LlamaIndex is a data framework for your LLM applications
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Updated
Jun 13, 2024 - Python
LlamaIndex is a data framework for your LLM applications
Data Infrastructure for Multimodal AI: Data, models, and orchestration in a unified declarative interface.
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Memory for AI agents
Framework for benchmarking vector search engines
A Python client for Aeca database
A pure Python-implemented, lightweight, server-optional, multi-end compatible, vector database deployable locally or remotely.
Radient turns many data types (not just text) into vectors for similarity search, clustering, regression analysis, and more.
Optimized local inference for LLMs with HuggingFace-like APIs for quantization, vision/language models, multimodal agents, speech, vector DB, and RAG.
Simple Graph Memory for AI applications
Python client for Qdrant vector search engine
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Backend library for conversational AI in biomedicine
aerospike-vector-search-examples
A lightweight library that leverages Language Models (LLMs) to enable natural language interactions, allowing you to source and converse with data.
local-first semantic code search engine
Redis Vector Library (RedisVL) interfaces with Redis' vector database for realtime semantic search, RAG, and recommendation systems.
This project is a chatbot application that utilizes Langchain and Ollama libraries to manage and process user queries using a large language model (LLM).
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