Add initial classifier

level_4/api.py

@@ -131,6 +131,22 @@ async def user_query_processor(payload: Payload):
     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:
+        decoded_payload = payload.payload
+
+        # Execute the query - replace this with the actual execution method
+        async with session_scope(session=AsyncSessionLocal()) as session:
+            from cognitive_architecture.classifiers.classifier import classify_user_query
+            # Assuming you have a method in Neo4jGraphDB to execute the query
+            result = await classify_user_query(session, decoded_payload['user_id'], decoded_payload['query'])
+        return JSONResponse(content={"response": result}, status_code=200)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 def start_api_server(host: str = "0.0.0.0", port: int = 8000):
     """
     Start the API server using uvicorn.

level_4/cognitive_architecture/classifiers/classifier.py

@@ -102,4 +102,37 @@ async def classify_call(query, context, document_types):
 
     print("This is the classifier value", classfier_value)
 
+    return classfier_value
+
+
+
+async def classify_user_query(query, context, document_types):
+
+    llm = ChatOpenAI(temperature=0, model=config.model)
+    prompt_classify = ChatPromptTemplate.from_template(
+        """You are a  classifier. You store user memories, thoughts and feelings. Determine if you need to use them to answer this query : {query}"""
+    )
+    json_structure = [{
+        "name": "classifier",
+        "description": "Classification",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "UserQueryClassifier": {
+                    "type": "bool",
+                    "description": "The classification of documents in groups such as legal, medical, etc."
+                }
+
+
+            }, "required": ["UserQueryClassiffier"] }
+    }]
+    chain_filter = prompt_classify | llm.bind(function_call={"name": "classifier"}, functions=json_structure)
+    classifier_output = await chain_filter.ainvoke({"query": query, "context": context, "document_types": document_types})
+    arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
+    print("This is the arguments string", arguments_str)
+    arguments_dict = json.loads(arguments_str)
+    classfier_value = arguments_dict.get('UserQueryClassifier', None)
+
+    print("This is the classifier value", classfier_value)
+
     return classfier_value