Fix context retireval

api.py

@@ -165,10 +165,7 @@ async def document_to_graph_db(payload: Payload):
 async def cognitive_context_enrichment(payload: Payload):
     try:
         decoded_payload = payload.payload
-
-        # Execute the query - replace this with the actual execution method
         async with session_scope(session=AsyncSessionLocal()) as session:
-            # Assuming you have a method in Neo4jGraphDB to execute the query
             result = await user_context_enrichment(session, user_id = decoded_payload['user_id'], query= decoded_payload['query'], generative_response=decoded_payload['generative_response'], memory_type= decoded_payload['memory_type'])
         return JSONResponse(content={"response": result}, status_code=200)
 
@@ -176,6 +173,19 @@ async def cognitive_context_enrichment(payload: Payload):
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@app.post("/classify-user-query")
+async def classify_user_query(payload: Payload):
+    try:
+        decoded_payload = payload.payload
+        async with session_scope(session=AsyncSessionLocal()) as session:
+            from main import relevance_feedback
+            result = await relevance_feedback(  query= decoded_payload['query'], input_type=decoded_payload['knowledge_type'])
+        return JSONResponse(content={"response": result}, status_code=200)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @app.post("/user-query-classifier")
 async def user_query_classfier(payload: Payload):
     try:

cognitive_architecture/classifiers/classifier.py

@@ -72,6 +72,35 @@ def classify_retrieval():
     pass
 
 
+async def classify_user_input(query, input_type):
+
+    llm = ChatOpenAI(temperature=0, model=config.model)
+    prompt_classify = ChatPromptTemplate.from_template(
+        """You are a  classifier. Determine with a True or False if the following input: {query}, is relevant for the following memory category: {input_type}"""
+    )
+    json_structure = [{
+        "name": "classifier",
+        "description": "Classification",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "InputClassification": {
+                    "type": "boolean",
+                    "description": "The classification of the input"
+                }
+            }, "required": ["InputClassification"] }
+    }]
+    chain_filter = prompt_classify | llm.bind(function_call={"name": "classifier"}, functions=json_structure)
+    classifier_output = await chain_filter.ainvoke({"query": query, "input_type": input_type})
+    arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
+    logging.info("This is the arguments string %s", arguments_str)
+    arguments_dict = json.loads(arguments_str)
+    logging.info("Relevant summary is %s", arguments_dict.get('DocumentSummary', None))
+    InputClassification = arguments_dict.get('InputClassification', None)
+    logging.info("This is the classification %s", InputClassification)
+    return InputClassification
+
+
 # classify documents according to type of document
 async def classify_call(query, document_summaries):
 
@@ -102,6 +131,7 @@ async def classify_call(query, document_summaries):
     arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
     print("This is the arguments string", arguments_str)
     arguments_dict = json.loads(arguments_str)
+    logging.info("Relevant summary is %s", arguments_dict.get('DocumentSummary', None))
     classfier_id = arguments_dict.get('d_id', None)
 
     print("This is the classifier id ", classfier_id)

main.py

@@ -392,7 +392,7 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
 
     if detect_language(query) != "en":
         query = translate_text(query, "sr", "en")
-    logging.info("Translated query is", query)
+    logging.info("Translated query is %s", str(query))
 
     neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
                                   password=config.graph_database_password)
@@ -404,8 +404,15 @@ async def user_context_enrichment(session, user_id:str, query:str, generative_re
     # summaries = [record.get("summary") for record in result]
     # logging.info('Possible document categories are', str(result))
     # logging.info('Possible document categories are', str(categories))
-    relevant_summary_id = await classify_call( query= query, document_summaries=str(summaries))
 
+    max_attempts = 3
+    relevant_summary_id = None
+
+    for _ in range(max_attempts):
+        relevant_summary_id = await classify_call( query= query, document_summaries=str(summaries))
+
+        if relevant_summary_id is not None:
+            break
 
     # logging.info("Relevant categories after the classifier are %s", relevant_categories)
     neo4j_graph_db = Neo4jGraphDB(url=config.graph_database_url, username=config.graph_database_username,
@@ -602,7 +609,11 @@ async def unlink_user_from_memory(user_id: str=None, labels:list=None, topic:str
         logging.error(f"Error creating public memory node: {e}")
         return None
 
+async def relevance_feedback(  query: str, input_type: str):
+    from cognitive_architecture.classifiers.classifier import classify_user_input
 
+    result = await classify_user_input( query, input_type=input_type)
+    return result
 
 async def main():
     user_id = "user"
@@ -687,8 +698,11 @@ async def main():
 
         # await attach_user_to_memory(user_id=user_id, labels=['sr'], topic="PublicMemory")
 
-        return_ = await user_context_enrichment(user_id=user_id, query="Koja je minimalna visina ograde na balkonu na stambenom objektu", session=session, memory_type="PublicMemory", generative_response=True)
+        # return_ = await user_context_enrichment(user_id=user_id, query="hi how are you", session=session, memory_type="PublicMemory", generative_response=True)
-        print(return_)
+        # print(return_)
+        aa = await relevance_feedback("I need to understand how to build a staircase in an apartment building", "PublicMemory")
+        print(aa)
+
         # document_summary = {
         #     'DocumentCategory': 'Science',
         #     'Title': 'The Future of AI',