Share Memory Across Users Using Graphs

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# Initialize the Zep client
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zep_client = AsyncZep(api_key="YOUR_API_KEY")  # Ensure your API key is set appropriately
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# Add one example user
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user_id = uuid.uuid4().hex
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await zep_client.user.add(
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    user_id=user_id,
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    first_name="Alice",
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    last_name="Smith",
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    email="[email protected]"
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)
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# Create a new thread for the user
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thread_id = uuid.uuid4().hex
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await zep_client.thread.create(
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    thread_id=thread_id,
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    user_id=user_id,
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)

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graph_id = uuid.uuid4().hex
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await zep_client.graph.create(graph_id=graph_id)
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product_json_data = [
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    {
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        "type": "Sedan",
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        "gas_mileage": "25 mpg",
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        "maker": "Toyota"
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    },
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    # ... more cars
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]
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json_string = json.dumps(product_json_data)
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await zep_client.graph.add(
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    graph_id=graph_id,
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    type="json",
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    data=json_string,
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)

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# Initialize the OpenAI client
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oai_client = OpenAI()
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async def chatbot_response(user_message, thread_id):
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    # Retrieve user memory
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    user_memory = await zep_client.thread.get_user_context(thread_id)
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    # Search the graph using the user message as the query
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    results = await zep_client.graph.search(graph_id=graph_id, query=user_message, scope="edges")
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    relevant_graph_edges = results.edges
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    product_context_block = "Below are some facts related to our car inventory that may help you respond to the user: \n"
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    for edge in relevant_graph_edges:
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        product_context_block += f"{edge.fact}\n"
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    # Combine context blocks for the developer message
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    developer_message = f"You are a helpful chat bot assistant for a car sales company. Answer the user's message while taking into account the following background information:\n{user_memory.context}\n{product_context_block}"
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    # Generate a response using the OpenAI API
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    completion = oai_client.chat.completions.create(
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        model="gpt-4o-mini",
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        messages=[
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            {"role": "developer", "content": developer_message},
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            {"role": "user", "content": user_message}
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        ]
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    )
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    response = completion.choices[0].message
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    # Add the conversation to memory
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    messages = [
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        Message(name="Alice", role="user", content=user_message),
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        Message(name="AI assistant", role="assistant", content=response)
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    ]
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    await zep_client.thread.add_messages(thread_id, messages=messages)
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    return response