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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA basic retrieval-augmented generation (RAG) pipeline answers questions about a bounded set of documents by finding relevant passages and giving them to a language model as evidence. It has two parts: an indexing workflow that prepares documents ahead of time, and a query workflow that retrieves passages and generates an answer when someone asks a question.
What a beginner RAG pipeline does
RAG combines search with text generation. Instead of asking a model to answer from its general training alone, your application searches a collection for passages related to the question, then includes those passages in the model’s prompt. The model can use that retrieved context to form an answer.
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A vector database is only one possible component. A working system also needs document loading, chunking, embeddings, retrieval logic, a model prompt, and a way to check whether answers are supported. Frameworks can bundle some of these steps, but the underlying flow remains the same.
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Separate indexing from answering
Prepare the collection once, then reuse it for questions. When documents change, update or rebuild the index so retrieval reflects the current collection.
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| Workflow | What it does |
|---|---|
| Offline indexing | Load documents, optionally clean them, split them into chunks, create embeddings, and store the chunks with useful metadata. |
| Online question answering | Accept a question, retrieve relevant chunks, give the question and evidence to a model, and return an answer that can be checked against the retrieved passages. |
Choose your implementation before writing the pipeline
You can assemble these components directly or use a framework such as LangChain or LlamaIndex. Neither is required. LlamaIndex describes RAG in stages that include loading, indexing, retrieval, generation, and evaluation; framework abstractions may combine or conceal individual operations.
Choose components based on where you want the data to live, how much infrastructure you want to operate, your data-handling requirements, how much control you need over chunking and metadata, and which Python interface best fits your application. A managed vector store may reduce the infrastructure you run yourself; local storage may suit a local workflow. In either case, the store must fit into both indexing and retrieval.
The code below is a framework-neutral blueprint, not a drop-in program: load_documents, embed, vector_store, and model stand for the loader, embedding service, storage implementation, and language-model client you select. Their concrete method names and setup vary by package. Keep the stages explicit while wiring those interfaces to your chosen stack.
Build the indexing workflow
1. Load and prepare source documents
Start with a small, bounded collection that you are allowed to process. Each loaded item should retain its text and enough metadata to identify its source later, such as a filename, document identifier, or page number. Remove boilerplate or irrelevant material when appropriate, but do not discard context needed to understand a passage.
2. Split documents into chunks
Split long documents into smaller passages before embedding them. Retrieval works at the chunk level, so chunk boundaries affect what evidence can be found and shown to the model. Keep source metadata on every chunk. Chunk size and overlap are configurable choices, not universal constants. OpenAI’s retrieval guide currently uses a maximum chunk size of 800 tokens and an overlap of 400 tokens in its vector-store file-indexing flow; those are OpenAI-specific defaults, not a general recommendation for every corpus or stack.
3. Embed and index the chunks
An embedding represents a piece of text in a form that supports similarity search. Create an embedding for each chunk and store it alongside the chunk text and metadata. Indexing also gives you a chance to customize chunking, metadata, and the embedding choice. Begin with a simple setup, then change one choice at a time if retrieval checks show a problem.
# Framework-neutral indexing blueprint
documents = load_documents("your-document-folder")
chunks = []
for document in documents:
text = clean_if_needed(document.text)
for passage in split_into_chunks(text):
chunks.append({
"text": passage,
"metadata": {
"source": document.source,
"page": document.page,
},
})
vectors = embed([chunk["text"] for chunk in chunks])
vector_store.add(chunks=chunks, vectors=vectors)
vector_store.persist_if_needed()split_into_chunks should implement the chunking behavior you chose, and persist_if_needed applies when your store requires an explicit save step. If your chosen vector-store integration performs chunking or embedding during file indexing, those steps may be hidden behind its API; the conceptual sequence is unchanged.
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4. Retrieve passages for a question
At question time, use the same compatible embedding setup to represent the question and search the index for relevant chunks. Retrieve a manageable set of candidates and retain their text and source metadata. The ideal number depends on your model, prompt, and chunking; the sources here do not establish one universal value.
5. Give the model evidence and an instruction
Put retrieved passages and the user’s question in clearly separated parts of the prompt. Instruct the model to answer from the supplied evidence and to say when the evidence does not contain the answer. This reduces the chance that an unsupported answer is presented as if it came from your documents; it does not guarantee that the model will always follow the instruction.
# Framework-neutral query blueprint
def answer_question(question):
matches = vector_store.search(query=question)
evidence = [
{"text": match.text, "source": match.metadata.get("source")}
for match in matches
]
prompt = make_prompt(
instruction=(
"Answer using the evidence provided. "
"If it does not support an answer, say you cannot find the answer in the documents."
),
question=question,
evidence=evidence,
)
answer = model.generate(prompt)
return {"answer": answer, "evidence": evidence}Show or log the retrieved passages and their source labels alongside the generated answer. That makes it possible for a reader or developer to inspect whether the system found relevant material and whether the response is grounded in it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate with representative questions
Before relying on a pipeline, assemble questions that reflect how people will use the collection. Include questions whose answers are present, questions that need information spread across passages, and questions the documents cannot answer. For each one, inspect both retrieval and generation:
- Did retrieval return passages that actually address the question?
- Are the useful passages complete enough to preserve their meaning?
- Does the answer follow from those passages, rather than adding unsupported details?
- When the collection lacks the answer, does the system acknowledge that?
When a result fails, trace the cause before changing everything. Missing source content cannot be recovered by retrieval. A poor chunk boundary may separate a fact from its context. Irrelevant results point to a retrieval or indexing issue. Relevant evidence paired with an unsupported answer points to the generation or prompt stage. Change one component and repeat the same questions so you can see what improved or worsened. Evaluation is part of the workflow, not a final polish step; LlamaIndex describes it as a way to assess a flow against alternatives and after changes.
What to add after the first working flow
Once the basic path works, decide whether you need custom metadata, different chunking, a local or managed storage setup, or additional evaluation. Keep changes tied to an observed need. There is no evidence here for a universal chunk size, a best-performing framework, or a general performance advantage for one storage approach.
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