DepsRAG

DepsRAG is a chatbot that answers user's questions about software dependencies after representing them as a Knowledge Graph (KG). DepsRAG offers the following features:

Constructing the software dependencies (direct and transitive) as a KG.
Supporting 4 popular software ecosystems (i.e. PyPI, NPM, Cargo, and Go).
Generatiing atutomatically Cypher queries to retrieve information from the KG.
Augmenting users' questions with the retrieved information.

DepsRAG Architecture

DepsRAG uses the following set of tools to accomplish its process:

DepGraphTool to build the dependency graph for a given pkg version, using the API at DepsDev
GoogleSearchTool to find package version and type information. It also can answer other question from the web about other aspects after obtaining the intended information from the dependency graph. For examples:
- does the dpendency use latest version for this package verion?
- Can I upgrade this package in the dependency graph?
GraphSchemaTool: get schema of Neo4j knowledge-graph
CypherRetrievalTool: generate cypher queries to get information from Neo4j knowledge-graph (Cypher is the query language for Neo4j)
VulnerabilityCheck: search OSV vulnerability DB based on package name, version, and its ecosystem.
VisualizeGraph: visualize the entire dependency grpah

DepsRAG Workflow

The workflow of DepsRAG as follows:

The chatbot will ask you to provide the name and ecosystem of the software package.
It will then the tool GoogleSearchTool to get the version of this package (you can skip this process by providing the intended version).
The chatbot will ask to confirm the version number before proceeding with constructing the dependencies as knowledge graph.
Finally, after constructing the dependency graph, you can ask the chatbot questions about the dependency graph such as these (specific package names are used here for illustration purposes, but of course you can use other names):
- what's the depth of the graph?
- what are the direct dependencies?
- any dependency on pytorch? which version?
- Is this package pytorch vunlnerable? (Note that in this case the chatbot will consult the tool GoogleSearchTool to get an answer from the internet.)
- tell me 3 interesting things about this package or dependency graph
- what's the path between package-1 and package-2? (provide names of package-1 and -2)
- Tell me the names of all packages in the dependency graph that use pytorch.

🔥 Updates/Releases

Click to expand

Aug 2024:
- Adding DuckDuck web search tool.
July 2024:
- Creating containerized version of DepsRAG that support UI and CLI mode.
May 2024:
- Adding integration with OSV vulnerability database to search for vulnerabilities
April 2024:
- Supporting the construction of dependency graph for Go, Cargo, and NPM.
March 2024:
- Supporting Chainlit to run DepsRAG via UI
Feb 2024:
- Adding tool to visualize the dependency graph

⚙️ Installation and Setup

DepsRAG requires Python 3.11+. We recommend using a virtual environment.

# clone the repo and cd into repo root
git clone https://github.com/Mohannadcse/DepsRAG.git
cd DepsRAG

# create a virtual env under project root, .venv directory
python3 -m venv .venv

# activate the virtual env
. .venv/bin/activate

# install dependencies from pyproject.toml:
# This installs DepsRAG
poetry install

Set up environment variables (API keys, etc)

DepsRAG requires a set of environment variables to operate. In the root of the repo, copy the .env-template file to a new file .env to set the values of these environment variables.

cp .env-template .env

Following is a description of these environment variables:

LLM Settings: by default DepsRAG support OpenAI model gpt-4o. Therefore, DepsRAG requires OPENAI_API_KEY=your-key-here-without-quotes. However, DepsRAG supports other models that are enabled by Langroid like Azure, Open/Local LLMs, and other non-OpenAI proprietary LLMs. Therefore, you need to provide the corresponding environment settings for the required LLM. For example, Azure settings required are listed inside .env-template and start with AZURE_.
Google Custom Search API Credentials: Only needed to enable an Agent to use the GoogleSearchTool. To use Google Search as an LLM Tool//function-call, you'll need to set up a Google API key, then setup a Google Custom Search Engine (CSE) and get the CSE ID. (Documentation for these can be challenging, we suggest asking GPT4 for a step-by-step guide.) After obtaining these credentials, store them as values of GOOGLE_API_KEY and GOOGLE_CSE_ID in your .env file.
Neo4j: The easiest way to get access to neo4j is by creating a cloud account at Neo4j Aura. OR you can use Neo4j Docker image using this command:

docker run --rm \
    --name neo4j \
    -p 7474:7474 -p 7687:7687 \
    -e NEO4J_AUTH=neo4j/password \
    neo4j:latest

Upon creating the cloud account successfully, neo4j will create a text file contains account settings. Please provide the following Neo4j environment variables:

NEO4J_USERNAME=typically neo4j
NEO4J_PASSWORD=your-neo4j-password
NEO4J_URI=uri-to-access-neo4j-dayabase OR bolt://neo4j:7687 if you use Neo4j Docker image
NEO4J_DATABASE=typically neo4j

🐳 Docker Instructions

We provide a containerized version of DepsRAG, where you can run DepsRAG using Chainlit in UI mode or CLI mode.
All you need to do is set up environment variables in the .env (as shown in .env-template) file after clonning DepsRAG repository. We created ths script run_depsrag_docker.sh. So everything will be working in an automated manner. Once you run this script, it will ask you to select the mode for running DepsRAG. Then you can interact with DepsRAG chatbot.

git clone https://github.com/Mohannadcse/DepsRAG.
cd DepsRAG
docker compose build
chmod +x run_depsrag_docker.sh
./run_depsrag_docker.sh

The script run_depsrag_docker.sh spins up two containers: Neo4j and DepsRAG app. After finishing the interaction with DepsRAG chatbot, you can run the command docker compose down to shut down the containers.

Running DepsRAG

DepsRAG supports two modes: CLI and UI (using chainlit).

Run DepsRAG in the CLI mode using this command:

python3 dependencyrag/dependency_chatbot.py
 -m <LLM-Name>

The flag -m provides the option to overwrite the default LLM (gpt-4o) used by DepsRAG. If you want to use Azure, set the flag -m azure, while for other LLMs, please check Langroid documentation (Open/Local LLMs and other non-OpenAI proprietary LLMs).

Here is a recording shows the CLI mode in action:

Run DepsRAG in the UI mode using this command:

chainlit run dependencyrag/chainlit/chainlit_dependency_chatbot.py

Here is a recording shows the UI moded in action:

NOTE: the dependency graph is constructed based on DepsDev API. Therefore, the Chatbot will not be able to construct the dependency graph if this API doesn't provide dependency metadata infromation.

DepsRAG Paper Citation

You can find the paper that describes the details of DepsRAG HERE

@misc{alhanahnah2024depsrag,
      title={DepsRAG: Towards Managing Software Dependencies using Large Language Models}, 
      author={Mohannad Alhanahnah and Yazan Boshmaf and Benoit Baudry},
      year={2024},
      eprint={2405.20455},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

Mohannadcse / DepsRAG