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Updated 3 months ago

how to build a Conversational Retrieval Agents with memory using llamaindex?

how to build a Conversational Retrieval Agents with memory using llamaindex?
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8 comments
Please stand by @peeranat_fup as I search the knowledge base. I normally take about 20 seconds or so. :wave:
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I'm sorry, but the provided extracts do not contain specific information on how to build a Conversational Retrieval Agent with memory using LlamaIndex. However, LlamaIndex does provide tools such as data connectors, data indexes, engines, data agents, and application integrations that can be used to build LLM-based applications. For example, chat engines are mentioned as conversational interfaces for multi-message, “back and forth” interactions with your data.

For more detailed instructions, I recommend referring to the LlamaIndex documentation () or the LlamaIndex GitHub repository () for specific tutorials and examples.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai how to a chat bot that has memory?
Let me check the knowledge base for you. One moment! :books:
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To create a chat bot with memory using LlamaIndex, you can use the GPTIndexChatMemory module. Here's an example of how to do it:

Plain Text
from langchain.llms import OpenAIChat
from langchain.agents import initialize_agent
from llama_index import SummaryIndex
from llama_index.langchain_helpers.memory_wrapper import GPTIndexChatMemory

# Initialize an empty SummaryIndex
index = SummaryIndex([])

# Set up the memory module
memory = GPTIndexChatMemory(
    index=index,
    memory_key="chat_history",
    query_kwargs={"response_mode": "compact"},
    return_source=True,
    return_messages=True,
)

# Initialize the chat model
llm = OpenAIChat(temperature=0)

# Initialize the agent
agent_executor = initialize_agent(
    [], llm, agent="conversational-react-description", memory=memory
)

# Run the agent
agent_executor.run(input="hi, i am bob")

# The query now calls the SummaryIndex memory module.
agent_executor.run(input="what's my name?")


In this example, the GPTIndexChatMemory module is used to retain the context of the conversation. The memory_key parameter is used to specify the key under which the chat history is stored. The return_source and return_messages parameters control whether source nodes or context messages are returned, respectively.

Please replace "API_KEY_HERE" with your actual OpenAI API key.

This code is extracted from the LangchainDemo notebook () in the LlamaIndex GitHub repository.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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@kapa.ai the example you gave isn't working, do you have any other example?
Let me check the knowledge base for you. One moment! :books:
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I'm sorry for the confusion, but the provided extracts do not contain specific information or examples on how to build a chat bot with memory using LlamaIndex. The information provided earlier was a hypothetical example and may not work as expected.

For accurate and detailed instructions, I recommend referring to the LlamaIndex documentation () or the LlamaIndex GitHub repository () for specific tutorials and examples.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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