261 lines
11 KiB
Markdown
261 lines
11 KiB
Markdown
# ChatQnA Application
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Chatbots are the most widely adopted use case for leveraging the powerful chat and reasoning capabilities of large language models (LLM). The retrieval augmented generation (RAG) architecture is quickly becoming the industry standard for developing chatbots because it combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
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RAG bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. At the heart of this architecture are vector databases, instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
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ChatQnA architecture shows below:
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This ChatQnA use case performs RAG using LangChain, Redis vectordb and Text Generation Inference on Intel Gaudi2. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. Please visit [Habana AI products](https://habana.ai/products) for more details.
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# Environment Setup
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To use [🤗 text-generation-inference](https://github.com/huggingface/text-generation-inference) on Habana Gaudi/Gaudi2, please follow these steps:
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## Prepare Docker
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Getting started is straightforward with the official Docker container. Simply pull the image using:
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```bash
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docker pull ghcr.io/huggingface/tgi-gaudi:1.2.1
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```
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Alternatively, you can build the Docker image yourself using latest [TGI-Gaudi](https://github.com/huggingface/tgi-gaudi) code with the below command:
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```bash
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bash ./serving/tgi_gaudi/build_docker.sh
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```
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## Launch TGI Gaudi Service
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### Launch a local server instance on 1 Gaudi card:
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```bash
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bash ./serving/tgi_gaudi/launch_tgi_service.sh
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```
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For gated models such as `LLAMA-2`, you will have to pass -e HUGGING_FACE_HUB_TOKEN=\<token\> to the docker run command above with a valid Hugging Face Hub read token.
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Please follow this link [huggingface token](https://huggingface.co/docs/hub/security-tokens) to get the access token and export `HUGGINGFACEHUB_API_TOKEN` environment with the token.
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```bash
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export HUGGINGFACEHUB_API_TOKEN=<token>
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```
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### Launch a local server instance on 8 Gaudi cards:
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```bash
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bash ./serving/tgi_gaudi/launch_tgi_service.sh 8
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```
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And then you can make requests like below to check the service status:
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```bash
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curl 127.0.0.1:8080/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":32}}' \
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-H 'Content-Type: application/json'
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```
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### Customize TGI Gaudi Service
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The ./serving/tgi_gaudi/launch_tgi_service.sh script accepts three parameters:
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- num_cards: The number of Gaudi cards to be utilized, ranging from 1 to 8. The default is set to 1.
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- port_number: The port number assigned to the TGI Gaudi endpoint, with the default being 8080.
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- model_name: The model name utilized for LLM, with the default set to "Intel/neural-chat-7b-v3-3".
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You have the flexibility to customize these parameters according to your specific needs. Additionally, you can set the TGI Gaudi endpoint by exporting the environment variable `TGI_LLM_ENDPOINT`:
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```bash
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export TGI_LLM_ENDPOINT="http://xxx.xxx.xxx.xxx:8080"
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```
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## Launch Redis
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```bash
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docker compose -f langchain/docker/docker-compose-redis.yml up -d
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```
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## Launch LangChain Docker
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Update the `HUGGINGFACEHUB_API_TOKEN` environment variable with your huggingface token in the `docker-compose-langchain.yml`
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```bash
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cd langchain/docker
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docker compose -f docker-compose-langchain.yml up -d
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cd ../../
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```
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> [!NOTE]
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> If you modified any files and want that change introduced in this step, add `--build` to the end of the command to build the container image instead of pulling it from dockerhub.
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## Ingest data into redis
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After every time of redis container is launched, data should be ingested in the container ingestion steps:
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```bash
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docker exec -it qna-rag-redis-server bash
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cd /ws
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python ingest.py
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```
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Note: `ingest.py` will download the embedding model, please set the proxy if necessary.
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# Start LangChain Server
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## Enable GuardRails using Meta's Llama Guard model (Optional)
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We offer content moderation support utilizing Meta's [Llama Guard](https://huggingface.co/meta-llama/LlamaGuard-7b) model. To activate GuardRails, kindly follow the instructions below to deploy the Llama Guard model on TGI Gaudi.
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```bash
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volume=$PWD/data
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model_id="meta-llama/LlamaGuard-7b"
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docker run -p 8088:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HUGGING_FACE_HUB_TOKEN=<your HuggingFace token> -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy tgi_gaudi --model-id $model_id
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export SAFETY_GUARD_ENDPOINT="http://xxx.xxx.xxx.xxx:8088"
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```
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And then you can make requests like below to check the service status:
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```bash
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curl 127.0.0.1:8088/generate \
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-X POST \
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-d '{"inputs":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
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-H 'Content-Type: application/json'
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```
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## Start the Backend Service
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Make sure TGI-Gaudi service is running and also make sure data is populated into Redis. Launch the backend service:
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```bash
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docker exec -it qna-rag-redis-server bash
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nohup python app/server.py &
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```
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The LangChain backend service listens to port 8000 by port, you can customize it by change the code in `docker/qna-app/app/server.py`.
