An LLM based Chatbot using Langchain
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Updated
Apr 19, 2024 - Jupyter Notebook
An LLM based Chatbot using Langchain
🤖 DataSciencePilot 🚀 is an innovative chat-based interface designed to interact with custom PDF files. It leverages the power of Pinecone for efficient vector database management and LLaMA-2 for advanced query response capabilities.
console based game based on a llm
RAG-based Streamlit app that uses Langchain, OpenAI Embeddings, GPT, and Pinecone Vector Database to answer questions about a user-provided document
Retrieval Augmented Generation Example with SemaDB
🔎📚 This document processing system is designed to efficiently analyze user documents and provide accurate responses to user queries related to the content. Powered by advanced algorithms, it offers a seamless experience for users seeking insights or information within their documents.
Spring AI RAG vector store sentiment search on custom data loaded by tiko with a REST API.
Trained chat-gpt 3.5 turbo model on 1000+ FAQs for students by vectorizing data using Pinecone DB. Used Langchain API & Reddit API for embedding & querying data, hosted w/ AWS Elastic Beanstalk.
"if-then-else" over topics made up of free-form sentences. Build conversations, not LLM chains!
NoSQL project
Streamline PDF data retrieval with PDFIntellect, harnessing the intelligence of LLMs via an intuitive Streamlit interface.
LangChain is a framework, which is very helpful and easy to build applications based on available Large Language Models.
Building and Deploying LLM models
RAG (Retrieval Augmented Generation) and vector search to translate natural language into SQL queries for PostgreSQL databases.
Elasticsearch demo of naive RAG
A chatbot powered by a vector database containing all US supreme court cases
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