AI Chatbots with RAG - Docker Workflow

Download this resource to run enterprise RAG applications based on NVIDIA services with Docker Compose.

Example RAG Applications

Common Customizations

Common Prerequisites

\n
Install Docker Engine and Docker Compose.
\n

Verify NVIDIA GPU driver version 535 or later is installed.

$ nvidia-smi --query-gpu=driver_version --format=csv,noheader\n535.129.03\n\n$ nvidia-smi -q -d compute\n\n==============NVSMI LOG==============\n\nTimestamp                                 : Sun Nov 26 21:17:25 2023\nDriver Version                            : 535.129.03\nCUDA Version                              : 12.2\n\nAttached GPUs                             : 1\nGPU 00000000:CA:00.0\n    Compute Mode                          : Default\n

Refer to the NVIDIA Linux driver installation instructions for more information.

Install the NVIDIA Container Toolkit.

Verify the toolkit is installed and configured as the default container runtime.

$ cat /etc/docker/daemon.json\n{\n    \"default-runtime\": \"nvidia\",\n    \"runtimes\": {\n        \"nvidia\": {\n            \"path\": \"/usr/bin/nvidia-container-runtime\",\n            \"runtimeArgs\": []\n        }\n    }\n}\n\n$ sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi -L\nGPU 0: NVIDIA A100 80GB PCIe (UUID: GPU-d8ce95c1-12f7-3174-6395-e573163a2ace)\n

\n
Create an NGC account and API Key.\nRefer to the instructions to create an account and generate an NGC API key.
\n
Log in to the NVIDIA container registry using the following command:
\n
```
docker login nvcr.io\n
```
\n
Export the NGC_API_KEY
\n
\n\">
```
export NGC_API_KEY=<ngc-api-key>\n
```
\n
Refer to Accessing And Pulling an NGC Container Image via the Docker CLI for more information.
\n

01: Canonical RAG Llamaindex

This example showcases a baseline Llamaindex based RAG pipeline that deploys the NIM for LLMs microservice to host a TensorRT optimized LLM and the NeMo Retriever Embedding microservice.\nMilvus is deployed as the vector database to store embeddings and generate responses to queries.

\n\n \n \n \n \n \n \n \n \n\ntr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	Milvus	On Prem

Deployment

\n
Complete the Common Prerequisites.
\n

Run the pipeline.

cd rag-app-text-chatbot-llamaindex/\nUSERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-application-text-chatbot-langchain  Up 27 minutes\n02c8062f15da   nemo-retriever-embedding-microservice   Up 27 minutes (healthy)\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n55135224e8fd   milvus-standalone                       Up 48 minutes (healthy)\n5844248a08df   milvus-minio                            Up 48 minutes (healthy)\nc42df344bb25   milvus-etcd                             Up 48 minutes (healthy)\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

02: Canonical RAG Langchain

This example showcases a baseline Langchain based RAG pipeline that deploys the NIM for LLMs microservice to host a TensorRT optimized LLM and the NeMo Retriever Embedding microservice.\nMilvus is deployed as the vector database to store embeddings and generate responses to queries.

\n\n \n \n \n \n \n \n \n \n\ntr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	Milvus	On Prem

Deployment

\n
Complete the Common Prerequisites.
\n

Run the pipeline.

cd rag-app-text-chatbot-langchain/\nUSERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-application-text-chatbot-llamaindex Up 27 minutes\n02c8062f15da   nemo-retriever-embedding-microservice   Up 27 minutes (healthy)\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n55135224e8fd   milvus-standalone                       Up 48 minutes (healthy)\n5844248a08df   milvus-minio                            Up 48 minutes (healthy)\nc42df344bb25   milvus-etcd                             Up 48 minutes (healthy)\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

03: Multimodal RAG

This example showcases a multimodal use case in a RAG pipeline.\nThe example understands any kind of image in PDF, such as graphs and plots, alongside text and tables.\nThe example uses multimodal models from NVIDIA API Catalog to answer queries.

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LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct for response generation, ai-google-Deplot for graph to text convertion, ai-Neva-22B for image to text convertion	nv-embedqa-e5-v5	Custom Python	PDF with images/PPTX	Milvus	Cloud - NVIDIA API Catalog for ai-google-Deplot and ai-Neva-22B. On Prem NeMo Inference Microservices for meta/llama3-8b-instruct model

Deployment

\n
Complete the Common Prerequisites.
\n
\n
Export your NVIDIA API key in your terminal.\nThe key is used to access Neva 22B and Deplot models from NVIDIA API Catalog.
\n
```
export NVIDIA_API_KEY=\"nvapi-*\"\n
```
\n

Run the pipeline.

cd rag-app-multimodal-chatbot/\nUSERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-application-multimodal-chatbot      Up 27 minutes\n02c8062f15da   nemo-retriever-embedding-microservice   Up 27 minutes (healthy)\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n55135224e8fd   milvus-standalone                       Up 48 minutes (healthy)\n5844248a08df   milvus-minio                            Up 48 minutes (healthy)\nc42df344bb25   milvus-etcd                             Up 48 minutes (healthy)\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

04: Multi-Turn RAG

This example showcases a multi-turn use case in a RAG pipeline.\nThe example stores the conversation history and knowledge base in Milvus and retrieves them at runtime to understand contextual queries.\nThe example deploys NIM for LLMs and NeMo Retriever Embedding Microservice.

