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# 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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| agents | ||
| datasetio | ||
| eval | ||
| inference | ||
| ios/inference | ||
| post_training | ||
| safety | ||
| scoring | ||
| telemetry | ||
| tool_runtime | ||
| vector_io | ||
| __init__.py | ||