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To make a PHP app answer from your own documents, retrieve relevant passages at question time and give them to the language model as context. That pattern is retrieval-augmented generation (RAG): it lets an answer draw on a private handbook or current documentation instead of relying only on what the model learned during training.
The essential pipeline is document ingestion, chunking, embedding, retrieval, and answer generation. Laravel provides documented building blocks for that pipeline, but it is one PHP implementation path—not a requirement for RAG.
How RAG answers from your documents
A model can produce a plausible response even when it has never seen your internal policies or the latest version of a manual. RAG addresses that gap by finding relevant material in an external collection and supplying it to the model when it answers. It does not update the model’s built-in knowledge or guarantee that every retrieved passage is correct.
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- Split into passages: Divide long documents into manageable chunks, retaining metadata such as document ID, title, page, and access scope.
- Create embeddings: Convert each passage into a vector representation and store it alongside the text and metadata.
- Retrieve for a question: Embed the user’s question and search for passages with similar vectors.
- Generate an answer: Pass the question and selected passages to the model. If the application retains source metadata, it can show which documents informed the response.
Embeddings make semantic comparison possible: a search can find conceptually related text even when the question and passage do not share the same keywords. Laravel’s Search documentation distinguishes semantic search from keyword matching and describes creating and storing vectors for documents and queries.
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Build the retrieval path with Laravel
Laravel’s AI SDK documents embeddings, similarity search, and vector stores. Its SimilaritySearch tool can search documents similar to a query using embeddings stored in a database; vector stores are another documented way to work with searchable file collections. See the Laravel 12.x AI SDK documentation.
Choose a documented database option for your Laravel release
The database choice is tied to framework version and package setup. Laravel 12.x Search documentation describes vector search with PostgreSQL and pgvector alongside the Laravel AI SDK. Its example uses a 1536-dimensional vector for OpenAI’s text-embedding-3-small; that is an example model dimension, not a universal vector size. Laravel 13.x AI SDK materials also describe PostgreSQL with pgvector, MariaDB 11.7 or later, and MongoDB through the Laravel MongoDB package. Check the documentation for the exact Laravel release and integration you deploy: Laravel Search 12.x and Laravel AI SDK 13.x.
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For a Laravel 12.x PostgreSQL route, the high-level implementation sequence is:
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- Configure a supported AI provider and embedding model using the Laravel AI SDK. Confirm the model’s vector dimensions and configure the database column or vector index to match.
- Create storage for passage text, embedding vectors, and source metadata. Use PostgreSQL with pgvector for the path described in Laravel’s 12.x Search documentation.
- On document upload or update, extract text, split it into passages, generate embeddings, and persist each passage with its source details. Run costly ingestion asynchronously when appropriate for the application.
- At question time, create a query embedding and run similarity search against the stored vectors. Retrieve a bounded set of passages and their metadata.
- Inspect the selected passages and access permissions, then send the question and useful context to the model. Return the answer with source references when the application has retained enough metadata to provide them.
Laravel’s official document-search agent tutorial demonstrates file uploads, asynchronous processing, embeddings, semantic retrieval, streamed responses, and deployment using queue workers and pgvector-enabled PostgreSQL on Laravel Cloud. It is an example architecture, not a checklist that every PHP application must follow.
Check retrieval before trusting the generated answer
Similarity is a ranking signal, not proof that a passage is relevant, current, or authoritative. During development, inspect the retrieved text and source metadata for representative questions before passing the passages to the model. If the top results are unrelated, an eloquent answer can still be grounded in the wrong material.
- Show sources: Keep document identifiers and useful details such as title, page, or section with every chunk so the UI can expose supporting material.
- Handle weak retrieval: Decide what the application should do when no useful passage is found—for example, ask the model to say the documents do not contain an answer, or request clarification.
- Protect document access: Apply user or tenant permissions before returning passages to the model. Retrieval must not expose material a user is not authorized to see.
- Keep the index current: Reprocess changed documents and remove or replace stale chunks; otherwise retrieval may surface outdated versions.
- Track ingestion failures and duplicates: Make processing status visible and prevent repeated uploads or retries from silently multiplying equivalent chunks.
- Evaluate the full answer path: Test a representative set of real questions, checking retrieved passages, answer grounding, and source attribution separately. Tune chunking and retrieval for your corpus rather than assuming one setting works everywhere.
What changes outside Laravel
RAG is a workflow, not a Laravel-only feature. A PHP application using another framework still needs to extract and chunk documents, generate and store embeddings, retrieve relevant passages, and provide them to a model. The Laravel sources establish Laravel-specific APIs and database integrations; they do not establish one framework-neutral PHP package, best chunk size, provider, or performance profile. Choose those components for your stack, then validate retrieval and answers against your own documents and questions.
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