Generating Information Products with RAG

Posted on: 06 November 2025

Objective

This document explains how a Retrieval-Augmented Generation (RAG) pipeline processes local documents to generate new information products.

RAG architecture defines how multiple components (e.g., ingestion, embedding, retrieval, and generation) work together to produce accurate, verifiable results. RAG is both a framework and an architectural pattern that integrates search and generation into a unified workflow.

1. Preparing Source Documents

The RAG pipeline begins by scanning a designated directory to identify source documents. This scanning typically starts with a file discovery script or scheduling system, such as Apache Airflow, Cron, or Haystack’s indexing API. These tools monitor a local or shared directory for new or updated files. The pipeline performs this initial scan to locate every file in the defined repository that should become part of the knowledge base. It uses file-matching patterns and metadata filters to include only relevant document types, such as user manuals, support tickets, or marketing artifacts.

The ingestion module (e.g., Apache Tika or Haystack), which collects files, reads their contents, and extracts text from multiple formats, identifies and removes unnecessary markup, images, or formatting tags. It prepares a clean text stream that the rest of the RAG process can interpret efficiently.

Next, the pipeline standardizes the text using a normalization process that applies text-cleaning and formatting operations with libraries such as spaCy, NLTK, or regex-based scripts. The normalization stage runs before semantic processing so that every document entering the embedding phase follows the same linguistic and formatting conventions. It converts text into a common encoding such as UTF-8 and removes extraneous spaces and symbols. This step ensures that later stages handle data predictably across operating systems and file types. The normalization typically occurs right after ingestion, before any chunking or semantic processing begins.

After normalization, the ingestion module segments the document logically. By identifying these boundaries early, the ingestion module helps preserve document meaning and prepares each section for chunking.

The chunking engine—often implemented through frameworks such as LangChain or Haystack—divides documents into smaller, meaning-preserving units for semantic analysis. (Segmentation defines boundaries between sections based on headings, paragraph breaks, or structural cues such as Markdown or HTML tags. Segmentation and chunking differ in both purpose and scale: segmentation organizes long documents into logical sections for readability, while chunking later divides those sections into smaller semantic units that fit within the LLM’s context window.)

Although LangChain and Haystack are not dedicated chunking engines, both provide utilities for text splitting as part of their document-processing pipelines. These functions split text based on sentence structure, paragraph boundaries, or token counts to prepare data for embedding. Chunking exists to make large documents computationally manageable, ensuring that each chunk fits within the token limits of downstream models. The chunking engine ensures that each chunk contains enough context for accurate retrieval but still fits within the context window of the LLM.

The document store or vector database interface assigns a unique identifier to each chunk and logs its source document and location through a metadata management component—often integrated with tools like Haystack’s DocumentStore, Elasticsearch, or LangChain’s VectorStore. These systems automatically generate IDs (typically as UUIDs) when chunks are stored, allowing every text unit to be individually referenced and traced. This metadata layer records where each chunk originated, when it was last updated, and how it relates to its parent document. The processed chunks then move to temporary storage, where the pipeline holds them before conversion into mathematical representations such as embeddings.

Summary: At the end of this phase, the RAG pipeline transforms unstructured files into clean, segmented text units that can be consistently processed by later components. These chunks provide the traceable foundation for semantic understanding.

2. Creating Embeddings & Vectors

The embedding model now converts each chunk into an embedding. An embedding model (for example, SentenceTransformers or OpenAI’s text-embedding models) maps text into a numerical space where meaning corresponds to distance. These models are smaller, task-specific neural networks that have been fine-tuned for text similarity tasks and can run locally or as an API service. This process translates language into mathematical form, preserving semantic relationships among sentences and phrases. The closer two embeddings are in this space, the more similar their meanings.

Each embedding becomes a vector, which represents the text as a set of numerical values. These values exist in high-dimensional space, meaning the vector has hundreds or thousands of numerical dimensions that together capture subtle relationships between words and ideas. Each dimension reflects a latent linguistic feature—a pattern the model has learned during training, such as topic, syntax, or sentiment—that isn’t directly visible in the original text but helps describe how different pieces of text relate by meaning. These vectors can have hundreds or thousands of dimensions, allowing the system to capture subtle semantic differences. The embedding model ensures that semantically similar text produces vectors that occupy nearby positions, while unrelated text produces distant vectors. This mathematical organization allows semantic search by meaning rather than by keyword.

The ingestion module in this phase acts as a processing pipeline that manages data flow between embedding and storage components. In frameworks such as Haystack or LangChain, this module orchestrates embedding generation, batching, and submission to storage systems. Batching minimizes overhead by grouping multiple embeddings into a single write operation. The vector database (such as FAISS, Weaviate, or Chroma) stores these vectors, enabling high-speed comparison operations. The ingestion module links each stored vector with metadata that identifies its source and chunk ID.

Once stored, the vectors become searchable entities. The retrieval engine (e.g., Haystack’s Retriever or LangChain’s VectorStoreRetriever) can now measure similarity between vectors using distance metrics such as [cosine similarity](/posts/rag-terms-glossary/#cosine similarity). This measurement quantifies how much two text passages share conceptual meaning, supporting accurate retrieval even when wording differs. The retrieval engine later uses these relationships to locate relevant chunks when users submit queries.

Summary: This stage translates human-readable text into machine-readable meaning. By encoding chunks into embeddings and vectors, the system builds the mathematical structure that enables semantic search and knowledge retrieval.

