Rocket.Chat AI Setup Guide

This guide will help you to setup Rocket.Chat AI on your local machine.

Rocket.Chat AI enables you to integrate Open Source Models like Llama3.1 with your own knowledge base (using Retrieval Augmented Generation) with your Rocket.Chat instance. This allows the LLMs to generate responses based on the context of the docs in the knowledge base, this helps in generating more accurate responses. The Rocket.Chat AI App provides other features like Thread summarization, Omnichannel Conversation Summarization.

The project is in beta and we are working on improving the setup process. If you face any issues or have any feedbacks, please reach out to us on the Rocket.Chat AI channel.

Here is a high-level overview of the overall workflow:

Prerequisites

• Docker

• Instance with a GPU

• NVIDIA GPU(s) with CUDA support

• CPU: x86_64 architecture

• OS: any Linux distros which:

  • • Are supported by the NVIDIA Container toolkit
  • • Have glibc >= 2.35 (see output of ld -v)

• CUDA drivers: Follow the installation guide here

  We only support the cuda and versions as 12.5, 12.2 and 12.1. If you have a different version, please upgrade or downgrade to the supported versions. Otherwise, you can reach out to the Rocket.Chat team for feedback on supporting the version you have.

• NVIDIA Container Toolkit

• Docker with GPU support

  To test if the GPU is accessible in the docker container, follow the steps listed in the Compose GPU Support

• Rocket.Chat License (Starter, Pro, or Enterprise), Starter license is free for small teams. For more information, please refer to the Rocket.Chat Pricing page.

Recommended Hardware

• Tested on an AWS EC2 instance with the following configuration:
  • Instance type: g5.2xlarge
  • vCPUs: 8
  • Memory: 32 GB
  • GPU: NVIDIA A10G
  • VRAM: 24 GB
  • Storage: 450 GB

Minimum requirements:

• vCPUs: 4

• Memory: 12 GB

• GPU VRAM:

• • For Llama3.1-8B model: 8 GB
• • For Llama3.1-70B model: 40 GB

• Storage:

• • For Llama3.1-8B model: 100 GB
• • For Llama3.1-70B model: 500 GB

Installation

1. Clone/Init the repository If using https:

git clone https://github.com/RocketChat/Rocket.Chat.AI.Preview.git

For zip download:

unzip Rocket.Chat.AI.Preview-main.zip -d Rocket.Chat.AI.Preview

2. Change the directory

cd Rocket.Chat.AI.Preview

RAG Pipeline (Rubra.AI)

Modifying the configuration files is optional. The pipeline will work without modifications, but certain features will be disabled. For more information, refer to the About the Config Files section.

3. Start Rubra.AI

docker-compose -f docker-compose.yaml --profile rubra up -d

If you're using a newer version of Docker (Docker CLI versions 1.27.0 and newer), you may need to use the following command:

docker compose -f docker-compose.yaml --profile rubra up -d

Once everything is running. To verify that every service is running, you can run the following command:

docker ps --format "{{.Names}}"

Should return the following services:

$ docker ps --format "{{.Names}}"
ui
api-server
task-executor
vector-db-api
milvus
milvus-minio
mongodb
litellm
milvus-etcd
text-embedding-api
redis

In case you don't see any of the services listed above, you can reach out to the Rocket.Chat team for support.

If everything is running, you can now access the Rubra UI at http://localhost:8501. Now move on to the next step to start the LLM service.

Note localhost is the default hostname. If you are using a different hostname, replace localhost with your hostname.

Deploy LLM

Note we support two methods to run LLM, one with Docker and the other with helm. For the Docker method, follow the steps below. For scaling and production use cases we recommend using our optimized Docker and Helm Deployments, for access, please reach out to us on the [email protected].

To verify the installed CUDA version on your system, execute the following command:

nvidia-smi

This command will produce an output similar to the following:

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 555.42.02              Driver Version: 555.42.02      CUDA Version: 12.5     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A10G                    Off |   00000000:00:1E.0 Off |                    0 |
|  0%   31C    P0             58W /  300W |       1MiB /  23028MiB |      5%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Additionally, to confirm the version of the CUDA Compiler Driver, use the command:

nvcc --version

The expected output is as follows:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Wed_Apr_17_19:19:55_PDT_2024
Cuda compilation tools, release 12.5, V12.5.40
Build cuda_12.5.r12.5/compiler.34177558_0

In the provided outputs, the CUDA Version is identified as 12.5, and the version of the CUDA compilation tools is also 12.5.

