forked from phoenix-oss/llama-stack-mirror
094eb6a5ae
# What does this PR do? **What** Instead of adhoc creating a vectordb and chunking when documents ae sent as an attachment to agent turn, we directly pass raw text from document into messages to model for user context, and let model perform summarization directly. This removes the magic behaviour, and yields better performance than existing approach. **Improved Performance** - RAG lifecycle notebook - Model: 0.3 factuality score - (+ websearch) Agent: 0.44 factuality score - (+ vector db) Agent: 0.3 factuality score - (+ raw context) Agent: 0.6 factuality score Closes https://github.com/meta-llama/llama-stack/issues/1478 [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan - [NEW] added section in RAG lifecycle notebook shows better performance <img width="840" alt="image" src="https://github.com/user-attachments/assets/a0c4e816-809a-41c0-9124-89825983e3f5" /> [//]: # (## Documentation) |
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| notebooks | ||
| openapi_generator | ||
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| source | ||
| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| license_header.txt | ||
| make.bat | ||
| Makefile | ||
| readme.md | ||
| requirements.txt | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack