Nuclia integrations for modular RAG
Get Nuclia’s modular RAG-as-a-Service and build the perfect RAG pipeline for your specific use case.
Vector DBs
Nuclia end-to-end RAG seamlessly integrates with several vector databases, including Pinecone and our very own NucliaDB. These integrations enable powerful and efficient retrieval capabilities, allowing you to maximize the potential of your unstructured data with the best-in-class vector search technology.
Pinecone enables rapid search and retrieval of vectors, essential for applications that demand real-time data processing. Whether you’re working with large datasets or complex queries, Pinecone ensures that you get the results you need without delay.
NucliaDB is the open-source vector database developed by Nuclia, designed to efficiently manage and retrieve unstructured data using vector search technology.
Language models
Nuclia integrates effortlessly with a variety of leading Large Language Models (LLMs), giving you the flexibility to choose the best model for your specific needs. Whether you’re using OpenAI, Mistral, Anthropic, or other cutting-edge LLMs, Nuclia ensures seamless deployment and optimal performance across all your AI-driven projects.
OpenAI models available in Nuclia:
- OpenAI + Azure
- ChatGPT-4 Turbo
- OpenAI + Azure
- ChatGPT-4o
- OpenAI + Azure
- ChatGPT-3.5
- OpenAI ChatGPT-Vision
- OpenAI ChatGPT-4
- OpenAI ChatGPT-4o
- OpenAI ChatGPT-4o-mini
Anthropic models available in Nuclia:
- Anthropic Claude 3
- OpusAnthropic Claude 3 Sonet
- Anthropic Claude 3.5 Sonet
Google Gemini models available in Nuclia:
- Google Gemini Pro 1.5
- Google Gemini Pro 1.5 Vision
Mistral models available in Nuclia:
- Mistral AI Mixtral
- Mistral + Azure Mistral large
Use Nuclia’s integration with Hugging Face to leverage any model available on Hugging Face for your own RAG.
Embedding models
Nuclia empowers you to take full control of your data processing by allowing you to choose the embedding model that best aligns with your specific data needs. Whether you’re dealing with text, images, or other types of unstructured data, Nuclia offers the flexibility to select from a range of advanced embedding models.
Use Nuclia’s integration with Hugging Face to leverage any embedding model available on Hugging Face for your own RAG.
Nuclia embeddings
OpenAI 3 large & small
Google Gecko
Google’s Gecko embeddings come in small, efficient vectors for simpler tasks and large, detailed vectors for deep, complex data analysis and understanding.
Retrieval integrated strategies
Textual Hierarchy
Pass Entire Resources as Context
Pass Specific Field(s) as Context
Include images
For users utilizing visual LLMs like OpenAI ChatGPT-Vision or Google Gemini Pro Vision, appending images to the context can be valuable. Nuclia offers two strategies to include images:
• Page Images: Append images present on the same page as the matched paragraph.
• Paragraph Images: Append images present next to the matched paragraph.
Add Extra Context
The extra_context parameter allows users to add a list of text snippets to the context.
- When the Knowledge Box does not contain all the relevant information to generate an answer. For instance, if the Knowledge Box contains your product catalog but lacks user preferences, you can add the current user’s preferences as extra context.
• When you need to run multiple distinct searches to gather all necessary information. The extra_context parameter can pass the combined results.
• When you need to chain several questions to generate the answer. The extra_context parameter can pass the results of previous questions.