neuml/txtai
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
2 releases in the last 90 days, the latest 34 days ago, with 90 contributors on GitHub.
Use Cases & Benefits
- Provides an all-in-one AI framework for semantic search, large language model orchestration, and language model workflows.
- Combines vector indexes, graph networks, and relational databases to enable powerful, scalable semantic search and LLM-powered applications locally or at scale.
- Use for building semantic search engines that understand natural language meaning beyond keywords across text, images, audio, and video.
- Use for orchestrating autonomous AI agents and retrieval augmented generation (RAG) pipelines to create context-aware chatbots and knowledge-driven workflows.
- Use for constructing language model workflows that integrate multiple specialized models for tasks like summarization, transcription, translation, and text-to-speech.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
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From the README
txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.
The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.
This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.
Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.
txtai is built with Python 3.10+, Hugging Face Transformers, Sentence Transformers and FastAPI. txtai is open-source under an Apache 2.0 license.
New vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?
The following sections introduce common txtai use cases. A comprehensive set of over 70 example notebooks and applications are also available.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+331 stars over 73 days across 8 snapshot days; best day +77 on Nov 10.
Forks
984+98
Open issues
18+9
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| D4Vinci/Scrapling | 101.1K | +6.3K | 75 | 6 days ago |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| heygen-com/hyperframes | 82.3K | +4.0K | 75 | today |
| harry0703/MoneyPrinterTurbo | 142.0K | +3.8K | 75 | 3 days ago |
| github/spec-kit | 149.2K | +2.6K | 75 | today |
| neuml/txtai | 13.2K | — | 58 | 34 days ago |
| Category median | 115.8K | +4.0K | 75 | — |
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