参数配置统一

backend/rag_core/__init__.py

@@ -5,9 +5,17 @@ RAG Core - 公共 RAG 组件包
 """
 
 from .embedders import LlamaCppEmbedder
-from .vector_store import QdrantVectorStore, QDRANT_URL, QDRANT_API_KEY
+from .vector_store import QdrantVectorStore
 from .store import PostgresDocStore, create_docstore
 from .retriever_factory import create_parent_retriever
+from .config import (
+    QDRANT_URL,
+    QDRANT_API_KEY,
+    LLAMACPP_EMBEDDING_URL,
+    LLAMACPP_API_KEY,
+    DB_URI,
+    DOCSTORE_URI,
+)
 
 
 __all__ = [
@@ -15,6 +23,10 @@ __all__ = [
     "QdrantVectorStore",
     "QDRANT_URL",
     "QDRANT_API_KEY",
+    "LLAMACPP_EMBEDDING_URL",
+    "LLAMACPP_API_KEY",
+    "DB_URI",
+    "DOCSTORE_URI",
     "PostgresDocStore",
     "create_docstore",
     "create_parent_retriever",

backend/rag_core/client.py

@@ -1,27 +1,30 @@
 # rag_core/client.py
 import os
 from .config import QDRANT_URL, QDRANT_API_KEY
-from typing import Optional
 from qdrant_client import QdrantClient
 
 
+def create_qdrant_client(timeout: int = 300) -> QdrantClient:
+    """
+    创建并返回一个配置好的 Qdrant 客户端。
 
-def create_qdrant_client(
-    url: Optional[str] = None,
-    api_key: Optional[str] = None,
-    timeout: int = 300,  # 索引构建需要较长超时
-) -> QdrantClient:
-    effective_url = url or QDRANT_URL
-    effective_api_key = api_key or QDRANT_API_KEY
+    Args:
+        timeout: 请求超时时间（秒），默认 300 秒（索引构建需要较长超时）。
 
-    if not effective_url:
+    Returns:
+        配置好的 QdrantClient 实例。
+
+    Raises:
+        ValueError: 如果 QDRANT_URL 未配置。
+    """
+    if not QDRANT_URL:
         raise ValueError("Qdrant URL 未配置")
 
     client_kwargs = {
-        "url": effective_url,
+        "url": QDRANT_URL,
         "timeout": timeout,
     }
-    if effective_api_key:
-        client_kwargs["api_key"] = effective_api_key
+    if QDRANT_API_KEY:
+        client_kwargs["api_key"] = QDRANT_API_KEY
 
-    return QdrantClient(**client_kwargs)
+    return QdrantClient(**client_kwargs)

backend/rag_core/embedders.py

@@ -5,21 +5,21 @@
 import os
 from .config import LLAMACPP_EMBEDDING_URL, LLAMACPP_API_KEY
 import httpx
-from typing import List, Optional
+from typing import List
 
 from langchain_core.embeddings import Embeddings
 
+
 class LlamaCppEmbedder:
     """通过 OpenAI 兼容 API 封装 llama.cpp 嵌入服务。"""
 
-    def __init__(
-        self,
-        base_url: Optional[str] = None,
-        api_key: Optional[str] = None,
-        model: str = "Qwen3-Embedding-0.6B-Q8_0",
-    ):
-        self.base_url = base_url or LLAMACPP_EMBEDDING_URL
-        self.api_key = api_key or LLAMACPP_API_KEY
+    def __init__(self, model: str = "Qwen3-Embedding-0.6B-Q8_0"):
+        """
+        Args:
+            model: 嵌入模型名称，默认 "Qwen3-Embedding-0.6B-Q8_0"。
+        """
+        self.base_url = LLAMACPP_EMBEDDING_URL
+        self.api_key = LLAMACPP_API_KEY
         self.model = model
 
     def as_langchain_embeddings(self) -> Embeddings:
@@ -30,7 +30,7 @@ class LlamaCppEmbedder:
         """嵌入一批文档。"""
         return self._call_embedding_api(texts)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> List[List[float]]:
         """嵌入单个查询。"""
         return self._call_embedding_api([text])[0]
 
@@ -70,6 +70,7 @@ class LlamaCppEmbedder:
             else:
                 raise ValueError(f"未知的嵌入 API 响应格式: {data}")
 
+
 class _LlamaCppLangchainAdapter(Embeddings):
     """将 LlamaCppEmbedder 适配为 LangChain Embeddings 接口。"""
 
@@ -79,5 +80,5 @@ class _LlamaCppLangchainAdapter(Embeddings):
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         return self._embedder.embed_documents(texts)
 
