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86 lines
2.3 KiB
Plaintext
86 lines
2.3 KiB
Plaintext
---
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title: Integrating M3E Embedding Model
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description: Integrating the private M3E embedding model with FastGPT
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---
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## Introduction
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FastGPT uses OpenAI's embedding model by default. For private deployment, you can replace it with the M3E embedding model. M3E is a lightweight model with low resource requirements -- it can even run on CPU. The following tutorial is based on an image provided by community contributor "睡大觉".
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## Deploy the Image
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Image: `stawky/m3e-large-api:latest`
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China mirror: `registry.cn-hangzhou.aliyuncs.com/fastgpt_docker/m3e-large-api:latest`
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Port: 6008
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Environment variables:
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```
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# Set the security token (used as the channel key in OneAPI)
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Default: sk-aaabbbcccdddeeefffggghhhiiijjjkkk
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You can also set it via the environment variable: sk-key. Refer to Docker documentation for how to pass environment variables.
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```
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## Connect to One API
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Add a channel with the following parameters:
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## Test
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curl example:
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```bash
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curl --location --request POST 'https://domain/v1/embeddings' \
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--header 'Authorization: Bearer xxxx' \
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--header 'Content-Type: application/json' \
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--data-raw '{
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"model": "m3e",
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"input": ["What is laf"]
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}'
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```
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Set Authorization to your sk-key. The model field should match the custom model name you entered in One API.
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## Integrate with FastGPT
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Edit the config.json file and add the M3E model to `vectorModels`:
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```json
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"vectorModels": [
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{
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"model": "text-embedding-ada-002",
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"name": "Embedding-2",
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"price": 0.2,
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"defaultToken": 500,
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"maxToken": 3000
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},
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{
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"model": "m3e",
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"name": "M3E (for testing)",
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"price": 0.1,
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"defaultToken": 500,
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"maxToken": 1800
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}
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]
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```
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## Usage
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1. Select the M3E model when creating a Knowledge Base.
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Note: once selected, the embedding model for the Knowledge Base cannot be changed.
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2. Import data
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3. Test search
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4. Bind the Knowledge Base to an app
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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.
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