The example supports ingestion of PDF and .txt files.\nThe documents are ingested in a dedicated document vector store.\nThe prompt for the example is tuned to act as a document chat bot.\nFor maintaining the conversation history, the chain server stores the previous user queries and the generated answers as a text entry in a different dedicated vector store for conversation history.\nBoth vector stores are part of a LangChain LCEL chain as LangChain Retrievers.\nWhen the chain is invoked with a query, the query is passed through both retrievers.\nThe retriever retrieves context from the document vector store and the closest matching conversation history from conversation history vector store. The document chunks retrieved from the document vector store are then passed through a reranker model to determine the most relevant top_k context. The context is then passed onto the LLM prompt for response generation.

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LLM Model	Embedding	Ranking(Optional)	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	nv-rerankqa-mistral-4b-v3	LangChain Expression Language	PDF/Text	Milvus	On Prem

Deployment

\n
Complete the Common Prerequisites.
\n

Run the pipeline.

cd rag-app-multiturn-chatbot/\nUSERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

Deploy the NVIDIA NeMo Retriever Text Reranking NIM container.

USERID=$(id -u) docker compose -f docker-compose-nim-ms.yaml up -d ranking-ms\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-application-multiturn-chatbot       Up 27 minutes\n02c8062f15da   nemo-retriever-embedding-microservice   Up 27 minutes (healthy)\n02c8062f15da   nemo-retriever-reranking-microservice   Up 27 minutes (healthy)\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n55135224e8fd   milvus-standalone                       Up 48 minutes (healthy)\n5844248a08df   milvus-minio                            Up 48 minutes (healthy)\nc42df344bb25   milvus-etcd                             Up 48 minutes (healthy)\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

05: Query Decomposition RAG

This example showcases a RAG use case built using the task decomposition paradigm.\nThe chain server breaks down a query into smaller subtasks and then combines results from different subtasks to formulate the final answer.\nIt uses models from NVIDIA API Catalog and LangChain as the framework.

\n\n \n \n \n \n \n \n \n \n\ntr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-70b-instruct	nv-embedqa-e5-v5	LangChain Agent	PDF/Text	Milvus	On Prem

Deployment

\n
Complete the Common Prerequisites.
\n

Run the pipeline.

cd rag-app-query-decomposition-agent/\nUSERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-app-query-decomposition-agent       Up 27 minutes\n02c8062f15da   nemo-retriever-embedding-microservice   Up 27 minutes (healthy)\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n55135224e8fd   milvus-standalone                       Up 48 minutes (healthy)\n5844248a08df   milvus-minio                            Up 48 minutes (healthy)\nc42df344bb25   milvus-etcd                             Up 48 minutes (healthy)\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

06: Structured Data RAG

This example demonstrates a use case of RAG with structured CSV data.\nThis approach does not involve embedding models or vector database solutions and instead uses PandasAI for interacting with the CSV data.\nFor ingestion, the structured data is loaded from a CSV file into a Pandas dataframe.\nThe pipeline can ingest multiple CSV files, provided they have identical columns.\nIngestion of CSV files with differing columns is not currently supported and results in an exception.

The core functionality utilizes a PandasAI agent to extract information from the dataframe.\nThis agent combines the query with the structure of the dataframe into an LLM prompt.\nThe LLM then generates Python code to extract the required information from the dataframe.\nSubsequently, this generated code is executed on the dataframe, yielding the output dataframe.

To test the example, sample CSV files are available.\nThese are part of the structured data example Helm chart and represent a subset of the Microsoft Azure Predictive Maintenance from Kaggle.\nThe CSV data retrieval prompt is specifically tuned for three CSV files from this dataset: PdM_machines.csv, PdM_errors.csv, and PdM_failures.csv.\nSpecify the CSV file to use in the rag-app-structured-data-chatbot.yaml file within this resource by updating the environment variable CSV_NAME.\nBy default, the file is set to PdM_machines but you can change it to PdM_errors or PdM_failures.\nCurrently, customization of the CSV data retrieval prompt is not supported.

