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Intern project task using langchain and chat with multiple document

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ChatBotIntern

Palm Mind

gemini

langchain

The provided code implements a chatbot system that utilizes Langchain for natural language processing and document indexing. Here's a breakdown of the code:

Setting Up Langchain and Indexing Documents: Langchain's OpenAI model is initialized with the provided API key. The construct_index function reads text documents from a specified directory path and constructs an index using VectorStoreIndex from llama_index.core. The index is saved to disk using pickle for later retrieval.

Collecting User Information: The collect_user_info function prompts the user to input their name, phone number, and email. It validates the input using regular expressions and calls the call_user function if the input is valid.

Initiating a Call: The call_user function simulates calling the user by printing a message with their provided information. You can add code here to initiate a real call using a telephony service like Twilio.

Asking the AI: The ask_ai function prompts the user to input queries. If the query is "call me," it triggers the user information collection process. Otherwise, it generates a response using Langchain and displays it.

Running the Chatbot: The ask_ai function is called with the constructed index to start the chatbot interaction. This code allows users to interact with the chatbot, ask queries, and request a call by providing their information. The chatbot generates responses based on the queries using Langchain's AI model.

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Intern project task using langchain and chat with multiple document

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