vss-ctx-rag 1.0.1

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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 [e-mail]: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-1.0.0-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=<IF USING NVIDIA>
   NVIDIA_VISIBLE_DEVICES=<GPU ID>

   OPENAI_API_KEY=<IF USING OPENAI>

   VSS_CTX_PORT_RET=<DATA RETRIEVAL PORT>
   VSS_CTX_PORT_IN=<DATA INGESTION PORT>

   GRAPH_DB_USERNAME=<GRAPH_DB_USERNAME>
   GRAPH_DB_PASSWORD=<GRAPH_DB_PASSWORD>
   ARANGO_DB_USERNAME=root
   ARANGO_DB_PASSWORD=<ARANGO_DB_PASSWORD>
   MINIO_USERNAME=<MINIO_USERNAME>
   MINIO_PASSWORD=<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://<HOST>:<VSS_CTX_PORT_IN>

• ctx-rag-data-retrieval

  • Service available at http://<HOST>:<VSS_CTX_PORT_RET>

• neo4j

  • UI available at http://<HOST>:7474

• milvus

• otel-collector

• Phoenix

  • UI available at http://<HOST>:16686

• prometheus

  • UI available at http://<HOST>:9090

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://<HOST>:<VSS_CTX_PORT_IN>"

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 document
for add_doc_data in add_doc_data_list:
    response = requests.post(
        f"{base_url}/add_doc", headers=headers, data=json.dumps(add_doc_data)
    )
    print(response.text)

response = requests.post(
    f"{base_url}/complete_ingestion", headers=headers, data=json.dumps({"uuid": "1"})
)
print(response.text)

Data Retrieval Example

import requests
import json


base_url = "http://<HOST>:<VSS_CTX_PORT_RET>"

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

init_data = {"config_path": "/app/config/config.yaml", "uuid": "1"}
response = requests.post(
    f"{base_url}/init", headers=headers, data=json.dumps(init_data)
)

chat_data = {
    "model": "meta/llama-3.1-70b-instruct",
    "messages": [{"role": "user", "content": "Who mentioned the fire?"}],
    "uuid": "1"
}

response = requests.post(f"{base_url}/chat/completions", headers=headers, data=json.dumps(chat_data))
print(response.json()["choices"][0]["message"]["content"])

Summary Data Retrieval Example

Summary data retrieval can be made to the system using the /summary endpoint of the Retrieval Service.

Example Query

import requests

url = "http://<HOST>:<VSS_CTX_PORT_RET>/summary"
headers = {"Content-Type": "application/json"}
data = {
    "uuid": "1",
    "summarization": {
        "start_index": 0,
        "end_index": -1
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.json()["result"])

Acknowledgements

We would like to thank the following projects that made Context Aware RAG possible:

• FastAPI
• LangChain
• Neo4j
• ArangoDB
• Elasticsearch
• Milvus
• uv
• OpenTelemetry