\n\n \n \n \n \n \n \n \n \n \n\ntr:nth-child(odd)]:bg-surface-sunken\">\n \n \n \n \n \n \n \n \n \n\n

LLM Model (Data Retrieval)	LLM Model (Response Paraphrasing)	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-70b-instruct	meta/llama3-70b-instruct	Not Used	PandasAI	CSV	Not Used	On Prem

Deployment

\n
Complete the Common Prerequisites.
\n

Run the pipeline.

cd rag-app-structured-data-chatbot/\nUSERID=$(id -u) docker compose --profile local-nim up -d\n

Check status of the containers.

docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\nCONTAINER ID   NAMES                                   STATUS\n32515fcb8ad2   rag-playground                          Up 26 minutes\nd60e0cee49f7   rag-app-structured-data-chatbot         Up 27 minutes\n7bd4d94dc7a7   nemollm-inference-microservice          Up 27 minutes\n

\n
Open your browser and interact with the RAG Playground at http://localhost:3001/converse.
\n

Configuring Milvus as the Vector Database

Perform the following steps to use Milvus as the vector database for an example.

Edit the Docker Compose file for the example, such as rag-app-text-chatbot-llamaindex.yaml.

Update the environment variables within the chain server service:

services:\n  chain-server:\n    container_name: chain-server\n    environment:\n      APP_VECTORSTORE_NAME: \"milvus\"\n      APP_VECTORSTORE_URL: \"http://milvus:19530\"\n

Optional: Stop the services running

$ USERID=$(id -u) docker compose --profile local-nim --profile milvus down\n

Stop and then start the services:

$ USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n

\n
Optional: View the chain server logs to confirm the vector database.
\n
1. \n
  View the logs:
  \n
  \n\">
```
$ docker logs -f <rag-example>\n
```
  \n
2. \n
  Try out a query after uploading any document and confirm the log output includes the vector database:
  \n
  .pdf in vectorDB\nINFO:RetrievalAugmentedGeneration.common.utils:Using milvus as vector store\nINFO:RetrievalAugmentedGeneration.common.utils:Using milvus collection:
  \n\">
```
INFO:example:Ingesting <file-name>.pdf in vectorDB\nINFO:RetrievalAugmentedGeneration.common.utils:Using milvus as vector store\nINFO:RetrievalAugmentedGeneration.common.utils:Using milvus collection: <example-name>\n
```
  \n
\n

Configuring pgvector as the Vector Database

\n
Edit the Docker Compose file for the example to use pgvector as the vector database.
\n
Update the environment variables within the chain server service:
\n
```
services:\n  chain-server:\n    container_name: chain-server\n    environment:\n      APP_VECTORSTORE_NAME: \"pgvector\"\n      APP_VECTORSTORE_URL: \"pgvector:5432\"\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-password}\n      POSTGRES_USER: ${POSTGRES_USER:-postgres}\n      POSTGRES_DB: ${POSTGRES_DB:-api}\n
```
\n
The preceding example shows the default values for the database user, password, and database.\nTo override the defaults, edit the values in the Docker Compose file, or set the values in the compose.env file.
\n

Optional: Stop the services running

$ USERID=$(id -u) docker compose --profile local-nim --profile pgvector down\n

Stop and then start the services:

$ USERID=$(id -u) docker compose --profile local-nim --profile pgvector up -d\n

\n
Optional: View the chain server logs to confirm the vector database.
\n
1. \n
  View the logs:
  \n
  \n\">
```
$ docker logs -f <rag-example>\n
```
  \n
2. \n
  Try out a query after uploading any document and confirm the log output includes the vector database:
  \n
  .pdf in vectorDB\nINFO:RetrievalAugmentedGeneration.common.utils:Using pgvector as vector store\nINFO:RetrievalAugmentedGeneration.common.utils:Using PGVector collection:
  \n\">
```
INFO:example:Ingesting <file-name>.pdf in vectorDB\nINFO:RetrievalAugmentedGeneration.common.utils:Using pgvector as vector store\nINFO:RetrievalAugmentedGeneration.common.utils:Using PGVector collection: <example-name>\n
```
  \n
\n

Alternative LLM Model: Deploy Meta Llama 3 70B Instruct

The Llama 3 70B Instruct model with NVIDIA NIM for LLMs is the only supported alternative model.

Edit the docker-compose-nim-ms.yaml file and update the image for the nemollm-inference-microservice service.

