NVIDIA/context-aware-rag

NVIDIA/context-aware-rag

Description: Context-Aware RAG library for Knowledge Graph ingestion and retrieval functions.

Language: Python

License: Apache-2.0

Stars: 86

Forks: 23

Open issues: 4

Created: 2025-04-04T19:43:48Z

Pushed: 2026-06-23T20:44:51Z

Default branch: main

Fork: no

Archived: no

README:

NVIDIA Context Aware RAG


Context Aware RAG is a flexible library designed to seamlessly integrate into existing data processing workflows to build customized data ingestion and retrieval (RAG) pipelines.

Key Features

• **Data Ingestion Service:** Add data to the RAG pipeline from a variety of sources.
• **Data Retrieval Service:** Retrieve data from the RAG pipeline using natural language queries.
• **Function and Tool Components:** Easy to create custom functions and tools to support your existing workflows.
• **GraphRAG:** Seamlessly extract knowledge graphs from data to support your existing workflows.
• **Observability:** Monitor and troubleshoot your workflows with any OpenTelemetry-compatible monitoring tool.
• **Experimental Features:** CA-RAG also provides structured output mode for response and five important Model Context Protocol (MCP) tools for using CA-RAG with AI agentic workflows.

With Context Aware RAG, you can quickly build RAG pipelines to support your existing workflows.

Links

• Documentation: Explore the full documentation for Context Aware RAG.
• Context Aware RAG Architecture: Learn more about how Context Aware RAG works and its components.
• Getting Started Guide: Set up your environment and start integrating Context Aware RAG into your workflows.
• Examples: Explore examples of Context Aware RAG workflows.
• Troubleshooting: Get help with common issues.
• Release Notes: Learn about the latest features and improvements.

Getting Started

Prerequisites

Before you begin using Context Aware RAG, ensure that you have the following software installed.

• Install Git
• Install uv

Installation

Clone the repository

git clone git@github.com:NVIDIA/context-aware-rag.git
cd context-aware-rag/

Create a virtual environment using uv

uv venv --seed .venv
source .venv/bin/activate

Installing from source

uv pip install -e .

Installing optional plugins

##### Arango

uv pip install -e .[arango]

##### NAT

uv pip install -e .[nat]

Optional: Building and Installing the wheel file

uv build
uv pip install dist/vss_ctx_rag-1.0.2-py3-none-any.whl

Service Example

Setting up environment variables

Create a .env file in the root directory and set the following variables:

NVIDIA_API_KEY=
NVIDIA_VISIBLE_DEVICES=

OPENAI_API_KEY=

VSS_CTX_PORT_RET=
VSS_CTX_PORT_IN=

GRAPH_DB_USERNAME=
GRAPH_DB_PASSWORD=
ARANGO_DB_USERNAME=root
ARANGO_DB_PASSWORD=
MINIO_USERNAME=
MINIO_PASSWORD=

Build docker

make -C docker build

Start using docker compose

make -C docker start_compose

This will start the following services:

• ctx-rag-data-ingestion

• Service available at http://:

• ctx-rag-data-retrieval

• Service available at http://:

• neo4j

• UI available at http://:7474

• milvus

• otel-collector

• Phoenix

• UI available at http://:16686

• prometheus

• UI available at http://:9090

• elasticsearch
• UI available at http://:9200

• kibana
• UI available at http://:5601

To change the storage volumes, export DOCKER_VOLUME_DIRECTORY to the desired directory.

Stop using docker compose

make -C docker stop_compose

Data Ingestion Example

import requests
import json
from pyaml_env import parse_config

base_url = "http://:"

headers = {"Content-Type": "application/json"}

### Initialize the service with a unique uuid
init_data = {"uuid": "1"}
### Optional: Initialize the service with a config file or context config
"""
init_data = {"config_path": "/app/config/config.yaml", "uuid": "1"}
init_data = {"context_config": parse_config("/app/config/config.yaml"), "uuid": "1"}
"""
response = requests.post(
f"{base_url}/init", headers=headers, data=json.dumps(init_data)
)

# POST request to /add_doc to add documents to the service
add_doc_data_list = [
{
"document": "User1: Hi how are you?",
"doc_index": 0,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 0,
"file": "chat_conversation.txt",
"is_first": True,
"is_last": False,
"uuid": "1"
},
"uuid": "1"
},
{
"document": "User2: I am good. How are you?",
"doc_index": 1,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 1,
"file": "chat_conversation.txt",
"uuid": "1"
},
"uuid": "1"
},
{
"document": "User1: I am great too. Thanks for asking",
"doc_index": 2,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 2,
"file": "chat_conversation.txt",
"uuid": "1"
},
"uuid": "1"
},
{
"document": "User2: So what did you do over the weekend?",
"doc_index": 3,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 3,
"file": "chat_conversation.txt",
"uuid": "1"
},
"uuid": "1"
},
{
"document": "User1: I went hiking to Mission Peak",
"doc_index": 4,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 4,
"file": "chat_conversation.txt",
"uuid": "1"
},
"uuid": "1"
},
{
"document": "User3: Guys there is a fire. Let us get out of here",
"doc_index": 5,
"doc_metadata": {
"streamId": "stream1",
"chunkIdx": 5,
"file": "chat_conversation.txt",
"is_first": False,
"is_last": True,
"uuid": "1"
},
"uuid": "1"
},
]

# Send POST requests for each...