Practical overview
When it fits
Use it when a self-hosted document-search service needs configurable retrieval, LLM providers and application integrations.
Who it is for
- Teams building searchable collections from internal documents.
- Developers integrating document retrieval into Python applications, REST services or agent frameworks.
- Operators who want to configure a local Ollama provider or a cloud LLM provider.
Documented uses
- Ingest supported documents into a named searchable store.
- Find relevant material with keyword, semantic or hybrid search.
- Generate answers with an LLM provider and link results to source documents and chunks.
- Expose document search to another application through the Python SDK or REST API.
Requirements & limits
- Docker and Python package installation are both documented deployment paths.
- The README lists Gemini, OpenAI, Anthropic Claude, Ollama and OpenAI-compatible providers; each requires provider-specific configuration.
- The core package covers a subset of file parsing; PDF and office-document parsers and image OCR are optional extras, with Tesseract required for OCR.
- SQLite is listed as the default storage, while PostgreSQL with pgvector and Redis caching are optional.
- The repository specifies an MIT license.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the Python package and configure a provider
Prepare a Python installation with the REST API extra and credentials for one supported LLM provider.
Before you start
- Python 3.8 or later, as stated in the repository README.
- A configured provider credential; the documented Gemini option uses GEMINI_API_KEY.
- For the REST API server installed from Python, the api extra provides FastAPI and uvicorn.
Steps
Install the package with the command below.
pip install flamehaven-filesearch[api]Set
GEMINI_API_KEYfor the default Gemini provider, or configure the documented variables for another provider.Set
FLAMEHAVEN_ADMIN_KEYas required by the README configuration.
Expected outcome
The Python package and its REST API dependencies are installed, with the documented environment configuration prepared.
Guide limitations
- These steps cover installation and configuration only; they do not demonstrate starting the server or completing an upload and search.
- Other document parsers, image OCR, vector indexing and PostgreSQL support are listed as separate extras.
Compare options for Retrieval-augmented applications
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Flamehaven Filesearch | Ingest supported documents into a named searchable store. | Docker, Self-hosted, Server | |
| Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And Pdfs | Retrieve relevant passages from web and PDF content to answer a question. | Web | |
| LangChain in Supabase Edge Functions | Initialize and start a Supabase project locally. | Cloud, Server |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- flamehaven01/Flamehaven-Filesearch · Official homepage title · retained in its source language

