SambaNova AI Starter Kits

Overview

SambaNova AI Starter Kits are a collection of open-source examples and guides to facilitate the deployment of AI-driven use cases in the enterprise.

To run these examples, you need access to a SambaStudio environment with your models deployed to endpoints. Most code examples are written in Python, though the concepts can be applied in any language.

If you have any issues with the examples or to provide feedback, create an issue in GitHub.

Available AI Starter Kits

Code Copilot	This example guide shows a simple integration with Continue VSCode and JetBrains extension using SambaNova platforms, to use Sambanova's hosted models as your custom coding assistant.
Complex RAG	Sample implementation of a complex RAG workflow using the SambaNova platform to get answers to questions about your documents. Includes a runnable demo.
CoE jump start	This kit demonstrates how to call SambaNova CoE models using the Langchain framework. The script offers different approaches for calling CoE models, including using Sambaverse, using SambaStudio with a named expert, and using SambaStudio with routing.
Data Extraction	Series of notebooks that demonstrate methods for extracting text from documents in different input formats.
EDGAR Q&A	Example workflow that uses the SambaNova platform to answer questions about organizations using their 10-K annual reports. Includes a runnable local demo and a Docker container to simplify remote deployment.
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.
Fine tuning embeddings	Example workflow for fine-tuning embeddings from unstructured data, leveraging Large Language Models (LLMs) and open-source embedding models to enhance NLP task performance.
Fine tuning SQL	Example workflow for fine-tuning an SQL model for Question-Answering purposes, leveraging Large Language Models (LLMs) and open-source embedding models to enhance SQL generation task performance.
Image Search	This example workflow shows a simple approach to image search by image description or image similarity. All workflows are built using the SambaNova platform.
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.
Post Call Analysis	Example workflow that shows a systematic approach to post-call analysis including Automatic Speech Recognition (ASR), diarization, large language model analysis, and retrieval augmented generation (RAG) workflows. All workflows are built using the SambaNova platform.
Prompt Engineering	Starting point demo for prompt engineering using SambaNova's API to experiment with different use case templates. Provides useful resources to improve prompt crafting, making it an ideal entry point for those new to this AISK.
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.
Web Crawled Data Retrieval	Sample implementation of a semantic search workflow built using the SambaNova platform to get answers to your questions using website crawled information as the source. Includes a runnable demo.
YoDA: Your Data Your model	Sample training recipe to train a Language Model (LLM) using a customer's private data.

Get started with SambaNova AI starter kit

Setting your model

Use Sambaverse models (Option 1)

SambaverseSambaverse allows you to interact with multiple open-source models. You can view the list of available models and interact with them in the playground.

Please note that Sambaverse's free offering is performance-limited. Companies that are ready to evaluate the production tokens-per-second performance, volume throughput, and 10x lower total cost of ownership (TCO) of SambaNova should contact us for a non-limited evaluation instance.

Begin by creating a Sambaverse account, then get your API key from the username button. Use the available models.

Deploy your model in SambaStudio (Option 2)

Begin by deploying your LLM of choice (e.g. Llama 2 13B chat, etc) to an endpoint for inference in SambaStudio. Use either the GUI or CLI, as described in the SambaStudio endpoint documentation.

Integrate your model in the starter kit

Integrate your LLM deployed on SambaStudio with this AI starter kit in two simple steps:

1. Clone this repo

  git clone https://github.com/sambanova/ai-starter-kit.git

2. Update API information for the SambaNova LLM

These are represented as configurable variables in the environment variables file in sn-ai-starter-kit/.env.

SambaStudio deployed model

For example, enter an endpoint with the URL "https://api-stage.sambanova.net/api/predict/nlp/12345678-9abc-def0-1234-56789abcdef0/456789ab-cdef-0123-4567-89abcdef0123" in the env file (with no spaces) as:

BASE_URL="https://api-stage.sambanova.net"
PROJECT_ID="12345678-9abc-def0-1234-56789abcdef0"
ENDPOINT_ID="456789ab-cdef-0123-4567-89abcdef0123"
API_KEY="89abcdef-0123-4567-89ab-cdef01234567"

Sambaverse model

Enter a Sambaverse API key, for example "456789ab-cdef-0123-4567-89abcdef0123", in the env file (with no spaces) as:

SAMBAVERSE_API_KEY="456789ab-cdef-0123-4567-89abcdef0123"

3. Update API information for SambaNova Embeddings model (optional).

You can use SambaStudio E5 embedding model endpoint instead of using default in cpu HugginFace embeddings to increase inference speed, follow this guide to deploy your SambaStudio embedding model

be sure to set batch size model parameter to 32

Update API information for the SambaNova embedding endpoint. These are represented as configurable variables in the environment variables file in the root repo directory sn-ai-starter-kit/.env. For example, an endpoint with the URL "https://api-stage.sambanova.net/api/predict/nlp/12345678-9abc-def0-1234-56789abcdef0/456789ab-cdef-0123-4567-89abcdef0123" would be entered in the env file (with no spaces) as:

EMBED_BASE_URL="https://api-stage.sambanova.net"
EMBED_PROJECT_ID="12345678-9abc-def0-1234-56789abcdef0"
EMBED_ENDPOINT_ID="456789ab-cdef-0123-4567-89abcdef0123"
EMBED_API_KEY="89abcdef-0123-4567-89ab-cdef01234567"

Note that using different embedding models (cpu or sambastudio) may change the results, and change the way they are set and their parameters

with CPU Huggingface embeddings:

           embeddings = HuggingFaceInstructEmbeddings(
               model_name="hkunlp/instructor-large",
               embed_instruction="",
               query_instruction="Represent this sentence for searching relevant passages:",
               encode_kwargs={"normalize_embeddings": True},
           )

with Sambastudio embeddings:

embeddings = SambaNovaEmbeddingModel()

4. 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.

Use Sambanova's LLMs and Langchain wrappers

LLM Wrappers

Set your environment as shown in integrate your model.

Using Sambaverse LLMs

1. Import the samabanova_endpoint langchain wrapper in your project and define your SambaverseEndpoint LLM:

from utils.sambanova_endpoint import SambaverseEndpoint

load_dotenv('.env')

llm = SambaverseEndpoint(
    sambaverse_model_name="Meta/llama-2-7b-chat-hf",
    model_kwargs={
      "do_sample": False,
      "temperature": 0.0,
      "max_tokens_to_generate": 512,
      "select_expert": "llama-2-7b-chat-hf"
      },
)

2. Use the model

llm.invoke("your prompt")

Using Sambastudio LLMs

1. Import the samabanova_endpoint langchain wrapper in your project and define your SambaNovaEndpoint LLM:

from utils.sambanova_endpoint import SambaNovaEndpoint

load_dotenv('.env')

llm = SambaNovaEndpoint(
    model_kwargs={
      "do_sample": False,
      "max_tokens_to_generate": 512,
      "temperature": 0.0
      },
)

2. Use the model

llm.invoke("your prompt")

See utils/usage.ipynb for an example.

Embedding Wrapper

1. Import the samabanova_endpoint langchain wrapper in your project and define your SambaNovaEmbeddingModel embedding:

from utils.sambanova_endpoint import SambaNovaEmbeddingModel

load_dotenv('.env')

embedding = SambaNovaEmbeddingModel()

2. Use your embedding model in your langchain pipeline

See utils/usage.ipynb for an example.

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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.