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State-of-the-art Generative AI examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs.

## NVIDIA NGC
Generative AI Examples uses resources from the [NVIDIA NGC AI Development Catalog](https://ngc.nvidia.com).

Generative AI Examples can use models and GPUs from the [NVIDIA NGC: AI Development Catalog](https://catalog.ngc.nvidia.com).

Sign up for a [free NGC developer account](https://ngc.nvidia.com/signin) to access:

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## Retrieval Augmented Generation (RAG)

A RAG pipeline embeds multimodal data -- such as documents, images, and video -- into a database connected to a LLM. RAG lets users chat with their data!
A RAG pipeline embeds multimodal data -- such as documents, images, and video -- into a database connected to a LLM.
RAG lets users chat with their data!

### Developer RAG Examples

The developer RAG examples run on a single VM. They demonstrate how to combine NVIDIA GPU acceleration with popular LLM programming frameworks using NVIDIA's [open source connectors](#open-source-integrations). The examples are easy to deploy via [Docker Compose](https://docs.docker.com/compose/).

Examples support local and remote inference endpoints. If you have a GPU, you can inference locally via [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM). If you don't have a GPU, you can inference and embed remotely via [NVIDIA AI Foundations endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/).

| Model | Embedding | Framework | Description | Multi-GPU | TRT-LLM | NVIDIA AI Foundation | Triton | Vector Database |
|---------------|-----------------------|------------|-------------------------|-----------|------------|-------------|---------|--------|
| llama-2 | e5-large-v2 | Llamaindex | Canonical QA Chatbot | [YES](RetrievalAugmentedGeneration/README.md#3-qa-chatbot-multi-gpu----a100h100l40s) | [YES](RetrievalAugmentedGeneration/README.md#2-qa-chatbot----a100h100l40s-gpu) | No | YES | Milvus/[PGVector]((RetrievalAugmentedGeneration/README.md#2-qa-chatbot----a100h100l40s-gpu))|
| mixtral_8x7b | nvolveqa_40k | Langchain | [Nvidia AI foundation based QA Chatbot](RetrievalAugmentedGeneration/README.md#1-qa-chatbot----nvidia-ai-foundation-inference-endpoint) | No | No | YES | YES | Milvus|
| llama-2 | all-MiniLM-L6-v2 | Llama Index | [QA Chatbot, GeForce, Windows](https://github.com/NVIDIA/trt-llm-rag-windows/tree/release/1.0) | NO | YES | NO | NO | FAISS |
| llama-2 | nvolveqa_40k | Langchain | [QA Chatbot, Task Decomposition Agent](./RetrievalAugmentedGeneration/README.md#5-qa-chatbot-with-task-decomposition-example----a100h100l40s) | No | No | YES | YES | FAISS
| mixtral_8x7b | nvolveqa_40k | Langchain | [Minimilastic example showcasing RAG using Nvidia AI foundation models](./examples/README.md#rag-in-5-minutes-example) | No | No | YES | YES | FAISS|


The developer RAG examples run on a single VM.
The examples demonstrate how to combine NVIDIA GPU acceleration with popular LLM programming frameworks using NVIDIA's [open source connectors](#open-source-integrations).
The examples are easy to deploy with [Docker Compose](https://docs.docker.com/compose/).

Examples support local and remote inference endpoints.
If you have a GPU, you can inference locally with [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM).
If you don't have a GPU, you can inference and embed remotely with [NVIDIA API Catalog endpoints](https://build.nvidia.com/explore/discover).

