I built proxy that keeps RAG working while hiding PII
Consistent pseudonymization beats redaction when RAG embeddings must survive.

LLM operators inside DuckDB SQL with WASM support and multi-provider swapping.
Data engineers and analysts using DuckDB for AI workflows
MindsDB · pgvector · LanceDB
In this new release, we've added a few major updates:
- Anthropic (Claude) & Multi-Provider Support: We now support OpenAI, Azure, Ollama, and Anthropic. You can define a model once with CREATE MODEL, then swap providers on the admin side without changing the downstream SQL queries.
- WASM Support: Flock now compiles and runs entirely inside DuckDB-WASM.
- LLM Metrics Tracking: We added end-to-end observability so you can track token usage, latency, and call counts for all LLM invocations directly within a query.
- Audio Transcription: We expanded our multimodal capabilities to include audio transcription, alongside our existing image support.
You can try it out via the community extension catalog: INSTALL flock FROM community;.
We'd love your feedback and contributions! Let us know what you think of the architecture or if you run into any edge cases.
Docs: https://dais-polymtl.github.io/flock/ Paper: https://dl.acm.org/doi/10.14778/3750601.3750685
Consistent pseudonymization beats redaction when RAG embeddings must survive.
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Structured memory layers for agents—but vector search already solves this problem.
Parallel translation comparison beats single-source RAG for theological accuracy.
Full ReAct agent loop running locally with WebGPU and Pyodide—no server required.