debug error

app.py

@@ -54,16 +54,18 @@ try:
     model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
 
     logging.info("Successfully loaded the model and tokenizer with transformers.")
+    
     # Initialize the Qdrant searcher after the model is successfully loaded
     global searcher  # Ensure searcher is accessible globally if needed
     searcher = QdrantSearcher(encoder=model, qdrant_url=qdrant_url, access_token=access_token)
+
 except Exception as e:
-    logging.error(f"Failed to load the model: {e}")
-    raise HTTPException(status_code=500, detail="Failed to load the custom model.")
+    logging.error(f"Failed to load the model or initialize searcher: {e}")
+    raise HTTPException(status_code=500, detail="Failed to load the custom model or initialize searcher.")
 
 # Function to embed text using the model
-def embed_texts(texts):
-    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+def embed_text(text):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
     outputs = model(**inputs)
     embeddings = outputs.last_hidden_state.mean(dim=1)  # Example: mean pooling
     return embeddings
@@ -76,10 +78,6 @@ class SearchDocumentsRequest(BaseModel):
 class GenerateRAGRequest(BaseModel):
     search_query: str
 
-@app.get("/")
-async def root():
-    return {"message": "Welcome to the Search/RAG Response API!"}
-
 # Define the search documents endpoint
 @app.post("/api/search-documents")
 async def search_documents(
@@ -94,7 +92,13 @@ async def search_documents(
 
     logging.info("Received request to search documents")
     try:
-        hits, error = searcher.search_documents("documents", body.query, user_id, body.limit)
+        logging.info("Starting document search")
+
+        # Encode the query using the custom embedding function
+        query_embedding = embed_text(body.query)
+
+        # Assuming searcher.search_documents uses these embeddings for search
+        hits, error = searcher.search_documents("documents", query_embedding, user_id, body.limit)
         
         if error:
             logging.error(f"Search documents error: {error}")
@@ -119,16 +123,21 @@ async def generate_rag_response_api(
 
     logging.info("Received request to generate RAG response")
     try:
-        hits, error = searcher.search_documents("documents", body.search_query, user_id)
+        logging.info("Starting document search")
+
+        # Encode the query using the custom embedding function
+        query_embedding = embed_text(body.search_query)
+
+        # Perform search using the encoded query
+        hits, error = searcher.search_documents("documents", query_embedding, user_id)
         
         if error:
             logging.error(f"Search documents error: {error}")
             raise HTTPException(status_code=500, detail=error)
 
-        # Example: Use custom embedding logic
-        # embeddings = embed_texts([hit['text'] for hit in hits])
-        # Use embeddings for further processing...
+        logging.info("Generating RAG response")
 
+        # Assuming generate_rag_response uses the retrieved documents to generate a response
         response, error = generate_rag_response(hits, body.search_query)
         
         if error: