增加文字转embeddings接口

fast_api.py

@@ -1,3 +1,4 @@
+# coding=gbk
 from fastapi import FastAPI, HTTPException, BackgroundTasks
 from qa_Ask import QAService, match_query, store_data
 from pydantic import BaseModel
@@ -9,27 +10,31 @@ import uuid
 import json
 import shutil
 import yaml
+import sys
 import logging
-
+from typing import List
+from faiss_kb_service import FaissKBService
+from faiss_kb_service import EmbeddingsFunAdapter
 
 app = FastAPI()
 
-import sys
+# 配置日志记录到文件和终端
-
-# 配置日志记录到文件和终端
 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - %(levelname)s - %(message)s',
     handlers=[
         logging.FileHandler('log/app.log'),
-        logging.StreamHandler(sys.stdout)  # 这里添加控制台处理程序
+        logging.StreamHandler(sys.stdout)  # 这里添加控制台处理程序
     ]
 )
 logger = logging.getLogger(__name__)
 
 class QuestionRequest(BaseModel):
     question: str
+    scoreThreshold: float
 
+class EmbeddingResponse(BaseModel):
+    embeddings: List[float]
 
 
 class QuestionResponse(BaseModel):
@@ -39,9 +44,11 @@ class QuestionResponse(BaseModel):
 
 
 class QuestionItem(BaseModel):
-    questionCode: str
+    questionId: str
     questionList: list[str]
 
+class EmbeddingRequest(BaseModel):
+    text: str
 
 with open('config/config.yaml', 'r') as config_file:
     config_data = yaml.safe_load(config_file)
@@ -53,7 +60,7 @@ max_knowledge_bases = config_data['max_knowledge_bases']
 
 
 def load_knowledge_bases():
-    """加载知识库名称列表"""
+    """加载知识库名称列表"""
     if os.path.exists(knowledge_base_file):
         with open(knowledge_base_file, "r") as file:
             return file.read().splitlines()
@@ -62,13 +69,13 @@ def load_knowledge_bases():
 
 
 def save_knowledge_bases(names):
-    """保存知识库名称列表到文件"""
+    """保存知识库名称列表到文件"""
     with open(knowledge_base_file, "w") as file:
         file.write("\n".join(names))
 
 
 def update_kb(kb_name, qa_service, path, max_knowledge_bases):
-    """更新知识库"""
+    """更新知识库"""
     store_data(qa_service, path)
 
     if len(recent_knowledge_bases) == max_knowledge_bases:
@@ -80,11 +87,11 @@ def update_kb(kb_name, qa_service, path, max_knowledge_bases):
 
     os.remove(path)
     logger.info(f"Knowledge base updated: {kb_name}\n"
-                f"Please wait while the database is being updated···")
+                f"Please wait while the database is being updated···")
 
 
 def fetch_and_write_data(api_url, path):
-    """从API获取数据并写入文件"""
+    """从API获取数据并写入文件"""
     try:
         response = requests.get(api_url)
         response_data = response.json()
@@ -102,11 +109,26 @@ def fetch_and_write_data(api_url, path):
     except Exception as e:
         logger.error(f"Error fetching data from API: {e}")
         return False
+bge_large_zh_v1_5_config = config_data.get('bge_large_zh_v1_5', {})
+embed_model_path = bge_large_zh_v1_5_config.get('embed_model_path', 'default_path_if_not_provided')
 
+recent_knowledge_bases = deque(load_knowledge_bases(), maxlen=max_knowledge_bases)
+faiss_service = FaissKBService(kb_name= recent_knowledge_bases[-1], embed_model_path=embed_model_path, device=None)
+
+@app.post("/embeddings/", response_model=EmbeddingResponse)
+async def get_embeddings(request: EmbeddingRequest):
+    """使用FaissKBService实例来获取嵌入向量"""
+    embed_func = EmbeddingsFunAdapter(faiss_service.embed_model_path, faiss_service.device)
+    try:
+        embeddings = embed_func.embed_query(request.text)
+        embeddings_list = embeddings
+        return EmbeddingResponse(embeddings=embeddings_list)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
 
 @app.post("/updateDatabase")
 async def save_to_json(question_items: list[QuestionItem], background_tasks: BackgroundTasks):
-    """接收问题数据并异步保存为JSON文件，触发后台更新任务"""
+    """接收问题数据并异步保存为JSON文件，触发后台更新任务"""
     try:
         json_data = json.dumps([item.dict() for item in question_items], ensure_ascii=False, indent=2)
         path = "output.json"
@@ -123,16 +145,17 @@ async def save_to_json(question_items: list[QuestionItem], background_tasks: Bac
             update_kb, kb_name, qa_service, path, max_knowledge_bases
         )
 
-        return {"status": "success", "message": "Please wait while the database is being updated···"}
+        return {"status": "success", "message": "Please wait while the database is being updated···"}
 
     except Exception as e:
         logger.error(f"Error saving data to file or scheduling knowledge base update task: {e}")
         # raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}")
-        return {"status": "error", "message": "update task error···"}
+        return {"status": "error", "message": "update task error···"}
+
 
 @app.post("/matchQuestion")
 def match_question(request: QuestionRequest):
-    """匹配问题的端点"""
+    """匹配问题的端点"""
     try:
         logger.info(f"match_question:Request: {request}")
         start_time = time.time()
@@ -141,12 +164,11 @@ def match_question(request: QuestionRequest):
         newest = recent_knowledge_bases[-1]
 
         top_k = 3
-        score_threshold = 0.1
 
         device = None
         qa_service = QAService(newest, device)
 
-        result = match_query(qa_service, query, top_k, score_threshold)
+        result = match_query(qa_service, query, top_k, request.scoreThreshold)
 
         response = QuestionResponse(code=200, msg="success", data=result)
         stop_time = time.time()

qa_Ask.py

@@ -51,7 +51,7 @@ def load_testing_data(file_path):
     with open(file_path, encoding='utf-8') as f:
         data = json.load(f)
         for item in data:
-            question_code = item['questionCode']
+            question_code = item['questionId']
             question_list.extend(item['questionList'])
             id_list.extend([create_question_id(question_code, j, q) for j, q in enumerate(item['questionList'])])