mirror of
https://github.com/labring/FastGPT.git
synced 2026-04-27 02:08:10 +08:00
Archer
87b0bca30c
* cloud doc * doc refactor * doc move * seo * remove doc * yml * doc * fix: tsconfig * fix: tsconfig
117 lines
3.1 KiB
Plaintext
117 lines
3.1 KiB
Plaintext
---
|
|
title: Integrating ChatGLM2 and M3E Models
|
|
description: Integrating private ChatGLM2 and m3e-large models with FastGPT
|
|
---
|
|
|
|
## Introduction
|
|
|
|
FastGPT uses OpenAI's LLM and embedding models by default. For private deployment, you can use ChatGLM2 and m3e-large as replacements. The following method was contributed by community user @不做了睡大觉. This image bundles both M3E-Large and ChatGLM2-6B models, ready to use out of the box.
|
|
|
|
## Deploy the Image
|
|
|
|
- Image: `stawky/chatglm2-m3e:latest`
|
|
- China mirror: `registry.cn-hangzhou.aliyuncs.com/fastgpt_docker/chatglm2-m3e:latest`
|
|
- Port: 6006
|
|
|
|
```
|
|
# Set the security token (used as the channel key in OneAPI)
|
|
Default: sk-aaabbbcccdddeeefffggghhhiiijjjkkk
|
|
You can also set it via the environment variable: sk-key. Refer to Docker documentation for how to pass environment variables.
|
|
```
|
|
|
|
## Connect to OneAPI
|
|
|
|
Documentation: [One API](/docs/self-host/config/model/one-api/)
|
|
|
|
Add a channel for chatglm2 and m3e-large respectively, with the following parameters:
|
|
|
|
|
|
|
|
Here, m3e is used as the embedding model and chatglm2 as the language model.
|
|
|
|
## Test
|
|
|
|
curl examples:
|
|
|
|
```bash
|
|
curl --location --request POST 'https://domain/v1/embeddings' \
|
|
--header 'Authorization: Bearer sk-aaabbbcccdddeeefffggghhhiiijjjkkk' \
|
|
--header 'Content-Type: application/json' \
|
|
--data-raw '{
|
|
"model": "m3e",
|
|
"input": ["What is laf"]
|
|
}'
|
|
```
|
|
|
|
```bash
|
|
curl --location --request POST 'https://domain/v1/chat/completions' \
|
|
--header 'Authorization: Bearer sk-aaabbbcccdddeeefffggghhhiiijjjkkk' \
|
|
--header 'Content-Type: application/json' \
|
|
--data-raw '{
|
|
"model": "chatglm2",
|
|
"messages": [{"role": "user", "content": "Hello!"}]
|
|
}'
|
|
```
|
|
|
|
Set Authorization to sk-aaabbbcccdddeeefffggghhhiiijjjkkk. The model field should match the custom model name you entered in One API.
|
|
|
|
## Integrate with FastGPT
|
|
|
|
Edit the config.json file. Add chatglm2 to `llmModels` and M3E to `vectorModels`:
|
|
|
|
```json
|
|
"llmModels": [
|
|
// Other chat models
|
|
{
|
|
"model": "chatglm2",
|
|
"name": "chatglm2",
|
|
"maxToken": 8000,
|
|
"price": 0,
|
|
"quoteMaxToken": 4000,
|
|
"maxTemperature": 1.2,
|
|
"defaultSystemChatPrompt": ""
|
|
}
|
|
],
|
|
"vectorModels": [
|
|
{
|
|
"model": "text-embedding-ada-002",
|
|
"name": "Embedding-2",
|
|
"price": 0.2,
|
|
"defaultToken": 500,
|
|
"maxToken": 3000
|
|
},
|
|
{
|
|
"model": "m3e",
|
|
"name": "M3E (for testing)",
|
|
"price": 0.1,
|
|
"defaultToken": 500,
|
|
"maxToken": 1800
|
|
}
|
|
],
|
|
```
|
|
|
|
## Usage
|
|
|
|
**M3E model:**
|
|
|
|
1. Select the M3E model when creating a Knowledge Base.
|
|
|
|
Note: once selected, the embedding model for the Knowledge Base cannot be changed.
|
|
|
|
|
|
|
|
2. Import data
|
|
3. Test search
|
|
|
|
|
|
|
|
4. Bind the Knowledge Base to an app
|
|
|
|
Note: an app can only bind Knowledge Bases that use the same embedding model -- cross-model binding is not supported. You may also need to adjust the similarity threshold, as different embedding models produce different similarity (distance) scores. Test and tune accordingly.
|
|
|
|
|
|
|
|
**ChatGLM2 model:**
|
|
|
|
Simply select chatglm2 as the model.
|