| Model | Embedding | Framework | Description | Multi-GPU | TRT-LLM | NVIDIA Endpoints | Triton | Vector Database |
| ---------------------------------- | ---------------- | ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ------- | ---------------- | ------ | ------------------ |
| mixtral_8x7b | nvolveqa_40k | LangChain | NVIDIA API Catalog endpoints chat bot [[code](./RetrievalAugmentedGeneration/examples/nvidia_api_catalog/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/api-catalog.html)] | No | No | Yes | Yes | Milvus or pgvector |
| llama-2 | e5-large-v2 | LlamaIndex | Canonical QA Chatbot [[code](./RetrievalAugmentedGeneration/examples/developer_rag/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/local-gpu.html)] | [Yes](https://nvidia.github.io/GenerativeAIExamples/latest/multi-gpu.html) | Yes | No | Yes | Milvus or pgvector |
| llama-2 | all-MiniLM-L6-v2 | LlamaIndex | Chat bot, GeForce, Windows [[repo](https://github.com/NVIDIA/trt-llm-rag-windows/tree/release/1.0)] | No | Yes | No | No | FAISS |
| llama-2 | nvolveqa_40k | LangChain | Chat bot with query decomposition agent [[code](./RetrievalAugmentedGeneration/examples/query_decomposition_rag/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/query-decomposition.html)] | No | No | Yes | Yes | Milvus or pgvector |
| mixtral_8x7b | nvolveqa_40k | LangChain | Minimilastic example: RAG with NVIDIA AI Foundation Models [[code](./examples/5_mins_rag_no_gpu/), [README](./examples/README.md#rag-in-5-minutes-example)] | No | No | Yes | Yes | FAISS |
| mixtral_8x7b<br>Deplot<br>Neva-22b | nvolveqa_40k | Custom | Chat bot with multimodal data [[code](./RetrievalAugmentedGeneration/examples/multimodal_rag/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/multimodal-data.html)] | No | No | Yes | No | Milvus or pvgector |
| llama-2 | e5-large-v2 | LlamaIndex | Chat bot with quantized LLM model [[docs](https://nvidia.github.io/GenerativeAIExamples/latest/quantized-llm-model.html)] | Yes | Yes | No | Yes | Milvus or pgvector |
| mixtral_8x7b | none | PandasAI | Chat bot with structured data [[code](./RetrievalAugmentedGeneration/examples/structured_data_rag/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/structured-data.html)] | No | No | Yes | No | none |
| llama-2 | nvolveqa_40k | LangChain | Chat bot with multi-turn conversation [[code](./RetrievalAugmentedGeneration/examples/multi_turn_rag/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/multi-turn.html)] | No | No | Yes | No | Milvus or pgvector |

### Enterprise RAG Examples

The enterprise RAG examples run as microservies distributed across multiple VMs and GPUs. They show how RAG pipelines can be orchestrated with [Kubernetes](https://kubernetes.io/) and deployed with [Helm](https://helm.sh/).
The enterprise RAG examples run as microservices distributed across multiple VMs and GPUs.
These examples show how to orchestrate RAG pipelines with [Kubernetes](https://kubernetes.io/) and deployed with [Helm](https://helm.sh/).

Enterprise RAG examples include a [Kubernetes operator](https://kubernetes.io/docs/concepts/extend-kubernetes/operator/) for LLM lifecycle management. It is compatible with the [NVIDIA GPU operator](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/gpu-operator) that automates GPU discovery and lifecycle management in a Kubernetes cluster.
Enterprise RAG examples include a [Kubernetes operator](https://kubernetes.io/docs/concepts/extend-kubernetes/operator/) for LLM lifecycle management.
It is compatible with the [NVIDIA GPU operator](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/gpu-operator) that automates GPU discovery and lifecycle management in a Kubernetes cluster.

Enterprise RAG examples also support local and remote inference via [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) and [NVIDIA AI Foundations endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/).
Enterprise RAG examples also support local and remote inference with [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) and [NVIDIA API Catalog endpoints](https://build.nvidia.com/explore/discover).

| Model | Embedding | Framework | Description | Multi-GPU | Multi-node | TRT-LLM | NVIDIA AI Foundation | Triton | Vector Database |
|---------------|-----------------------|------------|--------|-------------------------|-----------|------------|-------------|---------|--------|
| llama-2 | NV-Embed-QA | Llamaindex | QA Chatbot, Helm, k8s | NO | NO | [YES](./docs/developer-llm-operator/) | NO | YES | Milvus|
| Model | Embedding | Framework | Description | Multi-GPU | Multi-node | TRT-LLM | NVIDIA Endpoints | Triton | Vector Database |
| ------- | ----------- | ---------- | -------------------------------------------------------------------------- | --------- | ---------- | ------- | ---------------- | ------ | --------------- |
| llama-2 | NV-Embed-QA | LlamaIndex | Chat bot, Kubernetes deployment [[README](./docs/developer-llm-operator/)] | No | No | Yes | No | Yes | Milvus |

## Tools

Example tools and tutorials to enhance LLM development and productivity when using NVIDIA RAG pipelines.

| Name | Description | Deployment | Tutorial |
|------|-------------|------|--------|
| Evaluation | Example open source RAG eval tool that uses synthetic data generation and LLM-as-a-judge | [Docker compose file](./deploy/compose/docker-compose-evaluation.yaml) | [README](./docs/rag/evaluation.md) |]
| Observability | Observability serves as an efficient mechanism for both monitoring and debugging RAG pipelines. | [Docker compose file](./deploy/compose/docker-compose-observability.yaml) | [README](./docs/rag/observability.md) |]
| Name | Description | NVIDIA Endpoints |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| Evaluation | RAG evaluation using synthetic data generation and LLM-as-a-judge [[code](./tools/evaluation/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/evaluation.html)] | Yes |
| Observability | Monitoring and debugging RAG pipelines [[code](./tools/observability/), [docs](https://nvidia.github.io/GenerativeAIExamples/latest/observability.html)] | Yes |

