feat: Enhanced Model Provider Support and Configuration Flexibility For Rag Service (#2056)
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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@@ -37,14 +37,24 @@ M._defaults = {
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tokenizer = "tiktoken",
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---@type string | (fun(): string) | nil
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system_prompt = nil,
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rag_service = {
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enabled = false, -- Enables the rag service, requires OPENAI_API_KEY to be set
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host_mount = os.getenv("HOME"), -- Host mount path for the rag service (docker will mount this path)
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runner = "docker", -- The runner for the rag service, (can use docker, or nix)
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provider = "openai", -- The provider to use for RAG service. eg: openai or ollama
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llm_model = "", -- The LLM model to use for RAG service
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embed_model = "", -- The embedding model to use for RAG service
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endpoint = "https://api.openai.com/v1", -- The API endpoint for RAG service
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rag_service = { -- RAG service configuration
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enabled = false, -- Enables the RAG service
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host_mount = os.getenv("HOME"), -- Host mount path for the RAG service (Docker will mount this path)
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runner = "docker", -- The runner for the RAG service (can use docker or nix)
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llm = { -- Configuration for the Language Model (LLM) used by the RAG service
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provider = "openai", -- The LLM provider
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endpoint = "https://api.openai.com/v1", -- The LLM API endpoint
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api_key = "OPENAI_API_KEY", -- The environment variable name for the LLM API key
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model = "gpt-4o-mini", -- The LLM model name
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extra = nil, -- Extra configuration options for the LLM
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},
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embed = { -- Configuration for the Embedding model used by the RAG service
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provider = "openai", -- The embedding provider
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endpoint = "https://api.openai.com/v1", -- The embedding API endpoint
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api_key = "OPENAI_API_KEY", -- The environment variable name for the embedding API key
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model = "text-embedding-3-large", -- The embedding model name
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extra = nil, -- Extra configuration options for the embedding model
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},
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docker_extra_args = "", -- Extra arguments to pass to the docker command
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},
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web_search_engine = {
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