Chatbot with real time data

[surabhi-cb]

I want to create a chatbot which is capable to return answers from real-time based data.

[dosubot[bot]]

🤖

Hi @surabhi-cb! I'm Dosu, a friendly bot here to assist you while we're waiting for a human maintainer. I'm here to help you solve bugs, answer questions, and guide you on becoming a contributor. Let's get started!

To create a chatbot using the LangChain framework that can provide responses based on real-time data, you can utilize the ConversationalChatAgent class. This class is designed to facilitate a conversation and use tools for processing and response generation.

Here's a step-by-step guide:

1. Create an instance of the BaseLanguageModel class. This instance represents the language model that your chatbot will use to generate responses.

2. Create a sequence of BaseTool instances. These tools will be used by the chatbot to process the input and generate responses.

3. Create an instance of the ConversationalChatAgent class using the from_llm_and_tools class method. This method accepts the following parameters:

   • llm: The instance of the BaseLanguageModel class.
   • tools: The sequence of BaseTool instances.
   • callback_manager: An optional instance of the BaseCallbackManager class. This parameter can be used to manage callbacks.
   • output_parser: An optional instance of the AgentOutputParser class. This parameter can be used to parse the output of the agent.
   • system_message: An optional string that represents the system message.
   • human_message: An optional string that represents the human message.
   • input_variables: An optional list of strings that represent the input variables.

Here's an example of how you can create a chatbot:

from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain.agents.conversational_chat.base import ConversationalChatAgent

# Create an instance of the BaseLanguageModel class
llm = BaseLanguageModel()

# Create a sequence of BaseTool instances
tools = [BaseTool(), BaseTool()]

# Create an instance of the ConversationalChatAgent class
chatbot = ConversationalChatAgent.from_llm_and_tools(llm, tools)

Please note that you need to replace BaseLanguageModel() and BaseTool() with the actual instances of the language model and tools that you want to use.

As for the specific functionalities of the BaseLanguageModel class in the LangChain framework, I wasn't able to find detailed information within the repository. It's possible that this information is available elsewhere or I may have missed it. If you have more specific questions about this class or need further assistance, please provide more details.

You can find more information about the ConversationalChatAgent class in the source code.

Sources

• libs/langchain/langchain/agents/conversational_chat/base.py

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[pr0mila]

🔥 GroqStreamChain is here! I built real-time AI chat app powered by FastAPI, WebSocket, LangChain and Groq. 💬 Seamless, interactive AI responses for building smarter chatbots! 🤖

Explore the project: https://github.com/pr0mila/GroqStreamChain