Update app.py

app.py

@@ -16,6 +16,7 @@ from langchain_core.messages import (
 from langchain_huggingface import ChatHuggingFace
 from langchain_core.output_parsers import StrOutputParser
 from langchain_huggingface import HuggingFaceEndpoint
+from qdrant_client.http import models as rest
 #from qdrant_client import QdrantClient
 from dotenv import load_dotenv
 import pkg_resources
@@ -108,12 +109,34 @@ async def chat(query,history,sources,reports,subtype,year):
     else:
         vectorstore = vectorstores["allreports"]
 
-    ##------------------------------get context----------------------------------------------------   
+    ###-------------------------------------Construct Filter------------------------------------
+    if len(reports) == 0:
+        filter=rest.Filter(
+                must=[
+                    rest.FieldCondition(
+                        key="metadata.subtype",
+                        match=rest.MatchValue(value=subtype)
+                    ),
+                    rest.FieldCondition(
+                        key="metadata.year",
+                        match=rest.MatchAny(any=year)
+                    )])
+    else:
+        filter=rest.Filter(
+                must=[
+                    rest.FieldCondition(
+                        key="metadata.filename",
+                        match=rest.MatchAny(any=reports)
+                    )])
+        
+
+    ##------------------------------get context----------------------------------------------------  
+    if 
     context_retrieved_lst = []
     question_lst= [query]
     for question in question_lst:
         retriever = vectorstore.as_retriever(
-          search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.6, "k": 3})
+          search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.6, "k": 3, filter=filter})
         
         context_retrieved = retriever.invoke(question)