https://github.com/truongnhatbui/qabotwebapp
Construct a QA Bot That Leverages the LangChain and LLM to Answer Questions from Loaded Documents
https://github.com/truongnhatbui/qabotwebapp
genai genai-chatbot langchain large-language-models llm python
Last synced: 5 months ago
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Construct a QA Bot That Leverages the LangChain and LLM to Answer Questions from Loaded Documents
- Host: GitHub
- URL: https://github.com/truongnhatbui/qabotwebapp
- Owner: truongnhatbui
- Created: 2024-12-26T12:31:15.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-26T12:48:48.000Z (10 months ago)
- Last Synced: 2025-02-17T21:46:27.338Z (8 months ago)
- Topics: genai, genai-chatbot, langchain, large-language-models, llm, python
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Build a QA Bot Web App
A QA bot is an automated system designed to answer questions posed by users. These bots can handle a wide range of queries, providing accurate and contextually relevant responses. When integrated with LangChain and an LLM, a QA bot can analyze large documents and deliver precise answers based on the content within those documents.
It simulates a real-world scenario where you are required to build a bot that will leverage LangChain and a large language model (LLM) to answer questions based on content from loaded PDF documents.
The project involves several key components and techniques including:
Document loading using document loaders
Splitting long documents using text splitters
Generating embeddings using embedding models
Storing embeddings using vector databases
Defining retrievers
Setting up Gradio as the front-end interface
Using these elements, you will build a fully functional question-answering (QA) system. By leveraging LangChain and an LLM, the bot will be able to read and understand the content of loaded PDF documents and provide accurate and relevant answers to user queries.