---
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:

![](/imgs/model-m3e1.png)

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.

   ![](/imgs/model-m3e2.png)

2. Import data
3. Test search

   ![](/imgs/model-m3e3.png)

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.

   ![](/imgs/model-m3e4.png)

**ChatGLM2 model:**

Simply select chatglm2 as the model.