AI & Vector Search
Opensolr's AI features go beyond traditional keyword matching. With vector search, your search engine understands the meaning behind every query — not just the words. Combined with AI Hints and AI Reader, your users get answers, not just links.
New to AI search? Read the Hybrid Search deep-dive, try the live interactive demo, or follow the step-by-step testing guide.
How Hybrid Search Works
When a user types a query, Opensolr sends it through two paths at the same time and merges the results. This gives you the precision of keyword search plus the intelligence of AI semantic search.
What Is Vector Search?
In Plain English
Normal search works like a dictionary: it looks up the exact words you typed. If you search for “car repair”, it only finds pages that literally contain the words “car” and “repair”.
Vector search understands meaning. It knows that “car repair” is similar to “auto mechanic”, “vehicle maintenance”, and “fixing my automobile” — even though those pages never use the word “car” or “repair”. This means users find what they need, even when they do not know the exact terminology.
How It Works Under the Hood
Every piece of text — your documents and the user's query — gets converted into a list of 1,024 numbers (called a “vector”). Texts with similar meanings have similar numbers, so finding related content is as simple as finding nearby vectors.
Your documents and your users' queries go through the exact same AI model. That is why a query like “affordable restaurants” can match a document about “budget-friendly dining” — the AI produces similar number patterns for similar meanings. For the full technical details on how to create these embeddings via the API, see Bulk Embed All Documents.
Hybrid Search: Best of Both Worlds
Opensolr does not make you choose between keyword search and vector search. Hybrid search runs both at the same time and merges the results. Here is why that matters:
Keyword Search
Finds exact matches. Great for product codes, names, technical terms. If a user searches “SKU-12345”, keyword search finds it instantly.
Vector Search
Finds similar meanings. Great for natural language questions. If a user asks “how to fix a leaky faucet”, vector search finds plumbing guides even if they never use the word “leaky”.
Hybrid (Both)
Gets the right answer regardless of how the user phrases their query. Precise when they are specific, intelligent when they are vague. The best of both worlds.
AI Hints
When a user searches your index, an AI-generated answer can appear above the search results. This answer is sourced entirely from YOUR content — it does not make things up. Think of it as a smart preview that saves your users time.
AI Hints are generated from the actual content in your Opensolr index. The AI reads your documents and summarizes the answer. It cites which document the answer came from so users can verify and click through.
AI Reader
Every search result gets a “Read” button. When a user clicks it, the AI fetches the full page content and generates a concise summary right in the search page. Users get the key information without navigating away.
One-Click Summaries
Click “Read” on any result. The AI reads the entire page and produces a 2–3 paragraph summary highlighting the key points.
Streaming Response
The summary streams in word by word, so users see content appearing instantly. No waiting for the full response before text shows up.
API Endpoints
Developers can use the Opensolr AI and vector APIs directly. All endpoints are available via HTTPS and return JSON. Authentication is done with your API key.
/api/embed
Send a single piece of text and get back its 1,024-dimension vector embedding. Use this to convert text into a vector before storing or searching. Full API docs.
/api/batch_embed
Send up to 50 texts at once and get all their vectors back in a single response. Much faster than calling /api/embed 50 times. Full API docs.
/api/embed_and_search
The all-in-one endpoint. Send a query, and Opensolr converts it to a vector, searches your index, and returns ranked results — all in one API call.
/api/ai_summary
Send a URL and get a streaming AI-generated summary of the page content. This is the same API that powers the “Read” button on the search page. Full API docs.
Code Example: embed_and_search
Here is a complete example that sends a natural language query and gets back semantically-ranked results from your index:
AI Enrichment Pipeline
When you index a document on a vector-enabled plan, Opensolr automatically enriches it with AI-generated metadata. This happens behind the scenes — you do not need to do anything.
A 1,024-dimension numerical representation of the document's meaning. This is what powers semantic similarity search.
Detects whether the document's tone is positive, negative, or neutral. Useful for filtering results by sentiment or sorting by tone.
Automatically identifies the language of each document (English, German, French, etc.). Enables language-aware search and filtering. See also: NLP Features & Named Entity Recognition.
AI and vector search features are available on plans that include vector indexing. If your current plan does not include vector search, contact support@opensolr.com to learn about upgrade options and pricing.
Related FAQ Articles
Hybrid Search Deep-Dive
Technical explanation of how keyword and vector search work together in Opensolr.
Live Demo: Try It Yourself
Interactive demo where you can test hybrid search against real data.
Single Embed API
API reference for generating a single vector embedding from text.
Batch Embed API
Embed up to 50 texts in a single API call for efficient bulk processing.
Related Documentation
Search & Embed
Learn about the hosted search page, embed code, and how to add search to your own website.
Search Tuning
Configure field boosts, relevance weights, and ranking strategies for optimal results.
API Reference
Full API documentation for all Opensolr endpoints including authentication and rate limits.