Should there be a discrepancy in versions, it is recommended to align your system's CUDA version with the supported versions by either upgrading or downgrading.

This version information (excluding the period) is utilized to configure the PLATFORM_TAG within the .env file.

For instance, with a CUDA version of 12.5, the PLATFORM_TAG should be set to cuda125.

For example, if it is 12.5, the PLATFORM_TAG should be cuda125.

Note: Supported CUDA versions include 12.5, 12.2, and 12.1. If your system's version does not match any of the supported versions, please consider updating your CUDA installation. Alternatively, for assistance with unsupported versions, contact the Rocket.Chat team for guidance on compatibility.

Start with defining the environment variables in the .env file. You can copy the .env.llm.example file and rename it to .env. Then, modify the following variables as needed:

# For the model weights
MODEL_NAME=Llama-3.1-8B-Instruct-q4f16_1-MLC

# For the MLC library
PLATFORM_TAG=cuda125
RELEASE=0.0.1

docker-compose -f docker-compose.yaml --profile mlc-llm up -d

If you're using a newer version of Docker (Docker CLI versions 1.27.0 and newer), you may need to use the following command:

docker compose -f docker-compose.yaml --profile mlc-llm up -d

Once the Docker container is running, you can call the LLM API using the following command:

curl -X POST \
  -H "Content-Type: application/json" \
  -d '{
        "model": "Llama-3.1-8B-Instruct-q4f16_1-MLC",
        "messages": [
            {"role": "user", "content": "Hello! Our project is MLC LLM. What is the name of our project?"}
        ]
  }' \
  http://localhost:1234/v1/chat/completions

Note localhost and 1234 are the default hostname and port. If you are using a different hostname and port, replace localhost and 1234 with your hostname and port.

If you get a response, the LLM service is running successfully.

Now you have successfully set up Rocket.Chat AI on your local machine. You can now integrate it with your Rocket.Chat instance.

Integration

Note: Replace the service names with the actual hostname and port if you are using a different hostname and port.

1. Go to your Rocket.Chat instance.

2. Install the Rocket.Chat AI app from the marketplace. You can find the app by searching for Rocket.Chat AI under the (Admininistration > Marketplace > Explore). It's an premium app, so you need to have a valid license (Starter or Pro or Enterprise) to install the app.

3. After installing the app, go to the Rocket.Chat AI app settings page (Admininistration > Marketplace > Installed > Rocket.Chat AI > Settings).

4. Enter the Model URL with the LLM API URL. For example, http://llama3-8b:1234/v1. (don't include the /chat/completions part).

5. Enter the URL of the service milvus with the port 19530. For example, http://milvus:19530 in the setting "Vector database URL".

6. Enter the URL of the service text-embedding-api with the port 8020. For example, http://text-embedding-api:8020/embed_multiple in the setting "Text embedding API URL".

7. For setting up the Knowledge base refer to the Knowledge base setup video.

8. For the setting Vector database collection, you have two options:

   a. Call the endpoint http://api-server:8000/assistants and search for the assistant you want to integrate with. Example response looks like:

{
  "object": "list",
  "data": [
    {
      "_id": {},
      "id": "asst_226796",
      "object": "assistant",
      "created_at": 1718975287,
      "name": "Demo Assistant",
      "description": "An assistant for RAG",
      "model": "custom",
      "instructions": "You are a helpful assistant",
      "tools": [
        {
          "type": "retrieval"
        }
      ],
      "file_ids": ["file_0cff17", "file_9b02be"],
      "metadata": {}
    }
  ],
  "first_id": "asst_226796",
  "last_id": "asst_226796",
  "has_more": false
}

Now copy the id of the assistant you want to integrate with the Rocket.Chat AI, from the field id in the example we have it as asst_226796. Once copied enter the same in the Rocket.Chat AI app settings page in the field Vector database collection.

b. You can directly enter the http://api-server:8000?name=Demo Assistant in the field Vector database collection in the Rocket.Chat AI app settings page. If the assistant existst, it will automatically fetch the assistant and replace the settings with asst_XYZ where XYZ is the id of the assistant. If the field didn't change, it means the assistant doesn't exist or there is an issue with the API.

