.5452 10.5764 26.2223 10.5167C25.8993 10.457 25.5676 10.5596 25.3349 10.7913L26.0404 11.5ZM26 16C26 21.5228 21.5228 26 16 26V28C22.6274 28 28 22.6274 28 16H26ZM16 26C10.4772 26 6 21.5228 6 16H4C4 22.6274 9.37258 28 16 28V26ZM6 16C6 10.4772 10.4772 6 16 6V4C9.37258 4 4 9.37258 4 16H6ZM21.6 16C21.6 19.0928 19.0928 21.6 16 21.6V23.6C20.1974 23.6 23.6 20.1974 23.6 16H21.6ZM16 21.6C12.9072 21.6 10.4 19.0928 10.4 16H8.4C8.4 20.1974 11.8026 23.6 16 23.6V21.6ZM10.4 16C10.4 12.9072 12.9072 10.4 16 10.4V8.4C11.8026 8.4 8.4 11.8026 8.4 16H10.4ZM16 10.4C16.2895 10.4 16.5732 10.4219 16.8497 10.4639L17.1503 8.48662C16.7746 8.42952 16.3904 8.4 16 8.4V10.4ZM16 6C17.4274 6 18.7824 6.29844 20.0082 6.83538L20.8106 5.00343C19.3366 4.35776 17.7088 4 16 4V6ZM17.7218 10.1674L21.1312 6.61149L19.6876 5.22732L16.2782 8.78318L17.7218 10.1674ZM21.5361 15.1503C21.5781 15.4268 21.6 15.7105 21.6 16H23.6C23.6 15.6096 23.5705 15.2254 23.5134 14.8497L21.5361 15.1503ZM25.1281 11.9095C25.688 13.1569 26 14.5407 26 16H28C28 14.2529 27.626 12.5904 26.9527 11.0905L25.1281 11.9095ZM23.2303 15.7087L26.7459 12.2087L25.3349 10.7913L21.8192 14.2913L23.2303 15.7087Z" fill="#94D609"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.2 KiB |
@@ -0,0 +1,8 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M4 27V20H6.5C7.60457 20 8.5 20.8954 8.5 22C8.5 23.1046 7.60457 24 6.5 24H4" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M27 27V20M25 20H29" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M22.5644 20.4399C21.6769 19.7608 19 19.6332 19 21.7961C19 24.1915 23 22.5657 23 25.0902C23 26.9875 20.33 27.5912 19 26.3537" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M11 23.5C11 20.7 12.6667 20 13.5 20C14.3333 20 16 20.7 16 23.5C16 26.3 14.3333 27 13.5 27C12.6667 27 11 26.3 11 23.5Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.1 KiB |
@@ -1,8 +1,8 @@
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { CallbackManagerForChainRun } from 'langchain/callbacks'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { CallbackManagerForChainRun } from '@langchain/core/callbacks/manager'
|
||||
import { BaseChain, ChainInputs, LLMChain, SerializedAPIChain } from 'langchain/chains'
|
||||
import { BasePromptTemplate, PromptTemplate } from 'langchain/prompts'
|
||||
import { ChainValues } from 'langchain/schema'
|
||||
import { BasePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
|
||||
import { ChainValues } from '@langchain/core/utils/types'
|
||||
import fetch from 'node-fetch'
|
||||
|
||||
export const API_URL_RAW_PROMPT_TEMPLATE = `You are given the below API Documentation:
|
||||
|
||||
@@ -1,14 +1,19 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConversationChain } from 'langchain/chains'
|
||||
import { getBaseClasses, mapChatHistory } from '../../../src/utils'
|
||||
import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from 'langchain/prompts'
|
||||
import { BufferMemory } from 'langchain/memory'
|
||||
import { BaseChatModel } from 'langchain/chat_models/base'
|
||||
import { ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate } from '@langchain/core/prompts'
|
||||
import { RunnableSequence } from '@langchain/core/runnables'
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers'
|
||||
import { HumanMessage } from '@langchain/core/messages'
|
||||
import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
|
||||
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
|
||||
import { formatResponse } from '../../outputparsers/OutputParserHelpers'
|
||||
import { addImagesToMessages } from '../../../src/multiModalUtils'
|
||||
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
|
||||
import { FlowiseMemory, ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { flatten } from 'lodash'
|
||||
import { Document } from 'langchain/document'
|
||||
import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
|
||||
|
||||
let systemMessage = `The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.`
|
||||
const inputKey = 'input'
|
||||
|
||||
class ConversationChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -20,19 +25,20 @@ class ConversationChain_Chains implements INode {
|
||||
baseClasses: string[]
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Conversation Chain'
|
||||
this.name = 'conversationChain'
|
||||
this.version = 1.0
|
||||
this.version = 3.0
|
||||
this.type = 'ConversationChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'conv.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chat models specific conversational chain with memory'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(ConversationChain)]
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Language Model',
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
@@ -41,6 +47,14 @@ class ConversationChain_Chains implements INode {
|
||||
name: 'memory',
|
||||
type: 'BaseMemory'
|
||||
},
|
||||
{
|
||||
label: 'Chat Prompt Template',
|
||||
name: 'chatPromptTemplate',
|
||||
type: 'ChatPromptTemplate',
|
||||
description: 'Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable',
|
||||
optional: true
|
||||
},
|
||||
/* Deprecated
|
||||
{
|
||||
label: 'Document',
|
||||
name: 'document',
|
||||
@@ -49,87 +63,177 @@ class ConversationChain_Chains implements INode {
|
||||
'Include whole document into the context window, if you get maximum context length error, please use model with higher context window like Claude 100k, or gpt4 32k',
|
||||
optional: true,
|
||||
list: 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: 'System Message',
|
||||
name: 'systemMessagePrompt',
|
||||
type: 'string',
|
||||
rows: 4,
|
||||
description: 'If Chat Prompt Template is provided, this will be ignored',
|
||||
additionalParams: true,
|
||||
optional: true,
|
||||
placeholder: 'You are a helpful assistant that write codes'
|
||||
default: systemMessage,
|
||||
placeholder: systemMessage
|
||||
}
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model as BaseChatModel
|
||||
const memory = nodeData.inputs?.memory as BufferMemory
|
||||
const prompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const docs = nodeData.inputs?.document as Document[]
|
||||
|
||||
const flattenDocs = docs && docs.length ? flatten(docs) : []
|
||||
const finalDocs = []
|
||||
for (let i = 0; i < flattenDocs.length; i += 1) {
|
||||
if (flattenDocs[i] && flattenDocs[i].pageContent) {
|
||||
finalDocs.push(new Document(flattenDocs[i]))
|
||||
}
|
||||
}
|
||||
|
||||
let finalText = ''
|
||||
for (let i = 0; i < finalDocs.length; i += 1) {
|
||||
finalText += finalDocs[i].pageContent
|
||||
}
|
||||
|
||||
const replaceChar: string[] = ['{', '}']
|
||||
for (const char of replaceChar) finalText = finalText.replaceAll(char, '')
|
