Fix context retireval

cognitive_architecture/classifiers/classifier.py

@@ -77,7 +77,7 @@ async def classify_call(query, document_summaries):
 
     llm = ChatOpenAI(temperature=0, model=config.model)
     prompt_classify = ChatPromptTemplate.from_template(
-        """You are a  classifier. Determine what document  are relevant for the given query: {query}, Document summaries:{document_summaries}"""
+        """You are a  classifier. Determine what document  are relevant for the given query: {query}, Document summaries and ids:{document_summaries}"""
     )
     json_structure = [{
         "name": "classifier",
@@ -88,6 +88,10 @@ async def classify_call(query, document_summaries):
                 "DocumentSummary": {
                     "type": "string",
                     "description": "The summary of the document and the topic it deals with."
+                },
+                "d_id": {
+                    "type": "string",
+                    "description": "The id of the document"
                 }
 
 
@@ -98,11 +102,11 @@ 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)
-    classfier_value = arguments_dict.get('DocumentSummary', None)
+    classfier_id = arguments_dict.get('d_id', None)
 
-    print("This is the classifier value", classfier_value)
+    print("This is the classifier id ", classfier_id)
 
-    return classfier_value
+    return classfier_id
 
 
 

cognitive_architecture/database/create_database_tst.py

@@ -107,4 +107,6 @@ if __name__ == "__main__":
         create_database(username, password, host, database_name)
         print(f"Database {database_name} created successfully.")
 
-    create_tables(engine)
+    create_tables(engine)
+
+

cognitive_architecture/database/graph_database/graph.py

@@ -476,7 +476,6 @@ class Neo4jGraphDB(AbstractGraphDB):
 
         return create_statements
 
-
     async def get_memory_linked_document_summaries(self, user_id: str, memory_type: str = "PublicMemory"):
         """
         Retrieve a list of summaries for all documents associated with a given memory type for a user.
@@ -486,7 +485,7 @@ class Neo4jGraphDB(AbstractGraphDB):
             memory_type (str): The type of memory node ('SemanticMemory' or 'PublicMemory').
 
         Returns:
-            List[str]: A list of document categories associated with the memory type for the user.
+            List[Dict[str, Union[str, None]]]: A list of dictionaries containing document summary and d_id.
 
         Raises:
             Exception: If an error occurs during the database query execution.
@@ -498,18 +497,18 @@ class Neo4jGraphDB(AbstractGraphDB):
         try:
             query = f'''
             MATCH (user:User {{userId: '{user_id}'}})-[:{relationship}]->(memory:{memory_type})-[:HAS_DOCUMENT]->(document:Document)
-            RETURN document.summary AS summary
+            RETURN document.d_id AS d_id, document.summary AS summary
             '''
             logging.info(f"Generated Cypher query: {query}")
             result = self.query(query)
-            logging.info("Result: ", result)
-            return [record.get("summary", "No summary available") for record in result]
+            logging.info(f"Result: {result}")
+            return [{"d_id": record.get("d_id", None), "summary": record.get("summary", "No summary available")} for
+                    record in result]
 
         except Exception as e:
             logging.error(f"An error occurred while retrieving document summary: {str(e)}")
             return None
 
-
     # async def get_document_categories(self, user_id: str):
     #     """
     #     Retrieve a list of categories for all documents associated with a given user.
@@ -568,13 +567,13 @@ class Neo4jGraphDB(AbstractGraphDB):
     #         logging.error(f"An error occurred while retrieving document IDs: {str(e)}")
     #         return None
 
-    async def get_memory_linked_document_ids(self, user_id: str, summary: str, memory_type: str = "PUBLIC"):
+    async def get_memory_linked_document_ids(self, user_id: str, summary_id: str, memory_type: str = "PublicMemory"):
         """
         Retrieve a list of document IDs for a specific category associated with a given memory type for a user.
 
         Args:
             user_id (str): The unique identifier of the user.
-            summary (str): The specific document summary to filter by.
+            summary_id (str): The specific document summary id to filter by.
             memory_type (str): The type of memory node ('SemanticMemory' or 'PublicMemory').
 
         Returns:
@@ -591,7 +590,7 @@ class Neo4jGraphDB(AbstractGraphDB):
         try:
             query = f'''
             MATCH (user:User {{userId: '{user_id}'}})-[:{relationship}]->(memory:{memory_type})-[:HAS_DOCUMENT]->(document:Document)
-            WHERE apoc.text.fuzzyMatch(document.summary, '{summary}') > 0.8
+            WHERE document.d_id = '{summary_id}'
             RETURN document.d_id AS d_id
             '''
             logging.info(f"Generated Cypher query: {query}")

cognitive_architecture/database/vectordb/basevectordb.py

@@ -278,7 +278,7 @@ class BaseMemory:
         n_of_observations: Optional[int] = 2,
     ):
         logging.info(namespace)
-        logging.info("The search type is %", search_type)
+        logging.info("The search type is %", str(search_type))
         logging.info(params)
         logging.info(observation)
 

cognitive_architecture/database/vectordb/vectordb.py

@@ -199,7 +199,7 @@ class WeaviateVectorDB(VectorDB):
         client = self.init_weaviate(namespace =self.namespace)
         if search_type is None:
             search_type = 'hybrid'
-        logging.info("The search type is s%", search_type)
+        logging.info("The search type is s%", (search_type))