Practical overview
When it fits
Use it when you want to explore a LangChain-based GraphRAG workflow that turns document collections into graph-backed local or global queries, or when comparing replaceable model and retrieval components.
Who it is for
- Developers building graph-based retrieval or question-answering prototypes over document collections.
- LangChain users exploring local searches about entities and relationships or global questions about collection-wide themes.
Documented uses
- Index a text file with configurable language-model and embedding choices.
- Ask focused questions about entities and relationships using local search.
- Ask broad questions about themes across an indexed collection using global search.
- Experiment with replaceable GraphRAG components that implement the required interfaces.
Requirements & limits
- The core is a Python library; the repository also includes a separate Typer-based CLI example.
- The example CLI documents OpenAI, Azure OpenAI and Ollama language-model choices; its available configuration options are limited.
- The library describes components as replaceable when alternatives implement the required interface.
- The README documents Python 3.10+ and uv for the non-devcontainer dependency setup.
- The repository specifies the Apache-2.0 license.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the repository environment
Clone the repository and install its Python dependencies using the documented non-devcontainer setup.
Before you start
- Python 3.10 or later
- uv
Steps
Clone the repository and change into its directory.
Install uv if it is not already available.
Run the command below to set up the environment and install dependencies.
uv sync
Expected outcome
The repository environment and dependencies are set up for working with the project.
Guide limitations
- This covers environment setup only; it does not configure a model provider or run indexing or search.
Index a text file with the example CLI
Use the documented base indexing command to process a file and specify the model and embedding configuration.
Before you start
- A prepared repository environment with dependencies installed
- An input text file
- An output directory and cache directory
- A supported LLM type and model, plus embedding type and model
Steps
Choose the input file, output directory and cache directory for the indexing command.
Set the LLM type and model and the embedding type and model using the command options.
Run the
simple-app indexer indexcommand with those values.
Expected outcome
The indexing command is configured to process the input file and write artifacts to the specified output directory.
Guide limitations
- The documented CLI example describes configuration and indexing; this guide does not cover running a subsequent query.
- OpenAI or Azure OpenAI configuration requires renaming
.env.exampleto.envand filling in the necessary environment variables.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- ksachdeva/langchain-graphrag · Official homepage title · retained in its source language
Related options
Microsoft GraphRAG — The README identifies it as the official implementation by the authors of the research paper; Langchain Graphrag is a separate implementation using LangChain.