Remove unused api system and support qdrant valid

infra_ai_service/api/ai_enhance/embedding.py

@@ -1,33 +1,88 @@
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
+from typing import List
 from fastembed.embedding import DefaultEmbedding
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams, PointStruct
+import numpy as np
+import uuid
 
 router = APIRouter()
 
 
-class EmbeddingRequest(BaseModel):
-    text: str
+class TextInput(BaseModel):
+    content: str
 
 
-class EmbeddingResponse(BaseModel):
-    embedding: list[float]
+class EmbeddingOutput(BaseModel):
+    id: str
+    embedding: List[float]
 
 
-# Load a FastEmbed model
+# 初始化FastEmbed模型
 fastembed_model = DefaultEmbedding()
 
+# 初始化Qdrant客户端
+qdrant_client = QdrantClient(url="http://localhost:6333")
+collection_name = 'test_simi'
 
-@router.post("/embed_text/", response_model=EmbeddingResponse)
-async def embed_text(request: EmbeddingRequest) -> EmbeddingResponse:
+# 检查集合是否存在，如果不存在则创建
+try:
+    qdrant_client.get_collection(collection_name)
+    print(f"Collection {collection_name} already exists")
+except HTTPException as e:
+    # 获取向量维度
+    sample_embedding = next(fastembed_model.embed(["Sample text"]))
+    vector_size = len(sample_embedding)
+
+    # 创建集合
+    qdrant_client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
+    )
+    print(f"Created collection: {collection_name}")
+
+
+@router.post("/embed/", response_model=EmbeddingOutput)
+async def embed_text(input_data: TextInput):
     try:
         # 生成嵌入
-        embeddings = list(fastembed_model.embed([request.text]))
+        embeddings = list(fastembed_model.embed([input_data.content]))
         if not embeddings:
             raise ValueError("Failed to generate embedding")
 
-        # 将numpy数组转换为普通Python列表
-        embedding_list = embeddings[0].tolist()
+        embedding_vector = embeddings[0]
+
+        # 生成唯一ID
+        point_id = str(uuid.uuid4())
+
+        # 将嵌入存储到Qdrant
+        qdrant_client.upsert(
+            collection_name=collection_name,
+            points=[
+                PointStruct(
+                    id=point_id,
+                    vector=embedding_vector.tolist(),
+                    payload={"text": input_data.content}
+                )
+            ]
+        )
 
-        return EmbeddingResponse(embedding=embedding_list)
+        return EmbeddingOutput(id=point_id, embedding=embedding_vector.tolist())
+    except Exception as e:
+        raise HTTPException(status_code=400,
+                            detail=f"Error processing embedding: {str(e)}")
+
+
+@router.get("/status/")
+async def get_collection_status():
+    try:
+        collection_info = qdrant_client.get_collection(collection_name)
+        return {
+            "collection_name": collection_name,
+            "vectors_count": collection_info.vectors_count,
+            "status": "ready" if collection_info.status == "green" else "not ready"
+        }
     except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Error generating embedding: {str(e)}")
+        raise HTTPException(status_code=400,
+                            detail=f"Error getting collection status: {str(e)}")

infra_ai_service/api/ai_enhance/text_process.py

@@ -5,23 +5,22 @@
 router = APIRouter()
 
 
-class TextRequest(BaseModel):
+class TextInput(BaseModel):
     content: str
 
 
-class TextResponse(BaseModel):
-    processed_content: str
+class TextOutput(BaseModel):
+    modified_content: str
 
 
 def clean_text(text: str) -> str:
-    cleaned_text = re.sub(r'[{}[\]()@.#\\_\':\/-]', '', text)
-    return cleaned_text
+    return re.sub(r'[{}[\]()@.#\\_\':\/-]', '', text)
 
 
-@router.post("/process_text/", response_model=TextResponse)
-async def process_text(request: TextRequest) -> TextResponse:
+@router.post("/process/", response_model=TextOutput)
+async def process_text(input_data: TextInput):
     try:
-        processed_text = clean_text(request.content)
-        return TextResponse(processed_content=processed_text)
+        modified_text = clean_text(input_data.content)
+        return TextOutput(modified_content=modified_text)
     except Exception as e:
         raise HTTPException(status_code=400, detail=f"Error processing text: {str(e)}")

infra_ai_service/api/ai_enhance/vector_search.py

@@ -1,12 +1,19 @@
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
+from typing import List
 from fastembed.embedding import DefaultEmbedding
 from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams
+import logging
 
 router = APIRouter()
 
+# 设置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-class SearchRequest(BaseModel):
+
+class SearchInput(BaseModel):
     query_text: str
     top_n: int = 5
     score_threshold: float = 0.7
@@ -17,15 +24,39 @@ class SearchResult(BaseModel):
     score: float
 
 
+class SearchOutput(BaseModel):
+    results: List[SearchResult]
+
+
 # 初始化FastEmbed模型和Qdrant客户端
 fastembed_model = DefaultEmbedding()
 qdrant_client = QdrantClient(url="http://localhost:6333")
 collection_name = 'test_simi'
 
+# 检查集合是否存在，如果不存在则创建
+try:
+    qdrant_client.get_collection(collection_name)
+    print(f"Collection {collection_name} already exists")
+except HTTPException as e:
+    # 获取向量维度
+    sample_embedding = next(fastembed_model.embed(["Sample text"]))
+    vector_size = len(sample_embedding)
 
-@router.post("/search_vectors/", response_model=SearchResult)
-async def search_vectors(input_data: SearchRequest) -> SearchResult:
+    # 创建集合
+    qdrant_client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
+    )
+    print(f"Created collection: {collection_name}")
+
+
+@router.post("/query/", response_model=SearchOutput)
+async def vector_search(input_data: SearchInput):
     try:
+        # 检查集合是否存在
+        if not qdrant_client.get_collection(collection_name):
+            raise ValueError(f"Collection {collection_name} does not exist")
+
         # 生成查询文本的嵌入
         query_vector = list(fastembed_model.embed([input_data.query_text]))
         if not query_vector:
@@ -41,10 +72,15 @@ async def search_vectors(input_data: SearchRequest) -> SearchResult:
 
         # 转换搜索结果为输出格式
         results = [
-            SearchResult(id=str(result.id), score=result.score)
+            SearchResult(
+                id=str(result.id),
+                score=result.score,
+                text=result.payload.get('text', 'No text available')
+            )
             for result in search_results
         ]
 
-        return SearchResult(results=results)
+        return SearchOutput(results=results)
     except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Error performing vector search: {str(e)}")
+        logger.error(f"Error in vector search: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=f"Error performing vector search: {str(e)}")

infra_ai_service/api/router.py

@@ -1,12 +1,10 @@
 from fastapi.routing import APIRouter
 
-from infra_ai_service.api.system.views import router as system_router
 from infra_ai_service.api.ai_enhance.text_process import router as text_process_router
 from infra_ai_service.api.ai_enhance.embedding import router as embedding_router
 from infra_ai_service.api.ai_enhance.vector_search import router as vector_search_router
 
 api_router = APIRouter()
-api_router.include_router(system_router, prefix="/system", tags=["system"])
-api_router.include_router(text_process_router, prefix="/text", tags=["Text Processing"])
-api_router.include_router(embedding_router, prefix="/embed", tags=["Embedding"])
-api_router.include_router(vector_search_router, prefix="/search", tags=["Vector Search"])
+api_router.include_router(text_process_router, prefix="/text", tags=["text"])
+api_router.include_router(embedding_router, prefix="/embedding", tags=["embedding"])
+api_router.include_router(vector_search_router, prefix="/search", tags=["search"])