Flamehaven Filesearch
Self-hosted RAG search engine for private document search — BM25 plus hybrid retrieval across 34 file formats.
If your corpus can't touch a vendor cloud, this is one of the few RAG stacks you can audit before you trust it — retrieval, key hashing, rate limits and audit logs all sit in a public MIT repository, not behind a demo call. The cost is genuine: you own the host, the model endpoint and the upgrades, and the docs lean heavily on the README. Buy it for the transparency, budget for the ops.
Verified 5d ago · liveness 61/100 · cite: rightaichoice.com/tools/flamehaven-filesearch
- Teams that cannot send sensitive documents to third-party SaaS
- Security and compliance owners who need audit logs and per-key rate limits
- Python and FastAPI developers comfortable running Docker in production
- Organizations standardizing on a local Ollama model for air-gapped retrieval
- Teams wanting a fully managed cloud service with no containers to run
- Non-technical users who need a point-and-click document search UI
- Organizations with nobody to own Docker host maintenance and upgrades
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Skip Flamehaven Filesearch if nobody on your team wants to own a Docker host and model credentials, or if you need vendor support SLAs and a procurement contract attached to your search stack.
You pay for the compute yourself: the Docker host plus any hosted Gemini, OpenAI or Claude calls are your bill, and a large document corpus means a larger instance.
Self-hosting the MIT-licensed engine avoids a subscription, but the total cost of ownership sits in the infrastructure: a Docker host and either hosted model API spend or the hardware to run Ollama locally. That trades against per-seat managed document-QA services, where you pay monthly per user but nobody on your team owns the host, the credentials or the upgrades.
In short
Flamehaven Filesearch — Self-hosted RAG search engine for private document search — BM25 plus hybrid retrieval across 34 file formats. Best for Teams that cannot send sensitive documents to third-party SaaS, Security and compliance owners who need audit logs and per-key rate limits, Python and FastAPI developers comfortable running Docker in production. Free to use.
What's new in Flamehaven Filesearch
Checked 5 days agoAcross the latest 2 updates: 1 changelog entry and 1 news mention.
Repository updated September 03, 2026
The public repository shows a September 03, 2026 update, with the project remaining MIT-licensed, Python-based and publicly readable with 108 stars and 14 forks.
Announcements feed synced Sep 15, 2026
The project page's announcements feed is synced to September 15, 2026 and shows no mirrored announcements yet, meaning repository activity is the primary source of release information.
What people actually say about Flamehaven Filesearch — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
3 mentions across 2 sources (Hacker News, GitHub) · researched Jul 4, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Self-hosted keeps documents private on your infrastructure.
- +Supports 34 file formats for broad document indexing.
- +Hybrid retrieval combining BM25 and semantic search.
- +Multi-LLM integration: Gemini, OpenAI, Claude, Ollama.
- +FastAPI + Docker setup claims production-ready in 3 minutes.
- −Community feedback is too sparse to validate claims.
- −Scalability and performance under load are untested.
- −No listed integrations or plugin ecosystem yet.
- −Documentation quality and depth remain unknown.
- −Single-developer project poses long-term risk.
- • Self-hosting requires your own infrastructure and maintenance
Viability Score
How well maintained and how widely used is Flamehaven Filesearch? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Self-hosted RAG search engine for private document search
- BM25 keyword retrieval combined with vector hybrid search
- Source-grounded answers traced back to retrieved documents
- Document parsing across 34 file formats
- Multi-LLM support: Gemini, OpenAI, Claude and Ollama
- Ollama option for fully offline or air-gapped retrieval
- Docker deployment described as production-ready in about 3 minutes
- Python / FastAPI backend
- API key hashing with SHA256 plus salt
- Per-key rate limiting, default 100 requests per minute
- Granular per-key permission control
- Complete request audit logging
- OWASP security headers enabled by default
- Lazy imports for LangChain, LlamaIndex, CrewAI and Haystack SDKs
- Natural language querying over indexed document repositories
About Flamehaven Filesearch
Flamehaven Filesearch is a self-hosted RAG search engine built for teams whose documents cannot leave their own infrastructure. It runs BM25 keyword retrieval alongside vector hybrid search across 34 file formats, then grounds every answer in retrieved source text so a response traces back to a document you control rather than a model's memory. Deployment is Docker-first: the vendor claims a production-ready instance in about three minutes, on a Python/FastAPI backend maintained by Flamehaven (Kwansub Yun), an independent B2B AI systems studio. Model choice is yours — Gemini, OpenAI, Claude, or Ollama — and the Ollama path is what makes air-gapped and regulated sites realistic, since the entire retrieval loop can stay on your hardware. Operational security is part of the engine, not a bolt-on: API keys hashed with SHA256 plus salt, per-key rate limiting defaulting to 100 requests per minute, granular permissions, complete request audit logging, and OWASP security headers on by default. Framework SDKs including LangChain, LlamaIndex, CrewAI and Haystack are imported lazily, so you install only what your stack actually calls. The public repository is MIT licensed and readable end to end; as of the September 2026 scrape it shows 108 stars, 14 forks, and a last update of September 03, 2026, with release history tracked in CHANGELOG.md because the announcements feed carries no mirrored posts yet. The trade is deliberate: you absorb Docker and FastAPI operations, and in return no document crosses your perimeter and the retrieval layer stays inspectable.