||||
|
||||
if (finalText) systemMessage = `${systemMessage}\nThe AI has the following context:\n${finalText}`
|
||||
|
||||
const obj: any = {
|
||||
llm: model,
|
||||
memory,
|
||||
verbose: process.env.DEBUG === 'true' ? true : false
|
||||
}
|
||||
|
||||
const chatPrompt = ChatPromptTemplate.fromMessages([
|
||||
SystemMessagePromptTemplate.fromTemplate(prompt ? `${prompt}\n${systemMessage}` : systemMessage),
|
||||
new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
|
||||
HumanMessagePromptTemplate.fromTemplate('{input}')
|
||||
])
|
||||
obj.prompt = chatPrompt
|
||||
|
||||
const chain = new ConversationChain(obj)
|
||||
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
|
||||
const chain = prepareChain(nodeData, options, this.sessionId)
|
||||
return chain
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
|
||||
const chain = nodeData.instance as ConversationChain
|
||||
const memory = nodeData.inputs?.memory as BufferMemory
|
||||
memory.returnMessages = true // Return true for BaseChatModel
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | object> {
|
||||
const memory = nodeData.inputs?.memory
|
||||
|
||||
if (options && options.chatHistory) {
|
||||
const chatHistoryClassName = memory.chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
memory.chatHistory = mapChatHistory(options)
|
||||
const chain = prepareChain(nodeData, options, this.sessionId)
|
||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||
|
||||
if (moderations && moderations.length > 0) {
|
||||
try {
|
||||
// Use the output of the moderation chain as input for the LLM chain
|
||||
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)
|
||||
}
|
||||
}
|
||||
|
||||
chain.memory = memory
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
const additionalCallback = await additionalCallbacks(nodeData, options)
|
||||
|
||||
let res = ''
|
||||
let callbacks = [loggerHandler, ...additionalCallback]
|
||||
|
||||
if (process.env.DEBUG === 'true') {
|
||||
callbacks.push(new LCConsoleCallbackHandler())
|
||||
}
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId)
|
||||
const res = await chain.call({ input }, [loggerHandler, handler, ...callbacks])
|
||||
return res?.response
|
||||
callbacks.push(handler)
|
||||
res = await chain.invoke({ input }, { callbacks })
|
||||
} else {
|
||||
const res = await chain.call({ input }, [loggerHandler, ...callbacks])
|
||||
return res?.response
|
||||
res = await chain.invoke({ input }, { callbacks })
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: res,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
return res
|
||||
}
|
||||
}
|
||||
|
||||
const prepareChatPrompt = (nodeData: INodeData, humanImageMessages: HumanMessage[]) => {
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const prompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const chatPromptTemplate = nodeData.inputs?.chatPromptTemplate as ChatPromptTemplate
|
||||
|
||||
if (chatPromptTemplate && chatPromptTemplate.promptMessages.length) {
|
||||
const sysPrompt = chatPromptTemplate.promptMessages[0]
|
||||
const humanPrompt = chatPromptTemplate.promptMessages[chatPromptTemplate.promptMessages.length - 1]
|
||||
const messages = [sysPrompt, new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'), humanPrompt]
|
||||
if (humanImageMessages.length) messages.push(...humanImageMessages)
|
||||
|
||||
const chatPrompt = ChatPromptTemplate.fromMessages(messages)
|
||||
if ((chatPromptTemplate as any).promptValues) {
|
||||
// @ts-ignore
|
||||
chatPrompt.promptValues = (chatPromptTemplate as any).promptValues
|
||||
}
|
||||
|
||||
return chatPrompt
|
||||
}
|
||||
|
||||
const messages = [
|
||||
SystemMessagePromptTemplate.fromTemplate(prompt ? prompt : systemMessage),
|
||||
new MessagesPlaceholder(memory.memoryKey ?? 'chat_history'),
|
||||
HumanMessagePromptTemplate.fromTemplate(`{${inputKey}}`)
|
||||
]
|
||||
if (humanImageMessages.length) messages.push(...(humanImageMessages as any[]))
|
||||
|
||||
const chatPrompt = ChatPromptTemplate.fromMessages(messages)
|
||||
|
||||
return chatPrompt
|
||||
}
|
||||
|
||||
const prepareChain = (nodeData: INodeData, options: ICommonObject, sessionId?: string) => {
|
||||
const chatHistory = options.chatHistory
|
||||
let model = nodeData.inputs?.model as ChatOpenAI
|
||||
const memory = nodeData.inputs?.memory as FlowiseMemory
|
||||
const memoryKey = memory.memoryKey ?? 'chat_history'
|
||||
|
||||
let humanImageMessages: HumanMessage[] = []
|
||||
if (model instanceof ChatOpenAI) {
|
||||
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
|
||||
|
||||
if (messageContent?.length) {
|
||||
// Change model to gpt-4-vision
|
||||
model.modelName = 'gpt-4-vision-preview'
|
||||
|
||||
// Change default max token to higher when using gpt-4-vision
|
||||
model.maxTokens = 1024
|
||||
|
||||
for (const msg of messageContent) {
|
||||
humanImageMessages.push(new HumanMessage({ content: [msg] }))
|
||||
}
|
||||
} else {
|
||||
// revert to previous values if image upload is empty
|
||||
model.modelName = model.configuredModel
|
||||
model.maxTokens = model.configuredMaxToken
|
||||
}
|
||||
}
|
||||
|
||||
const chatPrompt = prepareChatPrompt(nodeData, humanImageMessages)
|
||||
let promptVariables = {}
|
||||
const promptValuesRaw = (chatPrompt as any).promptValues
|
||||
if (promptValuesRaw) {
|
||||
const promptValues = handleEscapeCharacters(promptValuesRaw, true)
|
||||
for (const val in promptValues) {
|
||||
promptVariables = {
|
||||
...promptVariables,
|
||||
[val]: () => {
|
||||
return promptValues[val]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const conversationChain = RunnableSequence.from([
|
||||
{
|
||||
[inputKey]: (input: { input: string }) => input.input,
|
||||
[memoryKey]: async () => {
|
||||
const history = await memory.getChatMessages(sessionId, true, chatHistory)
|
||||
return history
|
||||
},
|
||||
...promptVariables
|
||||
},
|
||||
prepareChatPrompt(nodeData, humanImageMessages),
|
||||
model,
|
||||
new StringOutputParser()
|
||||
])
|
||||
|
||||
return conversationChain
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ConversationChain_Chains }
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
@@ -0,0 +1,8 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M25 22L27 29L29 22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M8 23C7.68717 22.3986 7.2 22 6.4 22C4.8536 22 4 23.567 4 25.5C4 27.433 4.8536 29 6.4 29C7.2 29 7.76299 28.4812 8 28" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M18 29V22L22 29V22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M10.5 25.5C10.5 22.7 12.1667 22 13 22C13.8333 22 15.5 22.7 15.5 25.5C15.5 28.3 13.8333 29 13 29C12.1667 29 10.5 28.3 10.5 25.5Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.0 KiB |
