sambanova/ai-starter-kit

sambanova/ai-starter-kit

Language: Jupyter Notebook

License: NOASSERTION

Stars: 249

Forks: 80

Open issues: 24

Created: 2023-10-06T16:34:46Z

Pushed: 2026-06-03T15:51:01Z

Default branch: main

Fork: no

Archived: no

README:

SambaNova AI Starter Kits

Overview

SambaNova AI Starter Kits are a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for both developers and enterprises.

To run these examples, you can obtain a free API key using SambaCloud. Alternatively, if you are a current SambaNova customer, you can deploy your models using SambaStack or SambaManaged. Most of the code examples are written in Python, although the concepts can be applied to any programming language.

Questions? Just message us on SambaNova Community or create an issue in GitHub. We're happy to help live!

Available AI Starter Kits

The table below lists the available kits, which are grouped into four categories: 1) Data Ingestion & Preparation, 2) Model Development & Optimization, 3) Intelligent Information Retrieval, and 4) Advanced AI Capabilities.

For functionalities related to third-party integrations, find a list in our Integrations Repository and Integrations Docs.

Name Kit Description Category

Data Extraction Series of notebooks that demonstrate methods for extracting text from documents in different input formats. Data Ingestion & Preparation

Enterprise Knowledge Retrieval Sample implementation of the semantic search workflow using the SambaNova platform to get answers to questions about your documents. Includes a runnable demo. Intelligent Information Retrieval

Multimodal Knowledge Retriever Sample implementation of the semantic search workflow leveraging the SambaNova platform to get answers using text, tables, and images to questions about your documents. Includes a runnable demo. Intelligent Information Retrieval

RAG Evaluation Kit A tool for evaluating the performance of LLM APIs using the RAG Evaluation methodology. Intelligent Information Retrieval

Search Assistant Sample implementation of the semantic search workflow built using the SambaNova platform to get answers to your questions using search engine snippets, and website crawled information as the source. Includes a runnable demo. Intelligent Information Retrieval

Benchmarking This kit evaluates the performance of multiple LLM models hosted in SambaNova Solutions. It offers various performance metrics and configuration options. Users can also see these metrics within a chat interface. Advanced AI Capabilities

Financial Assistant This app demonstrates the capabilities of LLMs in extracting and analyzing financial data using function calling, web scraping, and RAG. Advanced AI Capabilities

Function Calling Example of tools calling implementation and a generic function calling module that can be used inside your application workflows. Advanced AI Capabilities

Custom Chat Templates complete workflow of how modern chat models format, send, and interpret conversations. From Jinja chat templates to Completions API invocation and tool-call parsing. Advanced AI Capabilitie

Getting Started

Go to [SambaNova Quickstart Guide](./quickstart/README.md) if it's your first time using the AI State Kits and you want to try out simple examples.

Getting a SambaNova API key and setting your generative models

Currently, there are two ways to obtain an API key from SambaNova. You can get a free API key using SambaCloud. Alternatively, if you are a current SambaNova customer, you can deploy your models using SambaStack or SambaManaged.

Use SambaCloud (Option 1)

To generate an API key, go to the API section of the SambaCloud portal.

To integrate SambaCloud LLMs with this AI starter kit, update the API information by configuring the environment variables in the ai-starter-kit/.env file, use [.env-example](.env-example) as template:

• Enter the SambaCloud API key in the ai-starter-kit/.env file, for example:

SAMBANOVA_API_KEY = "your-sambanova-api-key"

Use SambaStack/SambaManaged (Option 2)

Begin by deploying your LLM of choice (e.g., Llama 3.3 70B) to an endpoint for inference in SambaStack/SambaManaged.

To integrate your LLM deployed on SambaStack/SambaManaged with this AI starter kit, update the API information by configuring the environment variables in the ai-starter-kit/.env file, use [.env-example](.env-example) as template:

• Set your SambaStack/SambaManaged variables, for example:

SAMBANOVA_API_KEY = "your-sambanova-api-key"
SAMBANOVA_API_BASE = "you-sambanova-base-url"

Run the desired starter kit

Go to the README.md of the starter kit you want to use and follow the instructions. See [Available AI Starter Kits](#available-ai-starter-kits).

Additional information

Setting up your virtual environment

There are two approaches to setting up your virtual environment for the AI Starter Kits:

1. Individual Kit Setup (Traditional Method) 2. Base Environment Setup

1. Individual Kit Setup

Each starter kit (see table [above](#available-ai-starter-kits)) has its own README.md and requirements.txt file. You can set up a separate virtual environment for each kit by following the instructions in their respective directories. This method is suitable if you're only interested in running a single kit or prefer isolated environments for each project.

To use this method: 1. Navigate to the specific kit's directory 2. Create a virtual environment 3. Install the requirements 4. Follow the kit-specific instructions

2. Base Environment Setup

For users who plan to work with multiple kits or prefer a unified development environment, we recommend setting up a base environment. This approach uses a Makefile to automate the setup of a consistent Python environment that works across all kits.

Benefits of the base environment approach:

• Consistent Python version across all kits
• Centralized dependency management
• Simplified setup process
• Easier switching between different kits

Prerequisites

• pyenv: The Makefile will attempt to install pyenv if it's not already installed.
• Docker: (Optional) If you want to use the...