Update the image to nvcr.io/nim/meta/llama3-70b-instruct:1.0.0, as shown in the following sample.

services:\n  nemollm-inference:\n    container_name: nemollm-inference-microservice\n    image: nvcr.io/nim/meta/llama3-70b-instruct:1.0.0\n    volumes:\n    - ${MODEL_DIRECTORY}:/opt/nim/.cache\n    ports:\n    - \"8000:8000\"\n    expose:\n    - \"8000\"\n    environment:\n      NGC_API_KEY: ${NGC_API_KEY}\n    shm_size: 20gb\n    deploy:\n      resources:\n        reservations:\n          devices:\n            - driver: nvidia\n              count: ${INFERENCE_GPU_COUNT:-all}\n              capabilities: [gpu]\n      profiles: [\"local-nim\", \"nemo-retriever\"]\n

\n
Start the container.
\n
\ndocker compose --profile local-nim up -d\n\">
```
cd <example_dir>\ndocker compose --profile local-nim up -d\n
```
\n

Custom Chain Server

Clone the repository:

git lfs clone https://github.com/NVIDIA/GenerativeAIExamples.git\n

After you modify the chain server code, build the image:

USERID=$(id -u) docker compose build --no-cache chain-server\n

Tag and push the container to a private registry, such as NVIDIA NGC:

//:\n\ndocker push nvcr.io///: \n\">

docker tag chain-server:latest nvcr.io/<org-name>/<team-name>/<example-name>:<version>\n\ndocker push nvcr.io/<org-name>/<team-name>/<example-name>:<version> \n

Modify the compose file for the enterprise example to run and set your image for the chain server:

//:\n ...\n\">

services:\n  chain-server:\n    container_name: rag-application-text-chatbot\n    image: nvcr.io/<org-name>/<team-name>/<example-name>:<version>\n    ...\n

\n
Start the containers by running docker compose up -d.
\n

Additional Resources

Learn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.

Security considerations

The RAG applications are shared as reference architectures and are provided “as is”. The security of them in production environments is the responsibility of the end users deploying it. When deploying in a production environment, please have security experts review any potential risks and threats (including direct and indirect prompt injection); define the trust boundaries, secure the communication channels, integrate AuthN & AuthZ with appropriate access controls, keep the deployment including the containers up to date, ensure the containers are secure and free of vulnerabilities.

Licenses

By downloading or using this artifact included in the AI Chatbot workflows you agree to the terms of the NVIDIA Software License Agreement and Product-specific Terms for AI products.

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[Canonical RAG Llamaindex](#01-canonical-rag-llamaindex)\n2. [Canonical RAG Langchain](#02-canonical-rag-langchain)\n3. [Multimodal RAG](#03-multimodal-rag)\n4. [Multi-Turn RAG](#04-multi-turn-rag)\n5. [Query Decomposition RAG](#05-query-decomposition-rag)\n6. [Structured Data based RAG](#06-structured-data-rag)\n\n## Common Customizations\n\n1. [Configuring Milvus as the Vector Database](#configuring-milvus-as-the-vector-database)\n2. [Configuring pgvector as the Vector Database](#configuring-pgvector-as-the-vector-database)\n3. [Inference Model: Llama 3 70B Instruct](#alternative-llm-model-deploy-meta-llama-3-70b-instruct)\n4. [Custom Chain Server](#custom-chain-server)\n\n## Common Prerequisites\n\n1. Install [Docker Engine and Docker Compose](https://docs.docker.com/engine/install/ubuntu/).\n\n2. Verify NVIDIA GPU driver version 535 or later is installed.\n\n ```console\n $ nvidia-smi --query-gpu=driver_version --format=csv,noheader\n 535.129.03\n\n $ nvidia-smi -q -d compute\n\n ==============NVSMI LOG==============\n\n Timestamp : Sun Nov 26 21:17:25 2023\n Driver Version : 535.129.03\n CUDA Version : 12.2\n\n Attached GPUs : 1\n GPU 00000000:CA:00.0\n Compute Mode : Default\n ```\n\n Refer to the [NVIDIA Linux driver installation instructions](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html) for more information.\n\n3. Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).\n\n Verify the toolkit is installed and configured as the default container runtime.\n\n ```console\n $ cat /etc/docker/daemon.json\n {\n \"default-runtime\": \"nvidia\",\n \"runtimes\": {\n \"nvidia\": {\n \"path\": \"/usr/bin/nvidia-container-runtime\",\n \"runtimeArgs\": []\n }\n }\n }\n\n $ sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi -L\n GPU 0: NVIDIA A100 80GB PCIe (UUID: GPU-d8ce95c1-12f7-3174-6395-e573163a2ace)\n ```\n\n4. Create an NGC account and API Key.\n Refer to the [instructions](https://docs.nvidia.com/ngc/gpu-cloud/ngc-overview/index.html) to create an account and generate an NGC API key.\n\n Log in to the NVIDIA container registry using the following command:\n\n ```console\n docker login nvcr.io\n ```\n\n Export the `NGC_API_KEY`\n\n ```console\n export NGC_API_KEY=\n ```\n\n Refer to [Accessing And Pulling an NGC Container Image via the Docker CLI](https://docs.nvidia.com/ngc/gpu-cloud/ngc-catalog-user-guide/index.html#accessing_registry) for more information.\n\n\n# 01: Canonical RAG Llamaindex\n\nThis example showcases a baseline Llamaindex based RAG pipeline that deploys the NIM for LLMs microservice to host a TensorRT optimized LLM and the NeMo Retriever Embedding microservice.\nMilvus is deployed as the vector database to store embeddings and generate responses to queries.\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	Milvus	On Prem

