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AI and RAG with Gemma, Ollama, and Logi Symphony

By: Terrence Sheflin
Native LLMs have gotten mainstream with websites like HuggingFace selling open sharing of skilled LLMs. These LLMs are sometimes very small however nonetheless extraordinarily correct, particularly for domain-specific duties like drugs, finance, legislation, and others. Gemma is a multi-purpose LLM and, whereas small, is aggressive and correct.

Native LLMs even have the benefit of being utterly run inside your individual surroundings. There isn’t a probability of information leakage, and P3+ knowledge is ensured to be safe because it by no means leaves the protected community. Not too long ago Google shared its personal native mannequin: Gemma.

Gemma is a household of light-weight, state-of-the-art open fashions constructed from the identical analysis and expertise used to create the Gemini fashions. Developed by Google DeepMind and different groups throughout Google, Gemma is impressed by Gemini, and the title displays the Latin gemma, which means “valuable stone.” Accompanying our mannequin weights, we’re additionally releasing instruments to assist developer innovation, foster collaboration, and information accountable use of Gemma fashions.

Gemma has been shared on HuggingFace, and can be out there within the common LLM internet hosting software program Ollama. Utilizing Ollama, Gemma, and Logi Symphony, this text will present how you can rapidly create a chatbot that makes use of RAG so you’ll be able to work together together with your knowledge, domestically. Not one of the knowledge or questions are ever uncovered to the web or any on-line service outdoors of the native community.


Right here is an instance dashboard in Logi Symphony utilizing Google’s Gemma 2b mannequin on Ollama to reply questions concerning the knowledge.

All the knowledge and the LLM is totally safe with no data leaving the native cluster.



Step one is to deploy Logi Symphony in Kubernetes as per the set up directions (or use our SaaS providing). As soon as deployed, the subsequent step is so as to add Ollama into the cluster. This may be completed through their current helm chart with the next run inside the kubectl context:

helm repo add ollama

helm repo replace

helm set up ollama ollama/ollama –set ollama.defaultModel=”gemma” –set persistentVolume.enabled=true

Ollama will now be deployed and accessible inside the cluster as http://olama:11434 and have already got the default gemma mannequin preloaded. For this instance, we additionally use an area embeddings mannequin. So as to add that, run:

kubectl exec -it — ollama pull nomic-embed-text

The place ollama-pod-name is the title of the Ollama pod deployed above. Deployment is now full!


After deployment, create any visible or dashboard with any knowledge you’d like in Logi Symphony, even Excel knowledge. For this instance I used Logi Symphony’s new built-in IBCS Variance management containing gross sales knowledge for this yr and final yr.

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Chat movement setup

After deployment and dashboard creation, the subsequent step is to create the chat movement that can use Gemma to do RAG with knowledge accessible from Logi Symphony. This knowledge could possibly be from any database you will have! So long as Logi Symphony can entry it, Gemma will be capable to as properly.

Last chat movement

Beneath is how the chat movement ought to take care of being arrange. Every of those nodes could be discovered within the + icon and added.

Image 3

Setup steps

To set this up, begin with a Conversational Retrieval QA Chain. So as to add this, click on the + and increase Chains, then add it.

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Observe the identical steps so as to add the Chat Ollama from Chat Fashions, In-Reminiscence Vector Retailer from Vector Shops, Logi Symphony from the doc loaders, and at last Ollama Embeddings from embeddings.

Configuration notes

For Chat Ollama, you should specify the native URL of Ollama. That is usually the native title inside the Kubernetes cluster. For those who used the identical helm deployment as above, then it must be http://olama:11434.

As well as, the mannequin should be specified. If utilizing the default Gemma mannequin, this could merely be gemma. The default gemma mannequin is a 7b-instruct mannequin that has been gotten smaller by way of quantization. There may be additionally a 2b-instruct mannequin if sources are constrained, however it will likely be much less correct.

For Ollama Embeddings, you want to specify the identical URL because the chat and the mannequin. For this instance, nomic-embed-text was used, and set up was completed within the above deployment part.

For embeddings, click on Extra Parameters and guarantee Use MMap is chosen.

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It’s additionally suggested to switch the immediate as Gemma appears to do significantly better with a quite simple immediate schema.

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Lastly, set a dashboard or visible ID has been set on the Logi Symphony node in order that knowledge could be retrieved even when it’s not embedded. That is an elective step, but it surely permits utilizing the chatbot when it’s not embedded inside a dashboard.

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Ask it questions!

Now that the whole lot is setup, you’ll be able to ask questions concerning the knowledge proper in Logi AI.

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Embed it

The chatbot doesn’t must be accessed solely inside Logi AI, it can be accessed proper on the dashboard for anybody to make use of it, and even utterly individually when embedded inside a buyer’s portal.

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