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To build a chat-with-your-PDFs app, prepare document content, index it for search, retrieve relevant passages for each question, and give those passages to a language model to formulate an answer. This pattern is called retrieval-augmented generation (RAG). You can use a hosted search service for the shortest implementation path or build the parsing, indexing, and retrieval pipeline yourself for more control. Neither approach guarantees that every answer is correct.
How a PDF question-answering app works
RAG has two main flows: ingestion and question answering. During ingestion, the app reads a PDF, divides its content into searchable pieces, and indexes them. At question time, it searches that index for relevant passages and sends the question and retrieved content to a language model.
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Semantic search can find material that is relevant in meaning even when it shares few or no keywords with the question. The model then uses the retrieved passages as context to compose a response. Retrieval supplies evidence to work from; it does not establish that the evidence is complete or that the generated answer is correct.
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| Consideration | Hosted retrieval | Custom pipeline |
|---|---|---|
| What you build | Prepare a vector store and upload files, then call the hosted retrieval tool through the API. OpenAI describes File Search as a hosted tool in the Responses API: OpenAI File Search guide. | Choose and connect a PDF reader or parser, chunking logic, an embedding model, a search index or vector store, retrieval, and answer generation. Langflow illustrates separate ingestion and retrieval flows: Langflow documentation. |
| Control | The service manages the search workflow after setup; its behavior and available settings define your choices. | You choose components and can adjust parsing, chunking, metadata, and storage. That control also means you own integration and operation of those components. |
| Visual PDF content | Responses API PDF input on vision-capable models can include extracted text and page images. This is distinct from assuming every file-search workflow will interpret every visual layout reliably; see OpenAI PDF inputs. | Support depends on the parser and models you select. Test diagrams, tables, scans, and multi-column layouts that matter to your users. |
| Infrastructure | You still need to create and populate the vector store and manage your app’s file access and retention choices. | You manage the selected storage and processing components. A larger cloud design may involve object storage, event-driven processing, a vector database, and model services, as in Google Cloud’s RAG architecture. |
| Latency and answer quality | Not established as universally faster or more accurate than a custom implementation. | Not established as universally faster or more accurate than a hosted implementation. |
For a first small app, hosted retrieval reduces the number of systems you must assemble. A custom pipeline is useful when you need direct control over extraction, chunking, storage, or source metadata. Choose based on the controls and responsibilities you need, not on an assumed quality or speed advantage.
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Build the first version in five steps
1. Set file and access rules
Decide which file types your app accepts, who may upload and view documents, and how long files and indexes are retained. Apply appropriate size limits and access controls in your own application rather than treating a provider’s upload ceiling as a safe default.
OpenAI’s Retrieval guide lists PDF as a supported file type and, when checked on October 4, 2026, stated a maximum of 512 MB and 5,000,000 tokens per file. Those are OpenAI-specific limits, not limits for PDFs generally; confirm the current OpenAI Retrieval guide before relying on them.
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2. Extract the document content
In a custom setup, use a PDF parser to extract text; in a hosted setup, upload the PDF to the service’s document workflow. Keep document identity and, where supported, page or section information alongside the extracted content. Those references let the app show readers where a retrieved passage came from.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11PDFs can contain scans, charts, diagrams, tables, and layouts that ordinary text extraction may not preserve well. OpenAI’s Responses API documentation says PDF inputs to vision-capable models can include both extracted text and page images. That can help with visual content, but it is not a guarantee that a parser or retrieval system will interpret every layout correctly.
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3. Split and index content
Divide extracted content into chunks large enough to retain useful context but focused enough to retrieve relevant material. Keep useful metadata with each chunk, such as the source document and page, if your chosen pipeline supports it.
OpenAI’s Retrieval guide says vector-store files are automatically chunked, embedded, and indexed. The guide displayed a default chunk size of 800 tokens with 400 tokens of overlap, a supported range of 100–4096 tokens, and a constraint that overlap be no more than half the chunk size when checked on October 4, 2026. These are service defaults and constraints, not a universal best setting. With a custom pipeline, adjust chunking to the documents and questions you need to support, then test the results.
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4. Retrieve passages and generate an answer
When a user asks a question, search the index and pass the most relevant retrieved passages together with the original question to the language model. With OpenAI File Search, create and populate a vector store before invoking the hosted search tool. The OpenAI cookbook PDF question-answering example demonstrates this general route, but the recipe is archived; use the current API guide for implementation details.
In your prompt and application behavior, ask the model to answer from the supplied material, say when the material does not support an answer, and preserve source references for display. These are design choices, not guarantees: retrieval may miss relevant text, and a model can still misread or overstate the passages it receives.
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5. Test with questions your users will ask
Build a small evaluation set from representative questions about the PDFs your app is meant to handle. For each question, note the expected answer and the source page or passage. Check both whether retrieval finds the right material and whether the generated response stays faithful to it.
- Include questions whose answers are absent, and check whether the app acknowledges that rather than inventing a response.
- Test scanned files, tables, charts, and multi-column pages if users are likely to upload them.
- Check that displayed source references identify the right document and page.
- Repeat the checks when you change parsers, chunk sizes, retrieval settings, or models.
What does File Search cost?
OpenAI’s Retrieval documentation displayed 1 GB of vector-store storage included across vector stores, then $0.10 per GB per day beyond that, when checked on October 4, 2026: OpenAI pricing for vector stores. This is storage pricing only, not the total cost of a RAG app. Model use and other parts of an application may add costs, so check current pricing for the services you use.
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