The Google GenAI LLM Snap Pack is a powerful collection of connectors (Snaps) that brings Google cutting-edge large language models directly into your SnapLogic workflows. Whether you’re building conversational AI, automating content creation, or extracting insights from unstructured data, this Snap Pack gives you ready-to-use connectors for a wide range of natural language processing tasks—from text generation and translation to summarization, question answering, and sophisticated chatbot experiences.
What makes this Snap powerful
The Google GenAI LLM Snap Pack offers fine-grained control over how AI responds and behaves in your workflows. Generate advanced text outputs by selecting specific models and tuning parameters to match your use case. Build chatbots that engage in natural, human-like conversations. Create multilingual experiences with machine translation capabilities. Extract key insights through intelligent summarization. Convert questions into precise answers. And work seamlessly across multiple content types—text, images, and more—with multimodal support that standardizes diverse inputs into a unified format for your language models.
Customize AI behavior to your needs
Advanced response configuration options let you fine-tune exactly how the AI behaves in different scenarios. Whether you need creative storytelling, factual responses, technical documentation, or customer service interactions, you can adjust parameters and settings to get precisely the output your business requires—all within the SnapLogic environment you already know.
This Snap Pack includes the following key features:
- Enables advanced text generation with specified models and parameters.
- Supports tasks such as embedding, text generation, machine translation, text summarization, and question answering.
- Facilitates the creation of chatbots that can engage in human-like conversations.
- Supports advanced response configurations that enable you to customize response behavior through detailed settings, allowing for precise tuning of outputs based on your specific needs and scenarios.
- Supports the processing and generation of content across multiple modalities, converting diverse inputs into a standardized response model for seamless integration with LLMs.
This Snap Pack includes the following Snaps:
- Google Gemini API Tool Calling: Provides external tools for the model to call, supplying internal data and information for the model’s response
- Google Gemini Embedder: Generates an embedding vector based on the provided input data.
- Google Gemini Generate: Enables text generation tasks such as content creation, response generation, and more.
- Google GenAI Function Generator: Generates a tool definition in the output to inform the model about the tools that you want to call.
- Google GenAI Function Result Generator: Formats the results generated by user-run functions into a unified structure within SnapLogic.
- Google GenAI Multimodal Content Generator: Generates multimodal content for the user prompt with Google AI.
- Google GenAI Prompt Generator: Generates the augmented user prompt as per the specified prompt template using the mustache template format.
- Google VertexAI Embedder: Generates an embedding vector based on the provided input data using Vertex AI.
- Google VertexAI Generate: Generates text generation tasks such as content creation, response generation.
- Google VertexAI Tool Calling: Provides external tools for the model to call, supplying internal data and information for the Vertex model’s response.
- Google VertexAI RAG Create Corpus: Creates a RAG corpus.
- Google VertexAI RAG Delete Corpus: Deletes a RAG corpus.
- Google VertexAI RAG Get Corpus: Gets a RAG corpus.
- Google VertexAI RAG List Corpus: Lists a RAG corpus.
- Google VertexAI RAG Corpus Add File: Add a file to the RAG corpus.
- Google VertexAI RAG Corpus Get File: Gets details about a RAG file for use by the LLM.
- Google VertexAI RAG Corpus List File: Lists all of the files in the RAG corpus
- Google VertexAI RAG Corpus Remove File: Removes a file from the RAG corpus.
- Google VertexAI RAG Retrieval Query: Retrieves embeddings from the RAG corpus.
To learn more, please check out the documentation page.
