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!
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 |
Go to SambaNova Quickstart Guide if it's your first time using the AI State Kits and you want to try out simple examples.
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.
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 as template:
ai-starter-kit/.env file, for example:SAMBANOVA_API_KEY = "your-sambanova-api-key"
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 as template:
SAMBANOVA_API_KEY = "your-sambanova-api-key"
SAMBANOVA_API_BASE = "you-sambanova-base-url"
Go to the README.md of the starter kit you want to use and follow the instructions. See Available AI Starter Kits.
There are two approaches to setting up your virtual environment for the AI Starter Kits:
Each starter kit (see table above) 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:
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:
make all
This command will set up the base ai-starter-kit environment, including installing all necessary tools and dependencies.
source .venv/bin/activate
cd path/to/starter_kit
Within the starter kit there will be instructions on how to start the kit. You can skip the virtual environment creation part in the kits README.md as we've done it here.
For certain kits, we utilise a standard parsing service. By Default it's started automatically with the base environment. To work with this service in isolation, following the steps in this section.
make start-parsing-service
make stop-parsing-service
make parsing-status
make parsing-log
To use the Docker-based setup:
make docker-build
make docker-run-kit KIT=<kit_name>
Replace <kit_name> with the name of the starter kit you want to run (e.g., function_calling).
make docker-shell
To clean up all virtual environments created by the makefile and stop parsing services run the following command:
make clean
This command removes all virtual environments created with the makefile, stops the parsing service, and cleans up any temporary files.
If you encounter issues while setting up or running the AI Starter Kit, here are some common problems and their solutions:
If you're having problems with Python versions:
make ensure-pyenvmake install-python-versionsIf you're experiencing dependency conflicts:
make cleanpoetry lock --no-updatemake installIf you encounter an error while installing pikepdf, such as:
ERROR: Failed building wheel for pikepdf
Failed to build pikepdf
This is likely due to missing qpdf dependency. The Makefile should automatically install qpdf for you, but if you're still encountering issues:
qpdf using Homebrew:
brew install qpdf
sudo apt-get update && sudo apt-get install -y qpdf
qpdf, try running make install again.If you continue to face issues, please ensure your system meets all the requirements for building pikepdf and consider checking the pikepdf documentation for more detailed installation instructions.
If the parsing service isn't starting or is behaving unexpectedly:
make parsing-statusmake parsing-logmake stop-parsing-service followed by make start-parsing-serviceIf you encounter issues related to Tesseract OCR or Poppler:
brew install tesseract poppler
sudo apt-get update && sudo apt-get install -y tesseract-ocr poppler-utils
If you're using the Docker-based setup and encounter issues:
make docker-buildmake clean all.env file is correctly set up in the ai-starter-kit root with all necessary environment variables.If you continue to experience issues, please open an issue with details about your environment, the full error message, and steps to reproduce the problem.
Note: These AI Starter Kit code samples are provided "as-is," and are not production-ready or supported code. Bugfix/support will be on a best-effort basis only. Code may use third-party open-source software. You are responsible for performing due diligence per your organization policies for use in your applications.
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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!
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 |
Go to SambaNova Quickstart Guide if it's your first time using the AI State Kits and you want to try out simple examples.
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.
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 as template:
ai-starter-kit/.env file, for example:SAMBANOVA_API_KEY = "your-sambanova-api-key"
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 as template:
SAMBANOVA_API_KEY = "your-sambanova-api-key"
SAMBANOVA_API_BASE = "you-sambanova-base-url"
Go to the README.md of the starter kit you want to use and follow the instructions. See Available AI Starter Kits.
There are two approaches to setting up your virtual environment for the AI Starter Kits:
Each starter kit (see table above) 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:
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:
make all
This command will set up the base ai-starter-kit environment, including installing all necessary tools and dependencies.
source .venv/bin/activate
cd path/to/starter_kit
Within the starter kit there will be instructions on how to start the kit. You can skip the virtual environment creation part in the kits README.md as we've done it here.
For certain kits, we utilise a standard parsing service. By Default it's started automatically with the base environment. To work with this service in isolation, following the steps in this section.
make start-parsing-service
make stop-parsing-service
make parsing-status
make parsing-log
To use the Docker-based setup:
make docker-build
make docker-run-kit KIT=<kit_name>
Replace <kit_name> with the name of the starter kit you want to run (e.g., function_calling).
make docker-shell
To clean up all virtual environments created by the makefile and stop parsing services run the following command:
make clean
This command removes all virtual environments created with the makefile, stops the parsing service, and cleans up any temporary files.
If you encounter issues while setting up or running the AI Starter Kit, here are some common problems and their solutions:
If you're having problems with Python versions:
make ensure-pyenvmake install-python-versionsIf you're experiencing dependency conflicts:
make cleanpoetry lock --no-updatemake installIf you encounter an error while installing pikepdf, such as:
ERROR: Failed building wheel for pikepdf
Failed to build pikepdf
This is likely due to missing qpdf dependency. The Makefile should automatically install qpdf for you, but if you're still encountering issues:
qpdf using Homebrew:
brew install qpdf
sudo apt-get update && sudo apt-get install -y qpdf
qpdf, try running make install again.If you continue to face issues, please ensure your system meets all the requirements for building pikepdf and consider checking the pikepdf documentation for more detailed installation instructions.
If the parsing service isn't starting or is behaving unexpectedly:
make parsing-statusmake parsing-logmake stop-parsing-service followed by make start-parsing-serviceIf you encounter issues related to Tesseract OCR or Poppler:
brew install tesseract poppler
sudo apt-get update && sudo apt-get install -y tesseract-ocr poppler-utils
If you're using the Docker-based setup and encounter issues:
make docker-buildmake clean all.env file is correctly set up in the ai-starter-kit root with all necessary environment variables.If you continue to experience issues, please open an issue with details about your environment, the full error message, and steps to reproduce the problem.
Note: These AI Starter Kit code samples are provided "as-is," and are not production-ready or supported code. Bugfix/support will be on a best-effort basis only. Code may use third-party open-source software. You are responsible for performing due diligence per your organization policies for use in your applications.
(top 30 of 32)
1,363 commits
1,083 commits
579 commits
530 commits
Jupyter Notebook
75.7%
Python
22.3%