+278
-120
@@ -1,20 +1,25 @@
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses, mapChatHistory } from '../../../src/utils'
|
||||
import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
|
||||
import { BaseRetriever } from 'langchain/schema/retriever'
|
||||
import { BufferMemory, BufferMemoryInput } from 'langchain/memory'
|
||||
import { PromptTemplate } from 'langchain/prompts'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import {
|
||||
default_map_reduce_template,
|
||||
default_qa_template,
|
||||
qa_template,
|
||||
map_reduce_template,
|
||||
CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
|
||||
refine_question_template,
|
||||
refine_template
|
||||
} from './prompts'
|
||||
import { applyPatch } from 'fast-json-patch'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { BaseRetriever } from '@langchain/core/retrievers'
|
||||
import { PromptTemplate, ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts'
|
||||
import { Runnable, RunnableSequence, RunnableMap, RunnableBranch, RunnableLambda } from '@langchain/core/runnables'
|
||||
import { BaseMessage, HumanMessage, AIMessage } from '@langchain/core/messages'
|
||||
import { ConsoleCallbackHandler as LCConsoleCallbackHandler } from '@langchain/core/tracers/console'
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers'
|
||||
import type { Document } from '@langchain/core/documents'
|
||||
import { BufferMemoryInput } from 'langchain/memory'
|
||||
import { ConversationalRetrievalQAChain } from 'langchain/chains'
|
||||
import { convertBaseMessagetoIMessage, getBaseClasses } from '../../../src/utils'
|
||||
import { ConsoleCallbackHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { FlowiseMemory, ICommonObject, IMessage, INode, INodeData, INodeParams, MemoryMethods } from '../../../src/Interface'
|
||||
import { QA_TEMPLATE, REPHRASE_TEMPLATE, RESPONSE_TEMPLATE } from './prompts'
|
||||
|
||||
type RetrievalChainInput = {
|
||||
chat_history: string
|
||||
question: string
|
||||
}
|
||||
|
||||
const sourceRunnableName = 'FindDocs'
|
||||
|
||||
class ConversationalRetrievalQAChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -26,21 +31,22 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||
baseClasses: string[]
|
||||
description: string
|
||||
inputs: INodeParams[]
|
||||
sessionId?: string
|
||||
|
||||
constructor() {
|
||||
constructor(fields?: { sessionId?: string }) {
|
||||
this.label = 'Conversational Retrieval QA Chain'
|
||||
this.name = 'conversationalRetrievalQAChain'
|
||||
this.version = 1.0
|
||||
this.version = 2.0
|
||||
this.type = 'ConversationalRetrievalQAChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'qa.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Document QA - built on RetrievalQAChain to provide a chat history component'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(ConversationalRetrievalQAChain)]
|
||||
this.inputs = [
|
||||
{
|
||||
label: 'Language Model',
|
||||
label: 'Chat Model',
|
||||
name: 'model',
|
||||
type: 'BaseLanguageModel'
|
||||
type: 'BaseChatModel'
|
||||
},
|
||||
{
|
||||
label: 'Vector Store Retriever',
|
||||
@@ -60,6 +66,29 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||
type: 'boolean',
|
||||
optional: true
|
||||
},
|
||||
{
|
||||
label: 'Rephrase Prompt',
|
||||
name: 'rephrasePrompt',
|
||||
type: 'string',
|
||||
description: 'Using previous chat history, rephrase question into a standalone question',
|
||||
warning: 'Prompt must include input variables: {chat_history} and {question}',
|
||||
rows: 4,
|
||||
additionalParams: true,
|
||||
optional: true,
|
||||
default: REPHRASE_TEMPLATE
|
||||
},
|
||||
{
|
||||
label: 'Response Prompt',
|
||||
name: 'responsePrompt',
|
||||
type: 'string',
|
||||
description: 'Taking the rephrased question, search for answer from the provided context',
|
||||
warning: 'Prompt must include input variable: {context}',
|
||||
rows: 4,
|
||||
additionalParams: true,
|
||||
optional: true,
|
||||
default: RESPONSE_TEMPLATE
|
||||
}
|
||||
/** Deprecated
|
||||
{
|
||||
label: 'System Message',
|
||||
name: 'systemMessagePrompt',
|
||||
@@ -70,6 +99,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||
placeholder:
|
||||
'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
|
||||
},
|
||||
// TODO: create standalone chains for these 3 modes as they are not compatible with memory
|
||||
{
|
||||
label: 'Chain Option',
|
||||
name: 'chainOption',
|
||||
@@ -95,124 +125,252 @@ class ConversationalRetrievalQAChain_Chains implements INode {
|
||||
additionalParams: true,
|
||||
optional: true
|
||||
}
|
||||
*/
|
||||
]
|
||||
this.sessionId = fields?.sessionId
|
||||
}
|
||||
|
||||
async init(nodeData: INodeData): Promise<any> {
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
|
||||
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
const chainOption = nodeData.inputs?.chainOption as string
|
||||
const externalMemory = nodeData.inputs?.memory
|
||||
const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
|
||||
const responsePrompt = nodeData.inputs?.responsePrompt as string
|
||||
|
||||
const obj: any = {
|
||||
verbose: process.env.DEBUG === 'true' ? true : false,
|
||||
questionGeneratorChainOptions: {
|
||||
template: CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
|
||||
}
|
||||
let customResponsePrompt = responsePrompt
|
||||
// If the deprecated systemMessagePrompt is still exists
|
||||
if (systemMessagePrompt) {
|
||||
customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
|
||||
}
|
||||
|
||||
if (returnSourceDocuments) obj.returnSourceDocuments = returnSourceDocuments
|
||||
|
||||
if (chainOption === 'map_reduce') {
|
||||
obj.qaChainOptions = {
|
||||
type: 'map_reduce',
|
||||
combinePrompt: PromptTemplate.fromTemplate(
|
||||
systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
|
||||
)
|
||||
} as QAChainParams
|
||||
} else if (chainOption === 'refine') {
|
||||
const qprompt = new PromptTemplate({
|
||||
inputVariables: ['context', 'question'],
|
||||
template: refine_question_template(systemMessagePrompt)
|
||||
})
|
||||
const rprompt = new PromptTemplate({
|
||||
inputVariables: ['context', 'question', 'existing_answer'],
|
||||
template: refine_template
|
||||
})
|
||||
obj.qaChainOptions = {
|
||||
type: 'refine',
|
||||
questionPrompt: qprompt,
|
||||
refinePrompt: rprompt
|
||||
} as QAChainParams
|
||||
} else {
|
||||
obj.qaChainOptions = {
|
||||
type: 'stuff',
|
||||
prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
|
||||
} as QAChainParams
|
||||
}
|
||||
|
||||
if (externalMemory) {
|
||||
externalMemory.memoryKey = 'chat_history'
|
||||
