recipes

Welcome to Weaviate Recipes 🌟

Weaviate Recipes is a repository that provides comprehensive examples demonstrating various features and integrations of Weaviate. This project's goal is to help users understand and utilize Weaviate's capabilities effectively. Below is a detailed exploration of what Weaviate Recipes has to offer.

Integration Highlights 🌐

Weaviate offers extensive integration possibilities with several prominent technologies, allowing users to leverage combined capabilities for powerful results. Here’s a look at the categories and some of the major companies involved:

• Cloud Hyperscalers: This includes big names like Google, AWS, and NVIDIA, providing cloud infrastructure services to scale Weaviate deployments.

• Compute Infrastructure: Partners such as Modal and Replicate offer infrastructure tailored to run Weaviate efficiently.

• Data Platforms: Companies like Databricks, Confluent Cloud, and Spark help in managing and analyzing data, creating seamless data workflows.

• LLM Frameworks: Integration with frameworks like DSPy, LangChain, and Semantic Kernel assists in leveraging large language models with Weaviate.

• Operations Management: Tools like Arize and Langtrace help in monitoring and optimizing Weaviate operations, ensuring smooth and efficient performance.

For more detailed integration documentation, one can refer to Weaviate's Integrations Documentation.

Key Features of Weaviate 🔧

Weaviate features a suite of functionalities designed to enhance search capabilities and data management. Here are some of its standout features:

• Similarity Search: Use the nearText operator to conduct semantic searches, allowing more contextually relevant results.

• Hybrid Search: Combines multiple search methods for more comprehensive results using the hybrid operator.

• Generative Search: Facilitates a retrieval-augmented generation (RAG) workflow to produce new content based on search results.

• Filters: Enables users to refine their search outcomes by applying specific criteria.

• Reranking: Enhances search result accuracy by reordering results according to relevance.

• Media Search: With nearImage and nearVideo operators, users can search using visual data like images and videos.

• Classification: Implements KNN and zero-shot classification techniques to categorize data within Weaviate.

• Multi-Tenancy: Supports storing tenants on different data shards, ensuring complete data segmentation and privacy.

• Product Quantization: Compresses vector data to reduce memory usage while maintaining search performance.

• Evaluation Tools: Provides metrics and methods to assess the effectiveness of search solutions.

• CRUD APIs: Facilitates database management through Create, Read, Update, and Delete operations.

• Generative Feedback Loops: Allows users to input and store output from language models back into the database, fostering an iterative learning process.

Ongoing Development and Feedback ❓

Weaviate Recipes is continually evolving, with frequent updates and enhancements. The project welcomes user input and collaboration. If there are specific features desired or suggestions for improvement, users are encouraged to create an issue on GitHub or contribute directly to the project. This collaborative approach ensures that Weaviate Recipes remains a valuable resource for everyone involved.

Embark on your journey with Weaviate today, and explore the myriad possibilities this project presents in leveraging and extending Weaviate's capabilities!