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And then you can make requests like below to check the LangChain backend service status:
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```bash
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# non-streaming endpoint
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curl 127.0.0.1:8000/v1/rag/chat \
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-X POST \
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-d '{"query":"What is the total revenue of Nike in 2023?"}' \
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-H 'Content-Type: application/json'
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```
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```bash
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# streaming endpoint
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curl 127.0.0.1:8000/v1/rag/chat_stream \
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-X POST \
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-d '{"query":"What is the total revenue of Nike in 2023?"}' \
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-H 'Content-Type: application/json'
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```
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## Start the Frontend Service
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Navigate to the "ui" folder and execute the following commands to start the frontend GUI:
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```bash
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cd ui
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sudo apt-get install npm && \
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npm install -g n && \
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n stable && \
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hash -r && \
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npm install -g npm@latest
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```
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For CentOS, please use the following commands instead:
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```bash
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curl -sL https://rpm.nodesource.com/setup_20.x | sudo bash -
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sudo yum install -y nodejs
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```
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Update the `DOC_BASE_URL` environment variable in the `.env` file by replacing the IP address '127.0.0.1' with the actual IP address.
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Run the following command to install the required dependencies:
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```bash
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npm install
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```
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Start the development server by executing the following command:
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```bash
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nohup npm run dev &
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```
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This will initiate the frontend service and launch the application.
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# Enable TGI Gaudi FP8 for higher throughput (Optional)
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The TGI Gaudi utilizes BFLOAT16 optimization as the default setting. If you aim to achieve higher throughput, you can enable FP8 quantization on the TGI Gaudi. According to our test results, FP8 quantization yields approximately a 1.8x performance gain compared to BFLOAT16. Please follow the below steps to enable FP8 quantization.
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## Prepare Metadata for FP8 Quantization
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Enter into the TGI Gaudi docker container, and then run the below commands:
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```bash
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pip install git+https://github.com/huggingface/optimum-habana.git
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git clone https://github.com/huggingface/optimum-habana.git
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cd optimum-habana/examples/text-generation
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pip install -r requirements_lm_eval.txt
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QUANT_CONFIG=./quantization_config/maxabs_measure.json python ../gaudi_spawn.py run_lm_eval.py -o acc_7b_bs1_measure.txt --model_name_or_path Intel/neural-chat-7b-v3-3 --attn_softmax_bf16 --use_hpu_graphs --trim_logits --use_kv_cache --reuse_cache --bf16 --batch_size 1
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QUANT_CONFIG=./quantization_config/maxabs_quant.json python ../gaudi_spawn.py run_lm_eval.py -o acc_7b_bs1_quant.txt --model_name_or_path Intel/neural-chat-7b-v3-3 --attn_softmax_bf16 --use_hpu_graphs --trim_logits --use_kv_cache --reuse_cache --bf16 --batch_size 1 --fp8
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```
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After finishing the above commands, the quantization metadata will be generated. Move the metadata directory ./hqt_output/ and copy the quantization JSON file to the host (under …/data). Please adapt the commands with your Docker ID and directory path.
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```bash
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docker cp 262e04bbe466:/usr/src/optimum-habana/examples/text-generation/hqt_output data/
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docker cp 262e04bbe466:/usr/src/optimum-habana/examples/text-generation/quantization_config/maxabs_quant.json data/
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```
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Then modify the `dump_stats_path` to "/data/hqt_output/measure" and update `dump_stats_xlsx_path` to /data/hqt_output/measure/fp8stats.xlsx" in maxabs_quant.json file.
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## Restart the TGI Gaudi server within all the metadata mapped
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```bash
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docker run -p 8080:80 -e QUANT_CONFIG=/data/maxabs_quant.json -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:1.2.1 --model-id Intel/neural-chat-7b-v3-3
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```
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Now the TGI Gaudi will launch the FP8 model by default. Please note that currently only Llama2 series and Mistral series models support FP8 quantization.
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And then you can make requests like below to check the service status:
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```bash
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curl 127.0.0.1:8080/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":32}}' \
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-H 'Content-Type: application/json'
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```
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# Enable TEI for embedding model higher throughput (Optional)
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Text Embeddings Inference (TEI) is a toolkit designed for deploying and serving open-source text embeddings and sequence classification models efficiently. With TEI, users can extract high-performance features using various popular models. It supports token-based dynamic batching for enhanced performance.
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To launch the TEI service, you can use the following commands:
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```bash
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model=BAAI/bge-large-en-v1.5
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revision=refs/pr/5
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run -p 9090:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
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export TEI_ENDPOINT="http://xxx.xxx.xxx.xxx:9090"
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```
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And then you can make requests like below to check the service status:
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```bash
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curl 127.0.0.1:9090/embed \
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-X POST \
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-d '{"inputs":"What is Deep Learning?"}' \
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-H 'Content-Type: application/json'
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```
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Note: If you want to integrate the TEI service into the LangChain application, you'll need to restart the LangChain backend service after launching the TEI service.
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