Update app/retriever.py

app/retriever.py

@@ -3,6 +3,8 @@ from qdrant_client.http import models as rest
 from langchain.schema import Document
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
 from langchain.retrievers.document_compressors import CrossEncoderReranker
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('BAAI/bge-m3') 
 import logging
 import os
 from .utils import getconfig
@@ -220,15 +222,21 @@ def get_context(
         search_kwargs = {
             "model_name": config.get("embeddings", "MODEL_NAME")
         }
-        
+        model = SentenceTransformer(config.get("embeddings", "MODEL_NAME"))
+        query_vector = model.encode(query).tolist()
+        retrieved_docs = client.search(
+                      collection_name="EUDR",
+                      query_vector=query_vector,
+                      limit=top_k,
+                      with_payload=True)
         # filter support for QdrantVectorStore
-        if isinstance(vectorstore, QdrantVectorStore):
-            filter_obj = create_filter(reports, sources, subtype, year)
-            if filter_obj:
-                search_kwargs["filter"] = filter_obj
+        #if isinstance(vectorstore, QdrantVectorStore):
+        #    filter_obj = create_filter(reports, sources, subtype, year)
+        #    if filter_obj:
+        #        search_kwargs["filter"] = filter_obj
         
         # Perform initial retrieval
-        retrieved_docs = vectorstore.search(query, top_k, **search_kwargs)
+        #retrieved_docs = vectorstore.search(query, top_k,)
         
         logging.info(f"Retrieved {len(retrieved_docs)} documents for query: {query[:50]}...")