# 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. 

```bash
# 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](https://huggingface.co/) account and generate
[user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token).

Setup the HuggingFace token
```bash
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
```bash
export host_ip=$(hostname -I | awk '{print $1}')
```

Make sure to setup Proxies if you are behind a firewall
```bash
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](https://ollama.com/) 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:

```bash
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"'.

```bash
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

```bash
Environment="https_proxy=Your_HTTPS_Proxy"
```

#### Restart Ollama services

```bash
sudo systemctl daemon-reload
sudo systemctl restart ollama.service
```

#### Check the service started

```bash
netstat -tuln | grep  11434
```

The output are:

```bash
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`.

```bash
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:

```bash
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.

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

The outputs are similar to these:

```bash
{"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

::::::{tab-set}

:::::{tab-item} Pull
:sync: 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.

:::::
:::::{tab-item} Build
:sync: 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.

```bash
cd $WORKSPACE/GenAIComps
```

#### Build Dataprep Image

```bash
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

```bash
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

```bash
 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

```bash
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

::::{tab-set}

:::{tab-item} Ollama
:sync: Ollama


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

```bash
cd $WORKSPACE/GenAIExamples/ChatQnA
```

```bash
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*

```bash
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:

::::{tab-set}
:::{tab-item} Ollama
:sync: 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

::::{tab-set}

:::{tab-item} Ollama
:sync: 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.

```bash
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.

::::{tab-set}
:::{tab-item} Ollama
:sync: Ollama

```bash
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.

::::{tab-set}
:::{tab-item} Ollama
:sync: 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.

::::{tab-set}

:::{tab-item} Ollama
:sync: Ollama

```bash
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:

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

Upload the file:

```bash
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:

```bash
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:

```bash
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
```bash
# 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

```bash
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

```bash
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.

```bash
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.

```bash
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.

```bash
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:
```bash
{"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.
```bash
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.

```bash
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:

```bash
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:

```bash
{"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

::::{tab-set}

:::{tab-item} Ollama
:sync: Ollama

```bash
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:

```bash
{"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
```bash
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:

```bash
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

```bash
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:

```bash
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.


::::{tab-set}

:::{tab-item} Ollama
:sync: Ollama

```bash
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:
```yaml
  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:
::::{tab-set}

:::{tab-item} Ollama
:sync: Ollama

```bash
docker compose -f compose.yaml down
```
:::
::::