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-text-chatbot-llamaindex/\n USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-application-text-chatbot-langchain Up 27 minutes\n 02c8062f15da nemo-retriever-embedding-microservice Up 27 minutes (healthy)\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n 55135224e8fd milvus-standalone Up 48 minutes (healthy)\n 5844248a08df milvus-minio Up 48 minutes (healthy)\n c42df344bb25 milvus-etcd Up 48 minutes (healthy)\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n\n# 02: Canonical RAG Langchain\n\nThis example showcases a baseline Langchain based RAG pipeline that deploys the NIM for LLMs microservice to host a TensorRT optimized LLM and the NeMo Retriever Embedding microservice.\nMilvus is deployed as the vector database to store embeddings and generate responses to queries.\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	llama-index	PDF/Text	Milvus	On Prem

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-text-chatbot-langchain/\n USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-application-text-chatbot-llamaindex Up 27 minutes\n 02c8062f15da nemo-retriever-embedding-microservice Up 27 minutes (healthy)\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n 55135224e8fd milvus-standalone Up 48 minutes (healthy)\n 5844248a08df milvus-minio Up 48 minutes (healthy)\n c42df344bb25 milvus-etcd Up 48 minutes (healthy)\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n\n# 03: Multimodal RAG\n\nThis example showcases a multimodal use case in a RAG pipeline.\nThe example understands any kind of image in PDF, such as graphs and plots, alongside text and tables.\nThe example uses multimodal models from NVIDIA API Catalog to answer queries.\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct for response generation, ai-google-Deplot for graph to text convertion, ai-Neva-22B for image to text convertion	nv-embedqa-e5-v5	Custom Python	PDF with images/PPTX	Milvus	Cloud - NVIDIA API Catalog for ai-google-Deplot and ai-Neva-22B. On Prem NeMo Inference Microservices for meta/llama3-8b-instruct model

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n1. Export your NVIDIA API key in your terminal.\n The key is used to access Neva 22B and Deplot models from NVIDIA API Catalog.\n\n ```console\n export NVIDIA_API_KEY=\"nvapi-*\"\n ```\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-multimodal-chatbot/\n USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-application-multimodal-chatbot Up 27 minutes\n 02c8062f15da nemo-retriever-embedding-microservice Up 27 minutes (healthy)\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n 55135224e8fd milvus-standalone Up 48 minutes (healthy)\n 5844248a08df milvus-minio Up 48 minutes (healthy)\n c42df344bb25 milvus-etcd Up 48 minutes (healthy)\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n\n# 04: Multi-Turn RAG\n\nThis example showcases a multi-turn use case in a RAG pipeline.\nThe example stores the conversation history and knowledge base in Milvus and retrieves them at runtime to understand contextual queries.\nThe example deploys NIM for LLMs and NeMo Retriever Embedding Microservice.\n\nThe example supports ingestion of PDF and .txt files.\nThe documents are ingested in a dedicated document vector store.\nThe prompt for the example is tuned to act as a document chat bot.\nFor maintaining the conversation history, the chain server stores the previous user queries and the generated answers as a text entry in a different dedicated vector store for conversation history.\nBoth vector stores are part of a LangChain [LCEL](https://python.langchain.com/docs/expression_language/) chain as LangChain Retrievers.\nWhen the chain is invoked with a query, the query is passed through both retrievers.\nThe retriever retrieves context from the document vector store and the closest matching conversation history from conversation history vector store. The document chunks retrieved from the document vector store are then passed through a reranker model to determine the most relevant top_k context. The context is then passed onto the LLM prompt for response generation.\n\n\n\n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Ranking(Optional)	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-8b-instruct	nv-embedqa-e5-v5	nv-rerankqa-mistral-4b-v3	LangChain Expression Language	PDF/Text	Milvus	On Prem

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-multiturn-chatbot/\n USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n \n1. Deploy the NVIDIA NeMo Retriever Text Reranking NIM container.\n ```console\n USERID=$(id -u) docker compose -f docker-compose-nim-ms.yaml up -d ranking-ms\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-application-multiturn-chatbot Up 27 minutes\n 02c8062f15da nemo-retriever-embedding-microservice Up 27 minutes (healthy)\n 02c8062f15da nemo-retriever-reranking-microservice Up 27 minutes (healthy)\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n 55135224e8fd milvus-standalone Up 48 minutes (healthy)\n 5844248a08df milvus-minio Up 48 minutes (healthy)\n c42df344bb25 milvus-etcd Up 48 minutes (healthy)\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n# 05: Query Decomposition RAG\n\nThis example showcases a RAG use case built using the task decomposition paradigm.\nThe chain server breaks down a query into smaller subtasks and then combines results from different subtasks to formulate the final answer.\nIt uses models from NVIDIA API Catalog and LangChain as the framework.\n\n\n\n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n\n

