QihuAI Design
771c8456b8
Root-cause fix for mixed CRLF/LF in the repo (no .gitattributes previously). Adds '* text=auto eol=lf' and explicit binary rules so all future checkouts are LF. Renormalizes all tracked text files (no logical content change). Backup tags: image qihuaiflow:backup-lineend-20260725-121632, git backup-lineend-pre-20260725-121632.
380 lines
12 KiB
Python
380 lines
12 KiB
Python
"""
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PGVector 向量检索模块
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====================
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使用 PostgreSQL + pgvector 实现语义向量检索，
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与现有 MySQL BM25 检索配合，实现混合检索。
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使用方式:
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from app.pgvector_search import vector_search
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results = await vector_search(query, kb_id, top_k=5)
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"""
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import os
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import logging
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import asyncio
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from typing import List, Dict, Optional
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from datetime import datetime
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logger = logging.getLogger("aiflow.pgvector")
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# PostgreSQL 连接配置
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PG_HOST = os.environ.get("POSTGRES_HOST", "qihu-aiflow-pgvector")
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PG_PORT = int(os.environ.get("POSTGRES_PORT", "5432"))
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PG_USER = os.environ.get("POSTGRES_USER", "aiflow")
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PG_PASSWORD = os.environ.get("POSTGRES_PASSWORD", "pwd2026")
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PG_DB = os.environ.get("POSTGRES_DB", "aiflow_vectors")
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# 嵌入模型配置
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EMBEDDING_API_URL = os.environ.get("SILICONFLOW_BASE_URL", "https://api.siliconflow.cn")
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EMBEDDING_API_KEY = os.environ.get("SILICONFLOW_API_KEY", "")
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "BAAI/bge-large-zh-v1.5")
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# ---- 本地嵌入缓存（SQLite 持久化）：破解"每次嵌入消耗外部 API 额度"的隐性成本障碍 ----
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import sqlite3, hashlib, json as _json
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_EMBED_CACHE_DB = os.environ.get("EMBED_CACHE_DB", "/app/backend/python/.embed_cache.db")
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def _embed_cache_get(text, model):
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try:
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key = hashlib.sha256(f"{model}::{text}".encode("utf-8")).hexdigest()
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conn = sqlite3.connect(_EMBED_CACHE_DB, timeout=5)
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row = conn.execute("SELECT vec FROM embed_cache WHERE k=?", (key,)).fetchone()
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conn.close()
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if row:
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return _json.loads(row[0])
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except Exception:
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return None
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return None
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def _embed_cache_put(text, model, vec):
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try:
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key = hashlib.sha256(f"{model}::{text}".encode("utf-8")).hexdigest()
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conn = sqlite3.connect(_EMBED_CACHE_DB, timeout=5)
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conn.execute("CREATE TABLE IF NOT EXISTS embed_cache (k TEXT PRIMARY KEY, vec TEXT, ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP)")
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conn.execute("INSERT OR REPLACE INTO embed_cache (k, vec) VALUES (?, ?)", (key, _json.dumps(vec)))
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conn.commit()
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conn.close()
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except Exception:
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pass
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# 连接池
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_pg_pool = None
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_pool_lock = asyncio.Lock()
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async def get_pg_pool():
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"""获取 PostgreSQL 异步连接池"""
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global _pg_pool
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if _pg_pool is not None:
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return _pg_pool
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async with _pool_lock:
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if _pg_pool is not None:
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return _pg_pool
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try:
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import asyncpg
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except ImportError:
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logger.warning("asyncpg not installed, vector search disabled")
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return None
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try:
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_pg_pool = await asyncpg.create_pool(
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host=PG_HOST,
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port=PG_PORT,
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user=PG_USER,
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password=PG_PASSWORD,
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database=PG_DB,
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min_size=2,
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max_size=10,
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command_timeout=30,
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)
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logger.info(f"PostgreSQL connection pool created: {PG_HOST}:{PG_PORT}/{PG_DB}")
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# 初始化表结构
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async with _pg_pool.acquire() as conn:
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS document_embeddings (
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id SERIAL PRIMARY KEY,
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doc_id VARCHAR(64) NOT NULL,
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kb_id VARCHAR(64) NOT NULL,
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chunk_id VARCHAR(64),
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content TEXT NOT NULL,
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embedding vector(1024),
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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""")
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await conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_embeddings_kb
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ON document_embeddings(kb_id);
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""")
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await conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_embeddings_doc
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ON document_embeddings(doc_id);
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""")
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await conn.execute("""
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CREATE INDEX IF NOT EXISTS idx_embeddings_vector
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ON document_embeddings USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = 100);
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""")
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logger.info("PGVector tables initialized")
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return _pg_pool
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except Exception as e:
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logger.error(f"Failed to create PostgreSQL pool: {e}")
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_pg_pool = None
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return None
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async def get_embedding(text: str) -> Optional[List[float]]:
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"""调用嵌入API获取文本向量"""
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if not EMBEDDING_API_KEY:
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logger.warning("No embedding API key configured")
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return None
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cached = _embed_cache_get(text, EMBEDDING_MODEL)
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if cached:
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return cached
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try:
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# 收敛到共享标准模型层（app.core.llm_client.aembed），与聊天共用代理/超时/failover 语义
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from app.core.llm_client import aembed
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embedding = await aembed(
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EMBEDDING_API_URL, EMBEDDING_API_KEY, text[:8000], EMBEDDING_MODEL, timeout=30
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)
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if embedding:
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_embed_cache_put(text, EMBEDDING_MODEL, embedding)
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return embedding
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logger.error("Embedding API returned empty")
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return None
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except Exception as e:
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logger.error(f"Embedding generation failed: {e}")
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return None
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async def vector_search(
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query: str,
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kb_id: Optional[str] = None,
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top_k: int = 5,
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threshold: float = 0.5,
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) -> List[Dict]:
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"""
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向量语义检索
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Args:
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query: 查询文本
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kb_id: 知识库ID (可选，不指定则搜索全部)
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top_k: 返回结果数
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threshold: 相似度阈值 (0-1)
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Returns:
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[{"doc_id": ..., "content": ..., "score": ..., "metadata": ...}, ...]
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"""
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pool = await get_pg_pool()
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if pool is None:
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return []
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# 获取查询向量
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query_embedding = await get_embedding(query)
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if query_embedding is None:
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return []
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try:
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async with pool.acquire() as conn:
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# 将向量转换为 PostgreSQL 格式
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embedding_str = f"[{','.join(str(x) for x in query_embedding)}]"
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if kb_id:
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rows = await conn.fetch(
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"""
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SELECT doc_id, chunk_id, content, metadata,
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1 - (embedding <=> $1::vector) as score
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FROM document_embeddings
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WHERE kb_id = $2
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ORDER BY embedding <=> $1::vector
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LIMIT $3;
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""",
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embedding_str, kb_id, top_k,
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)
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else:
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rows = await conn.fetch(
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"""
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SELECT doc_id, chunk_id, content, metadata,
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1 - (embedding <=> $1::vector) as score
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FROM document_embeddings
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ORDER BY embedding <=> $1::vector
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LIMIT $2;
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""",
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embedding_str, top_k,
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)
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results = []
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for row in rows:
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score = float(row["score"])
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if score >= threshold:
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results.append({
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"doc_id": row["doc_id"],
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"chunk_id": row["chunk_id"],
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"content": row["content"],
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"score": score,
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"metadata": row["metadata"] if isinstance(row["metadata"], dict) else {},
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})
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return results
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except Exception as e:
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logger.error(f"Vector search failed: {e}")
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return []
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async def index_document(
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doc_id: str,
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kb_id: str,
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content: str,
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chunk_id: Optional[str] = None,
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metadata: Optional[Dict] = None,
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) -> bool:
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"""
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将文档内容索引到 PGVector
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Args:
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doc_id: 文档ID
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kb_id: 知识库ID
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content: 文档内容
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chunk_id: 分块ID (可选)
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metadata: 元数据
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Returns:
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是否成功
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"""
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pool = await get_pg_pool()
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if pool is None:
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return False
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# 分块处理（简单按长度分块）
