RA

RAG Firewall – retrieval-time guardrails for LangChain/LlamaIndex

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RAG Firewall – retrieval-time guardrails for LangChain/LlamaIndex

RAG pipelines are great, but they can still retrieve "toxic" chunks: – prompt injection attempts – leaked API keys/secrets – stale or conflicting content – unapproved external URLs We built an open-source "retrieval firewall" that scans chunks before they reach the LLM: – denies injection & secrets – flags/reranks PII, encoded blobs, untrusted URLs – audit log (JSONL) of all decisions – drop-in wrappers for LangChain and LlamaIndex retrievers Install: pip install rag-firewall Repo: https://github.com/taladari/rag-firewall Curious if others here handle retrieval-time risks, or just ingest/output filtering. Would love feedback and red-team payloads.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, pipe, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
18%18% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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