Behind the Verdict
The reason to pick Flamehaven Filesearch is not that it retrieves better than a managed document-QA service. It might, it might not — nobody outside your corpus can tell you. The reason is that you can read the code that touches your documents, and that the whole loop can run on your hardware via Ollama when policy says it has to. That makes it a fit for a narrow but real buyer: a Python team already running Docker, with a compliance owner who needs per-key permissions, 100/min default rate limits and a complete request log they can hand to an auditor. If that describes you, the 3-minute deployment claim is plausible and the operational security features save you from bolting on your own key management. Pass when nobody on staff owns a Docker host. Containers need patching, model endpoints time out, and the upgrade path for a self-hosted retrieval engine is not a background service you can forget. Teams without that person are better served by a hosted document-QA product, even at the cost of sending documents out. Two caveats we'd flag before you commit. First, the announcements feed has no mirrored posts as of September 15, 2026, so release information lives in CHANGELOG.md and the repository — check commit cadence yourself before betting a production pipeline on it. Second, the repository is small at 108 stars and 14 forks, which means a thin third-party knowledge base when something breaks; plan on reading the source rather than finding a Stack Overflow answer. Neither is disqualifying, but both shift effort onto your team. Where it bites is retrieval tuning. BM25 plus hybrid search needs chunking and weighting decisions that generic SaaS hides from you; here they're yours to make, which is the point and also the work. We'd spin it up on a pilot corpus with the
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Real-world workflow fit
Concrete scenarios for the personas Flamehaven Filesearch actually fits — and what changes day-one when you adopt it.
They clone the repository, run the Docker deployment on an internal host, point it at an Ollama instance so nothing leaves the network, and index the policy and procedure document set. Each API key is scoped to a team with its own permissions.
Outcome: Internal search answers cite the source document, the OWASP headers and audit logging are already on, and the security review has something concrete to read rather than a vendor questionnaire.
They run the FastAPI service locally, import the LangChain or LlamaIndex SDK they already use, and call the search API from their application; hybrid BM25-plus-vector retrieval handles both exact identifiers and paraphrased questions.
Outcome: The feature ships without them writing a chunking, embedding and reranking pipeline from scratch, and they install only the SDK their stack actually calls thanks to lazy imports.
They review the MIT-licensed source directly, confirm SHA256-plus-salt key hashing and per-key rate limiting, and check that every search request is recorded in the audit log.
Outcome: They can sign off on the retrieval layer from the code itself instead of relying on a vendor's security page.
Use Cases
- Index a private document repository and query it in natural language with answers cited to source.
- Run a self-hosted RAG server behind a corporate firewall so no document leaves the perimeter.
- Combine BM25 keyword search with vector retrieval so part numbers and paraphrased questions both work.
- Keep a complete audit log of every search request for compliance and internal review.
- Point retrieval at a local Ollama model for an air-gapped document search stack.
- Drop a RAG service into an existing FastAPI application through its HTTP API.
Models Under the Hood
as of 2026-10-17
Limitations
- You run the infrastructure: Docker host, model endpoint, credentials and upgrades are all yours to maintain.
- The default per-key rate limit of 100 requests per minute is a starting cap, not a tuned production value, and busy internal deployments will need it raised.
