vector-stores

Building blocks for rapid development of GenAI applications

#自然语言处理#Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.

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#大语言模型#A Python package to access different LLMs, embeddings, vector stores etc.

#自然语言处理#Course LangChain Chat with Your Data

#大语言模型#Developed A LLM Powered Recommendation System, Based on Instructor-XL, Google Flan / GPT3.5 and FAISS. Conducted a consumer survey to understand the problems of a consumer, created the problem stateme...

A proof of concept for Question-Answering API on a specific topic using RAG from pre-defined document(s) on the topic

Demonstrate two types of chat interactions with a Mattermost instance leveraging Mattermost OpenAPI v3 spec and Spring AI.

A simple demo of a Flutter application that implements Retrieval-Augmented Generation (RAG) using OpenAI's APIs.

This repository showcases Python scripts demonstrating interactions with various models using the LangChain library. From fine-tuning to custom runnables, explore examples with Gemini, Hugging Face, a...

#大语言模型#Examples of using different retrievers in LangChain, including Wikipedia, Vector Store, MMR, MultiQuery, and Contextual Compression retrievers. Demonstrates how to fetch relevant context for semantic ...

#大语言模型#Examples of integrating LangChain with vector stores for semantic search and RAG. Covers Chroma and FAISS. Ideal for learning how to store, index, and query embeddings with LLMs.