LLM Model	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-70b-instruct	nv-embedqa-e5-v5	LangChain Agent	PDF/Text	Milvus	On Prem

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-query-decomposition-agent/\n USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-app-query-decomposition-agent Up 27 minutes\n 02c8062f15da nemo-retriever-embedding-microservice Up 27 minutes (healthy)\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n 55135224e8fd milvus-standalone Up 48 minutes (healthy)\n 5844248a08df milvus-minio Up 48 minutes (healthy)\n c42df344bb25 milvus-etcd Up 48 minutes (healthy)\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n\n# 06: Structured Data RAG\n\nThis example demonstrates a use case of RAG with structured CSV data.\nThis approach does not involve embedding models or vector database solutions and instead uses [PandasAI](https://docs.pandas-ai.com/en/latest/) for interacting with the CSV data.\nFor ingestion, the structured data is loaded from a CSV file into a Pandas dataframe.\nThe pipeline can ingest multiple CSV files, provided they have identical columns.\nIngestion of CSV files with differing columns is not currently supported and results in an exception.\n\nThe core functionality utilizes a [PandasAI](https://docs.pandas-ai.com/en/latest/) agent to extract information from the dataframe.\nThis agent combines the query with the structure of the dataframe into an LLM prompt.\nThe LLM then generates Python code to extract the required information from the dataframe.\nSubsequently, this generated code is executed on the dataframe, yielding the output dataframe.\n\nTo test the example, sample CSV files are available.\nThese are part of the structured data example Helm chart and represent a subset of the [Microsoft Azure Predictive Maintenance](https://www.kaggle.com/datasets/arnabbiswas1/microsoft-azure-predictive-maintenance) from Kaggle.\nThe CSV data retrieval prompt is specifically tuned for three CSV files from this dataset: `PdM_machines.csv, PdM_errors.csv, and PdM_failures.csv`.\nSpecify the CSV file to use in the `rag-app-structured-data-chatbot.yaml` file within this resource by updating the environment variable `CSV_NAME`.\nBy default, the file is set to `PdM_machines` but you can change it to `PdM_errors` or `PdM_failures`.\nCurrently, customization of the CSV data retrieval prompt is not supported.\n\n\n\n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n\n

LLM Model (Data Retrieval)	LLM Model (Response Paraphrasing)	Embedding	Framework	Document Type	Vector Database	Model deployment platform
meta/llama3-70b-instruct	meta/llama3-70b-instruct	Not Used	PandasAI	CSV	Not Used	On Prem

