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- Run `pnpm generate-embeddings` (or `generate-embeddings:yes`) to populate/update the vector store.
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- Run the embeddings generator to populate/update the vector store:
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- Prefer: `bun run src/generateEmbeddings.ts` (or `bun run src/generateEmbeddings.ts -y`)
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- If you have scripts wired: `bun run generate-embeddings` (or `generate-embeddings:yes`)
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2. Agents (TS)
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2. Agents (Python)
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- Add the new enum value to `packages/agents/src/types/index.ts` under `DocumentSource`.
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- Verify Postgres vector store accepts the new `source` and filters on it (`packages/agents/src/db/postgresVectorStore.ts`).
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- Add the new enum value to `python/src/cairo_coder/core/types.py` under `DocumentSource`.
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- Ensure filtering by `metadata->>'source'` works with the new value in `python/src/cairo_coder/dspy/document_retriever.py`.
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- Update the query processor resource descriptions in `python/src/cairo_coder/dspy/query_processor.py` (`RESOURCE_DESCRIPTIONS`). The module validates that every `DocumentSource` has a description.
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3. Retrieval Pipeline (Python)
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- Add the new enum value to `python/src/cairo_coder/core/types.py` under `DocumentSource`.
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- Ensure filtering by `metadata->>'source'` works with the new value in `python/src/cairo_coder/dspy/document_retriever.py`.
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- Update the query processor resource descriptions in `python/src/cairo_coder/dspy/query_processor.py` (`RESOURCE_DESCRIPTIONS`).
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- No extra steps beyond the above; the retriever already supports filtering by `metadata->>'source'`.
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4. Optimized Program Files (Python) — required
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@@ -34,7 +35,7 @@ When adding a new documentation source (e.g., a new docs site or SDK) make sure
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- Ensure the new source appears where appropriate (e.g., `/v1/agents` output and documentation tables):
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