externalMemory.inputKey = 'question'
|
||||
externalMemory.outputKey = 'text'
|
||||
externalMemory.returnMessages = true
|
||||
if (chainOption === 'refine') externalMemory.outputKey = 'output_text'
|
||||
obj.memory = externalMemory
|
||||
} else {
|
||||
const fields: BufferMemoryInput = {
|
||||
memoryKey: 'chat_history',
|
||||
inputKey: 'question',
|
||||
outputKey: 'text',
|
||||
returnMessages: true
|
||||
}
|
||||
if (chainOption === 'refine') fields.outputKey = 'output_text'
|
||||
obj.memory = new BufferMemory(fields)
|
||||
}
|
||||
|
||||
const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, obj)
|
||||
return chain
|
||||
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
|
||||
return answerChain
|
||||
}
|
||||
|
||||
async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
|
||||
const chain = nodeData.instance as ConversationalRetrievalQAChain
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
const externalMemory = nodeData.inputs?.memory
|
||||
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
|
||||
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
|
||||
const rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
|
||||
const responsePrompt = nodeData.inputs?.responsePrompt as string
|
||||
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
|
||||
const chainOption = nodeData.inputs?.chainOption as string
|
||||
|
||||
let model = nodeData.inputs?.model
|
||||
|
||||
// Temporary fix: https://github.com/hwchase17/langchainjs/issues/754
|
||||
model.streaming = false
|
||||
chain.questionGeneratorChain.llm = model
|
||||
|
||||
const obj = { question: input }
|
||||
|
||||
if (options && options.chatHistory && chain.memory) {
|
||||
const chatHistoryClassName = (chain.memory as any).chatHistory.constructor.name
|
||||
// Only replace when its In-Memory
|
||||
if (chatHistoryClassName && chatHistoryClassName === 'ChatMessageHistory') {
|
||||
;(chain.memory as any).chatHistory = mapChatHistory(options)
|
||||
}
|
||||
let customResponsePrompt = responsePrompt
|
||||
// If the deprecated systemMessagePrompt is still exists
|
||||
if (systemMessagePrompt) {
|
||||
customResponsePrompt = `${systemMessagePrompt}\n${QA_TEMPLATE}`
|
||||
}
|
||||
|
||||
let memory: FlowiseMemory | undefined = externalMemory
|
||||
if (!memory) {
|
||||
memory = new BufferMemory({
|
||||
returnMessages: true,
|
||||
memoryKey: 'chat_history',
|
||||
inputKey: 'input'
|
||||
})
|
||||
}
|
||||
|
||||
const answerChain = createChain(model, vectorStoreRetriever, rephrasePrompt, customResponsePrompt)
|
||||
|
||||
const history = ((await memory.getChatMessages(this.sessionId, false, options.chatHistory)) as IMessage[]) ?? []
|
||||
|
||||
const loggerHandler = new ConsoleCallbackHandler(options.logger)
|
||||
const callbacks = await additionalCallbacks(nodeData, options)
|
||||
const additionalCallback = await additionalCallbacks(nodeData, options)
|
||||
|
||||
if (options.socketIO && options.socketIOClientId) {
|
||||
const handler = new CustomChainHandler(
|
||||
options.socketIO,
|
||||
options.socketIOClientId,
|
||||
chainOption === 'refine' ? 4 : undefined,
|
||||
returnSourceDocuments
|
||||
)
|
||||
const res = await chain.call(obj, [loggerHandler, handler, ...callbacks])
|
||||
if (chainOption === 'refine') {
|
||||
if (res.output_text && res.sourceDocuments) {
|
||||
return {
|
||||
text: res.output_text,
|
||||
sourceDocuments: res.sourceDocuments
|
||||
}
|
||||
}
|
||||
return res?.output_text
|
||||
}
|
||||
if (res.text && res.sourceDocuments) return res
|
||||
return res?.text
|
||||
} else {
|
||||
const res = await chain.call(obj, [loggerHandler, ...callbacks])
|
||||
if (res.text && res.sourceDocuments) return res
|
||||
return res?.text
|
||||
let callbacks = [loggerHandler, ...additionalCallback]
|
||||
|
||||
if (process.env.DEBUG === 'true') {
|
||||
callbacks.push(new LCConsoleCallbackHandler())
|
||||
}
|
||||
|
||||
const stream = answerChain.streamLog(
|
||||
{ question: input, chat_history: history },
|
||||
{ callbacks },
|
||||
{
|
||||
includeNames: [sourceRunnableName]
|
||||
}
|
||||
)
|
||||
|
||||
let streamedResponse: Record<string, any> = {}
|
||||
let sourceDocuments: ICommonObject[] = []
|
||||
let text = ''
|
||||
let isStreamingStarted = false
|
||||
const isStreamingEnabled = options.socketIO && options.socketIOClientId
|
||||
|
||||
for await (const chunk of stream) {
|
||||
streamedResponse = applyPatch(streamedResponse, chunk.ops).newDocument
|
||||
|
||||
if (streamedResponse.final_output) {
|
||||
text = streamedResponse.final_output?.output
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('end')
|
||||
if (Array.isArray(streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output)) {
|
||||
sourceDocuments = streamedResponse?.logs?.[sourceRunnableName]?.final_output?.output
|
||||
if (isStreamingEnabled && returnSourceDocuments)
|
||||
options.socketIO.to(options.socketIOClientId).emit('sourceDocuments', sourceDocuments)
|
||||
}
|
||||
}
|
||||
|
||||
if (
|
||||
Array.isArray(streamedResponse?.streamed_output) &&
|
||||
streamedResponse?.streamed_output.length &&
|
||||
!streamedResponse.final_output
|
||||
) {
|
||||
const token = streamedResponse.streamed_output[streamedResponse.streamed_output.length - 1]
|
||||
|
||||
if (!isStreamingStarted) {
|
||||
isStreamingStarted = true
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('start', token)
|
||||
}
|
||||
if (isStreamingEnabled) options.socketIO.to(options.socketIOClientId).emit('token', token)
|
||||
}
|
||||
}
|
||||
|
||||
await memory.addChatMessages(
|
||||
[
|
||||
{
|
||||
text: input,
|
||||
type: 'userMessage'
|
||||
},
|
||||
{
|
||||
text: text,
|
||||
type: 'apiMessage'
|
||||
}
|
||||
],
|
||||
this.sessionId
|
||||
)
|
||||
|
||||
if (returnSourceDocuments) return { text, sourceDocuments }
|
||||
else return { text }
|
||||
}
|
||||
}
|
||||
|
||||
const createRetrieverChain = (llm: BaseLanguageModel, retriever: Runnable, rephrasePrompt: string) => {
|
||||
// Small speed/accuracy optimization: no need to rephrase the first question
|
||||
// since there shouldn't be any meta-references to prior chat history
|
||||
const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(rephrasePrompt)
|
||||
const condenseQuestionChain = RunnableSequence.from([CONDENSE_QUESTION_PROMPT, llm, new StringOutputParser()]).withConfig({
|
||||
runName: 'CondenseQuestion'
|
||||
})
|
||||
|
||||
const hasHistoryCheckFn = RunnableLambda.from((input: RetrievalChainInput) => input.chat_history.length > 0).withConfig({
|
||||
runName: 'HasChatHistoryCheck'
|
||||
})
|
||||
|
||||
const conversationChain = condenseQuestionChain.pipe(retriever).withConfig({
|
||||
