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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIf a paper is missing from a local AI search, changing the language model is rarely the first fix. The failure usually occurs earlier: the file was never collected, indexing did not finish, the PDF produced no usable text, metadata was mapped incorrectly, or filters removed the document. Diagnose the pipeline in that order—collection, ingestion, extraction, indexing, retrieval, then answer generation.
This approach also explains common symptoms. A paper may appear when you search its title but not its contents because only metadata was indexed. A scanned PDF may look perfect on screen yet contain no machine-readable words. A relevant passage may be retrieved without enough section context because chunking split the claim from its heading or table.
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Start with the failure stage, not the model
Local literature search is a chain of dependent stages. A later stage cannot recover a document that an earlier stage never supplied.
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| Symptom | Most likely stage | First check |
|---|---|---|
| The paper is absent from every search | Collection or ingestion | Confirm the file is in an included folder or connector, then check the last completed import and error log. |
| The title or author is found, but body phrases are not | Extraction or field mapping | Inspect the indexed body-text field. Metadata-only indexing is not full-text indexing. |
| A scanned article returns no words | OCR | Open extracted text and test a sentence visible on the page. Image-only pages need OCR. |
| Exact names work, but conceptual questions fail | Retrieval mode or ranking | Compare lexical, semantic and hybrid retrieval on known relevant papers. |
| The right paper is retrieved but the answer is wrong | Chunking, reranking or generation | Verify the retrieved passage, its section context and the citation before changing the model. |
Run the checks below in sequence. Keep one known-missing paper and one known-easy paper as controls so every change has an observable effect.
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1. Confirm that the paper is actually in scope
Before inspecting embeddings, prove that the source file can enter the collection.
Check local folders and connectors
- Verify the file is inside a watched folder, import list or source connector.
- Check allowed extensions. A connector may accept PDF but exclude a companion format, archive or nested directory.
- Inspect collection-level filters, excluded subdirectories and access rules. A file can exist on disk yet be intentionally outside the searchable corpus.
- For web-crawled papers, confirm that the article URL is discoverable and that the crawler can read the page or PDF without authentication barriers.
Google Scholar’s inclusion guidance makes the same basic requirement explicit: scholarly PDFs need searchable text, and the crawler needs discoverable article URLs plus accessible full text or complete abstracts. A local index has the additional requirement that its connector can reach the file at all.
Use an inventory, not a visual guess
Create a simple inventory containing path or URL, file size, modification time, document identifier and expected collection. Compare it with the indexer’s processed-document count. If the paper is missing from the inventory, retrieval settings cannot fix the problem.
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A present file is not necessarily an indexed file. Import jobs can be delayed, fail on one document, time out, apply the wrong field mapping or skip records because change-tracking values are stale.
Inspect job state and logs
- Open the indexer or import job status page.
- Record the last successful run time and the number of documents processed.
- Search errors for the paper’s filename, identifier or connector path.
- Check whether the paper’s modification time is newer than the last run.
- Review mapping changes that may have redirected title, body or identifier fields.
- Inspect incremental-change markers. A stale value can cause an older record to be treated as already processed.
After correcting the cause, re-run the smallest possible import and verify that the document count increases. A full rebuild is useful only after you know why the incremental run skipped the file.
Test query filters separately
Filters can hide a document that is present. Remove date, collection, language, permissions and file-type filters one at a time. Then test a plain identifier query. Query syntax, field restrictions and accidental exclusion clauses are retrieval problems, not ingestion problems.
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3. Inspect extracted text before creating embeddings
Open the extracted representation of a known-missing paper. Do not infer extraction quality from how the PDF looks in a viewer.
Distinguish digital text from a scan
Search the extracted text for a distinctive sentence, author name and figure caption. If none appears, determine whether the PDF contains selectable characters or only page images. A scanned PDF can have a complete visual layout while exposing zero characters to a parser. Digital-text parsing alone will not manufacture body text for image-only pages.
