Single node on-prem deployment with Ollama on AIPC

This deployment section covers single-node on-prem deployment of the ChatQnA example with OPEA comps to deploy using Ollama. There are several slice-n-dice ways to enable RAG with vectordb and LLM models, but here we will be covering one option of doing it for convenience : we will be showcasing how to build an e2e chatQnA with Redis VectorDB and the llama-3 model, deployed on the client CPU.

Overview

There are several ways to setup a ChatQnA use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node Ollama megaservice solution.

1. Data Prep

2. Embedding

3. Retriever

4. Reranking

5. LLM with Ollama

The solution is aimed to show how to use Redis vectordb for RAG and the llama-3 model on Intel Client PCs. We will go through how to setup docker container to start microservices and megaservice. The solution will then utilize a sample Nike dataset which is in PDF format. Users can then ask a question about Nike and get a chat-like response by default for up to 1024 tokens. The solution is deployed with a UI.

Prerequisites

The first step is to clone the GenAIExamples and GenAIComps projects. GenAIComps are fundamental necessary components used to build the examples you find in GenAIExamples and deploy them as microservices. Set an environment variable for the desired release version with the number only (i.e. 1.0, 1.1, etc) and checkout using the tag with that version.

# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE

# Set desired release version - number only
export RELEASE_VERSION=<insert-release-version>

# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd ..

# GenAIExamples
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples
git checkout tags/v${RELEASE_VERSION}
cd ..

Setup your HuggingFace account and generate user access token.

Setup the HuggingFace token

export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"

The example requires you to set the host_ip to deploy the microservices on endpoint enabled with ports. Set the host_ip env variable

export host_ip=$(hostname -I | awk '{print $1}')

Make sure to setup Proxies if you are behind a firewall

export no_proxy=${your_no_proxy},$host_ip
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}

The examples utilize model weights from Ollama and langchain.

Set Up Ollama LLM Service

We use Ollama as our LLM service for AIPC.

Please follow the instructions to set up Ollama on your PC. This will set the entrypoint needed for the Ollama to suit the ChatQnA examples.

Install Ollama Service

Install Ollama service with one command:

curl -fsSL https://ollama.com/install.sh | sh

Set Ollama Service Configuration

Ollama Service Configuration file is /etc/systemd/system/ollama.service. Edit the file to set OLLAMA_HOST environment. Replace <host_ip> with your host IPV4 (please use external public IP). For example the host_ip is 10.132.x.y, then `Environment=”OLLAMA_HOST=10.132.x.y:11434”’.

Environment="OLLAMA_HOST=host_ip:11434"

Set https_proxy environment for Ollama

If your system access network through proxy, add https_proxy in Ollama Service Configuration file

Environment="https_proxy=Your_HTTPS_Proxy"

Restart Ollama services

sudo systemctl daemon-reload
sudo systemctl restart ollama.service

Check the service started

netstat -tuln | grep  11434

The output are:

tcp        0      0 10.132.x.y:11434      0.0.0.0:*               LISTEN

Pull Ollama LLM model

Run the command to download LLM models. The <host_ip> is the one set in the Set Ollama Service Configuration.

export host_ip=<host_ip>
export OLLAMA_HOST=http://${host_ip}:11434
ollama pull llama3.2

After downloaded the models, you can list the models by ollama list.

The output should be similar to the following:

NAME            ID                SIZE      MODIFIED
llama3.2:latest   a80c4f17acd5    2.0 GB    2 minutes ago

Consume Ollama LLM Service

Access ollama service to verify that the ollama is functioning correctly.

curl http://${host_ip}:11434/api/generate -d '{"model": "llama3.2", "prompt":"What is Deep Learning?"}'

The outputs are similar to these:

{"model":"llama3.2","created_at":"2024-10-12T12:55:28.098813868Z","response":"Deep","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" is","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.180420784Z","response":" a","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.229185873Z","response":" subset","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.263956118Z","response":" of","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.289097354Z","response":" machine","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.316838918Z","response":" learning","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.342309506Z","response":" that","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.367221264Z","response":" involves","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.39205893Z","response":" the","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.417933974Z","response":" use","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.443110388Z","response":" of","done":false}
...

Prepare (Building / Pulling) Docker images

This step will involve building/pulling relevant docker images with step-by-step process along with sanity check in the end. For ChatQnA, the following docker images will be needed: embedding, retriever, rerank, LLM and dataprep. Additionally, you will need to build docker images for ChatQnA megaservice, and UI. In total, there are 7 required docker images.

The docker images needed to setup the example needs to be build local, however the images will be pushed to docker hub soon by Intel.

Build/Pull Microservice images

Pull

If you decide to pull the docker containers and not build them locally, you can proceed to the next step where all the necessary containers will be pulled in from Docker Hub.

