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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchHybrid search for retrieval-augmented generation (RAG) sends each question down two retrieval paths over the same chunked documents. A lexical path matches exact words, usually with BM25-style scoring. A vector path matches meaning through embeddings. The two ranked lists are merged into one, usually with reciprocal rank fusion (RRF), and only the strongest passages, with their source identifiers, reach the language model. For internal documents the extra moving parts are worth it because each path fails in different places. Lexical search misses paraphrases, and vector search can underweight a rare ticket number or policy code. Whether the combination beats either path alone depends on your corpus and your queries, so this guide covers both the build and the test plan.
What is hybrid search in RAG?
In a working system, the retrieval step has three parts:
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- Lexical arm. The query is analyzed into terms and matched against the text fields of each chunk. It returns the top candidates by a text relevance score.
- Vector arm. The query is embedded with the same model used for the chunks, and the nearest chunk vectors are returned.
- Fusion. The two candidate lists are merged into one ordered list. Optionally, a reranker reorders the top of that list.
The vendor documentation describes this same shape, under different names. Microsoft’s Azure AI Search hybrid-search overview describes a single request that carries full-text and vector query components, with results merged by RRF. OpenSearch’s hybrid-search documentation describes a hybrid query over an index that holds both text and vectors, with rank-based and score-based combination. Elastic’s hybrid-search documentation describes a combined full-text and vector query and recommends RRF for merging. The product surfaces differ, but the design choices underneath are largely the same, which is why the sections below compare choices rather than products.
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The useful question is not which path is better in general. It is which query classes each path serves in your corpus. The table describes typical behavior for each query class, not measured results from any particular corpus. Use it to decide which query classes belong in your test set.
#1 Best Overall
| Query class | Lexical (BM25-style) path | Vector path | Practical implication |
|---|---|---|---|
| Exact identifiers, product codes, error strings | Strong when the token is indexed as searchable text | Often weak; similar-looking codes can land close together | Keep the lexical path for these and test them explicitly |
| Rare names and acronyms | Strong if the field is analyzed the way you expect | Varies with how the embedding model tokenizes the term | Put acronyms and internal project names in the judged set |
| Paraphrase and concept questions, such as “how do I get travel costs back” | Weak unless the wording overlaps or synonyms are configured | Strong | This is the main reason to add a vector path |
| Questions scoped to a section, date, or document version | Depends on metadata filters, not on scoring | Depends on metadata filters, not on scoring | Enforce scope with filters; neither path solves it alone |
| Questions with no answer in the corpus | May return few or no matches | Returns its nearest chunks even when none is relevant | Both paths return something; judge abstention in the generation layer |
How do I implement hybrid search for internal documents?
The build has five stages. Each one is a place where a small choice changes what the model eventually sees.
Inventory sources, owners, and update paths
Before writing any indexing code, record the following for every source system:
- Source systems and formats, such as wikis, ticket trackers, file shares, PDFs, and Office files, and which ones contain tables your answers depend on.
- Document owners and update rates, which set the re-index schedule.
- Authorization rules in each source and how they are expressed, such as group membership, per-document ACL lists, or sensitivity labels.
- A stable source identifier and canonical location for each document, so every retrieved passage can link back to the authoritative copy.
- Timestamps and version identifiers, so a question about a specific policy revision can be matched to the right text.
Then define how creations, edits, deletions, and permission changes reach the search index. Treat freshness and deletion behavior as acceptance criteria for the ingestion pipeline, with a maximum allowed lag agreed with the document owners. A deleted policy that still answers questions is a pipeline defect, not a search-quality issue.
Chunk the text and keep metadata beside it
No single chunk size, overlap, or field schema is generally optimal. Choose them from your documents’ structure, languages, and judged queries. Two practices hold across most corpora. First, prepend the document title and section path to each chunk, so the lexical path can match on headings as well as body text. Second, keep title, source system, source ID, section, timestamp, version, and access-control principals as fields next to the chunk text. Microsoft’s Azure Architecture Center guidance on RAG retrieval makes the same point: preserve meaningful text and metadata so lexical retrieval can use titles, content, keywords, and entities.
Index the text for full-text retrieval and store the embedding in a vector field. In OpenSearch’s example, an ingest pipeline applies a text_embedding processor and writes the resulting vector into a mapped k-NN vector field, while the original text stays indexed. One document therefore carries both representations, and a single chunk can be found by either path.
Rank #2
Embed chunks and queries the same way
Microsoft’s RAG retrieval guidance says to use the same embedding model that embedded the chunks and to apply the same preprocessing to the query. A mismatch rarely throws an error. It shows up as a vector arm that returns plausible-looking but irrelevant chunks while the lexical arm works fine.
You can check the path directly. Take the exact text of one indexed chunk, send it through the query-time embedding code, and compare the result with the vector stored for that chunk. With the same model and preprocessing, the two should be essentially identical. If they differ, the query path is transforming text differently from the indexing path. Changing the embedding model means re-embedding the whole corpus into a new index, because vectors from two models cannot be compared.
