feat: prompt - #7
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Pull request overview
This PR tweaks the RAG chat experience (retrieval size + prompt wording) and adjusts the data ingestion/training configuration that supports the guardrails and vector DB pipeline.
Changes:
- Increase Qdrant ColBERT query
limitand refine the assistant prompt text in the/predictendpoint. - Adjust ingestion chunk size (
max_tokens) for document chunking. - Update guardrails training configuration (notably
RandomForestClassifier.n_estimators) and a final summary print.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
ragia/app.py |
Updates retrieval limit and refines the LLM prompt used to answer user queries. |
ragia/03_train.py |
Modifies guardrails model training settings and adjusts the end-of-notebook output. |
ragia/01_ingest.py |
Changes document chunking size used before uploading vectors to Qdrant. |
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| Sumarize o contexto e responda de forma clara, objetiva e descontraída com no máximo 300 caracteres. | ||
| Sumarize a resposta com o contexto e responda de forma clara, objetiva e descontraída com no máximo 300 caracteres. Lembre-se que se trata de uma live aberta. |
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| clf = ensemble.RandomForestClassifier( | ||
| n_estimators=100, | ||
| n_estimators=500, | ||
| min_samples_leaf=3, | ||
| random_state=42, | ||
| ) |
| # %% | ||
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| print(np.mean(y)) | ||
| print(np.mean(y_train)) |
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