runName: 'RetrievalChainWithHistory'
|
||||
})
|
||||
|
||||
const basicRetrievalChain = RunnableLambda.from((input: RetrievalChainInput) => input.question)
|
||||
.withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
})
|
||||
.pipe(retriever)
|
||||
.withConfig({ runName: 'RetrievalChainWithNoHistory' })
|
||||
|
||||
return RunnableBranch.from([[hasHistoryCheckFn, conversationChain], basicRetrievalChain]).withConfig({ runName: sourceRunnableName })
|
||||
}
|
||||
|
||||
const formatDocs = (docs: Document[]) => {
|
||||
return docs.map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`).join('\n')
|
||||
}
|
||||
|
||||
const formatChatHistoryAsString = (history: BaseMessage[]) => {
|
||||
return history.map((message) => `${message._getType()}: ${message.content}`).join('\n')
|
||||
}
|
||||
|
||||
const serializeHistory = (input: any) => {
|
||||
const chatHistory: IMessage[] = input.chat_history || []
|
||||
const convertedChatHistory = []
|
||||
for (const message of chatHistory) {
|
||||
if (message.type === 'userMessage') {
|
||||
convertedChatHistory.push(new HumanMessage({ content: message.message }))
|
||||
}
|
||||
if (message.type === 'apiMessage') {
|
||||
convertedChatHistory.push(new AIMessage({ content: message.message }))
|
||||
}
|
||||
}
|
||||
return convertedChatHistory
|
||||
}
|
||||
|
||||
const createChain = (
|
||||
llm: BaseLanguageModel,
|
||||
retriever: Runnable,
|
||||
rephrasePrompt = REPHRASE_TEMPLATE,
|
||||
responsePrompt = RESPONSE_TEMPLATE
|
||||
) => {
|
||||
const retrieverChain = createRetrieverChain(llm, retriever, rephrasePrompt)
|
||||
|
||||
const context = RunnableMap.from({
|
||||
context: RunnableSequence.from([
|
||||
({ question, chat_history }) => ({
|
||||
question,
|
||||
chat_history: formatChatHistoryAsString(chat_history)
|
||||
}),
|
||||
retrieverChain,
|
||||
RunnableLambda.from(formatDocs).withConfig({
|
||||
runName: 'FormatDocumentChunks'
|
||||
})
|
||||
]),
|
||||
question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
}),
|
||||
chat_history: RunnableLambda.from((input: RetrievalChainInput) => input.chat_history).withConfig({
|
||||
runName: 'Itemgetter:chat_history'
|
||||
})
|
||||
}).withConfig({ tags: ['RetrieveDocs'] })
|
||||
|
||||
const prompt = ChatPromptTemplate.fromMessages([
|
||||
['system', responsePrompt],
|
||||
new MessagesPlaceholder('chat_history'),
|
||||
['human', `{question}`]
|
||||
])
|
||||
|
||||
const responseSynthesizerChain = RunnableSequence.from([prompt, llm, new StringOutputParser()]).withConfig({
|
||||
tags: ['GenerateResponse']
|
||||
})
|
||||
|
||||
const conversationalQAChain = RunnableSequence.from([
|
||||
{
|
||||
question: RunnableLambda.from((input: RetrievalChainInput) => input.question).withConfig({
|
||||
runName: 'Itemgetter:question'
|
||||
}),
|
||||
chat_history: RunnableLambda.from(serializeHistory).withConfig({
|
||||
runName: 'SerializeHistory'
|
||||
})
|
||||
},
|
||||
context,
|
||||
responseSynthesizerChain
|
||||
])
|
||||
|
||||
return conversationalQAChain
|
||||
}
|
||||
|
||||
class BufferMemory extends FlowiseMemory implements MemoryMethods {
|
||||
constructor(fields: BufferMemoryInput) {
|
||||
super(fields)
|
||||
}
|
||||
|
||||
async getChatMessages(_?: string, returnBaseMessages = false, prevHistory: IMessage[] = []): Promise<IMessage[] | BaseMessage[]> {
|
||||
await this.chatHistory.clear()
|
||||
|
||||
for (const msg of prevHistory) {
|
||||
if (msg.type === 'userMessage') await this.chatHistory.addUserMessage(msg.message)
|
||||
else if (msg.type === 'apiMessage') await this.chatHistory.addAIChatMessage(msg.message)
|
||||
}
|
||||
|
||||
const memoryResult = await this.loadMemoryVariables({})
|
||||
const baseMessages = memoryResult[this.memoryKey ?? 'chat_history']
|
||||
return returnBaseMessages ? baseMessages : convertBaseMessagetoIMessage(baseMessages)
|
||||
}
|
||||
|
||||
async addChatMessages(): Promise<void> {
|
||||
// adding chat messages will be done on the fly in getChatMessages()
|
||||
return
|
||||
}
|
||||
|
||||
async clearChatMessages(): Promise<void> {
|
||||
await this.clear()
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
@@ -1,64 +1,27 @@
|
||||
export const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const qa_template = `Use the following pieces of context to answer the question at the end.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const default_map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
|
||||
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
|
||||
|
||||
{summaries}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
|
||||
|
||||
{summaries}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const refine_question_template = (sysPrompt?: string) => {
|
||||
let returnPrompt = ''
|
||||
if (sysPrompt)
|
||||
returnPrompt = `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, ${sysPrompt}
|
||||
Answer the question: {question}.
|
||||
Answer:`
|
||||
if (!sysPrompt)
|
||||
returnPrompt = `Context information is below.
|
||||
---------------------
|
||||
{context}
|
||||
---------------------
|
||||
Given the context information and not prior knowledge, answer the question: {question}.
|
||||
Answer:`
|
||||
return returnPrompt
|
||||
}
|
||||
|
||||
export const refine_template = `The original question is as follows: {question}
|
||||
We have provided an existing answer: {existing_answer}
|
||||
We have the opportunity to refine the existing answer (only if needed) with some more context below.
|
||||
------------
|
||||
{context}
|
||||
------------
|
||||
Given the new context, refine the original answer to better answer the question.
|
||||
If you can't find answer from the context, return the original answer.`
|
||||
|
||||
export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, answer in the same language as the follow up question. include it in the standalone question.
|
||||
|
||||
Chat History:
|
||||
{chat_history}
|
||||
Follow Up Input: {question}
|
||||
Standalone question:`
|
||||
|
||||
export const RESPONSE_TEMPLATE = `I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". Using the provided context, answer the user's question to the best of your ability using the resources provided.
|
||||
If there is nothing in the context relevant to the question at hand, just say "Hmm, I'm not sure" and stop after that. Refuse to answer any question not about the info. Never break character.
|
||||
------------
|
||||
{context}
|
||||
------------
|
||||
REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm not sure". Don't try to make up an answer. Never break character.`
|
||||
|
||||
export const QA_TEMPLATE = `Use the following pieces of context to answer the question at the end.
