local-first semantic code search engine
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Updated
Jul 20, 2024 - Python
local-first semantic code search engine
Find Python Packages on PyPI with the help of vector embeddings
A simple web application to generate vector embeddings for PDF document, store them in a vector database (Pinecone), and enable semantic search and information retrieval using OpenAI's language models.
Vector embeddings generation for a csv file, storing embeddings in the vector database and query the csv file using openai language model
Nicolay is a digital history experiment that uses artificial intelligence to explore the speeches of Abraham Lincoln.
This repository demonstrates a workflow that integrates LangChain with a vector store (Pinecone) to enable semantic search and question answering using large language models (LLMs).
Learning semantic embeddings from OSM data: A Pytorch implementation of the loc2vec general method outlined in: https://sentiance.com/loc2vec-learning-location-embeddings-w-triplet-loss-networks.
AI Chatbot with Knowledge Base embeddings (prototype)
Seamlessly interact with PDF, CSV, Website and Handwritten Notes
Know Your Docs: Upload your documents and get instant answers to any questions related to them with this document knowledge platform
SSAT Analogy Solver: Test Vector Embedding Against Web Scrapped Questions
Use Cohere and OpenSearch to analyze customer feedback in an MLOps pipeline
Python scripts that converts PDF files to text, splits them into chunks, and stores their vector representations using GPT4All embeddings in a Chroma DB. It also provides a script to query the Chroma DB for similarity search based on user input.
V3CTRON | Vector Embeddings Data Retrieval | ChatGPT Plugin
Flask API for generating text embeddings using OpenAI or sentence_transformers
Semantic QA with a markdown database: Query any markdown file using vector embedding, Pinecone vector database and GPT (langchain). A weaker version of privateGPT
Detection as well as identification of faces
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