Build

Follow the steps below to build the docker images from within the GenAIComps folder. Note: For RELEASE_VERSIONS older than 1.0, you will need to add a ‘v’ in front of ${RELEASE_VERSION} to reference the correct image on Docker Hub.

cd $WORKSPACE/GenAIComps

Build Dataprep Image

docker build --no-cache -t opea/dataprep-redis:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/Dockerfile .

Build Embedding Image

docker build --no-cache -t opea/embedding-tei:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .

Build Retriever Image

 docker build --no-cache -t opea/retriever-redis:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/Dockerfile .

Build Rerank Image

docker build --no-cache -t opea/reranking-tei:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .

Build LLM Image

Ollama

Next, we’ll build the Ollama microservice docker. This will set the entry point needed for Ollama to suit the ChatQnA examples

docker build --no-cache -t opea/llm-ollama:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
   --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/ollama/langchain/Dockerfile .

Build Mega Service images

The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the chatqna.py file, say in this example the output of embedding microservice will be the input of retrieval microservice which will in turn passes data to the reranking microservice and so on. You can also add newer or remove some microservices and customize the megaservice to suit the needs.

Build the megaservice image for this use case

cd $WORKSPACE/GenAIExamples/ChatQnA

docker build --no-cache -t opea/chatqna:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f Dockerfile .

Build Other Service images

Build the UI Image

UI

cd $WORKSPACE/GenAIExamples/ChatQnA/ui/
docker build --no-cache -t opea/chatqna-ui:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .

Sanity Check

Check if you have the below set of docker images, before moving on to the next step:

Ollama

• opea/dataprep-redis:${RELEASE_VERSION}

• opea/embedding-tei:${RELEASE_VERSION}

• opea/retriever-redis:${RELEASE_VERSION}

• opea/reranking-tei:${RELEASE_VERSION}

• opea/llm-ollama:${RELEASE_VERSION}

• opea/chatqna:${RELEASE_VERSION}

• opea/chatqna-ui:${RELEASE_VERSION}

Use Case Setup

As mentioned the use case will use the following combination of the GenAIComps with the tools

Ollama

use case components	Tools	Model	Service Type
Data Prep	LangChain	NA	OPEA Microservice
VectorDB	Redis	NA	Open source service
Embedding	TEI	BAAI/bge-base-en-v1.5	OPEA Microservice
Reranking	TEI	BAAI/bge-reranker-base	OPEA Microservice
LLM	Ollama	llama3	OPEA Microservice
UI		NA	Gateway Service

Tools and models mentioned in the table are configurable either through the environment variable or compose.yaml file.

Set the necessary environment variables to setup the use case. If you want to swap out models, modify set_env.sh before running.

cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
source ./set_env.sh

Deploy the use case

In this tutorial, we will be deploying via docker compose with the provided YAML file. The docker compose instructions should be starting all the above mentioned services as containers.

Ollama

cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
docker compose -f compose.yaml up -d

Validate microservice

Check Env Variables

Check the start up log by docker compose -f ./compose.yaml logs. The warning messages print out the variables if they are NOT set.

Ollama

ubuntu@aipc:~/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc$ docker compose -f ./compose.yaml up -d
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string.
WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string.
WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/compose.yaml: `version` is obsolete

Check the container status

Check if all the containers launched via docker compose has started.

For example, the ChatQnA example starts 11 docker (services), check these docker containers are all running. That is, all the containers STATUS are Up. To do a quick sanity check, try docker ps -a to see if all the containers are running.

