Fireworks AI
Build AI applications by combining Fireworks AI's LLM models with Zilliz Cloud's vector database capabilities
Use this integration for FreeAbout Fireworks
AI Fireworks AI is a generative AI platform that lets developers run and customize AI models with high performance and reliability. The platform offers serverless models, on-demand deployments, and fine-tuning options across text, audio, image, and embedding models.
The platform uses a pay-as-you-go model and includes features like JSON mode, grammar mode, and function calling through their Flumina framework.
Why Zilliz Cloud and Fireworks
AI Combining Zilliz Cloud with Fireworks AI creates a robust foundation for building AI applications. Zilliz Cloud handles vector storage and similarity search, while Fireworks AI provides access to optimized language and embedding models.
This integration helps developers build production-ready AI applications without managing complex infrastructure. The combination is particularly useful for applications that need reliable vector search and high-performance LLM capabilities.
How Zilliz Cloud and Fireworks AI Works
The integration works by using Fireworks AI's models to generate embeddings from your data, which are then stored and searched in Zilliz Cloud. When you need to retrieve relevant information, Zilliz Cloud performs similarity search on these embeddings. For RAG applications, Zilliz Cloud retrieves the most relevant documents based on vector similarity, which are then used by Fireworks AI's LLMs to generate accurate, contextual responses.
Technical Implementation
Authentication Setup:
- Set up Fireworks API key as an environment variable
- Configure Zilliz Cloud connection using URI and API key
- Data Processing Flow:
- Generate embeddings using Fireworks AI's embedding models (e.g., nomic-ai/nomic-embed-text-v1.5)
- Store vectors in Zilliz Cloud collections with specified dimensions
- Use Inner Product (IP) or Cosine similarity for vector search
- Collection Management:
- Create collections with specific parameters
- Configure dimension size based on embedding model output
- Set consistency levels for data reliability
- Search and Retrieval:
- Perform semantic search with customizable limits
- Retrieve related documents with similarity scores
- Process results through Fireworks AI's LLM for final responses
- Set up Fireworks API key as an environment variable
Learn
Learn Section The best way to start is with a hands-on tutorial. This tutorial will walk you through how to build a RAG (Retrieval-Augmented Generation) system with Fireworks AI & Zilliz Cloud.
And here are a few more resources: