Best vector search Tools

Next-gen AI database with vector search and SQL analytics.

Build AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python!

Access over 6000 third-party GPTs with ease.

Build AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python!

Linq is an AI-powered enterprise search engine that allows you to build your own vector search engine in just one day. It offers hyper-accurate vector search over your data, using a single API to finish the process quickly. With Linq, you can effortlessly manage your knowledge base on a user-friendly admin page.

Pinecone is a vector database that allows users to search through billions of items and find similar matches to any object in milliseconds. It is a next-generation search solution that can be accessed through an API call.

Vellum is the development platform for building LLM apps with tools for prompt engineering, semantic search, version control, testing, and monitoring. Compatible with all major LLM providers.

Steamship is a platform that allows you to build, scale, and monitor AI Agents. It provides serverless cloud hosting, vector search, webhooks, callbacks, and more.

Convex is a full-stack TypeScript development platform that keeps you focused on your product. Use our realtime database to build apps that are reactive by default. Integrate OpenAI into your workflows with builtin functions, scheduling, and vector search.

Embedditor is an open-source MS Word equivalent for embedding that maximizes the effectiveness of vector searches. It offers a user-friendly interface for improving embedding metadata and tokens. With advanced NLP cleansing techniques, like TF-IDF normalization, users can enhance the efficiency and accuracy of their LLM-related applications. Embedditor also optimizes the relevance of content obtained from a vector database by intelligently splitting or merging the content based on its structure and adding void or hidden tokens. Furthermore, it provides secure data control by allowing local deployment on a PC or in a dedicated enterprise cloud or on-premises environment. By filtering out irrelevant tokens, users can save up to 40% on embedding and vector storage costs while achieving better search results.