Practical overview
When it fits
Use Techrag when building a technical-documentation RAG workflow where source-linked facts and graph context are useful alongside vector retrieval.
Who it is for
- Developers building retrieval-augmented question-answering systems for technical documents.
- Teams that want document retrieval to combine semantic search with structured entity and fact relationships.
- Python users who can configure OpenAI-compatible chat and embedding endpoints.
Documented uses
- Ingest PDF, Markdown and plain-text technical documents.
- Search documentation and ask questions through a CLI.
- Provide a language model with retrieved chunks and graph-derived facts linked to source material.
- Review answer metadata such as evidence coverage and reasoning score.
Requirements & limits
The project is a public Python repository with an MIT license. Its documented CLI setup uses uv and environment variables for OpenAI-compatible endpoints.
- The engine combines LanceDB vector search with ontology and knowledge-graph extraction rather than using semantic chunks alone.
- Atomic facts include source back-references, and answer metadata includes evidence coverage and a reasoning score.
- Chat and embedding models are accessed through separate OpenAI-compatible endpoints; embeddings are not produced by locally loaded transformer models.
- The README gives Ollama with qwen2.5:7b and bge-m3 as an example configuration.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Techrag with OpenAI-compatible model endpoints
Install the project and configure its chat and embedding endpoints before using the CLI.
Before you start
- Python project environment managed with uv
- OpenAI-compatible chat and embedding endpoints
- A local .env file based on .env.example or an existing .env
Steps
Run the command below in the project.
uv syncCopy
.env.exampleto.env, or use an existing.envfile.Set the LLM base URL, API key and model using the
ROE_LLM_*variables.Set the embedding base URL, API key and model using the
ROE_EMBED_*variables.
Expected outcome
The project dependencies are installed and endpoint settings are in place for the documented CLI workflow.
Guide limitations
- The guide covers setup only; it does not establish that a particular endpoint or model will work in a given environment.
- Embeddings are requested through an OpenAI-compatible endpoint; the code does not load local transformer models.
Ingest a document and query its contents
Use the documented CLI stages to add a supported document and ask a question against the indexed material.
Before you start
- Techrag installed and configured with OpenAI-compatible chat and embedding endpoints
- A PDF, Markdown or plain-text document
Steps
Ingest the document with the command below.
uv run roe ingestAsk a question with the command below to request an answer with audit information.
uv run roe query ""Use the command below when only an answer is wanted.
uv run roe ask ""
Expected outcome
The document enters the ingestion pipeline, and the query or ask command requests an answer using retrieved documentation context.
Guide limitations
- Results depend on the configured endpoints and the ingested source material.
- The README documents these commands but does not establish behavior for every document or environment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- FBR65/TechRAG · Official homepage title · retained in its source language