Ollama

CONTAINER ID   IMAGE                                                   COMMAND                  CREATED          STATUS                      PORTS                                                                                  NAMES
5db065a9fdf9   opea/chatqna-ui:${RELEASE_VERSION}                                  "docker-entrypoint.s…"   29 seconds ago   Up 25 seconds               0.0.0.0:5173->5173/tcp, :::5173->5173/tcp                                              chatqna-aipc-ui-server
6fa87927d00c   opea/chatqna:${RELEASE_VERSION}                                     "python chatqna.py"      29 seconds ago   Up 25 seconds               0.0.0.0:8888->8888/tcp, :::8888->8888/tcp                                              chatqna-aipc-backend-server
bdc93be9ce0c   opea/retriever-redis:${RELEASE_VERSION}                             "python retriever_re…"   29 seconds ago   Up 3 seconds                0.0.0.0:7000->7000/tcp, :::7000->7000/tcp                                              retriever-redis-server
add761b504bc   opea/reranking-tei:${RELEASE_VERSION}                               "python reranking_te…"   29 seconds ago   Up 26 seconds               0.0.0.0:8000->8000/tcp, :::8000->8000/tcp                                              reranking-tei-aipc-server
d6b540a423ac   opea/dataprep-redis:${RELEASE_VERSION}                              "python prepare_doc_…"   29 seconds ago   Up 26 seconds               0.0.0.0:6007->6007/tcp, :::6007->6007/tcp                                              dataprep-redis-server
6662d857a154   opea/embedding-tei:${RELEASE_VERSION}                               "python embedding_te…"   29 seconds ago   Up 26 seconds               0.0.0.0:6000->6000/tcp, :::6000->6000/tcp                                              embedding-tei-server
8b226edcd9db   ghcr.io/huggingface/text-embeddings-inference:cpu-1.5   "text-embeddings-rou…"   29 seconds ago   Up 27 seconds               0.0.0.0:8808->80/tcp, :::8808->80/tcp                                                  tei-reranking-server
e1fc81b1d542   redis/redis-stack:7.2.0-v9                              "/entrypoint.sh"         29 seconds ago   Up 27 seconds               0.0.0.0:6379->6379/tcp, :::6379->6379/tcp, 0.0.0.0:8001->8001/tcp, :::8001->8001/tcp   redis-vector-db
051e0d68e263   ghcr.io/huggingface/text-embeddings-inference:cpu-1.5   "text-embeddings-rou…"   29 seconds ago   Up 27 seconds               0.0.0.0:6006->80/tcp, :::6006->80/tcp                                                  tei-embedding-server
632a6634b06b   opea/llm-ollama                                         "bash entrypoint.sh"     29 seconds ago   Up 27 seconds               0.0.0.0:9000->9000/tcp, :::9000->9000/tcp                                              llm-ollama

Interacting with ChatQnA deployment

This section will walk you through what are the different ways to interact with the microservices deployed

Dataprep Microservice（Optional）

If you want to add/update the default knowledge base, you can use the following commands. The dataprep microservice extracts the texts from variety of data sources, chunks the data, embeds each chunk using embedding microservice and store the embedded vectors in the redis vector database.

nke-10k-2023.pdf is Nike’s annual report on a form 10-K. Run in a terminal window this command to download the file:

wget https://github.com/opea-project/GenAIComps/blob/v1.1/comps/retrievers/redis/data/nke-10k-2023.pdf

Upload the file:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F "files=@./nke-10k-2023.pdf"

This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.

Add Knowledge Base via HTTP Links:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F 'link_list=["https://opea.dev"]'

This command updates a knowledge base by submitting a list of HTTP links for processing.

Also, you are able to get the file list that you uploaded:

curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
     -H "Content-Type: application/json"

To delete the file/link you uploaded you can use the following commands:

Delete link

# The dataprep service will add a .txt postfix for link file

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "https://opea.dev.txt"}' \
     -H "Content-Type: application/json"

Delete file

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "nke-10k-2023.pdf"}' \
     -H "Content-Type: application/json"

Delete all uploaded files and links

curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "all"}' \
     -H "Content-Type: application/json"

TEI Embedding Service

The TEI embedding service takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model and returns this embedded vector.

curl ${host_ip}:6006/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

In this example the embedding model used is “BAAI/bge-base-en-v1.5”, which has a vector size of 768. So the output of the curl command is a embedded vector of length 768.

Embedding Microservice

The embedding microservice depends on the TEI embedding service. In terms of input parameters, it takes in a string, embeds it into a vector using the TEI embedding service and adds other default parameters that are required for the retrieval microservice and returns it.

curl http://${host_ip}:6000/v1/embeddings\
  -X POST \
  -d '{"text":"hello"}' \
  -H 'Content-Type: application/json'

Retriever Microservice

To consume the retriever microservice, you need to generate a mock embedding vector using Python script. The length of embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID=”BAAI/bge-base-en-v1.5”, which vector size is 768.

Check the vector dimension of your embedding model and set your_embedding dimension equal to it.

export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")

curl http://${host_ip}:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
  -H 'Content-Type: application/json'

The output of the retriever microservice comprises of the a unique id for the request, initial query or the input to the retrieval microservice, a list of top n retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned. The output is retrieved text that relevant to the input data:

