How to build document Q&A without managing vector databases

Use Ragex to build document question-answering without running vector databases, embedding pipelines, or parsing infrastructure. Upload documents, search with natural language, and feed results to your LLM — five API calls total.

TL;DR: Use Ragex to skip the vector database entirely. Upload documents to a knowledge base, search with a natural language query, and pass the ranked results as context to your LLM. The API handles parsing, chunking, embedding, and reranking — you go from zero to document Q&A in under 5 minutes with five API calls.

Why are vector databases so hard to manage for document Q&A?

Building document Q&A from scratch means running a vector database, choosing an embedding model, writing a document parser, implementing a chunking strategy, and wiring a reranker on top. Each layer is a separate system to provision, configure, scale, and debug.

Vector databases specifically add three categories of operational pain:

  1. Infrastructure management. You need to provision nodes, configure memory and disk, handle backups, and plan capacity. When your document count grows, you re-index and re-tune.
  2. Model selection and coupling. Every embedding model produces vectors with a specific dimension. If you switch models for better quality, you re-embed your entire corpus and update your index.
  3. Tuning and scaling. Index parameters (segment sizes, distance metrics, search algorithms) affect both result quality and latency. Getting these right requires experimentation, and the optimal settings change as your data grows.

For most teams, the vector database becomes the most time-consuming part of the stack — not the Q&A feature they actually wanted to build.

How does Ragex replace the vector database layer?

Ragex handles the full retrieval pipeline behind a single API key: document parsing (16 file types including PDFs, spreadsheets, and scanned images), chunking, embedding, indexing, and reranking. You never touch a vector database, choose an embedding model, or configure index parameters.

The workflow is three endpoints: create a knowledge base, upload documents, and search. Reranking is enabled by default, so results are ranked by relevance rather than raw vector similarity. When better models become available, the service upgrades them — your code stays the same.

How do I build document Q&A with this approach?

The full workflow has two parts: use Ragex to retrieve relevant chunks from your documents, then pass those chunks as context to your LLM to generate an answer. Here is a complete Python example that uploads a document, searches it, and feeds the results to an LLM for question-answering:

from ragex import RagexClient
import time
import openai

# Initialize the Ragex client
rag = RagexClient(api_key="YOUR_RAGEX_API_KEY")

# Step 1: Create a knowledge base
kb = rag.create_knowledge_base(name="Product Docs")

# Step 2: Upload a document
doc = rag.upload_document(kb["id"], "product-manual.pdf")

# Step 3: Wait for async processing to finish
while doc["status"] not in ("ready", "failed"):
    time.sleep(2)
    doc = rag.get_document(kb["id"], doc["id"])

# Step 4: Search with a natural language question
question = "What are the return policy requirements?"
results = rag.search(
    kb["id"],
    query=question,
    top_k=5,
)

# Step 5: Pass retrieved chunks as context to your LLM
context = "\n\n".join([r["text"] for r in results["results"]])

answer = openai.chat.completions.create(
    model="gpt-5-mini",
    messages=[
        {"role": "system", "content": f"Answer based on this context:\n\n{context}"},
        {"role": "user", "content": question},
    ],
)
print(answer.choices[0].message.content)

That is five API calls to Ragex, plus one call to your LLM of choice. No vector database to provision, no embedding model to select, no chunking parameters to tune.

Can I scope Q&A to specific documents using metadata?

Yes. Attach metadata at upload time and filter on it at query time. This is useful when your knowledge base contains documents from multiple products, teams, or versions and you want Q&A scoped to a subset. Instead of creating separate knowledge bases for each category, you upload everything to one knowledge base and use filters to narrow results at search time.

# Upload with metadata
doc = rag.upload_document(
    kb["id"],
    "billing-faq.pdf",
    metadata={"department": "billing", "year": 2026},
)

# Search only billing documents
results = rag.search(
    kb["id"],
    query="How do I get a refund?",
    top_k=5,
    filter={"department": {"$eq": "billing"}},
)

The filter supports operators like $eq, $ne, $gt, and $in, so you can build precise scoping rules without maintaining separate knowledge bases for each category.

What does this cost compared to running my own vector database?

Ragex starts at $29/mo on the Starter plan. A self-managed setup typically costs $70-200/mo for the vector database alone, plus $50-150/mo for embedding API calls, plus $30-100/mo for a document parsing service. The total infrastructure bill for DIY ranges from $150-450/mo before you count the engineering time to integrate and maintain it all.

The bigger cost is opportunity cost. Weeks spent configuring vector databases, debugging embedding pipelines, and tuning retrieval quality are weeks not spent on the Q&A experience your users actually care about.

FAQ

Do I need any vector database knowledge to use this approach?

No. Ragex abstracts the entire vector storage and retrieval layer. You never configure indexes, choose distance metrics, or manage database nodes. You upload documents and call a search endpoint. The API handles embedding, indexing, and reranking behind the scenes.

What file types can I upload for document Q&A?

The API supports 16 file types. Nine types get advanced parsing that handles tables, images, and complex layouts: PDF, DOCX, PPTX, XLSX, PNG, JPG, WEBP, TIFF, and CSV. Seven additional types are ingested as direct text: TXT, MD, HTML, TSV, JSON, and others.

Can I use any LLM for the question-answering step?

Yes. The search endpoint returns ranked text chunks with relevance scores. You pass those chunks as context to whatever LLM you prefer — OpenAI, Anthropic, a local model, or any other provider. The API handles retrieval; you handle generation. This keeps you free to swap LLMs without changing your retrieval setup.

How fast is document processing after upload?

Documents are processed asynchronously. Small files (a few pages) finish in seconds. Large PDFs with complex tables or scanned images take a few minutes. You can poll the document status endpoint or set up a webhook to get notified when processing completes. Most users have their first search result in under 5 minutes from signup.

Last updated: 2026-02-26