Note: http://api-server:8000 is the default hostname and port. If you are using a different hostname and port, replace http://api-server:8000 with your hostname and port.

Once you have integrated the Rocket.Chat AI with your Rocket.Chat instance, you can start using the AI features in your Rocket.Chat instance.

Bring your own Milvus vector database

If you have your own Milvus vector database, you can use it with the Rocket.Chat AI. You can follow the steps below to integrate your Milvus vector database with the Rocket.Chat AI.

1. Go to your Rocket.Chat instance.
2. Install the Rocket.Chat AI app from the marketplace. You can find the app by searching for Rocket.Chat AI under the (Admininistration > Marketplace > Explore). It's an premium app, so you need to have a valid license (Starter or Pro or Enterprise) to install the app.
3. After installing the app, go to the Rocket.Chat AI app settings page (Admininistration > Marketplace > Installed > Rocket.Chat AI > Settings).
4. Enter the Model URL with the LLM API URL. For example, http://llama3-8b:1234/v1. (don't include the /chat/completions part).
5. Enter the URL of your Milvus vector database with the port 19530. For example, http://milvus:19530 in the setting "Vector database URL".
6. Enter your API Key in the setting Vector database API key.
7. Enter the text field where the text data is stored in the collection schema in the setting Vector database text field.
8. Enter your embedding model (used when ingesting the data) in the field Embedding model URL.

Make sure your Embedding Model URL follows a certain format for request payload and response.

Input:

{
    [
        "text1", "text2", ...
    ]
}

Output:

{
    "embeddings": [
            [0.1, 0.2, 0.3, ...],
            [0.4, 0.5, 0.6, ...]

    ]
}

About the Config Files

Note: Once modified, you need to restart the services for the changes to take effect. You can restart the services using the following command:

docker-compose -f docker-compose.yaml --profile rubra restart

1. llm-config.yaml: This file contains the configuration for the LLM service of Rubra AI. You can modify the configuration as per your requirements.

OPENAI_API_KEY: sk-....X0FUz2bhgyRW32qF1 # OpenAI API key - Enables the use of OpenAI models in Rubra AI
  REDIS_HOST: redis # Redis host
  REDIS_PASSWORD: "" # Redis password
  REDIS_PORT: "6379" # Redis port

model_list:
  - litellm_params:
      api_base: http://host.docker.internal:1234/v1 # LLM API base URL
      api_key: None # LLM API key
      custom_llm_provider: openai # Don't change this for custom models
      model: openai/custom # Model name - must be in the format openai/custom
    model_name: custom

2. milvus.yaml: This file contains the configuration for the Milvus service of Rubra AI. For more information, refer to the Milvus documentation.

Troubleshooting

1. If following error:

 ✘ text-embedding-api Error context can...                           0.1s
 ✘ ui Error                 Head "https://ghcr.io/v2/ru...           0.1s
 ✘ task-executor Error      context canceled                         0.1s
 ✘ api-server Error         context canceled                         0.1s
 ✘ vector-db-api Error      context canceled                         0.1s
Error response from daemon: Head "https://ghcr.io/v2/rubra-ai/rubra/ui/manifests/main": denied: denied

Make sure you have logged in with the GitHub Container Registry (ghcr.io) using the following command:

echo $CR_PAT | docker login ghcr.io -u YOUR_GITHUB_USERNAME --password-stdin

Replace $CR_PAT with your personal access token (PAT) and YOUR_GITHUB_USERNAME with your GitHub username.

2. If you get the following error:

TVMError: after determining tmp storage requirements for inclusive_scan: cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device

This error occurs when the NVIDIA GPU architecture is less than the sm_80. Please refer to this website for the supported GPU with architectures sm_80 and above.

Credits

@software{mlc-llm,
    author = {MLC team},
    title = {{MLC-LLM}},
    url = {https://github.com/mlc-ai/mlc-llm},
    year = {2023}
}