Fix OpenAIEmbeddings initialization and Qdrant query methods for Hugging Face compatibility

streamlit_app.py

@@ -114,21 +114,27 @@ def get_embedding_model():
     from langchain_openai import OpenAIEmbeddings
     import os
     
+    # The simplest initialization possible - just model name
     try:
-        # First try the standard approach
         return OpenAIEmbeddings(model="text-embedding-3-small")
     except Exception as e:
-        if "proxies" in str(e):
-            # If there's a proxies error, create with additional kwargs to override defaults
+        print(f"OpenAIEmbeddings initialization error: {str(e)}")
+        
+        # Try with just API key, no other parameters
+        try:
+            return OpenAIEmbeddings(
+                model="text-embedding-3-small",
+                openai_api_key=os.environ.get("OPENAI_API_KEY")
+            )
+        except Exception as e2:
+            print(f"Second attempt failed: {str(e2)}")
+            
+            # Last resort - most minimal initialization
             return OpenAIEmbeddings(
                 model="text-embedding-3-small",
                 openai_api_key=os.environ.get("OPENAI_API_KEY"),
-                show_progress_bar=False,
-                client_kwargs={"proxies": None, "timeout": 60}
+                client=None  # Let the class create its own client
             )
-        else:
-            # Re-raise any other exceptions
-            raise
 
 @st.cache_resource
 def setup_qdrant_client():
@@ -172,15 +178,28 @@ def retrieve_documents(query, k=5):
             query_vector=query_embedding,
             limit=k
         )
+        print("Successfully used query_points method")
     except Exception as e:
         print(f"Error with query_points method: {str(e)}")
-        # Fall back to the deprecated method
-        results = client.search(
-            collection_name="kohavi_ab_testing_pdf_collection",
-            query_vector=query_embedding,
-            limit=k
-        )
-        print("Using deprecated search method")
+        try:
+            # Try a different parameter format
+            results = client.query_points(
+                collection_name="kohavi_ab_testing_pdf_collection",
+                query_vector=query_embedding,
+                with_payload=True,
+                with_vectors=False,
+                limit=k
+            )
+            print("Successfully used query_points with alternate parameters")
+        except Exception as e2:
+            print(f"Error with alternate query_points: {str(e2)}")
+            # Fall back to the deprecated method as last resort
+            results = client.search(
+                collection_name="kohavi_ab_testing_pdf_collection",
+                query_vector=query_embedding,
+                limit=k
+            )
+            print("Using deprecated search method")
     
     # Convert results to documents
     documents = []