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chunks = []
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chunk_size = 500
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overlap = 50
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if len(content) <= chunk_size:
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chunks = [(content, chunk_id or f"{doc_id}_0")]
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else:
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for i in range(0, len(content), chunk_size - overlap):
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chunk = content[i:i + chunk_size]
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if len(chunk.strip()) > 10:
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chunks.append((chunk, f"{doc_id}_{i // (chunk_size - overlap)}"))
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success_count = 0
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for chunk_text, cid in chunks:
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embedding = await get_embedding(chunk_text)
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if embedding is None:
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continue
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try:
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async with pool.acquire() as conn:
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embedding_str = f"[{','.join(str(x) for x in embedding)}]"
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import json
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await conn.execute(
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"""
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INSERT INTO document_embeddings (doc_id, kb_id, chunk_id, content, embedding, metadata)
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VALUES ($1, $2, $3, $4, $5::vector, $6)
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ON CONFLICT DO NOTHING;
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""",
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doc_id, kb_id, cid, chunk_text, embedding_str,
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json.dumps(metadata or {}),
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)
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success_count += 1
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except Exception as e:
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logger.error(f"Failed to index chunk {cid}: {e}")
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logger.info(f"Indexed document {doc_id}: {success_count}/{len(chunks)} chunks")
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return success_count > 0
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async def hybrid_search(
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query: str,
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kb_id: Optional[str] = None,
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top_k: int = 5,
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bm25_results: Optional[List[Dict]] = None,
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) -> List[Dict]:
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"""
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混合检索：BM25 + 向量检索融合
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Args:
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query: 查询文本
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kb_id: 知识库ID
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top_k: 返回结果数
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bm25_results: BM25 检索结果（来自MySQL）
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Returns:
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融合后的检索结果
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"""
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# 获取向量检索结果
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vec_results = await vector_search(query, kb_id, top_k)
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if not vec_results and not bm25_results:
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return []
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if not vec_results:
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return bm25_results or []
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if not bm25_results:
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return vec_results
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# 融合：Reciprocal Rank Fusion (RRF)
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rrf_k = 60
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score_map = {}
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for rank, item in enumerate(bm25_results):
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doc_id = item.get("doc_id", item.get("id", ""))
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score_map[doc_id] = {
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"content": item.get("content", item.get("text", "")),
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"score": 1.0 / (rrf_k + rank + 1),
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"bm25_rank": rank + 1,
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"metadata": item.get("metadata", {}),
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}
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for rank, item in enumerate(vec_results):
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doc_id = item["doc_id"]
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if doc_id in score_map:
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score_map[doc_id]["score"] += 1.0 / (rrf_k + rank + 1)
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score_map[doc_id]["vec_score"] = item["score"]
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score_map[doc_id]["vec_rank"] = rank + 1
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else:
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score_map[doc_id] = {
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"content": item["content"],
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"score": 1.0 / (rrf_k + rank + 1),
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"vec_score": item["score"],
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"vec_rank": rank + 1,
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"metadata": item.get("metadata", {}),
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}
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# 按融合分数排序
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merged = sorted(score_map.values(), key=lambda x: x["score"], reverse=True)
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return merged[:top_k]
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async def delete_document_embeddings(doc_id: str) -> bool:
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"""删除文档的所有向量索引"""
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pool = await get_pg_pool()
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if pool is None:
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return False
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try:
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async with pool.acquire() as conn:
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await conn.execute(
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"DELETE FROM document_embeddings WHERE doc_id = $1;",
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doc_id,
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)
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logger.info(f"Deleted embeddings for document {doc_id}")
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return True
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except Exception as e:
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logger.error(f"Failed to delete embeddings: {e}")
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return False
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async def close_pool():
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"""关闭连接池"""
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global _pg_pool
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if _pg_pool is not None:
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await _pg_pool.close()
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_pg_pool = None
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logger.info("PostgreSQL connection pool closed")
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