# Support Model Config

## Custom Model Endpoint Guide with Ollama

### 1. Prerequisites: Ollama Setup

First, download and install Ollama from the official website:

🔗 **Download Link**: <https://ollama.com/download>

📚 **Additional Resources**:

* Official Website: [https://ollama.com](https://ollama.com/)
* Model Library: <https://ollama.com/library>
* GitHub Repository: [https://github.com/ollama/ollama/](https://github.com/ollama/ollama)

***

### 2. Basic Ollama Commands

| Command                  | Description                |
| ------------------------ | -------------------------- |
| `ollama pull model_name` | Download a model           |
| `ollama serve`           | Start the Ollama service   |
| `ollama ps`              | List running models        |
| `ollama list`            | List all downloaded models |
| `ollama rm model_name`   | Remove a model             |
| `ollama show model_name` | Show model details         |

### 3. Using Ollama API for Custom Model

#### OpenAI-Compatible API

**Chat Request**

```bash
curl http://127.0.0.1:11434/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "qwen2.5:0.5b",
  "messages": [
    {"role": "user", "content": "Why is the sky blue?"}
  ]
}'
```

**Embedding Request**

```bash
curl http://127.0.0.1:11434/v1/embeddings -d '{
  "model": "snowflake-arctic-embed:110m",
  "input": "Why is the sky blue?"
}'
```

More Details: <https://github.com/ollama/ollama/blob/main/docs/openai.md>

### 4. Configuring Custom Embedding in Second Me

1. Start the Ollama service: `ollama serve`
2. Check your Ollama embedding model context length:

```bash
# Example: ollama show snowflake-arctic-embed:110m
$ ollama show snowflake-arctic-embed:110m

Model
  architecture        bert       
  parameters          108.89M    
  context length      512        
  embedding length    768        
  quantization        F16        

License
  Apache License               
  Version 2.0, January 2004
```

3. Modify `EMBEDDING_MAX_TEXT_LENGTH` in `Second_Me/.env` to match your embedding model's context window. This prevents chunk length overflow and avoids server-side errors (500 Internal Server Error).

```bash
# Embedding configurations

EMBEDDING_MAX_TEXT_LENGTH=embedding_model_context_length
```

4. Configure Custom Embedding in Settings

```
Chat:
Model Name: qwen2.5:0.5b
API Key: ollama
API Endpoint: http://127.0.0.1:11434/v1

Embedding:
Model Name: snowflake-arctic-embed:110m
API Key: ollama
API Endpoint: http://127.0.0.1:11434/v1
```

**When running Second Me in Docker environments**, please replace `127.0.0.1` in API Endpoint with `host.docker.internal`:

```
Chat:
Model Name: qwen2.5:0.5b
API Key: ollama
API Endpoint: http://host.docker.internal:11434/v1

Embedding:
Model Name: snowflake-arctic-embed:110m
API Key: ollama
API Endpoint: http://host.docker.internal:11434/v1
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://secondme.gitbook.io/secondme/guides/create-second-me/support-model-config.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.