📜 Briefly utilizes open-source LLM's with text embeddings and vectorstores to chat with your documents
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Updated
Jun 30, 2024 - Python
📜 Briefly utilizes open-source LLM's with text embeddings and vectorstores to chat with your documents
We are going to showcase how to build a superhero character AI - where users can chat with their favourite superheroes.
IntelliSearch is an advanced retrieval-based question-answering and recommendation system that leverages embeddings and a large language model (LLM) to provide accurate and relevant information to users.
AI agent for automated content moderation of movies and books, employing Retrieval-Augmented Generation (RAG) and natural language processing Large Language Models to identify, discover, and summarize potentially concerning content for informed decision-making.
Final Project for Information Retrival, this is an implementation that uses numpy of a vector store and a RAG PoC with ollama
MedChat - ✨RAG based AI Chatbot🤖 for Indian Medicines 🇮🇳
LangChain framework provides chat interaction with RAG by extracting information from URL or PDF sources using OpenAI embedding and Gemini LLM
In this project, I built a chatbot to answer customer simple inquiries about restaurants, such as displaying the food menu and contact, and providing information about the number of available tables for reservation.
ZeroPal: A concise RAG example for LightZero QA.
MindSQL: A Python Text-to-SQL RAG Library simplifying database interactions. Seamlessly integrates with PostgreSQL, MySQL, SQLite, Snowflake, and BigQuery. Powered by GPT-4 and Llama 2, it enables natural language queries. Supports ChromaDB and Faiss for context-aware responses.
Pinecone Explorer: Unleash text exploration's power with Pinecone Explorer. Efficiently process and query large datasets using Pinecone's vectors, langchain's text handling, and OpenAI's language model.
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