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
Helpful Answer:`
|
||||
|
||||
export const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
|
||||
|
||||
Chat History:
|
||||
{chat_history}
|
||||
Follow Up Input: {question}
|
||||
Standalone Question:`
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M18 27L20.5 20L23 27M19 26H22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M13 25L15 27M12 20C11 20 9 20.7 9 23.5C9 26.3 11 27 12 27C13 27 15 26.3 15 23.5C15 20.7 13 20 12 20Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 756 B |
@@ -1,13 +1,16 @@
|
||||
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { BaseLanguageModel, BaseLanguageModelCallOptions } from 'langchain/base_language'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { BaseOutputParser } from 'langchain/schema/output_parser'
|
||||
import { formatResponse, injectOutputParser } from '../../outputparsers/OutputParserHelpers'
|
||||
import { BaseLLMOutputParser } from 'langchain/schema/output_parser'
|
||||
import { BaseLanguageModel, BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
|
||||
import { BaseLLMOutputParser, BaseOutputParser } from '@langchain/core/output_parsers'
|
||||
import { HumanMessage } from '@langchain/core/messages'
|
||||
import { ChatPromptTemplate, FewShotPromptTemplate, PromptTemplate, HumanMessagePromptTemplate } from '@langchain/core/prompts'
|
||||
import { OutputFixingParser } from 'langchain/output_parsers'
|
||||
import { LLMChain } from 'langchain/chains'
|
||||
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { getBaseClasses, handleEscapeCharacters } from '../../../src/utils'
|
||||
import { checkInputs, Moderation, streamResponse } from '../../moderation/Moderation'
|
||||
import { formatResponse, injectOutputParser } from '../../outputparsers/OutputParserHelpers'
|
||||
import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
|
||||
import { addImagesToMessages } from '../../../src/multiModalUtils'
|
||||
|
||||
class LLMChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -27,7 +30,7 @@ class LLMChain_Chains implements INode {
|
||||
this.name = 'llmChain'
|
||||
this.version = 3.0
|
||||
this.type = 'LLMChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'LLM_Chain.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chain to run queries against LLMs'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(LLMChain)]
|
||||
@@ -82,7 +85,7 @@ class LLMChain_Chains implements INode {
|
||||
const model = nodeData.inputs?.model as BaseLanguageModel
|
||||
const prompt = nodeData.inputs?.prompt
|
||||
const output = nodeData.outputs?.output as string
|
||||
const promptValues = prompt.promptValues as ICommonObject
|
||||
let promptValues: ICommonObject | undefined = nodeData.inputs?.prompt.promptValues as ICommonObject
|
||||
const llmOutputParser = nodeData.inputs?.outputParser as BaseOutputParser
|
||||
this.outputParser = llmOutputParser
|
||||
if (llmOutputParser) {
|
||||
@@ -107,17 +110,24 @@ class LLMChain_Chains implements INode {
|
||||
verbose: process.env.DEBUG === 'true'
|
||||
})
|
||||
const inputVariables = chain.prompt.inputVariables as string[] // ["product"]
|
||||
promptValues = injectOutputParser(this.outputParser, chain, promptValues)
|
||||
const res = await runPrediction(inputVariables, chain, input, promptValues, options, nodeData)
|
||||
// eslint-disable-next-line no-console
|
||||
console.log('\x1b[92m\x1b[1m\n*****OUTPUT PREDICTION*****\n\x1b[0m\x1b[0m')
|
||||
// eslint-disable-next-line no-console
|
||||
console.log(res)
|
||||
|
||||
let finalRes = res
|
||||
if (this.outputParser && typeof res === 'object' && Object.prototype.hasOwnProperty.call(res, 'json')) {
|
||||
finalRes = (res as ICommonObject).json
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply string transformation to convert special chars:
|
||||
* FROM: hello i am ben\n\n\thow are you?
|
||||
* TO: hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?
|
||||
*/
|
||||
return handleEscapeCharacters(res, false)
|
||||
return handleEscapeCharacters(finalRes, false)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -154,12 +164,7 @@ const runPrediction = async (
|
||||
const socketIO = isStreaming ? options.socketIO : undefined
|
||||
const socketIOClientId = isStreaming ? options.socketIOClientId : ''
|
||||
const moderations = nodeData.inputs?.inputModeration as Moderation[]
|
||||
/**
|
||||
* Apply string transformation to reverse converted special chars:
|
||||
* FROM: { "value": "hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?" }
|
||||
* TO: { "value": "hello i am ben\n\n\thow are you?" }
|
||||
*/
|
||||
const promptValues = handleEscapeCharacters(promptValuesRaw, true)
|
||||
let model = nodeData.inputs?.model as ChatOpenAI
|
||||
|
||||
if (moderations && moderations.length > 0) {
|
||||
try {
|
||||
@@ -172,6 +177,46 @@ const runPrediction = async (
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply string transformation to reverse converted special chars:
|
||||
* FROM: { "value": "hello i am benFLOWISE_NEWLINEFLOWISE_NEWLINEFLOWISE_TABhow are you?" }
|
||||
* TO: { "value": "hello i am ben\n\n\thow are you?" }
|
||||
*/
|
||||
const promptValues = handleEscapeCharacters(promptValuesRaw, true)
|
||||
const messageContent = addImagesToMessages(nodeData, options, model.multiModalOption)
|
||||
|
||||
if (chain.llm instanceof ChatOpenAI) {
|
||||
const chatOpenAI = chain.llm as ChatOpenAI
|
||||
if (messageContent?.length) {
|
||||
// Change model to gpt-4-vision && max token to higher when using gpt-4-vision
|
||||
chatOpenAI.modelName = 'gpt-4-vision-preview'
|
||||
chatOpenAI.maxTokens = 1024
|
||||
// Add image to the message
|
||||
if (chain.prompt instanceof PromptTemplate) {
|
||||
const existingPromptTemplate = chain.prompt.template as string
|
||||
let newChatPromptTemplate = ChatPromptTemplate.fromMessages([
|
||||
HumanMessagePromptTemplate.fromTemplate(existingPromptTemplate)
|
||||
])
|
||||
newChatPromptTemplate.promptMessages.push(new HumanMessage({ content: messageContent }))
|
||||
chain.prompt = newChatPromptTemplate
|
||||
} else if (chain.prompt instanceof ChatPromptTemplate) {
|
||||
chain.prompt.promptMessages.push(new HumanMessage({ content: messageContent }))
|
||||
} else if (chain.prompt instanceof FewShotPromptTemplate) {
|
||||
let existingFewShotPromptTemplate = chain.prompt.examplePrompt.template as string
|
||||
let newFewShotPromptTemplate = ChatPromptTemplate.fromMessages([
|
||||
HumanMessagePromptTemplate.fromTemplate(existingFewShotPromptTemplate)
|
||||
])
|
||||
newFewShotPromptTemplate.promptMessages.push(new HumanMessage({ content: messageContent }))
|
||||
// @ts-ignore
|
||||
chain.prompt.examplePrompt = newFewShotPromptTemplate
|
||||
}
|
||||
} else {
|
||||
// revert to previous values if image upload is empty