3. Indexing and Metadata

The vector database management component builds an index over the database to enable efficient similarity searches. The index organizes vectors according to proximity and dimension. Tools like FAISS and Milvus implement these indices using approximate nearest neighbor algorithms. The indexing mechanism allows fast lookups without scanning every stored vector, making large-scale semantic search feasible. The indexing structure allows the retrieval engine to locate the most relevant vectors rapidly.

During indexing, the ingestion process adds vector metadata. Metadata stores key attributes such as file name, author, timestamp, and document structure information—for example, section titles or heading hierarchy—to ensure that the origin and position of every chunk remain known. This data supports filtering, relevance scoring, and regulatory compliance by preserving the document source and layout context. Including document structure as metadata improves retrieval accuracy because the retrieval engine can match queries to the most relevant sections, distinguish between similar terms that appear in different contexts, and retrieve related parent sections to provide more complete answers. It also enables users to request only specific document types, sections, or time periods when generating outputs.

The RAG architecture maintains and updates the index dynamically. Whenever a document changes, the pipeline reprocesses its affected chunks, regenerates embeddings, and replaces outdated vectors. These updates often run through scheduled batch jobs or file-system triggers that detect changes in source content, ensuring the knowledge base remains synchronized. This automatic updating prevents information drift, ensuring that every retrieved vector reflects the most current version of its source text.

Summary: Indexing and metadata management create a living, searchable map of organizational knowledge. The system continually refreshes this index to keep semantic retrieval accurate and up to date.

4. User Queries & Retrieval

When a user submits a question or request, the RAG pipeline processes it as input to the embedding model, which is the same embedding model used earlier for document chunks such as SentenceTransformers or MiniLM, so that both the query and stored chunks exist in the same semantic space. Using the same model for both documents and queries ensures that their numerical representations are directly comparable.

The retrieval engine (for example, Haystack or LangChain retrievers), which searches the indexed vector space for the most semantically related chunks, compares the query embedding generated from the user’s question or request against all stored vectors. It computes similarity scores and ranks the results. The engine retrieves the top-matching chunks with their metadata, ensuring that only the most relevant information proceeds to generation.

The retrieval engine ranks the top-k most similar chunks, where k is a configurable parameter that determines how many context passages are passed to the generator. Selecting an appropriate k balances completeness against prompt length.

The retrieval engine packages these chunks into a response set. It adds provenance data—structured information that records the document ID, source location, author, and timestamp for each retrieved chunk, allowing human reviewers to trace every output back to its origin and confirm its authenticity, which maintains transparency and allows auditing. The pipeline then passes this packaged context to the LLM. The LLM depends entirely on these retrieved chunks to create an informed, fact-based response.

Summary: Query processing transforms user intent into a semantic search task. The retrieval engine uses vector similarity to identify and deliver the most relevant context for generation.

5. Prompting & Response Generation

After retrieval, the prompt construction module builds a prompt that includes the user’s question, the retrieved chunks, and formatting instructions. The module arranges these elements so that the most relevant context appears first, maximizing the LLM’s attention on key details. Prompt construction is typically implemented with orchestration frameworks such as LangChain or LlamaIndex, which dynamically assemble retrieved text into templates that guide the model’s reasoning. This preparation step determines how effectively the model can answer complex or multi-part queries.

The LLM reads the prompt within its context window. It analyzes the text, identifies linguistic and logical patterns, and synthesizes new language grounded in the provided content. The LLM cannot access external data because it operates in a self-contained inference environment without network connectivity or live database access; it can only use the context explicitly provided in the prompt. Prompt quality directly determines output accuracy and completeness.

Generation quality is also influenced by model parameters such as temperature, top-p, and max tokens. For example, a lower temperature produces more focused, deterministic answers suitable for technical documentation, while a higher temperature encourages creative variation useful in exploratory writing.

Because RAG responses are grounded in retrieved context rather than model memory, this approach reduces hallucinations—statements that sound plausible but are not supported by source data.

The generation process produces new text that answers the query or repackages knowledge into the requested format. The LLM may rewrite, summarize, or create structured documents based on its instructions. The RAG pipeline monitors this output using validation scripts or rule-based checks (for example, LangChain output parsers) to confirm that responses follow organizational standards and formatting policies.

The quality of the generated output depends entirely on retrieval accuracy. If the retrieved information is incomplete, the LLM may generate incorrect conclusions. Human reviewers typically evaluate outputs for clarity and factual reliability before final publication.

Summary: The prompt and generation stages translate retrieved knowledge into coherent, formatted text. Validation ensures that automated writing remains accurate and consistent with verified sources.

Sum of the Parts

RAG merges two processes: retrieving factual information and generating natural language to produce reliable answers. Because the model bases its responses on retrieved content from trusted documents, RAG delivers more factual, verifiable results than generation alone.

RAG pipelines can turn static document collections into searchable knowledge systems that produce accurate outputs. The RAG pipeline, such as this one described here, continuously evolves. When new documents are added, the system triggers ingestion, embedding, and indexing automatically, ensuring the knowledge base stays synchronized with the file system. This automation allows organizations to keep AI-generated content aligned with their latest internal documentation without reconfiguring the system or retraining the model.

Tagged with:

• RAG

More posts:

• Next: RAG Pipeline Terms
• Previous: Setting up Local RAG Pipeline in Docker