Apply OCR where it is needed
OCR the image-only pages, then inspect the OCR output rather than assuming it is accurate. Common errors include confusing “1” with “l,” dropping superscripts, merging columns and misreading mathematical symbols. Test names, acronyms, references and a sentence from each major section.
Mixed PDFs require a mixed strategy. Some pages contain native text while others are scans; the parser should combine both streams without duplicating text. A layout-aware parser is preferable when reading order, tables, lists, equations or captions affects meaning.
Google Cloud’s current Agent Search documentation distinguishes digital-text parsing, PDF OCR and layout parsing. Its OCR parser documentation states a limit of processing the first 500 PDF pages; that is a constraint of that service, checked on October 13, 2026, not a universal limit for local OCR tools.
Verify structure, not just character count
- Headings should remain associated with the paragraphs they introduce.
- Table rows and column labels should retain their relationship.
- Footnotes and references should not be inserted into the middle of body sentences.
- Page numbers, captions and equations should be identifiable as such where your parser supports those types.
A large extracted-text file can still be unusable if reading order is scrambled. Compare several pages with complex layouts before proceeding to retrieval tests.
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4. Keep bibliographic metadata and body text distinct
Metadata and full text serve different search jobs. Store title, authors, year, DOI, abstract, file path and source identifier for lookup, filtering and display. Also verify that the paper’s body text—and the sections readers need—is indexed in a searchable content field.
Use a minimum document schema
| Field | Purpose | Validation |
|---|---|---|
| Document ID | Stable identity across re-indexes | Unique and unchanged when only text is reprocessed. |
| Title, authors, year, DOI | Exact lookup and filters | Compare with the first page and citation record. |
| Abstract and body text | Lexical and semantic retrieval | Search a distinctive sentence from each field. |
| File path or source URL | Traceability and re-ingestion | Opens the intended source and is retained after updates. |
| Section title and page/location | Context and verification | Every returned chunk points to a useful location. |
If a title search succeeds but a sentence search fails, inspect field mappings before changing the retrieval algorithm. The index may contain only bibliographic fields.
Preserve identity on every chunk
When a paper is split, copy its document ID, title, file path and location metadata onto every chunk. PaperRAG’s project documentation illustrates this local-first pattern by retaining file path and section title with chunks; it is an architecture example, not a performance guarantee. Without this linkage, a result may contain useful text but leave you unable to verify which paper or passage produced it.
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Chunking determines what a retriever can see as one unit. Academic papers contain sections, tables, equations, definitions and cross-references, so a fixed character count can separate a claim from the heading that qualifies it.
Structure-aware chunking
- Start new chunks at major headings when the parser identifies them.
- Keep a table’s header with its rows, or store a faithful text representation that preserves column relationships.
- Keep definitions, limitations and nearby qualifiers with the claims they modify.
- Retain page and section metadata even when a chunk overlaps adjacent text.
- Check cross-references such as “as shown in Table 2” so the referenced item is discoverable or linked in metadata.
There is no universally correct chunk size. The useful choice depends on paper layout, query length, context-window limits and whether readers search for exact passages or broad concepts. Compare alternatives on the same known-query set instead of adopting a value from an unrelated corpus.
6. Choose retrieval for the query you actually ask
Keyword and embedding search solve different problems.
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| Retrieval mode | Good at | Typical weakness | Useful test queries |
|---|---|---|---|
| Lexical or keyword | Exact titles, author names, DOI strings, acronyms and quoted phrases | Misses paraphrases and terminology differences | “10.1234/…”; an uncommon method name; an exact sentence. |
| Dense semantic | Conceptual questions and paraphrased descriptions | May overlook an exact identifier or return conceptually similar but incorrect papers | “Which study tests this intervention in older adults?” |
| Hybrid | Combines lexical matching with semantic similarity | Needs tuning and evaluation on your corpus; can still fail when extraction is poor | Mix an acronym query with a paraphrased research question. |
OpenSearch documentation defines the rationale plainly: “Hybrid search combines keyword and semantic search to improve search relevance.” That describes the design, not a guaranteed result for every library. In the 2020 paper Dense Passage Retrieval for Open-Domain Question Answering, Karpukhin and colleagues reported 9%–19% absolute top-20 passage-retrieval gains over a strong Lucene-BM25 baseline on their evaluated open-domain QA datasets. Those benchmark results should not be projected onto an unspecified local literature collection.
