Completely redid how memory works, now directly integrates with datab…

…ase.

server/agents/ai_agent.py

@@ -1,29 +1,35 @@
 import re
 
-from langchain_community.vectorstores import AzureCosmosDBVectorSearch
+from langchain_community.vectorstores.azure_cosmos_db import (
+    AzureCosmosDBVectorSearch,
+    CosmosDBSimilarityType,
+    CosmosDBVectorSearchType
+)
 from langchain.agents import Tool
 from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
 from langchain.tools import StructuredTool
 from langchain_core.messages import SystemMessage
 from langchain_core.vectorstores import VectorStoreRetriever
 
+from pymongo.database import Database
+
 from services.azure_mongodb import MongoDBClient
 from services.my_azure import get_azure_openai_variables, get_azure_openai_llm, get_azure_openai_embeddings
 
 class AIAgent:
     def __init__(self, system_message:str, schema:list[str]=[]):
-        self.db = (MongoDBClient.get_client())[MongoDBClient.get_db_name()]
+        self.db:Database = (MongoDBClient.get_client())[MongoDBClient.get_db_name()]
         self.llm = get_azure_openai_llm()
         self.embedding_model = get_azure_openai_embeddings()
 
         self.system_message = SystemMessage(content=system_message)
 
-        self.agent_executor = create_conversational_retrieval_agent(
-            llm=self.llm,
-            tools=self.__create_agent_tools(schema),
-            system_message = self.system_message,
-            verbose=True
-        )
+        # self.agent_executor = create_conversational_retrieval_agent(
+        #     llm=self.llm,
+        #     tools=self.__create_agent_tools(schema),
+        #     system_message = self.system_message,
+        #     verbose=True
+        # )
 
 
     def run(self, message:str) -> str:
@@ -43,10 +49,11 @@ def _get_cosmosdb_vector_store_retriever(self, collection_name, top_k=3) -> Vect
             connection_string = CONNECTION_STRING, 
             namespace = f"{db_name}.{collection_name}", 
             embedding = AOAI_EMBEDDINGS, 
-            index_name =f"{db_name}_{collection_name}_index",
+            index_name =f"VectorSearchIndex",
             embedding_key = "contentVector", #TODO: Find out what these are for
             text_key = "_id" #TODO: Find out what these are for
         )
+        vector_store.create_index()
         return vector_store.as_retriever(search_kwargs={"k": top_k})