- Documentation is concentrated in the repository README and docs rather than a dedicated documentation site, so whoever deploys this should be comfortable reading source.
- The September 2026 scrape shows 108 stars and 14 forks — a small project, which means a small community to draw on when you hit an issue.
as of 2026-10-02
Verification history
We have re-verified Flamehaven Filesearch 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Flamehaven Filesearch tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Engineering teams with Docker and Python skills who need private document search and would rather run the infrastructure than pay per seat for a managed service.
What this tier adds
Starting tier — MIT-licensed, free to self-host, with BM25 plus hybrid retrieval, 34 file formats, multi-LLM support and the full security layer included.
Where the pricing makes sense
The company stage and team size where Flamehaven Filesearch's pricing actually pencils out — and where peers do it cheaper.
Self-hosting the MIT-licensed engine avoids a subscription, but the total cost of ownership sits in the infrastructure: a Docker host and either hosted model API spend or the hardware to run Ollama locally. That trades against per-seat managed document-QA services, where you pay monthly per user but nobody on your team owns the host, the credentials or the upgrades.
Setup time & first value
How long it actually takes to get something useful out of Flamehaven Filesearch — broken out by persona, not the marketing-page minute.
For a developer who already runs Docker, the vendor describes production-ready deployment in about three minutes — pull the repository, start the container, supply model credentials and index a document set. Budget an afternoon rather than minutes to reach real value: indexing your first corpus and tuning the 100 requests/min per-key default to your traffic is the part that takes the time. If
Switching to or from Flamehaven Filesearch
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a managed document-QA SaaS: stand up the Docker deployment, point it at Ollama or your preferred provider, and re-index the same corpus so nothing leaves your perimeter.
- →From a hand-rolled LangChain or LlamaIndex pipeline: keep your existing SDK and call the Filesearch API instead of maintaining chunking and retrieval code yourself.
- →From a plain keyword search index: add the engine alongside it and compare hybrid BM25-plus-vector results on the queries your users actually type.
- ↗To a managed document-QA service: re-upload the corpus and rebuild the query layer, accepting that documents now leave your infrastructure.
- ↗To a bespoke RAG build: lift the retrieval approach rather than the code — you already know your 34-format parsing and hybrid ranking requirements from running this.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Flamehaven Filesearch”, and we withheld 6: 6 did not mention Flamehaven Filesearch. We are showing none, because we could not prove any of them are about Flamehaven Filesearch.
Official links
Tools that pair well with Flamehaven Filesearch
Common stack mates teams adopt alongside Flamehaven Filesearch, with the specific reason each pairing earns its keep.
RAGFlow
Open-source RAG engine with built-in ETL, hybrid vector/BM25 search and visual agent orchestration for enterprise AI agents.
Sourcebot
Self-hosted code search and AI code Q&A that gives your coding agents context across every repo you own
Pinecone
Pinecone is a managed vector database and knowledge engine for RAG, semantic search, and agent retrieval.
Featured Head-to-Head Comparisons
Flamehaven Filesearch vs Spider Cloud
For teams needing private, secure document RAG with no cloud dependency, Flamehaven Filesearch is the clear winner—it’s free, self-hosted, and packed with governance features. If your AI agents need live web data at scale, Spider Cloud’s freemium API with AI extraction and browser automation is the go-to pick. The two tools complement each other rather than compete directly.
Flamehaven Filesearch vs Temporal Ai
Choose Flamehaven Filesearch if your priority is a free, self-hosted RAG engine with strong security and multi-LLM support for private document search. Choose Temporal AI if you need durable execution, automatic retries, and workflow orchestration for AI agents or complex business processes, especially when you can leverage its freemium cloud tier. They solve different problems, but if your need is reliable AI agent pipelines, Temporal offers unmatched resilience.
Flamehaven Filesearch vs Screenplayiq
Choose Flamehaven Filesearch if you need a private, secure, self-hosted RAG engine for document search across 34 file formats, with flexible LLM backends and granular access control—ideal for teams handling sensitive data. Choose ScreenplayIQ if you're a screenwriting professional seeking AI-driven narrative analysis and box office predictions to evaluate script marketability. They serve entirely different domains; the decision hinges on your primary need: document search vs. screenplay analysis.
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