|
||||
chatOpenAI.modelName = model.configuredModel
|
||||
chatOpenAI.maxTokens = model.configuredMaxToken
|
||||
}
|
||||
}
|
||||
|
||||
if (promptValues && inputVariables.length > 0) {
|
||||
let seen: string[] = []
|
||||
|
||||
|
||||
@@ -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="M14 20V27H17.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M7 20V27H10.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M21 27V20L23.5 24.5L26 20V27" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 774 B |
@@ -1,7 +1,7 @@
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { MultiPromptChain } from 'langchain/chains'
|
||||
import { ICommonObject, INode, INodeData, INodeParams, PromptRetriever } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { MultiPromptChain } from 'langchain/chains'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
class MultiPromptChain_Chains implements INode {
|
||||
@@ -20,7 +20,7 @@ class MultiPromptChain_Chains implements INode {
|
||||
this.name = 'multiPromptChain'
|
||||
this.version = 1.0
|
||||
this.type = 'MultiPromptChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'prompt.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'Chain automatically picks an appropriate prompt from multiple prompt templates'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(MultiPromptChain)]
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
@@ -0,0 +1,8 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M17 29V22L19.5 26.5L22 22V29" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M3 29V22H5.22222C6.20406 22 7 22.8954 7 24C7 25.1046 6.20406 26 5.22222 26H3" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M10 29V22H12.2222C13.2041 22 14 22.8954 14 24C14 25.1046 13.2041 26 12.2222 26M12.2222 26H10M12.2222 26L14 29" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M27 29V22M25 22H29" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.0 KiB |
@@ -1,7 +1,7 @@
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { MultiRetrievalQAChain } from 'langchain/chains'
|
||||
import { ICommonObject, INode, INodeData, INodeParams, VectorStoreRetriever } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { MultiRetrievalQAChain } from 'langchain/chains'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
class MultiRetrievalQAChain_Chains implements INode {
|
||||
@@ -20,7 +20,7 @@ class MultiRetrievalQAChain_Chains implements INode {
|
||||
this.name = 'multiRetrievalQAChain'
|
||||
this.version = 1.0
|
||||
this.type = 'MultiRetrievalQAChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'qa.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'QA Chain that automatically picks an appropriate vector store from multiple retrievers'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(MultiRetrievalQAChain)]
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
@@ -0,0 +1,6 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M18 27L20.5 20L23 27M19 26H22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M13 25L15 27M12 20C11 20 9 20.7 9 23.5C9 26.3 11 27 12 27C13 27 15 26.3 15 23.5C15 20.7 13 20 12 20Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 756 B |
@@ -1,9 +1,9 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { BaseRetriever } from '@langchain/core/retrievers'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { RetrievalQAChain } from 'langchain/chains'
|
||||
import { BaseRetriever } from 'langchain/schema/retriever'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
|
||||
class RetrievalQAChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -21,7 +21,7 @@ class RetrievalQAChain_Chains implements INode {
|
||||
this.name = 'retrievalQAChain'
|
||||
this.version = 1.0
|
||||
this.type = 'RetrievalQAChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'qa.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'QA chain to answer a question based on the retrieved documents'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(RetrievalQAChain)]
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
@@ -0,0 +1,6 @@
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M18 27L20.5 20L23 27M19 26H22" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M13 25L15 27M12 20C11 20 9 20.7 9 23.5C9 26.3 11 27 12 27C13 27 15 26.3 15 23.5C15 20.7 13 20 12 20Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 756 B |
@@ -1,12 +1,12 @@
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { SqlDatabaseChain, SqlDatabaseChainInput, DEFAULT_SQL_DATABASE_PROMPT } from 'langchain/chains/sql_db'
|
||||
import { getBaseClasses, getInputVariables } from '../../../src/utils'
|
||||
import { DataSource } from 'typeorm'
|
||||
import { SqlDatabase } from 'langchain/sql_db'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { PromptTemplate, PromptTemplateInput } from 'langchain/prompts'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { DataSourceOptions } from 'typeorm/data-source'
|
||||
import { DataSource } from 'typeorm'
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { PromptTemplate, PromptTemplateInput } from '@langchain/core/prompts'
|
||||
import { SqlDatabaseChain, SqlDatabaseChainInput, DEFAULT_SQL_DATABASE_PROMPT } from 'langchain/chains/sql_db'
|
||||
import { SqlDatabase } from 'langchain/sql_db'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { getBaseClasses, getInputVariables } from '../../../src/utils'
|
||||
|
||||
type DatabaseType = 'sqlite' | 'postgres' | 'mssql' | 'mysql'
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-sql" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M12 8a2 2 0 0 1 2 2v4a2 2 0 1 1 -4 0v-4a2 2 0 0 1 2 -2z"></path>
|
||||
<path d="M17 8v8h4"></path>
|
||||
<path d="M13 15l1 1"></path>
|
||||
<path d="M3 15a1 1 0 0 0 1 1h2a1 1 0 0 0 1 -1v-2a1 1 0 0 0 -1 -1h-2a1 1 0 0 1 -1 -1v-2a1 1 0 0 1 1 -1h2a1 1 0 0 1 1 1"></path>
|
||||
</svg>
|
||||
<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M17 25L19 27M16 20C15 20 13 20.7 13 23.5C13 26.3 15 27 16 27C17 27 19 26.3 19 23.5C19 20.7 17 20 16 20Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M23 20V27H26.5" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M9.56442 20.4399C8.67691 19.7608 6 19.6332 6 21.7961C6 24.1915 10 22.5657 10 25.0902C10 26.9875 7.32996 27.5912 6 26.3537" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
<path d="M19 10C19 6.68629 16.2577 4 12.875 4H11.125C7.74226 4 5 6.68629 5 10C5 11.7572 5.77114 13.338 7 14.4353" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
<path d="M13 9C13 12.3137 15.7423 15 19.125 15H20.875C24.2577 15 27 12.3137 27 9C27 5.68629 24.2577 3 20.875 3" stroke="black" stroke-width="2" stroke-linecap="round"/>
|
||||
</svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 560 B After Width: | Height: | Size: 957 B |
@@ -1,9 +1,9 @@
|
||||
import fetch from 'node-fetch'
|
