llm-applications
Comprehensive guide to building RAG-based LLM applications for production
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
The llm-applications repository offers focused guidance on deploying RAG-based LLM apps in production environments with an emphasis on using Ray.
Good fit when
- You require a detailed guide specifically tailored to the development and deployment of RAG-based applications, leveraging Ray for performance and scalability.
- Your team has experience or interest in working with Jupyter Notebooks as the primary format for tutorials and documentation.
Avoid when
- If you are looking for a more generalized approach to LLM application development that does not specifically cater to RAG-based designs and Ray optimizations.
- When your project workflow is incompatible with or cannot support Jupyter Notebook dependencies and the resources assume.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (35d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/ray-project/llm-applicationsSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Offers instructions and resources focussed on developing large language model applications using Ray for production environments.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
OPENAI_API_BASE="https://api.openai.com/v1"Source link
Source: README excerpt (regex_v1, Sep 19, 2026)
pip install --user -r requirements.txtSource link
Source: README excerpt (regex_v1, Sep 19, 2026)
We'll be using [OpenAI](https://platform.openai.com/docs/models/) to access ChatGPT models like `gpt-3.5-turbo`, `gpt-4`, etc. and [Anyscale Endpoints](https://endSource link
Tags
README
LLM Applications A comprehensive guide to building RAG based LLM applications for production. Blog post : https://www.anyscale.com/blog/a comprehensive guide for building rag based llm applications part 1 GitHub repository : https://github.com/ray project/llm applications Interac...
For agents
This page has a .md twin and JSON over the API.