whyhow-ai / rule-based-retrievalLinks
The Rule-based Retrieval package is a Python package that enables you to create and manage Retrieval Augmented Generation (RAG) applications with advanced filtering capabilities. It seamlessly integrates with OpenAI for text generation and Pinecone or Milvus for efficient vector database management.
☆246Updated 10 months ago
Alternatives and similar repositories for rule-based-retrieval
Users that are interested in rule-based-retrieval are comparing it to the libraries listed below
Sorting:
- Automated knowledge graph creation SDK☆122Updated 8 months ago
- Repository to demonstrate Chain of Table reasoning with multiple tables powered by LangGraph☆147Updated last year
- Tuning and Evaluation of RAG pipeline. (Automated optimization to be added soon)☆264Updated last year
- Python SDK for running evaluations on LLM generated responses☆291Updated last month
- Schemas for WhyHow's automated knowledge graph creation SDK☆91Updated 11 months ago
- Semantic layer on top of a graph database to provide an LLM with a set of robust tools to interact with the database☆235Updated last year
- Benchmark various LLM Structured Output frameworks: Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, etc on task…☆173Updated 10 months ago
- End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.☆134Updated 7 months ago
- AGI SDK☆356Updated this week
- This project enhances the construction of RAG applications by addressing challenges, improving accessibility, scalability, and managing d…☆146Updated last year
- FastAPI wrapper around DSPy☆258Updated last year
- The long-term memory for your Superagents 🥷and LLMs 🤖. Built with GraphRAG, Knowledge graphs and autonomous ai agents☆63Updated 6 months ago
- An Awesome list of curated DSPy resources.☆390Updated 5 months ago
- Visualize Different Text Splitting Methods☆283Updated 7 months ago
- LLM-driven automated knowledge graph construction from text using DSPy and Neo4j.☆187Updated last year
- 🦜💯 Flex those feathers!☆253Updated 9 months ago
- ☆185Updated last year
- Super performant RAG pipelines for AI apps. Summarization, Retrieve/Rerank and Code Interpreters in one simple API.☆381Updated last year
- ☆173Updated last year
- In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples.☆434Updated last year
- LangChain chat model abstractions for dynamic failover, load balancing, chaos engineering, and more!☆82Updated last year
- Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, ba…☆252Updated last week
- ☆222Updated last year
- Research repository on interfacing LLMs with Weaviate APIs. Inspired by the Berkeley Gorilla LLM.☆133Updated last month
- Task-based Agentic Framework using StrictJSON as the core☆455Updated 2 weeks ago
- ☆122Updated 5 months ago
- GraphRAG database - hybrid graph / vector db☆127Updated 10 months ago
- An example of multi-agent orchestration with llama-index☆429Updated 6 months ago
- ☆89Updated last year
- ☆271Updated last year