sajad-dev
94355af62b
- Introduced LLM configuration in rag_config.yaml and corresponding LLMConfig class in config.py. - Updated load_rag_config function to parse LLM settings from the configuration file. - Added new API route for RAG in urls.py to facilitate access to the chat model. - Modified QdrantVectorStore to use query_points method for improved functionality.
30 lines
808 B
YAML
30 lines
808 B
YAML
# تنظیمات RAG برای پایگاه دانش CropLogic
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embedding:
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provider: "avalai" # Avalai API (OpenAI-compatible)
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model: "text-embedding-3-small"
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base_url: "https://api.avalai.ir/v1"
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api_key_env: "AVALAI_API_KEY"
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batch_size: 32
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# فاز یک: Qdrant بهعنوان vector store
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qdrant:
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host: "localhost" # یا qdrant در Docker
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port: 6333
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collection_name: "croplogic_kb"
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vector_size: 1536 # متناسب با text-embedding-3-small
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chunking:
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max_chunk_tokens: 500
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overlap_tokens: 50
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# تنظیمات مدل چت (LLM) — Avalai
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llm:
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model: "gpt-4o"
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base_url: "https://api.avalai.ir/v1"
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api_key_env: "AVALAI_API_KEY"
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tone_file: "config/tone.txt"
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knowledge_base_path: "config/knowledge_base"
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user_info_path: "config/user_info"
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