Add files via upload - #74
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Signed-off-by: Zyntro <zyntro.ai.studio@gmail.com>
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| # --- Placeholders for the imports referenced above (wire up for real) ---- | ||
| async def create_session(): ... |
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| # --- Placeholders for the imports referenced above (wire up for real) ---- | ||
| async def create_session(): ... | ||
| async def generate_embedding(text: str) -> list[float]: ... |
| # --- Placeholders for the imports referenced above (wire up for real) ---- | ||
| async def create_session(): ... | ||
| async def generate_embedding(text: str) -> list[float]: ... | ||
| async def upsert_embedding(doc_id: UUID, vector: list[float]) -> None: ... |
| async def create_session(): ... | ||
| async def generate_embedding(text: str) -> list[float]: ... | ||
| async def upsert_embedding(doc_id: UUID, vector: list[float]) -> None: ... | ||
| async def mark_doc_failed(doc_id: UUID, reason: str) -> None: ... |
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feat(skills): add fastapi-research SKILL.md with endpoint conventions
Adds Claude Code skill defining Pydantic Create/Response split,
async route standards, DI, and background-task patterns for
FastAPI research endpoints. Scoped to api/, services/, research/,
models/, schemas/.
feat(skills): add langchain-research SKILL.md with RAG chain conventions
Adds Claude Code skill defining LCEL chain composition, prompt
decoupling, structured output via Pydantic parsers, and retry
handling for LangChain research pipelines. Scoped to chains/,
prompts/, vectorstores/.
docs(examples): add reference FastAPI endpoint for doc_embeddings ingestion
Illustrates fastapi-research skill conventions: async route,
BackgroundTasks for embedding generation, paginated list endpoint.
Not wired to real Supabase client yet — placeholders left for
create_session/generate_embedding/upsert_embedding.
docs(examples): add reference LangChain RAG chain for doc_embeddings
Illustrates langchain-research skill conventions: SupabaseVectorStore
retriever, PydanticOutputParser for structured answers, tenacity
retry wrapper. Pairs with the FastAPI ingestion example.
docs(examples): add reference LangChain RAG chain for doc_embeddings
Illustrates langchain-research skill conventions: SupabaseVectorStore
retriever, PydanticOutputParser for structured answers, tenacity
retry wrapper. Pairs with the FastAPI ingestion example.