\n\n## Deployment\n\n1. Complete the [Common Prerequisites](#common-prerequisites).\n\n1. Run the pipeline.\n\n ```console\n cd rag-app-structured-data-chatbot/\n USERID=$(id -u) docker compose --profile local-nim up -d\n ```\n\n1. Check status of the containers.\n\n ```console\n docker ps --format \"table {{.ID}}\\t{{.Names}}\\t{{.Status}}\"\n\n CONTAINER ID NAMES STATUS\n 32515fcb8ad2 rag-playground Up 26 minutes\n d60e0cee49f7 rag-app-structured-data-chatbot Up 27 minutes\n 7bd4d94dc7a7 nemollm-inference-microservice Up 27 minutes\n ```\n\n1. Open your browser and interact with the RAG Playground at .\n\n\n\n## Configuring Milvus as the Vector Database\n\nPerform the following steps to use Milvus as the vector database for an example.\n\n1. Edit the Docker Compose file for the example, such as `rag-app-text-chatbot-llamaindex.yaml`.\n\n Update the environment variables within the chain server service:\n\n ```yaml\n services:\n chain-server:\n container_name: chain-server\n environment:\n APP_VECTORSTORE_NAME: \"milvus\"\n APP_VECTORSTORE_URL: \"http://milvus:19530\"\n ```\n\n2. Optional: Stop the services running\n\n ```console\n $ USERID=$(id -u) docker compose --profile local-nim --profile milvus down\n ```\n\n3. Stop and then start the services:\n\n ```console\n $ USERID=$(id -u) docker compose --profile local-nim --profile milvus up -d\n ```\n\n4. Optional: View the chain server logs to confirm the vector database.\n\n 1. View the logs:\n\n ```console\n $ docker logs -f \n ```\n\n 2. Try out a query after uploading any document and confirm the log output includes the vector database:\n\n ```output\n INFO:example:Ingesting .pdf in vectorDB\n INFO:RetrievalAugmentedGeneration.common.utils:Using milvus as vector store\n INFO:RetrievalAugmentedGeneration.common.utils:Using milvus collection: \n ```\n\n\n## Configuring pgvector as the Vector Database\n\n1. Edit the Docker Compose file for the example to use pgvector as the vector database.\n\n Update the environment variables within the chain server service:\n\n ```yaml\n services:\n chain-server:\n container_name: chain-server\n environment:\n APP_VECTORSTORE_NAME: \"pgvector\"\n APP_VECTORSTORE_URL: \"pgvector:5432\"\n POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-password}\n POSTGRES_USER: ${POSTGRES_USER:-postgres}\n POSTGRES_DB: ${POSTGRES_DB:-api}\n ```\n\n The preceding example shows the default values for the database user, password, and database.\n To override the defaults, edit the values in the Docker Compose file, or set the values in the `compose.env` file.\n\n2. Optional: Stop the services running\n\n ```console\n $ USERID=$(id -u) docker compose --profile local-nim --profile pgvector down\n ```\n\n3. Stop and then start the services:\n\n ```console\n $ USERID=$(id -u) docker compose --profile local-nim --profile pgvector up -d\n ```\n\n4. Optional: View the chain server logs to confirm the vector database.\n\n 1. View the logs:\n\n ```console\n $ docker logs -f \n ```\n\n 2. Try out a query after uploading any document and confirm the log output includes the vector database:\n\n ```output\n INFO:example:Ingesting .pdf in vectorDB\n INFO:RetrievalAugmentedGeneration.common.utils:Using pgvector as vector store\n INFO:RetrievalAugmentedGeneration.common.utils:Using PGVector collection: \n\n## Alternative LLM Model: Deploy Meta Llama 3 70B Instruct\n\nThe Llama 3 70B Instruct model with NVIDIA NIM for LLMs is the only supported alternative model.\n\n1. Edit the `docker-compose-nim-ms.yaml` file and update the image for the `nemollm-inference-microservice` service.\n\n Update the image to `nvcr.io/nim/meta/llama3-70b-instruct:1.0.0`, as shown in the following sample.\n\n ```yaml\n services:\n nemollm-inference:\n container_name: nemollm-inference-microservice\n image: nvcr.io/nim/meta/llama3-70b-instruct:1.0.0\n volumes:\n - ${MODEL_DIRECTORY}:/opt/nim/.cache\n ports:\n - \"8000:8000\"\n expose:\n - \"8000\"\n environment:\n NGC_API_KEY: ${NGC_API_KEY}\n shm_size: 20gb\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: ${INFERENCE_GPU_COUNT:-all}\n capabilities: [gpu]\n profiles: [\"local-nim\", \"nemo-retriever\"]\n ```\n\n2. Start the container.\n\n ```console\n cd \n docker compose --profile local-nim up -d\n ```\n\n## Custom Chain Server\n\n1. Clone the repository:\n\n ```console\n git lfs clone https://github.com/NVIDIA/GenerativeAIExamples.git\n ```\n\n1. After you modify the chain server code, build the image:\n\n ```console\n USERID=$(id -u) docker compose build --no-cache chain-server\n ```\n\n1. Tag and push the container to a private registry, such as NVIDIA NGC:\n\n ```console\n docker tag chain-server:latest nvcr.io///:\n\n docker push nvcr.io///: \n ```\n\n1. Modify the compose file for the enterprise example to run and set your image for the chain server:\n\n ```yaml\n services:\n chain-server:\n container_name: rag-application-text-chatbot\n image: nvcr.io///:\n ...\n ```\n\n1. Start the containers by running `docker compose up -d`.\n\n## Additional Resources\nLearn more about how to use NVIDIA NIM microservices for RAG through our Deep Learning Institute. Access the course here.\n\n## Security considerations\nThe RAG applications are shared as reference architectures and are provided “as is”. The security of them in production environments is the responsibility of the end users deploying it. When deploying in a production environment, please have security experts review any potential risks and threats (including direct and indirect prompt injection); define the trust boundaries, secure the communication channels, integrate AuthN & AuthZ with appropriate access controls, keep the deployment including the containers up to date, ensure the containers are secure and free of vulnerabilities.