||||
import { Document } from '@langchain/core/documents'
|
||||
import { VectaraStore } from '@langchain/community/vectorstores/vectara'
|
||||
import { VectorDBQAChain } from 'langchain/chains'
|
||||
import { INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { VectorDBQAChain } from 'langchain/chains'
|
||||
import { Document } from 'langchain/document'
|
||||
import { VectaraStore } from 'langchain/vectorstores/vectara'
|
||||
import fetch from 'node-fetch'
|
||||
|
||||
// functionality based on https://github.com/vectara/vectara-answer
|
||||
const reorderCitations = (unorderedSummary: string) => {
|
||||
@@ -69,22 +69,23 @@ class VectaraChain_Chains implements INode {
|
||||
options: [
|
||||
{
|
||||
label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
|
||||
name: 'vectara-summary-ext-v1.2.0'
|
||||
name: 'vectara-summary-ext-v1.2.0',
|
||||
description: 'base summarizer, available to all Vectara users'
|
||||
},
|
||||
{
|
||||
label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
|
||||
name: 'vectara-experimental-summary-ext-2023-10-23-small',
|
||||
description: 'In beta, available to both Growth and Scale Vectara users'
|
||||
description: `In beta, available to both Growth and <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
|
||||
},
|
||||
{
|
||||
label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
|
||||
name: 'vectara-summary-ext-v1.3.0',
|
||||
description: 'Only available to paying Scale Vectara users'
|
||||
description: 'Only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users'
|
||||
},
|
||||
{
|
||||
label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
|
||||
name: 'vectara-experimental-summary-ext-2023-10-23-med',
|
||||
description: 'In beta, only available to paying Scale Vectara users'
|
||||
description: `In beta, only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
|
||||
}
|
||||
],
|
||||
default: 'vectara-summary-ext-v1.2.0'
|
||||
@@ -228,7 +229,7 @@ class VectaraChain_Chains implements INode {
|
||||
|
||||
async run(nodeData: INodeData, input: string): Promise<object> {
|
||||
const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
|
||||
const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto'
|
||||
const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng'
|
||||
const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
|
||||
const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string
|
||||
const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7
|
||||
@@ -247,17 +248,31 @@ class VectaraChain_Chains implements INode {
|
||||
lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
|
||||
}))
|
||||
|
||||
// Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker)
|
||||
const mmrRerankerId = 272725718
|
||||
const mmrEnabled = vectaraFilter?.mmrConfig?.enabled
|
||||
|
||||
const data = {
|
||||
query: [
|
||||
{
|
||||
query: input,
|
||||
start: 0,
|
||||
numResults: topK,
|
||||
numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK,
|
||||
corpusKey: corpusKeys,
|
||||
contextConfig: {
|
||||
sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
|
||||
sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
|
||||
},
|
||||
corpusKey: corpusKeys,
|
||||
...(mmrEnabled
|
||||
? {
|
||||
rerankingConfig: {
|
||||
rerankerId: mmrRerankerId,
|
||||
mmrConfig: {
|
||||
diversityBias: vectaraFilter?.mmrConfig.diversityBias
|
||||
}
|
||||
}
|
||||
}
|
||||
: {}),
|
||||
summary: [
|
||||
{
|
||||
summarizerPromptName,
|
||||
@@ -285,6 +300,14 @@ class VectaraChain_Chains implements INode {
|
||||
const documents = result.responseSet[0].document
|
||||
let rawSummarizedText = ''
|
||||
|
||||
// remove responses that are not in the topK (in case of MMR)
|
||||
// Note that this does not really matter functionally due to the reorder citations, but it is more efficient
|
||||
const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length
|
||||
if (responses.length > maxResponses) {
|
||||
responses.splice(0, maxResponses)
|
||||
}
|
||||
|
||||
// Add metadata to each text response given its corresponding document metadata
|
||||
for (let i = 0; i < responses.length; i += 1) {
|
||||
const responseMetadata = responses[i].metadata
|
||||
const documentMetadata = documents[responses[i].documentIndex].metadata
|
||||
@@ -301,13 +324,13 @@ class VectaraChain_Chains implements INode {
|
||||
responses[i].metadata = combinedMetadata
|
||||
}
|
||||
|
||||
// Create the summarization response
|
||||
const summaryStatus = result.responseSet[0].summary[0].status
|
||||
if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
|
||||
throw new Error(
|
||||
`BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.`
|
||||
)
|
||||
}
|
||||
|
||||
if (
|
||||
summaryStatus.length > 0 &&
|
||||
summaryStatus[0].code === 'NOT_FOUND' &&
|
||||
@@ -316,8 +339,8 @@ class VectaraChain_Chains implements INode {
|
||||
throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`)
|
||||
}
|
||||
|
||||
// Reorder citations in summary and create the list of returned source documents
|
||||
rawSummarizedText = result.responseSet[0].summary[0]?.text
|
||||
|
||||
let summarizedText = reorderCitations(rawSummarizedText)
|
||||
let summaryResponses = applyCitationOrder(responses, rawSummarizedText)
|
||||
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
import { BaseLanguageModel } from '@langchain/core/language_models/base'
|
||||
import { VectorStore } from '@langchain/core/vectorstores'
|
||||
import { VectorDBQAChain } from 'langchain/chains'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
|
||||
import { getBaseClasses } from '../../../src/utils'
|
||||
import { VectorDBQAChain } from 'langchain/chains'
|
||||
import { BaseLanguageModel } from 'langchain/base_language'
|
||||
import { VectorStore } from 'langchain/vectorstores/base'
|
||||
import { ConsoleCallbackHandler, CustomChainHandler, additionalCallbacks } from '../../../src/handler'
|
||||
|
||||
class VectorDBQAChain_Chains implements INode {
|
||||
label: string
|
||||
@@ -21,7 +21,7 @@ class VectorDBQAChain_Chains implements INode {
|
||||
this.name = 'vectorDBQAChain'
|
||||
this.version = 1.0
|
||||
this.type = 'VectorDBQAChain'
|
||||
this.icon = 'chain.svg'
|
||||
this.icon = 'vectordb.svg'
|
||||
this.category = 'Chains'
|
||||
this.description = 'QA chain for vector databases'
|
||||
this.baseClasses = [this.type, ...getBaseClasses(VectorDBQAChain)]
|
||||
|
||||
@@ -1,6 +0,0 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
|
||||
<path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
|
||||
<path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
|
||||
<path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
|
||||
</svg>
|
||||
|
Before Width: | Height: | Size: 489 B |
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user