{"id":"27210945c7c6c054fa7355bdd4cde818","retrieved_docs":[{"id":"0c1dd04b31ab87a5468d65f98e33a9f6","text":"Company: Nike. financial instruments are subject to master netting arrangements that allow for the offset of assets and liabilities in the event of default or early termination of the contract.\nAny amounts of cash collateral received related to these instruments associated with the Company's credit-related contingent features are recorded in Cash and\nequivalents and Accrued liabilities, the latter of which would further offset against the Company's derivative asset balance. Any amounts of cash collateral posted related\nto these instruments associated with the Company's credit-related contingent features are recorded in Prepaid expenses and other current assets, which would further\noffset against the Company's derivative liability balance. Cash collateral received or posted related to the Company's credit-related contingent features is presented in the\nCash provided by operations component of the Consolidated Statements of Cash Flows. The Company does not recognize amounts of non-cash collateral received, such\nas securities, on the Consolidated Balance Sheets. For further information related to credit risk, refer to Note 12 — Risk Management and Derivatives.\n2023 FORM 10-K 68Table of Contents\nThe following tables present information about the Company's derivative assets and liabilities measured at fair value on a recurring basis and indicate the level in the fair\nvalue hierarchy in which the Company classifies the fair value measurement:\nMAY 31, 2023\nDERIVATIVE ASSETS\nDERIVATIVE LIABILITIES"},{"id":"1d742199fb1a86aa8c3f7bcd580d94af","text": ... }

TEI Reranking Service

The TEI Reranking Service reranks the documents returned by the retrieval service. It consumes the query and list of documents and returns the document index based on decreasing order of the similarity score. The document corresponding to the returned index with the highest score is the most relevant document for the input query.

curl http://${host_ip}:8808/rerank \
    -X POST \
    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
    -H 'Content-Type: application/json'

Output is: [{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]

Reranking Microservice

The reranking microservice consumes the TEI Reranking service and pads the response with default parameters required for the LLM microservice.

curl http://${host_ip}:8000/v1/reranking\
  -X POST \
  -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": \
     [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
  -H 'Content-Type: application/json'

The input to the microservice is the initial_query and a list of retrieved documents and it outputs the most relevant document to the initial query along with other default parameter such as the temperature, repetition_penalty, chat_template and so on. We can also get top n documents by setting top_n as one of the input parameters. For example:

curl http://${host_ip}:8000/v1/reranking\
  -X POST \
  -d '{"initial_query":"What is Deep Learning?" ,"top_n":2, "retrieved_docs": \
     [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
  -H 'Content-Type: application/json'

Here is the output:

{"id":"e1eb0e44f56059fc01aa0334b1dac313","query":"Human: Answer the question based only on the following context:\n    Deep learning is...\n    Question: What is Deep Learning?","max_new_tokens":1024,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}

You may notice reranking microservice are with state (‘ID’ and other meta data), while reranking service are not.

Ollama Service

Ollama

curl http://${host_ip}:11434/api/generate -d '{"model": "llama3", "prompt":"What is Deep Learning?"}'

Ollama service generates text for the input prompt. Here is the expected result from Ollama:

{"model":"llama3","created_at":"2024-09-05T08:47:17.160752424Z","response":"Deep","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:18.229472564Z","response":" learning","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:19.594268648Z","response":" is","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:21.129254135Z","response":" a","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:22.066555829Z","response":" sub","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:22.993695854Z","response":"field","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:24.315183296Z","response":" of","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:25.337741889Z","response":" machine","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:26.232468605Z","response":" learning","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:27.584534136Z","response":" that","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:28.50201424Z","response":" involves","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:29.895471763Z","response":" the","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:31.204128984Z","response":" use","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:32.231884525Z","response":" of","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:33.510913894Z","response":" artificial","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:34.516291108Z","response":" neural","done":false}
...

LLM Microservice

curl http://${host_ip}:9000/v1/chat/completions\
  -X POST \
  -d '{"query":"What is Deep Learning?","max_new_tokens":17,"top_k":10,"top_p":0.95,\
     "typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
  -H 'Content-Type: application/json'

You will get the below generated text from LLM:

data: b'\n'
data: b'\n'
data: b'Deep'
data: b' learning'
data: b' is'
data: b' a'
data: b' subset'
data: b' of'
data: b' machine'
data: b' learning'
data: b' that'
data: b' uses'
data: b' algorithms'
data: b' to'
data: b' learn'
data: b' from'
data: b' data'
data: [DONE]

MegaService

curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
     "model":  "'"${OLLAMA_MODEL}"'",
     "messages": "What is the revenue of Nike in 2023?"
     }'

Here is the output for your reference:

data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
data: b'en'
data: b'ues'
data: b' of'
data: b' $'
data: b'5'
data: b'1'
data: b'.'
data: b'2'
data: b' billion'
data: b'.'
data: b'</s>'
data: [DONE]

Check docker container log

Check the log of container by:

docker logs <CONTAINER ID> -t

Check the log by docker logs f7a08f9867f9 -t.

Also you can check overall logs with the following command, where the compose.yaml is the mega service docker-compose configuration file.

Ollama

docker compose -f compose.yaml logs

Launch UI

To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  chaqna-aipc-ui-server:
    image: opea/chatqna-ui${TAG:-latest}
    ...
    ports:
      - "5173:5173"

Stop the services

Once you are done with the entire pipeline and wish to stop and remove all the containers, use the command below:

Ollama

docker compose -f compose.yaml down