Criação de um agente de AI aprimorado para memória com o laudo do Aconnection no Amazon Cama do MongoDB Atlas

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       def mongodb_place_lookup_by_country(query_str: str) -> str:
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       """Retrieve place by Country Name"""
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       res = ""
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       res = collection.aggregate(
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           [
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               {"$match": {"Country": {"$regex": query_str, "$options": "i"}}},
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               {"$project": {"Place Name": 1}},
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           ]
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       )
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       places = []
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       for place in res:
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           places.append(place["Place Name"])
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       return str(places)

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    # filter the data using the criteria and do a Schematic search
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    def mongodb_search(query: str) -> str:
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       """Retrieve results from MongoDB related to the user input by performing vector search. Pass text input only."""
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       embeddings = BedrockEmbeddings(
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           client=bedrock_runtime,
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           model_id=embedding_model_id,
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       )
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       text_as_embeddings = embeddings.embed_documents([query])
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       embedding_value = text_as_embeddings[0]
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       # get the vector search results based on the filter conditions.
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       response = collection.aggregate(
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           [
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               {
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                   "$vectorSearch": {
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                       "index": "awsanthropic_vector_index",
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                       "path": "details_embedding",
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                       "queryVector": embedding_value,
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                       "numCandidates": 200,
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                       "limit": 10,
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                   }
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               },
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               {
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                   "$project": {
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                       "score": {"$meta": "searchScore"},
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                       field_name_to_be_vectorized: 1,
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                       "_id": 0,
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                   }
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               },
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           ]
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       )
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       # Result is a list of docs with the array fields
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       docs = list(response)
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       # Extract an array field from the docs
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       array_field = [doc[field_name_to_be_vectorized] for doc in docs]
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       # Join array elements into a string
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       llm_input_text = "\n \n".join(str(elem) for elem in array_field)
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       # utility
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       newline, bold, unbold = "\n", "\033[1m", "\033[0m"
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       logging.info(
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           newline
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           + bold
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           + "Given Input From MongoDB Vector Search: "
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           + unbold
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           + newline
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           + llm_input_text
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           + newline
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       )
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       return llm_input_text

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    def get_session_history(session_id: str) -> MongoDBChatMessageHistory:
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       return MongoDBChatMessageHistory(
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           utils.get_MongoDB_Uri(),
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           session_id,
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           database_name="anthropic-travel-agency",
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           collection_name="chat_history",
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       )
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    def interact_with_agent(sessionId, input_query, chat_history):
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       """Interact with the agent and store chat history. Return the response."""
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       # Initialize bedrock and llm
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       bedrock_runtime = setup_bedrock()
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       llm = initialize_llm(bedrock_runtime)
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       # Initialize tools
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       tool_mongodb_search = StructuredTool.from_function(mongodb_search)
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       tool_mongodb_place_lookup_by_country = StructuredTool.from_function(
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           mongodb_place_lookup_by_country
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       )
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       tool_mongodb_place_lookup_by_name = StructuredTool.from_function(
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           mongodb_place_lookup_by_name
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       )
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       tool_mongodb_place_lookup_by_best_time_to_visit = StructuredTool.from_function(
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           mongodb_place_lookup_by_best_time_to_visit
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       )
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       tools = [
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           tool_mongodb_search,
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           tool_mongodb_place_lookup_by_country,
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           tool_mongodb_place_lookup_by_name,
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           tool_mongodb_place_lookup_by_best_time_to_visit,
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       ]
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       chat_message_int = MessagesPlaceholder(variable_name="chat_history")
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       memory = ConversationBufferMemory(
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           memory_key="chat_history",
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           chat_memory=get_session_history(sessionId),
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           return_messages=True,
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       )
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       PREFIX = """You are a helpful and polite Travel recommendations assistant. Answer the following questions as best you can using only the provided tools and chat history. You have access to the following tools:"""
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       FORMAT_INSTRUCTIONS = """Always return only the final answer to the original input question in human readable format as text only without any extra special characters. Also tell all the tools you used to reach to this answer in brief."""
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       SUFFIX = """Begin!"""
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       agent_executor = initialize_agent(
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           tools,
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           llm,
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           agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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           agent_kwargs={
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               "memory_prompts": [chat_message_int],
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               "input_variables": ["input", "agent_scratchpad", "chat_history"],
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               "prefix":PREFIX,
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               "format_instructions":FORMAT_INSTRUCTIONS,
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               "suffix":SUFFIX
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           },
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           memory=memory,
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           verbose=True,
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           max_iterations=3
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       )
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       result = agent_executor.invoke(
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           {
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               "input": input_query,
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               "chat_history": chat_history,
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           }
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       )
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       chat_history.append(HumanMessage(content=input_query))
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       chat_history.append(AIMessage(content="Assistant: " + result["output"]))
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       return result