Skip to content

LLM Open Source

The prismeai-llm microservice uses LocalAi by default, with the pre-built image available here :

The prismeai-llm can also use Ollama instead of Localai.

Installation prerequisites

This service need access to:
- A volume on which you can load the models to use

For LocalAI, you can learn more about installing the models in the next section.


Using Helm Chart :
- Add the following lines under prismeai-llm: in prismeai-apps/values.yaml :

    repository: ollama/ollama
    tag: latest
    pullPolicy: Always

     - name: OLLAMA_HOST
     - name: OLLAMA_MODELS
       value: /models/models/ollama  

Installing models


You will need to provide some files in the ./models directory (relative to your installation method), for each model:
- A .yaml file describing the model
- A .tmpl file for the prompt format of the model
- GGUF (CPU-compatible) file containing the model

For embeddings models, you won't need a .tmpl file.

Examples for mistral, however you will need to download two additional files and place them in the ./models folder.
A is in the ./models folder.
- Mistral-7B-Instruct-v0.2-GGUF from

Some embeddings model can be used as .gguf like LLM, but other like MPNET requires to git clone a repository. MPNET provides way better results than bert.
- clone into your models folder git clone
- Replace the path in the mpnet.yaml file to match the cloned directory

The files should have the following names :
- multi-qa-mpnet-base-dot-v1
- Mistral-7B-Instruct-v0.2-GGUF

You can try more "compressed" versions of these model. Q8 is less compressed and Q4 is more compressed, leading to less accurate results but faster generation. Note that the gguf files are for CPU inference for proof of concept before dedicating to GPU. GPU will be miles ahead, able to reach chat-gpt speed on a 4090 or A100 (models have differing speed).

On Using 16 CPU on our early tests, using big prompts with a lot of context you can expect 3 minutes for Phi-2 and 10 from Mistral 7b. These models can handle up to 4096 tokens of context + generated text.

To add a new model or modify the configurations, follow the documentation here: This documentation can be summarized to :
- Find a model on huggingFace
- click on the download icon for the file and copy the link, in your machine download it in the /model section using curl -L urlWithModelName -O modelName
- Then find the appropriate template to use here and copy it in the /models folder along the model.
- Restart the service to be able to use it


Any model provided by can be downloaded within the LLM host / docker container with this command :

ollama run <modelName>

For example :

ollama run phi

Models will be automatically downloaded within OLLAMA_MODELS directory & made available to HTTP API.
In order to install models in an offline machine, this command can be run from any machine connected to Internet, and models directories can then be scp to the container :

root:/# ls /models/models/ollama/
blobs  manifests

By default & if not changed by a OLLAMA_MODELS environment variable, Ollama downloads models to ~/.ollama/models on macOS, /usr/share/ollama/.ollama/models on Linux and C:\Users\<username>\.ollama\models on Windows.


Microservice testing

You can call your API with the following query to test. You can use "orca" or "airoboros" if using the provided examples.

curl http://localhost:5000/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
     "model": "phi-2",
     "messages": [{
        "role": "user",
        "content": "Give me a random number."
     "temperature": 0.7,
     "max_token": 10,
     "stream": true

To test the embeddings, you can call the following:

curl http://localhost:8080/v1/embeddings -H "Content-Type: application/json" -d '{                                                                                                                                                 
     "model": "bert",
     "input": "A long time ago in a galaxy far, far away"


To debug the product if you don't receive any response after a long time (10+ minutes for the first inference) you can run the docker-compose with the DEBUG: true environment variable. This will provide useful logs to sends us.

Please note that when ran locally the LLM is expected to be really slow. For reference, on a macbook M2 the docker image can generate 1 token every 7 seconds for the above requests.

Usage on the AI Knowledge

To use these LLM on the Knowledge, in the project settings, you should specify the correct model names for text generation and embeddings. In the first model field enter either "orca" or "airoboros", and in the model-embeddings fields "bert" or "mpnet".

If you want to test change the embedding models of an AI:Knowledge project, you will have use another project. This is because the embeddings does not have the same vector size, which are used by redis for indexation.