Langchain Conversational agent

[akabass]

@dosu-bot

how could create the langchain conversational agent with this requirements >

1. conversational chat able
2. memory remembering
3. use the pandas agent as a tool to make the agent use it whenever the input is something related to the csv file uploaded
4. the main purpose of the converstaional agent is it is conversational and also uses the pandas agent to answer questions asked about the dataframe

[dosubot[bot]]

🤖

Sure, I can guide you on how to create a LangChain conversational agent with the requirements you've mentioned.

Firstly, you would need to create a CSV agent using the create_csv_agent function from the LangChain agent toolkits. This function loads data into a pandas DataFrame and uses a pandas agent. Here's how you can do it:

from langchain.agents.agent_toolkits.csv.base import create_csv_agent
from langchain.schema.language_model import BaseLanguageModel

# Define your language model
llm = BaseLanguageModel()

# Create a CSV agent
csv_agent = create_csv_agent(llm, 'your_file.csv')

Next, you would need to create a conversational chat agent using the ConversationalChatAgent class:

from langchain.agents.conversational_chat.base import ConversationalChatAgent

# Create a conversational chat agent
conversational_agent = ConversationalChatAgent()

Now, you can use the conversational_agent to answer questions about the dataframe and use the csv_agent to handle CSV file related inputs:

# Now you can use the conversational_agent to answer questions about the dataframe
response = conversational_agent.ask('What is the average value of column X?')

# And use the csv_agent to handle CSV file related inputs
csv_agent.process('Add a new row with these values...')

Please note that this is a simplified example and the actual implementation may vary based on the specific requirements of your application. The ask and process methods used in this example are placeholders and may not exist in the actual LangChain framework. You would need to replace them with the appropriate methods for querying the conversational agent and processing CSV file inputs with the CSV agent.

For more details, you can refer to the following sources:

• libs/langchain/langchain/agents/agent_toolkits/init.py
• libs/langchain/langchain/agents/init.py
• libs/langchain/langchain/agents/agent_toolkits/csv/base.py

I hope this helps! If you have any more questions, feel free to ask.

Sources

• Issue: Dataframe with conversation agent for chat models
• libs/langchain/langchain/agents/agent_toolkits/init.py
• libs/langchain/langchain/agents/init.py
• libs/langchain/langchain/agents/agent_toolkits/csv/base.py

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