Practical overview
When it fits
Use it when building a Python RAG application where knowledge-graph relationships, evidence traceability or graph-level tenant separation are useful to the retrieval design.
Who it is for
- Python developers adding graph-based retrieval to generative AI applications.
- Teams building document question-answering workflows that need connected-fact retrieval or source-context inspection.
- Developers prototyping applications that separate tenant data by using distinct graph names.
Documented uses
- Ingest text into a FalkorDB-backed knowledge graph for use in an RAG workflow.
- Retrieve connected evidence through graph relationships when relevant facts span multiple passages.
- Generate an answer from retrieved graph context and expose that context for application-level review.
- Prototype a tenant-separated GraphRAG application using named graphs.
Requirements & limits
The repository is public and identifies the project as Apache-2.0 licensed. The documented quick start requires Python 3.10 or later and a running FalkorDB instance.
- The documented architecture depends on FalkorDB as its graph database.
- The README quick start uses LiteLLM with OpenAI model names and an OpenAI API key; the supplied material does not establish other provider integrations.
- The SDK separates ingestion, graph finalization and completion into distinct stages.
- PDF ingestion is described as requiring an additional pdf package extra.
- The project documents source-chunk provenance and retrieval-context access, which developers can use for application-level validation.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the documented GraphRAG quick start
Prepare the local dependencies and Python objects shown for ingesting text and requesting a graph-grounded answer.
Before you start
- Python 3.10 or later
- FalkorDB running locally
- The graphrag-sdk[litellm] package
- An OpenAI API key, as shown in the setup commands
Steps
Install the SDK with its LiteLLM extra and start FalkorDB using the README’s Docker command.
Set the OPENAI_API_KEY environment variable.
Create a GraphRAG instance with a FalkorDB connection, an LLM and an embedder.
Ingest text with a document ID, call finalize(), then submit a question with completion().
Expected outcome
The example prints graph node and relationship counts and the answer returned for a question about the ingested text.
Guide limitations
- The example documents text ingestion; PDF ingestion requires the separate pdf extra.
- The example specifies OpenAI models and credentials; broader provider support is not detailed in the cited setup material.
- This is documented setup guidance, not a claim that the steps were executed or verified.
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 |
|---|---|---|---|
| Graphrag SDK | Ingest text into a FalkorDB-backed knowledge graph for use in an RAG workflow. | Cloud, Server | The repository is public and identifies the project as Apache-2.0 licensed. The documented quick start requires Python 3.10 or later and a running FalkorDB instance. |
| Langsearch | Crawl website or local data for a knowledge base. | CLI, Self-hosted, Server | |
| Hybrid Search Rag | Combine document retrieval and language-model generation in a configurable workflow. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 9 October 2026
- Original website title
- FalkorDB/GraphRAG-SDK · Official homepage title · retained in its source language