## Open Source Integrations

These are open source connectors for NVIDIA-hosted and self-hosted API endpoints. These open source connectors are maintained and tested by NVIDIA engineers.

| Name | Framework | Chat | Text Embedding | Python | Description |
|------|-----------|------|-----------|--------|-------------|
|[NVIDIA AI Foundation Endpoints](https://python.langchain.com/docs/integrations/providers/nvidia) | [Langchain](https://www.langchain.com/) |[YES](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints)|[YES](https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints)|[YES](https://pypi.org/project/langchain-nvidia-ai-endpoints/)|Easy access to NVIDIA hosted models. Supports chat, embedding, code generation, steerLM, multimodal, and RAG.|
|[NVIDIA Triton + TensorRT-LLM](https://github.com/langchain-ai/langchain/tree/master/libs/partners/nvidia-trt) | [Langchain](https://www.langchain.com/) |[YES](https://github.com/langchain-ai/langchain-nvidia/blob/main/libs/trt/docs/llms.ipynb)|[YES](https://github.com/langchain-ai/langchain-nvidia/blob/main/libs/trt/docs/llms.ipynb)|[YES](https://pypi.org/project/langchain-nvidia-trt/)|This connector allows Langchain to remotely interact with a Triton inference server over GRPC or HTTP tfor optimized LLM inference.|
|[NVIDIA Triton Inference Server](https://docs.llamaindex.ai/en/stable/examples/llm/nvidia_triton.html) | [LlamaIndex](https://www.llamaindex.ai/) |YES|YES|NO|Triton inference server provides API access to hosted LLM models over gRPC. |
|[NVIDIA TensorRT-LLM](https://docs.llamaindex.ai/en/stable/examples/llm/nvidia_tensorrt.html) | [LlamaIndex](https://www.llamaindex.ai/) |YES|YES|NO|TensorRT-LLM provides a Python API to build TensorRT engines with state-of-the-art optimizations for LLM inference on NVIDIA GPUs. |
|------|-----------|------|----------------|--------|-------------|
|[NVIDIA AI Foundation Endpoints](https://python.langchain.com/docs/integrations/providers/nvidia) | [Langchain](https://www.langchain.com/) |[Yes](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints)|[Yes](https://python.langchain.com/docs/integrations/text_embedding/nvidia_ai_endpoints)|[Yes](https://pypi.org/project/langchain-nvidia-ai-endpoints/)|Easy access to NVIDIA hosted models. Supports chat, embedding, code generation, steerLM, multimodal, and RAG.|
|[NVIDIA Triton + TensorRT-LLM](https://github.com/langchain-ai/langchain/tree/master/libs/partners/nvidia-trt) | [Langchain](https://www.langchain.com/) |[Yes](https://github.com/langchain-ai/langchain-nvidia/blob/main/libs/trt/docs/llms.ipynb)|[Yes](https://github.com/langchain-ai/langchain-nvidia/blob/main/libs/trt/docs/llms.ipynb)|[Yes](https://pypi.org/project/langchain-nvidia-trt/)|This connector allows Langchain to remotely interact with a Triton inference server over GRPC or HTTP tfor optimized LLM inference.|
|[NVIDIA Triton Inference Server](https://docs.llamaindex.ai/en/stable/examples/llm/nvidia_triton.html) | [LlamaIndex](https://www.llamaindex.ai/) |Yes|Yes|No|Triton inference server provides API access to hosted LLM models over gRPC. |
|[NVIDIA TensorRT-LLM](https://docs.llamaindex.ai/en/stable/examples/llm/nvidia_tensorrt.html) | [LlamaIndex](https://www.llamaindex.ai/) |Yes|Yes|No|TensorRT-LLM provides a Python API to build TensorRT engines with state-of-the-art optimizations for LLM inference on NVIDIA GPUs. |

## Support, Feedback, and Contributing

## NVIDIA support
In each example README we indicate the level of support provided.
We're posting these examples on GitHub to support the NVIDIA LLM community and facilitate feedback.
We invite contributions via GitHub Issues or pull requests!

## Feedback / Contributions
We're posting these examples on GitHub to support the NVIDIA LLM community, facilitate feedback. We invite contributions via GitHub Issues or pull requests!
## Known Issues

## Known issues
- In each of the READMEs, we indicate any known issues and encourage the community to provide feedback.
- The datasets provided as part of this project is under a different license for research and evaluation purposes.
- This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
- Some known issues are identified as TODOs in the Python code.
- The datasets provided as part of this project are under a different license for research and evaluation purposes.
- This project downloads and installs third-party open source software projects. Review the license terms of these open source projects before use.

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