-    def embed_query(self, text: str) -> List[float]:
-        return self._embedder.embed_query(text)
+    def embed_query(self, text: str) -> List[List[float]]:
+        return self._embedder.embed_query(text)

backend/rag_core/retriever_factory.py

@@ -1,38 +1,46 @@
-# rag_core/retriever_factory.py 
+# rag_core/retriever_factory.py
 from langchain_core.embeddings import Embeddings
 from langchain_classic.retrievers import ParentDocumentRetriever
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from typing import Optional
-from langchain_core.embeddings import Embeddings
-from langchain_core.stores import BaseStore
 from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
-from langchain_classic.retrievers import ParentDocumentRetriever
+from langchain_core.stores import BaseStore
 
 from rag_core import LlamaCppEmbedder, QdrantVectorStore, create_docstore
 
+
 def create_parent_retriever(
     collection_name: str = "rag_documents",
-    embeddings: Optional[Embeddings] = None,
-    parent_splitter: Optional[TextSplitter] = None,
-    child_splitter: Optional[TextSplitter] = None,
-    docstore: Optional[BaseStore] = None,
+    parent_splitter: TextSplitter | None = None,
+    child_splitter: TextSplitter | None = None,
+    docstore: BaseStore | None = None,
     search_k: int = 5,
-    # 若未传入切分器，则用以下参数创建默认切分器
     parent_chunk_size: int = 1000,
     parent_chunk_overlap: int = 100,
     child_chunk_size: int = 200,
     child_chunk_overlap: int = 20,
 ) -> ParentDocumentRetriever:
+    """
+    创建 ParentDocumentRetriever 实例。
+
+    Args:
+        collection_name: Qdrant 集合名称，默认 "rag_documents"
+        parent_splitter: 父文档切分器，默认 None（使用默认参数创建）
+        child_splitter: 子文档切分器，默认 None（使用默认参数创建）
+        docstore: 文档存储实例，默认 None（使用默认参数创建）
+        search_k: 检索时返回的结果数，默认 5
+        parent_chunk_size: 父文档块大小，默认 1000
+        parent_chunk_overlap: 父文档块重叠大小，默认 100
+        child_chunk_size: 子文档块大小，默认 200
+        child_chunk_overlap: 子文档块重叠大小，默认 20
+
+    Returns:
+        ParentDocumentRetriever 实例
+    """
     # 嵌入模型
-    if embeddings is None:
-        embedder = LlamaCppEmbedder()
-        embeddings = embedder.as_langchain_embeddings()
+    embedder = LlamaCppEmbedder()
+    embeddings = embedder.as_langchain_embeddings()
 
     # 向量存储（只读）
-    vector_store = QdrantVectorStore(
-        collection_name=collection_name,
-        embeddings=embeddings,
-    )
+    vector_store = QdrantVectorStore(collection_name=collection_name)
 
     # 切分器（若未提供则创建默认）
     if parent_splitter is None:
@@ -48,7 +56,7 @@ def create_parent_retriever(
 
     # 文档存储
     if docstore is None:
-        docstore, _ = create_docstore()  # 从环境变量读取连接
+        docstore, _ = create_docstore()
 
     return ParentDocumentRetriever(
         vectorstore=vector_store.get_langchain_vectorstore(),
@@ -56,4 +64,4 @@ def create_parent_retriever(
         child_splitter=child_splitter,
         parent_splitter=parent_splitter,
         search_kwargs={"k": search_k},
-    )
+    )

backend/rag_core/store/__init__.py

@@ -9,14 +9,13 @@
     
     >>> # 创建 PostgreSQL 存储
     >>> store, conn = create_docstore(
-    ...     connection_string="postgresql://user:pass@host:5432/db",
     ...     table_name="parent_docs"
     ... )
 """
 
 
 from .postgres import PostgresDocStore
-from .factory import create_docstore, get_docstore_uri, DEFAULT_DB_URI
+from .factory import create_docstore, get_docstore_uri
 
 __version__ = "2.0.0"
 
@@ -27,5 +26,4 @@ __all__ = [
     # 工厂函数
     "create_docstore",
     "get_docstore_uri",
-    "DEFAULT_DB_URI",
 ]

backend/rag_core/store/factory.py

@@ -5,17 +5,14 @@
 """
 
 import os
-from ..config import DB_URI, DOCSTORE_URI
+from ..config import DOCSTORE_URI
 import logging
-from typing import Optional, Tuple
+from typing import Tuple
 
 from langchain_core.stores import BaseStore
 from .postgres import PostgresDocStore
 
