Search Tuning
Search tuning lets you control how results are ranked when someone searches your Opensolr index. Think of it as a mixing board in a recording studio: each slider changes how much weight a particular signal carries in the final output. Adjusting these settings can dramatically improve the quality and relevance of your search results. For a deep dive into every tuning parameter, see the Search Tuning reference in the knowledge base.
How Search Tuning Works
Every document in your index has multiple fields: a title, a description, body text, the URL, and more. When a user searches, Opensolr scores each document by checking how well the query matches each field. The field weights you set determine how much each field contributes to the final relevance score.
Field Weights
Field weights tell Opensolr which parts of your documents matter most. A higher weight means matches in that field count more toward the relevance score. For example, if a user's search query appears in the title of a page, that is usually a stronger signal of relevance than if it only appears somewhere in the body text.
| Field | What It Contains | Default Weight | When to Increase |
|---|---|---|---|
| Title | The page title (from the HTML <title> tag) |
0.85 | When page titles are descriptive and unique |
| Description | The meta description or page summary | 0.70 | When descriptions are carefully written summaries |
| URI | The page URL (e.g., /products/blue-shoes) |
0.30 | When URLs contain meaningful keywords |
| Text | The full body text of the page | 0.50 | When pages have rich, well-written content |
| LD Text | Structured data text (from JSON-LD, schema.org markup) | 0.40 | When your site uses structured data extensively |
Each weight ranges from 0.01 (almost ignored) to 1.0 (maximum importance). Move the slider or type a value directly. Changes take effect on the next search query. You can always reset to defaults if you get lost. To understand how these weights interact with the eDisMax query parser under the hood, see The eDisMax Query Parser Explained.
The default weights work well for most websites. Only adjust them after testing your search results and finding room for improvement.
After changing weights, search for terms your users actually search for. Check whether the most relevant pages now appear near the top.
Minimum Match
Minimum Match (sometimes called mm) controls how many of the search words must appear in a document for it to be considered a match. This is one of the most powerful relevance controls you have.
Imagine a user searches for "best italian restaurant new york". That is 5 words. Should a document match if it contains only 2 of those words? Or should all 5 be required?
Flexible
Returns the most results. Great when users type long, exploratory queries and you want to show anything remotely relevant. Some results may feel loosely connected to the query.
Balanced (Default)
A good middle ground. Most of the search words must appear, but one or two can be missing. This is the recommended starting point for most websites.
Strict
Every single word in the query must appear in the document. Use this when precision matters more than finding a lot of results, like a product catalog or legal document search.
Advanced users can type a custom minimum match expression, like 2<75% (which means: for queries with more than 2 words, at least 75% must match). These use Solr's mm parameter syntax. Most users should stick with the three preset modes. For more on how these fields are analyzed and tokenized before matching, see Best Fulltext Solr Fields.
Content Quality Boost
The Content Quality Boost rewards documents that have rich, substantial content and gently pushes down documents with thin, sparse content. Think of it as a "depth detector" for your pages.
- 0.0 = Quality boost is turned off. All documents scored purely on keyword relevance.
- 0.5 = A moderate boost. Rich content gets a gentle lift; thin content is slightly downranked.
- 1.0 = Maximum boost. Pages with substantial, in-depth content are strongly favored.
Enable a moderate quality boost (0.3 to 0.6) if your site has a mix of long articles and short stub pages, and you want the detailed articles to rank higher. Set it to 0 if all your pages have roughly the same amount of content.
Results Per Page
This slider controls how many search results are shown on each page. The range is 10 to 200.
- 10-20 — Best for clean, focused search pages. Users see the top results quickly.
- 30-50 — Good for research-heavy use cases where users want to browse many results at once.
- 100-200 — Suitable for internal tools or data exploration dashboards. Note: larger numbers may make pages load slightly slower.
Vector Search Settings
The following settings appear only for indexes with vector search enabled. If your index uses keyword-only search, you will not see these options. Vector search uses AI to understand the meaning behind words, not just the exact words typed.
Semantic vs. Keyword Balance
This is the most important vector search setting. It controls the balance between traditional keyword matching (finding documents that contain the exact words typed) and semantic/AI matching (finding documents that are about the same topic, even if they use different words).
- 1.0 (far left) = Pure keyword search. Only exact word matches count. No AI understanding.
- 0.5 (center) = Equal mix. Both keyword matches and semantic similarity contribute equally.
- 0.0 (far right) = Pure semantic search. The AI decides what is relevant based entirely on meaning.
For most use cases, a value between 0.4 and 0.7 works best. This ensures exact keyword matches still rank high, while semantically similar content also surfaces. Pure semantic (0.0) can feel unpredictable since it ignores exact matches. For a detailed explanation of how hybrid search blends keyword and vector scoring, see Hybrid Search in Opensolr.
Search Mode
Search Mode controls how keyword results and semantic results are combined. Think of it as choosing how the two result lists are merged together.
Union (Broadest)
Shows results from either keyword matches or semantic matches. If a document appears in either list, it is included. This gives you the most results and the broadest coverage.
Keywords Required
A document must contain the search keywords, but its rank is also influenced by semantic similarity. Guarantees keyword relevance while using AI to improve ordering.
Meaning Required
A document must be semantically similar to the query (the AI must consider it relevant), but keyword matches boost the score further. Good for conceptual searches.
Intersection (Strictest)
A document must appear in both the keyword results and the semantic results. Only documents that match the exact words AND are semantically relevant are shown. Fewest results, highest precision.
Vector Candidate Pool (topK)
When Opensolr performs a semantic search, it first asks the AI to find the most similar documents from your entire index. The topK slider controls how many candidates the AI considers.
- Lower topK (e.g., 50) = Faster, but might miss some relevant documents that are further down the similarity list.
- Higher topK (e.g., 500) = Considers more candidates, potentially finding more relevant results, but takes slightly longer.
The default value of 200 works well for most indexes. Increase it only if you have a very large index (100,000+ documents) and feel that semantic search is missing relevant results.
Reset All Settings
If you have experimented with many settings and want to start fresh, click the Reset All button at the bottom of the search tuning panel. This restores every slider and option to its factory default. Your search results will go back to the standard relevance ranking.
Resetting is instant and cannot be undone. If you have a configuration you like, make a note of your settings before resetting.
Search Tuning Tips
Begin with the default settings. Only change things if your search results are not satisfactory. Small adjustments often make a big difference.
Adjust a single slider, run your test queries, and evaluate. Changing multiple settings at once makes it impossible to know which change helped (or hurt).
Test with the actual search queries your users type, not made-up examples. Check your Analytics page to see what people search for.
Product searches usually work best with a high Title weight (0.8-1.0) and a moderate Description weight. Users search by product name, and the title is where that lives.
Content-heavy sites often benefit from a higher Text weight (0.6-0.8). The answer to the user's query is usually buried deep in the page body, not just the title.
If your users search for very specific terms (part numbers, SKUs, exact phrases), keyword search will outperform semantic search. Use the balance slider to favor keywords in those cases.
For general guidance on building high-quality search experiences, see Solr Best Practices. It covers schema design, indexing strategies, and query optimization tips that complement the tuning controls described on this page.