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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo make a PHP app answer from your own documentation, retrieve relevant passages at question time and give them to the model as context. This pattern is called retrieval-augmented generation (RAG). It lets the app use private or up-to-date material that the model may not know; it does not make the model’s built-in memory a dependable source for that material.
How RAG answers from your documents
A RAG pipeline has two parts: preparing a searchable document collection and using that collection when a question arrives. A typical request follows this path:
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- Extract text: read the contents of each supported document format and retain its source information, such as filename, page, or document ID.
- Split into passages: divide long text into smaller chunks that can be retrieved and supplied as context. Chunk size and overlap depend on the documents and retrieval setup; the cited Laravel documentation does not prescribe universal settings.
- Create embeddings: turn each passage into a vector representation. Embeddings can be compared for semantic similarity, which helps find text related in meaning even when it does not repeat the query’s exact words.
- Store searchable records: save passage text, metadata, and its vector in a vector store or a database configured for vector search.
- Retrieve for each question: embed the question and search for passages with similar vectors. Keyword search is a different retrieval approach; Laravel’s Search documentation distinguishes it from semantic search.
- Generate an answer: pass the selected passages to the model alongside the question. If the app supports it, show source references so the reader can check where the answer came from.
The vector score is a relevance signal, not proof that a passage is correct or current. Inspect the retrieved text and its metadata before relying on it, and make the model’s instructions clear about answering from the supplied context rather than filling gaps with unsupported claims.
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A Laravel implementation path
Laravel is one way to build RAG in PHP, not a requirement. Laravel’s 12.x AI SDK documents embeddings, similarity search, and vector stores; its Search documentation describes semantic search. For the example below, use Laravel 12.x, its documented AI SDK and Search capabilities, and PostgreSQL with the pgvector extension. These documentation references do not establish a PHP runtime version or a specific model-provider setup, so confirm those requirements for the provider and packages you choose.
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Choose and configure the vector database
Laravel 12.x Search documentation says vector search requires PostgreSQL with pgvector and the Laravel AI SDK. Its example uses a 1536-dimensional vector for OpenAI’s text-embedding-3-small; that dimension count belongs to that example model and is not a universal embedding size. The chosen embedding model’s output dimensions must match the database column and index configuration.
Laravel’s 13.x AI SDK materials also describe PostgreSQL with pgvector, MariaDB 11.7 or later, and MongoDB through the Laravel MongoDB package. Those are version-specific options, not interchangeable requirements for the Laravel 12.x example. Check the documentation for the Laravel release and database package you actually deploy.
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Ingest and index the documents
Build an ingestion flow that accepts or locates documents, extracts their text, splits it into passages, generates an embedding for each passage, and stores the passage and useful source metadata with its vector. Laravel’s official tutorial demonstrates file uploads and asynchronous processing. Queuing ingestion can keep lengthy extraction and embedding work out of the upload request, but the tutorial is an example architecture rather than a universal production checklist.
Retrieve passages and generate a grounded answer
At query time, create an embedding for the user’s question, search the stored vectors for similar passages, and provide the selected text to the model for answer generation. Laravel’s 12.x AI SDK documents a SimilaritySearch tool for finding documents similar to a query using database-stored embeddings, as well as vector stores for searchable file collections. The exact agent or application wiring depends on the SDK and provider configuration you adopt.
Before generation, check what was actually retrieved: inspect the passage text, document identifier, and any page or section metadata. Return source references with the answer when your implementation can reliably connect each passage to its original location. If retrieval returns nothing useful, the application should make that limitation visible rather than presenting an ungrounded response as document-backed.
What the official tutorial demonstrates
Laravel’s tutorial walks through a document-search agent with file uploads, asynchronous processing, embeddings, semantic retrieval, streamed responses, and deployment to Laravel Cloud using queue workers and PostgreSQL with pgvector. Treat those details as one Laravel example; they do not imply that every RAG deployment needs that hosting platform or the same architecture. Read the Laravel document-search agent tutorial.
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Operational checks that protect answer quality
- Ingestion failures: record whether each file was extracted, chunked, embedded, and indexed successfully. Provide a way to identify and retry failed items.
- Stale or duplicate content: track document identity and update status so a replacement does not leave old passages available alongside new ones unintentionally.
- Access control: enforce the user’s document permissions before retrieval or otherwise constrain the searchable collection to authorized material. A correct answer from an unauthorized file is still a serious failure.
- Irrelevant retrieval: review retrieved passages for representative questions. Similarity alone cannot validate relevance or factual correctness.
- Source attribution: retain metadata that maps each chunk back to its document and location, then check that displayed references correspond to the passages used.
- Evaluation: test a representative set of real questions, including questions with no supporting document. Assess retrieval quality, whether answers stay grounded, and whether citations are accurate as separate checks.
The Laravel documentation establishes implementation primitives, not universal chunking settings or measured improvements in accuracy, latency, cost, or PHP performance. Measure those outcomes with your own corpus, provider, and workload.
Framework and database choices are separate decisions
Laravel provides a documented route through its AI SDK and Search features, while RAG itself is a retrieval-and-generation design that can be implemented in other PHP applications. The cited materials do not establish a framework-neutral PHP recipe or a quantitative winner among database options. When selecting an implementation, verify framework and package compatibility, required extensions, how indexing and updates will run, and who will operate migrations, queues, backups, and access controls. Then compare retrieval quality, source attribution, latency, and indexing and query cost against your own representative questions rather than assuming one option is best for every workload.
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Documentation for the Laravel path
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