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.
110 lines
3.0 KiB
Python
110 lines
3.0 KiB
Python
"""
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بارگذاری تنظیمات RAG از rag_config.yaml
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"""
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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import yaml
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@dataclass
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class EmbeddingConfig:
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provider: str
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model: str
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batch_size: int = 32
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api_key_env: str | None = None
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base_url: str | None = None
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@dataclass
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class QdrantConfig:
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host: str = "localhost"
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port: int = 6333
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collection_name: str = "croplogic_kb"
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vector_size: int = 384
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@dataclass
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class ChunkingConfig:
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max_chunk_tokens: int = 500
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overlap_tokens: int = 50
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@dataclass
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class LLMConfig:
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model: str = "gpt-4o"
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base_url: str | None = None
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api_key_env: str | None = None
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@dataclass
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class RAGConfig:
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embedding: EmbeddingConfig
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qdrant: QdrantConfig
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chunking: ChunkingConfig
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llm: LLMConfig = field(default_factory=LLMConfig)
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tone_file: str = "config/tone.txt"
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knowledge_base_path: str = "config/knowledge_base"
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user_info_path: str = "config/user_info"
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chromadb: dict[str, Any] = field(default_factory=dict)
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def load_rag_config(config_path: str | Path | None = None) -> RAGConfig:
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"""
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بارگذاری تنظیمات از YAML و env.
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QDRANT_HOST و QDRANT_PORT از متغیرهای محیطی override میشوند.
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"""
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if config_path is None:
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base = Path(__file__).resolve().parent.parent
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config_path = base / "config" / "rag_config.yaml"
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path = Path(config_path)
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if not path.exists():
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raise FileNotFoundError(f"RAG config not found: {path}")
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with open(path, encoding="utf-8") as f:
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data = yaml.safe_load(f) or {}
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emb = data.get("embedding", {})
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embedding = EmbeddingConfig(
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provider=emb.get("provider", "sentence_transformers"),
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model=emb.get("model", "text-embedding-3-small"),
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batch_size=emb.get("batch_size", 32),
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api_key_env=emb.get("api_key_env"),
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base_url=emb.get("base_url"),
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)
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qd = data.get("qdrant", {})
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qdrant = QdrantConfig(
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host=os.environ.get("QDRANT_HOST", qd.get("host", "localhost")),
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port=int(os.environ.get("QDRANT_PORT", qd.get("port", 6333))),
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collection_name=qd.get("collection_name", "croplogic_kb"),
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vector_size=qd.get("vector_size", 1536),
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)
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ch = data.get("chunking", {})
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chunking = ChunkingConfig(
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max_chunk_tokens=ch.get("max_chunk_tokens", 500),
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overlap_tokens=ch.get("overlap_tokens", 50),
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)
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llm_data = data.get("llm", {})
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llm = LLMConfig(
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model=llm_data.get("model", "gpt-4o"),
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base_url=llm_data.get("base_url"),
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api_key_env=llm_data.get("api_key_env"),
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)
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return RAGConfig(
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embedding=embedding,
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qdrant=qdrant,
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chunking=chunking,
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llm=llm,
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tone_file=data.get("tone_file", "config/tone.txt"),
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knowledge_base_path=data.get("knowledge_base_path", "config/knowledge_base"),
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user_info_path=data.get("user_info_path", "config/user_info"),
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chromadb=data.get("chromadb", {}),
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)
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