Run both arms in one request
Issue the lexical and vector queries together, with the same filters applied to both, so each candidate list is already scoped to what the user may see. Each arm returns its own candidate depth. Candidate depth is a tuning parameter: a document found by only one arm can still rank highly after fusion, so a depth that is too shallow can hide relevant chunks before fusion even starts. The official sources do not set a universal value; determine it on your judged queries.
Fuse the lists with reciprocal rank fusion
RRF merges the lists by rank rather than by raw score. BM25 scores and vector similarities live on different scales, so adding them directly produces an order that depends on arbitrary scale differences. For each document, RRF sums the reciprocal of its rank in each list:
RRF(d) = sum over arms of 1 / (k + rank of d in that arm)
Rank #3
A document absent from an arm contributes nothing from that arm. The constant k dampens the advantage of rank one over rank two. Some platforms expose it as a setting, so check the default in the version you run.
Here is an illustrative calculation with k = 60, using invented ranks rather than measured data. Suppose the lexical arm ranks document A first and document B third, and the vector arm ranks B first and document C second:
- A: 1/61 = 0.0164
- B: 1/63 + 1/61 = 0.0323
- C: 1/62 = 0.0161
B wins because both arms returned it, even though neither arm ranked it first on its own. That is the behavior hybrid search is meant to produce, and it is also why RRF is a sensible baseline before any tuning.
Pass bounded, attributable passages to generation
Send the answer model a bounded set of the top passages, each with its source identifier, location within the document, and a link to the original. Keeping the set small limits the context the model must reason over and makes it clear which passages support which claims. The prompt and generation behavior sit outside the search documentation, and retrieval alone does not guarantee a factually grounded answer.
Should I use reciprocal rank fusion or a reranker?
These options work at different points in the pipeline, and they are not mutually exclusive. Fusion merges the two candidate lists. A reranker, if used, reorders the top of the fused list.
| Option | Where it runs | What it changes | Cost | Choose it when |
|---|---|---|---|---|
| Reciprocal rank fusion (RRF) | After both arms return candidates | Merges order using ranks only | A merge step; no additional model call | You need a baseline, or the arms’ scores are not comparable |
| Score-based normalization | After both arms return candidates | Rescales each arm’s scores and combines them with weights; OpenSearch documents this option | Needs calibration and weight tuning on labeled queries | You have judged queries and need explicit control over the balance between arms |
| Reranker | After fusion, applied to the top candidates | Re-scores query and passage pairs with a deeper relevance calculation | Extra latency and compute on every query that uses it | Measured relevance gain on your judged queries justifies the latency |
A practical order of work follows from this. Start with RRF. Measure lexical-only, vector-only, and hybrid retrieval on the same queries. Add a reranker only if the hybrid results still leave relevant passages outside the top positions the generator uses, and measure the latency it adds. Move to score-based normalization only when you have enough labeled queries to tune weights without overfitting. Microsoft’s guidance makes the same point: compare approaches against test queries and benchmark relevance and latency before adopting a reranker in production.
Choosing between OpenSearch, Azure AI Search, and Elastic
The documentation cited here establishes hybrid-search mechanics. It does not establish a comparable matrix of price, regional availability, service limits, or feature tiers. Confirm those directly with each provider before you commit.
| Platform | Hybrid pattern documented | Fusion documented | Planning notes |
|---|---|---|---|
| OpenSearch | Hybrid query over an index with text fields and a k-NN vector field; ingest pipeline with a text_embedding processor |
Rank-based RRF and score-based normalization | RRF results depend on shard layout (see the fusion section). The OpenSearch tutorial includes guidance on dedicated ML nodes; follow it if embedding models run on the search cluster. |
| Azure AI Search | One hybrid request containing full-text and vector query components | RRF merge of the two result sets | Filters are listed among the capabilities available in the hybrid query context. Mapping identities and document permissions into those filters remains your design work. |
| Elastic (Elasticsearch) | Combined full-text and vector query | RRF recommended for hybrid search | Confirm query syntax against the version you deploy, since query APIs change between releases. |
When you compare platforms for your team, weigh these axes against your own constraints:
- Managed service versus self-managed operation, and the deployment constraints each implies.
- Existing infrastructure, identity provider, and data-source integrations.
- Supported lexical analyzers, vector index types, fusion controls, filter syntax, and reranking options.
- How permission filters are implemented and how queries are logged for audit.
- Corpus size, update frequency, latency targets, and scaling approach.
- Operational staffing, observability tooling, cost model, and deployment region.
- Measured retrieval quality on your own judged queries, which matters more than any vendor’s example.
Permissions for internal document retrieval
Internal search is only as safe as its authorization design, and the sources cited here do not prescribe one architecture. The following practices reduce the most common leaks.
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- Store access principals on every chunk, or on a parent document record that chunks reference. Use stable IDs from your identity provider, not display names, which change and collide.
- Filter inside the index query, for both arms. Filtering after retrieval in application code leaves unauthorized text inside the candidate lists, the fusion step, and any reranker. In-query filters keep those stages working only on material the user may see.
