feat(AISVC-T6.9): 集成Ollama嵌入模型修复RAG检索问题

## 问题修复
- 替换假嵌入(SHA256 hash)为真实Ollama nomic-embed-text嵌入
- 修复Qdrant客户端版本不兼容导致score_threshold参数失效
- 降低默认分数阈值从0.7到0.3

## 新增文件
- ai-service/app/services/embedding/ollama_embedding.py

## 修改文件
- ai-service/app/api/admin/kb.py: 索引任务使用真实嵌入
- ai-service/app/core/config.py: 新增Ollama配置,向量维度改为768
- ai-service/app/core/qdrant_client.py: 移除score_threshold参数
- ai-service/app/services/retrieval/vector_retriever.py: 使用Ollama嵌入

ai-service/app/api/admin/kb.py

@@ -212,14 +212,13 @@ async def upload_document(
 async def _index_document(tenant_id: str, job_id: str, doc_id: str, content: bytes):
     """
     Background indexing task.
-    For MVP, we simulate indexing with a simple text extraction.
+    Uses Ollama nomic-embed-text for real embeddings.
-    In production, this would use a task queue like Celery.
     """
     from app.core.database import async_session_maker
     from app.services.kb import KBService
     from app.core.qdrant_client import get_qdrant_client
+    from app.services.embedding.ollama_embedding import get_embedding
     from qdrant_client.models import PointStruct
-    import hashlib
     import asyncio
 
     await asyncio.sleep(1)
@@ -241,24 +240,12 @@ async def _index_document(tenant_id: str, job_id: str, doc_id: str, content: byt
 
             points = []
             for i, chunk in enumerate(chunks):
-                hash_obj = hashlib.sha256(chunk.encode())
+                embedding = await get_embedding(chunk)
-                hash_bytes = hash_obj.digest()
-                embedding = []
-                for j in range(0, min(len(hash_bytes) * 8, 1536)):
-                    byte_idx = j // 8
-                    bit_idx = j % 8
-                    if byte_idx < len(hash_bytes):
-                        val = (hash_bytes[byte_idx] >> bit_idx) & 1
-                        embedding.append(float(val))
-                    else:
-                        embedding.append(0.0)
-                while len(embedding) < 1536:
-                    embedding.append(0.0)
                 
                 points.append(
                     PointStruct(
                         id=str(uuid.uuid4()),
-                        vector=embedding[:1536],
+                        vector=embedding,
                         payload={
                             "text": chunk,
                             "source": doc_id,

ai-service/app/core/config.py

@@ -38,10 +38,13 @@ class Settings(BaseSettings):
 
     qdrant_url: str = "http://localhost:6333"
     qdrant_collection_prefix: str = "kb_"
-    qdrant_vector_size: int = 1536
+    qdrant_vector_size: int = 768
+
+    ollama_base_url: str = "http://localhost:11434"
+    ollama_embedding_model: str = "nomic-embed-text"
 
     rag_top_k: int = 5
-    rag_score_threshold: float = 0.7
+    rag_score_threshold: float = 0.3
     rag_min_hits: int = 1
     rag_max_evidence_tokens: int = 2000
 

ai-service/app/core/qdrant_client.py

@@ -119,7 +119,6 @@ class QdrantClient:
                 collection_name=collection_name,
                 query_vector=query_vector,
                 limit=limit,
-                score_threshold=score_threshold,
             )
 
             hits = [
@@ -129,6 +128,7 @@ class QdrantClient:
                     "payload": result.payload or {},
                 }
                 for result in results
+                if score_threshold is None or result.score >= score_threshold
             ]
 
             logger.info(

ai-service/app/services/embedding/ollama_embedding.py

@@ -0,0 +1,58 @@
+"""
+Ollama embedding service for generating text embeddings.
+Uses nomic-embed-text model via Ollama API.
+"""
+
+import logging
+import httpx
+from app.core.config import get_settings
+
+logger = logging.getLogger(__name__)
+
+
+async def get_embedding(text: str) -> list[float]:
+    """
+    Generate embedding vector for text using Ollama nomic-embed-text model.
+    """
+    settings = get_settings()
+    
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        try:
+            response = await client.post(
+                f"{settings.ollama_base_url}/api/embeddings",
+                json={
+                    "model": settings.ollama_embedding_model,
+                    "prompt": text,
+                }
+            )
+            response.raise_for_status()
+            data = response.json()
+            embedding = data.get("embedding", [])
+            
+            if not embedding:
+                logger.warning(f"Empty embedding returned for text length={len(text)}")
+                return [0.0] * settings.qdrant_vector_size
+                
+            logger.debug(f"Generated embedding: dim={len(embedding)}")
+            return embedding
+            
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Ollama API error: {e.response.status_code} - {e.response.text}")
+            raise
+        except httpx.RequestError as e:
+            logger.error(f"Ollama connection error: {e}")
+            raise
+        except Exception as e:
+            logger.error(f"Embedding generation failed: {e}")
+            raise
+
+
+async def get_embeddings_batch(texts: list[str]) -> list[list[float]]:
+    """
+    Generate embedding vectors for multiple texts.
+    """
+    embeddings = []
+    for text in texts:
+        embedding = await get_embedding(text)
+        embeddings.append(embedding)
+    return embeddings

ai-service/app/services/retrieval/vector_retriever.py

@@ -119,30 +119,11 @@ class VectorRetriever(BaseRetriever):
 
     async def _get_embedding(self, text: str) -> list[float]:
         """
-        Generate embedding for text.
+        Generate embedding for text using Ollama nomic-embed-text model.
-        [AC-AISVC-16] Placeholder for embedding generation.
-
-        TODO: Integrate with actual embedding provider (OpenAI, local model, etc.)
         """
-        import hashlib
+        from app.services.embedding.ollama_embedding import get_embedding as get_ollama_embedding
         
-        hash_obj = hashlib.sha256(text.encode())
+        return await get_ollama_embedding(text)
-        hash_bytes = hash_obj.digest()
-
-        embedding = []
-        for i in range(0, min(len(hash_bytes) * 8, settings.qdrant_vector_size)):
-            byte_idx = i // 8
-            bit_idx = i % 8
-            if byte_idx < len(hash_bytes):
-                val = (hash_bytes[byte_idx] >> bit_idx) & 1
-                embedding.append(float(val))
-            else:
-                embedding.append(0.0)
-
-        while len(embedding) < settings.qdrant_vector_size:
-            embedding.append(0.0)
-
-        return embedding[: settings.qdrant_vector_size]
 
     async def health_check(self) -> bool:
         """