\n\n## Licenses\n\nBy downloading or using this artifact included in the AI Chatbot workflows you agree to the terms of the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-specific Terms for AI products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).","displayName","AI Chatbot - Docker workflow",["D",1756966419975],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/aiworkflows/resources/ai-chatbot-docker-workflow","rawImageUrl","https://nvdam.widen.net/content/qhl26oo9hq/jpeg/ai-platform-social-ai-chatbots-with-rag-24-1200x628.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_152":1248,"_154":1249},"ngcApps",[1201],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1182":1183,"_1184":1185,"_1186":1187,"_1188":1189,"_1190":283,"_109":309,"_1191":1192,"_1193":59,"_1194":90,"_1195":90,"_1196":1197,"_1198":90,"_1199":1200},"amplitude",{"_1179":1180,"_821":-7,"_1181":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1173":1174,"_1175":1176,"_1177":1178},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787402988099,"1.12.0","defaultSearchSuggestions",{"_1061":220,"_1062":1063,"_1064":1065,"_1066":1067},"announcements",[],"consentState",{"_347":1051,"_1052":90,"_1053":34,"_1054":34,"_1055":1056},"consentConfig",{"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348},"showContactAdmin",1787402988100,[],[],[323,324,325,326,327],{"_48":62,"_50":73,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":328,"_58":59},5,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506],[370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386],{"_387":480,"_241":480,"_212":220,"_361":481},{"_387":476,"_241":476,"_212":220,"_361":477},{"_387":471,"_241":471,"_212":220,"_361":472},{"_387":467,"_241":467,"_212":220,"_361":468},{"_387":462,"_241":462,"_212":220,"_361":463},{"_387":457,"_241":457,"_212":220,"_361":458},{"_387":449,"_241":449,"_212":220,"_361":450},{"_387":441,"_241":441,"_212":220,"_361":442},{"_387":436,"_241":436,"_212":220,"_361":437},{"_387":431,"_241":431,"_212":220,"_361":432},{"_387":426,"_241":426,"_212":220,"_361":427},{"_387":421,"_241":421,"_212":220,"_361":422},{"_387":416,"_241":416,"_212":220,"_361":417},{"_387":409,"_241":409,"_212":220,"_361":410},{"_387":404,"_241":404,"_212":220,"_361":405},{"_387":397,"_241":397,"_212":220,"_361":398},{"_387":388,"_241":388,"_212":220,"_361":389},"hostName","amazon-adsystem.com",[390,391],{"_112":396,"_393":388,"_394":34,"_212":395},{"_112":392,"_393":388,"_394":34,"_212":395},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[399,400],{"_112":403,"_393":397,"_394":34,"_212":402},{"_112":401,"_393":397,"_394":34,"_212":402},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602],[549,550,551,552,553],{"_387":462,"_241":462,"_212":220,"_361":573},{"_387":436,"_241":436,"_212":220,"_361":568},{"_387":563,"_241":563,"_212":220,"_361":564},{"_387":558,"_241":558,"_212":220,"_361":559},{"_387":554,"_241":554,"_212":220,"_361":555},"nvidia.partners",[556],{"_112":557,"_393":554,"_394":34,"_212":430},"edge","app.hushly.com",[560],{"_112":561,"_393":558,"_394":34,"_212":562},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[565],{"_112":566,"_393":563,"_394":34,"_212":567},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689],[641,642,643,644],{"_387":656,"_241":656,"_212":220,"_361":657},{"_387":651,"_241":651,"_212":220,"_361":652},{"_387":449,"_241":449,"_212":220,"_361":648},{"_387":563,"_241":563,"_212":220,"_361":645},[646],{"_112":647,"_393":563,"_394":90,"_212":567},"_gh_sess",[649],{"_112":650,"_393":449,"_394":90,"_212":456},"YSC","bizible.com",[653],{"_112":654,"_393":651,"_394":34,"_212":655},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[658],{"_112":659,"_393":656,"_394":34,"_212":660},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_112":731,"_393":508,"_394":34,"_212":732},{"_112":716,"_393":730,"_394":90,"_212":717},{"_112":699,"_393":511,"_394":34,"_212":624},{"_112":699,"_393":612,"_394":34,"_212":624},{"_112":728,"_393":508,"_394":34,"_212":729},{"_112":716,"_393":514,"_394":90,"_212":717},{"_112":726,"_393":508,"_394":34,"_212":727},{"_112":716,"_393":511,"_394":90,"_212":717},{"_112":724,"_393":611,"_394":34,"_212":725},{"_112":716,"_393":633,"_394":90,"_212":717},{"_112":696,"_393":511,"_394":34,"_212":698},{"_112":722,"_393":508,"_394":90,"_212":723},{"_112":720,"_393":508,"_394":34,"_212":721},{"_112":718,"_393":508,"_394":90,"_212":719},{"_112":716,"_393":607,"_394":90,"_212":717},{"_112":714,"_393":508,"_394":34,"_212":715},{"_112":712,"_393":508,"_394":34,"_212":713},{"_112":710,"_393":508,"_394":90,"_212":711},{"_112":708,"_393":616,"_394":34,"_212":709},{"_112":696,"_393":607,"_394":34,"_212":698},{"_112":706,"_393":508,"_394":34,"_212":707},{"_112":704,"_393":508,"_394":90,"_212":705},{"_112":702,"_393":508,"_394":34,"_212":703},{"_112":700,"_393":508,"_394":34,"_212":701},{"_112":699,"_393":508,"_394":34,"_212":624},{"_112":696,"_393":697,"_394":34,"_212":698},{"_112":694,"_393":508,"_394":34,"_212":695},{"_112":692,"_393":508,"_394":90,"_212":693},{"_112":690,"_393":508,"_394":34,"_212":691},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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