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https://github.com/sdsc-ordes/kg-llm-interface
Langchain-powered natural language interface to knowledge-graphs.
https://github.com/sdsc-ordes/kg-llm-interface
k8s knowledge-graph llm question-answering rest-api
Last synced: about 2 months ago
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Langchain-powered natural language interface to knowledge-graphs.
- Host: GitHub
- URL: https://github.com/sdsc-ordes/kg-llm-interface
- Owner: sdsc-ordes
- License: apache-2.0
- Created: 2023-04-19T11:42:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-08T15:48:31.000Z (5 months ago)
- Last Synced: 2024-08-08T18:43:58.291Z (5 months ago)
- Topics: k8s, knowledge-graph, llm, question-answering, rest-api
- Language: Python
- Homepage:
- Size: 1.14 MB
- Stars: 13
- Watchers: 4
- Forks: 1
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# kg-llm-interface
Langchain-powered natural language interface to RDF knowledge-graphs.
## Installation
This repository uses poetry for package management. A Makefile rule is provided to install the dependencies:
```bash
make install
```
## Configuration
Configuration variables are loaded from the `.env` file or environment variables. A template configuration file is provided in `.env.example`.
The chat configuration (`config.chat.ChatConfig`) uses OpenAI by default, however you can run this tool with open source LLMs using a framework such as llamafile, openllm or localGPT. When doing so, simply provide your LLM server url using `openai_api_base` and the model name using`model`.
## Quickstart
You can read and run the [example notebook](aikg/notebooks/nl_sparql.ipynb) to get a quick overview of the system.
The notebook supports using the Openai API and can run locally on a laptop.
To run the notebook in a containerized environment, run:
`make notebook`
## Server
The server can be deployed as a standalone service using the script `scripts/standalone_server.sh`. It will start a uvicorn server on port 8001, use chromaDB in client-only mode and use an RDF file as knowledge graph. This should work for small datasets.
## Pipelines
Pipelines are used to execute one-time operations for preparing data before the chat server can operate. They load their configuration from the `.env` file as well, but the variables can be overriden using yaml files (run with `--help` for more info).
### Insert triples
```mermaid
flowchart LR
RDF[RDF file] -->|insert_triples.py| SPARQL(SPARQL endpoint)
```
Insert data from an input RDF file to a SPARQL endpoint. The input file can be in any format supported by rdflib (ttl, json-ld, rdf/xlm, ...).
Location: [insert_triples.py](aikg/flows/insert_triples.py):
SPARQL configuration can be overriden by providing a yaml file following the [aikg.config.sparql.SparqlConfig](aikg/config/sparql.py) schema:
`python insert_triples --sparql-config-path sparql.yaml`
```yaml
# sparql.yaml
endpoint: http://localhost:3030/ds/query
user: admin
password: admin
```
CLI usage: `python aikg/flows/insert_triples.py`
### Chroma build
```mermaid
flowchart LR
SPARQL(SPARQL endpoint) -->|chroma_build.py| CHROMA(ChromaDB)
```
Build the chromaDB index from a SPARQL endpoint.
Location: [chroma_build.py](aikg/flows/chroma_build.py):
CLI usage: `python aikg/flows/chroma_build.py`
Chroma and SPARQL configurations can be overriden by providing a yaml file following the [aikg.config.chroma.ChromaConfig](aikg/config/chroma.py) or [aikg.config.sparql.SparqlConfig](aikg/config/sparql.py) schemas respectively.
## Containerized service
:warning: WIP, not functional yet
The chat server can be deployed along with the front-end, SPARQL endpoint and chromaDB server using kubernetes.
```mermaid
sequenceDiagram
Front-end->>+Chat server: question
Chat server->>+ChromaDB: question
ChromaDB -->ChromaDB: embed
ChromaDB-->>-Chat server: ontology triples
Chat server-->Chat server: generate query
Chat server-->>+SPARQL endpoint: query
SPARQL endpoint-->SPARQL endpoint: run query
SPARQL endpoint-->>-Chat server: result
Chat server-->>-Front-end: answer
```
## Contributing
All contributions are welcome. New functions and classes should have associated docstrings following the [numpy style guide](https://numpydoc.readthedocs.io/en/latest/format.html).
The code formatting standard we use is [black](https://github.com/psf/black), with `--line-length=79` to follow [PEP8](https://peps.python.org/pep-0008/) recommendations. We use [pytest](https://docs.pytest.org/en/7.2.x/) as our testing framework. This project uses [pyproject.toml](https://pip.pypa.io/en/stable/reference/build-system/pyproject-toml/) to define package information, requirements and tooling configuration.
Tests can be executed with `make test`. Tests use [testcontainers](https://testcontainers.com) to temporarily deploy the required services.