-logger = logging.getLogger(__name__)        
-
-# 默认连接字符串（从环境变量读取）
-DEFAULT_DB_URI = DB_URI
+logger = logging.getLogger(__name__)
 
 
 def get_docstore_uri() -> str:
@@ -24,48 +21,36 @@ def get_docstore_uri() -> str:
 
 
 def create_docstore(
-    store_type: str = "postgres",
-    connection_string: Optional[str] = None,
     table_name: str = "parent_documents",
-    pool_config: Optional[dict] = None,
-    max_concurrency: Optional[int] = None
-) -> Tuple[BaseStore, Optional[str]]:
+    pool_config: dict | None = None,
+    max_concurrency: int | None = None
+) -> Tuple[BaseStore, str]:
     """
     工厂函数，创建 PostgreSQL 文档存储。
-    
+
     Args:
-        store_type: 存储类型，目前仅支持 "postgres"（默认）
-        connection_string: PostgreSQL 连接字符串
         table_name: PostgreSQL 表名（默认：parent_documents）
         pool_config: 连接池配置
         max_concurrency: 最大并发操作数，如果为 None 则不限制
-    
+
     Returns:
         元组 (存储实例, 连接字符串)
-    
+
     Raises:
-        ValueError: 不支持的存储类型
         ImportError: 缺少必要的依赖
-    
+
     Example:
         >>> # 创建 PostgreSQL 存储
         >>> store, conn = create_docstore(
-        ...     connection_string="postgresql://user:pass@host:5432/db",
         ...     table_name="parent_docs",
         ...     max_concurrency=10
         ... )
     """
-    store_type = store_type.lower()
-    
-    if store_type == "postgres":
-        conn_str = connection_string or get_docstore_uri()
-        store = PostgresDocStore(
-            connection_string=conn_str,
-            table_name=table_name,
-            pool_config=pool_config,
-            max_concurrency=max_concurrency
-        )
-        return store, conn_str
-    
-    else:
-        raise ValueError(f"不支持的存储类型: {store_type}。目前仅支持: postgres")
+    conn_str = get_docstore_uri()
+    store = PostgresDocStore(
+        connection_string=conn_str,
+        table_name=table_name,
+        pool_config=pool_config,
+        max_concurrency=max_concurrency
+    )
+    return store, conn_str

backend/rag_core/vector_store.py

@@ -4,7 +4,6 @@ Qdrant 向量数据库包装器。
 
 import logging
 import os
-from .config import QDRANT_URL, QDRANT_API_KEY
 import time
 from typing import List, Optional, Dict, Any
 
@@ -14,31 +13,28 @@ from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
 from httpx import RemoteProtocolError
 from qdrant_client.http.exceptions import ResponseHandlingException
+
 from .client import create_qdrant_client
+from .embedders import LlamaCppEmbedder
 
 logger = logging.getLogger(__name__)
 
 
-
 class QdrantVectorStore:
     """Qdrant 向量数据库操作包装器。"""
 
-    def __init__(
-        self,
-        collection_name: str,
-        embeddings: Optional[Any] = None,
-    ):
+    def __init__(self, collection_name: str):
+        """
+        Args:
+            collection_name: Qdrant 集合名称。
+        """
         self.collection_name = collection_name
         self._client: Optional[QdrantClient] = None
         self._connection_attempts = 0
         self._last_connection_time: Optional[float] = None
 
-        if embeddings is None:
-            from rag_core.embedders import LlamaCppEmbedder
-            embedder = LlamaCppEmbedder()
-            self.embeddings = embedder.as_langchain_embeddings()
-        else:
-            self.embeddings = embeddings
+        embedder = LlamaCppEmbedder()
+        self.embeddings = embedder.as_langchain_embeddings()
 
         self.create_collection()
 
@@ -92,12 +88,10 @@ class QdrantVectorStore:
             "client_initialized": self._client is not None,
         }
 
-    def create_collection(self, vector_size: Optional[int] = None, force_recreate: bool = False):
+    def create_collection(self, force_recreate: bool = False):
         """创建集合，设置合适的向量维度。"""
-        if vector_size is None:
-            from rag_core.embedders import LlamaCppEmbedder
-            embedder = LlamaCppEmbedder()
-            vector_size = embedder.get_embedding_dimension()
+        embedder = LlamaCppEmbedder()
+        vector_size = embedder.get_embedding_dimension()
 
         max_retries = 3
         base_delay = 2
@@ -177,4 +171,4 @@ class QdrantVectorStore:
 
     def get_qdrant_client(self):
         """返回原生 Qdrant 客户端（如需手动管理 collection）"""
-        return self.get_client()
+        return self.get_client()