- Define the permission-change lag. Revoked access must stop appearing within a window agreed with the security owner, and that window must be tested, not assumed.
- Log the filter applied to each query along with the user identity and returned source IDs, so you can reconstruct who could have seen what.
Test with a permission set that covers at least these cases: a user with full access, a user with no access to a sensitive collection, a user whose access was just revoked, a user who was just granted access, and a document moved between access groups.
Best Value
How do I evaluate RAG retrieval quality?
Evaluate retrieval separately from generated answers. If a generated answer is weak, you need to know whether the right evidence was missing from the candidates or whether the model failed to use evidence it was given.
Build a judged query set
For each query, record the chunks or documents that should answer it, and for policy questions, the specific section and version. Cover these query classes:
- Exact names, acronyms, IDs, product codes, and policy titles.
- Natural-language questions and paraphrases of the same content.
- Questions that require a particular section, date, or document version.
- Queries with no answer in the corpus, recorded with empty judgments so abstention can be measured.
- Permission-sensitive queries run as identities with different access levels.
Keep the per-class counts large enough to show differences between arms. Sources do not set a universal sample size, so start with what your team can judge reliably and grow the set as failures appear.
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Compare the arms on identical queries
- Run the lexical arm alone on every query, with the same filters.
- Run the vector arm alone on every query, with the same filters.
- Run the hybrid query with RRF on every query.
- Store the top-N results for each run, with their ranks and source IDs.
- Report results by query class, not only as one average. An overall average can hide a total failure on exact-identifier queries.
Choose metrics and latency targets
| Measure | What it answers | Why it matters for RAG |
|---|---|---|
| Recall at k | Whether a relevant chunk appears in the top k passages | The generator only sees roughly the top k, so this is often the first number to check |
| Mean reciprocal rank (MRR) | How early the first relevant result appears | Useful when one correct passage is usually enough to answer |
| nDCG at k | How well graded relevance is ordered in the top k | Useful when some passages are partial answers and order matters |
| Latency and failure rate | Response time distribution, timeouts, and empty or errored responses | Determines whether a reranker or deeper candidate lists are affordable |
The sources do not establish a universal metric, threshold, or top-k value. Set targets from your lexical-only and vector-only baselines on your own queries.
Tune the knobs against the same set
- Candidate depth for each arm.
- Fusion method: RRF, or score-based normalization with the weights you tuned.
- Filters on and off, to confirm they remove only unauthorized or out-of-scope material.
- Reranker on and off, and the number of candidates it receives.
Change one knob at a time, and rerun the full query set each time. Record latency alongside relevance, because a gain that costs too much time may not be worth shipping.
Check generation separately
For each generated answer, check two things. First, whether the cited passages actually support the claims made. Second, whether the system abstains on the no-answer queries instead of producing a confident response from loosely related passages. A low score here with good retrieval numbers points to generation behavior. A low score with poor recall points to retrieval.
Failure modes and fixes
| Symptom | Likely cause | Check or fix |
|---|---|---|
| A document is missed for a query that uses its exact title | The text field is not indexed as expected, or the analyzer changes the term | Inspect the indexed text field and run the lexical arm alone on that query |
| Paraphrased questions fail while keyword queries work | The vector arm contributes little, or query and chunk embeddings differ | Run the embedding-path check from the implementation section, and confirm the vector arm returns candidates |
| Vector results look unrelated after a model change | Vectors from two embedding models are mixed in one index | Re-embed the full corpus into a new index and switch over |
| Fused ordering looks arbitrary | Raw scores were added together | Switch to RRF, or normalize scores and tune weights on judged queries |
| Ranking changes after the index is resharded | Shard-level statistics and per-shard candidate lists differ (OpenSearch) | Benchmark at the production shard count before accepting a configuration |
| Latency jumps after enabling the reranker | The reranker receives too many candidates | Limit the rerank depth, and keep the reranker only if the measured gain justifies it |
| A user sees a passage they should not | Permission metadata is stale, or a filter is missing on one arm | Run the permission test set, including revocation cases, and review the logged filters |
| A deleted or superseded policy is still answered | Deletions or re-indexing did not propagate | Run the freshness acceptance test against the agreed maximum lag |
| An answer makes claims its passages do not contain | A generation problem, with retrieval working | Judge citation support and abstention separately from retrieval metrics |
Before going live
- Replace the quickstart configuration with a production configuration. Keep index mappings, ingest pipeline, filters, and fusion settings under version control.
- Record judged-query results for lexical-only, vector-only, and hybrid retrieval, by query class.
- Run benchmarks at the production shard layout and at production-like corpus size.
- Pass the permission test set, including revocation and new-grant cases.
- Measure update, deletion, and permission-change propagation against the agreed lag.
- Size capacity for ingestion and for query-time embedding, and confirm that the platform’s guidance on ML node placement is followed.
- Monitor empty-result rate, latency percentiles, ingestion lag, and failed embedding calls.
- Name owners for ingestion, search configuration, and permission mapping, and write a re-embedding plan for the day the embedding model changes.
Confirm pricing, regional availability, and service limits with the provider you select, since the cited documentation does not compare them.
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