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Match the method to corpus content
- Text-native, prose-heavy papers often support both lexical and dense retrieval once extraction is clean.
- Scans require reliable OCR before either method can work on body content.
- Table- or equation-heavy papers need layout-aware extraction and careful chunk validation.
- Mixed-language or acronym-heavy collections benefit from tests that include each language and notation style.
Do not select an embedding model, parser or chunk size as a universal winner. Hardware, language mix, supported file types, offline requirements and index-update behavior change the trade-off.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Evaluate retrieval independently from answer generation
A fluent answer can conceal a retrieval failure. Build a compact regression set of natural questions tied to known papers and passages.
- Record the expected paper and the relevant passage for each question.
- Confirm that the paper is indexed.
- Confirm that the relevant passage survived extraction.
- Run the query and record whether the paper appears among candidates.
- Record its rank and the rank of the relevant passage.
- Only then inspect whether the language model used the passage correctly and cited the right source.
Change one component at a time—OCR, metadata mapping, parser, chunking, embeddings, reranking or hybrid weighting. Compare every version against the same cases. This separates ingestion recall from ranking and generation errors.
Common symptoms and targeted fixes
“Why isn’t my paper showing up in local AI search?”
Check scope, connector reachability, import status and filters in that order. If the file is absent from the inventory or failed ingestion, retrieval tuning cannot help.
“Can AI search read scanned PDFs?”
Yes, after OCR converts page images into text that the index can store. Validate OCR output for names, symbols, columns and section headings; a visual PDF alone is not searchable body content.
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“Why does my PDF show up by title but not by its contents?”
The index probably contains title or other metadata but no usable body-text field, or the body field is excluded from the query. Inspect extracted text and field mappings, then test a distinctive sentence.
“Should I use keyword search or embeddings for papers?”
Use lexical retrieval for exact identifiers and wording, dense retrieval for paraphrased concepts, and evaluate hybrid retrieval when both query types matter. Judge the choice with representative questions from your own collection.
A practical local architecture
A dependable pipeline keeps each responsibility visible:
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- Validate: record file identity, type, size and modification time.
- Parse: extract native text, invoke OCR for image pages and use layout handling for complex documents.
- Normalize: preserve headings, tables, captions, references and page locations.
- Enrich: attach title, authors, year, DOI, abstract, path and stable document ID.
- Chunk: split at meaningful structural boundaries while retaining provenance.
- Index: write metadata, full text and vectors to fields that the intended queries can access.
- Retrieve: run lexical, dense or hybrid candidate searches, then rerank if needed.
- Generate: provide the model only with retrieved passages that retain paper identity and location.
Log counts and failures between stages. A sudden drop from 1,000 acquired files to 870 parsed documents is more actionable than a vague report that “AI search misses papers.”
Operational checklist
- Can you locate the source file or URL in the collection inventory?
- Did the latest import finish after the paper was added or changed?
- Was the document rejected, timed out or skipped by change tracking?
- Does extracted text contain a sentence visible in the paper?
- Did OCR handle image-only and mixed pages?
- Are body text and bibliographic metadata indexed in separate, queryable fields?
- Do chunks retain document ID, section and page/location?
- Can exact identifiers and paraphrased questions retrieve the expected paper?
- Does the regression set show the correct passage before answer generation?
- Can a reader verify every answer against the original paper and location?
When a paper disappears, walk this list from the top. Fixing the earliest failed stage usually produces a larger improvement than replacing the language model downstream.
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