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feat: Enhanced Model Provider Support and Configuration Flexibility For Rag Service (#2056)

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Co-authored-by: doodleEsc <cokie@foxmail.com>
Co-authored-by: pre-commit-ci-lite[bot] <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>
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doodleEsc
2025-06-06 23:07:07 +08:00
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parent ec0f4f9ae0
commit 2dd4c04088
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@@ -705,7 +705,7 @@ Given its early stage, `avante.nvim` currently supports the following basic func
> model = "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
> aws_profile = "bedrock",
> aws_region = "us-east-1",
>},
> },
> ```
>
> Note: Bedrock requires the [AWS CLI](https://aws.amazon.com/cli/) to be installed on your system.
@@ -884,21 +884,37 @@ For more information, see [Custom Providers](https://github.com/yetone/avante.nv
Avante provides a RAG service, which is a tool for obtaining the required context for the AI to generate the codes. By default, it is not enabled. You can enable it this way:
```lua
rag_service = {
enabled = false, -- Enables the RAG service
host_mount = os.getenv("HOME"), -- Host mount path for the rag service
provider = "openai", -- The provider to use for RAG service (e.g. openai or ollama)
llm_model = "", -- The LLM model to use for RAG service
embed_model = "", -- The embedding model to use for RAG service
endpoint = "https://api.openai.com/v1", -- The API endpoint for RAG service
},
rag_service = { -- RAG Service configuration
enabled = false, -- Enables the RAG service
host_mount = os.getenv("HOME"), -- Host mount path for the rag service (Docker will mount this path)
runner = "docker", -- Runner for the RAG service (can use docker or nix)
llm = { -- Language Model (LLM) configuration for RAG service
provider = "openai", -- LLM provider
endpoint = "https://api.openai.com/v1", -- LLM API endpoint
api_key = "OPENAI_API_KEY", -- Environment variable name for the LLM API key
model = "gpt-4o-mini", -- LLM model name
extra = nil, -- Additional configuration options for LLM
},
embed = { -- Embedding model configuration for RAG service
provider = "openai", -- Embedding provider
endpoint = "https://api.openai.com/v1", -- Embedding API endpoint
api_key = "OPENAI_API_KEY", -- Environment variable name for the embedding API key
model = "text-embedding-3-large", -- Embedding model name
extra = nil, -- Additional configuration options for the embedding model
},
docker_extra_args = "", -- Extra arguments to pass to the docker command
},
```
If your rag_service provider is `openai`, then you need to set the `OPENAI_API_KEY` environment variable!
The RAG Service can currently configure the LLM and embedding models separately. In the `llm` and `embed` configuration blocks, you can set the following fields:
If your rag_service provider is `ollama`, you need to set the endpoint to `http://localhost:11434` (note there is no `/v1` at the end) or any address of your own ollama server.
- `provider`: Model provider (e.g., "openai", "ollama", "dashscope", and "openrouter")
- `endpoint`: API endpoint
- `api_key`: Environment variable name for the API key
- `model`: Model name
- `extra`: Additional configuration options
If your rag_service provider is `ollama`, when `llm_model` is empty, it defaults to `llama3`, and when `embed_model` is empty, it defaults to `nomic-embed-text`. Please make sure these models are available in your ollama server.
For detailed configuration of different model providers, you can check [here](./py/rag-service/README.md).
Additionally, RAG Service also depends on Docker! (For macOS users, OrbStack is recommended as a Docker alternative).