---
title: "Production RAG: From Documents to Answers in One System"
description: "Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers, with no vector database or orchestrator required."
keywords:
  - production RAG implementation
  - RAG pipeline python
  - retrieval augmented generation
  - RAG best practices
  - document processing pipeline
  - embedding management
  - semantic search python
  - document AI workflow
  - AI automation workflow
complexity: "intermediate"
estimated_time: "30 min"
url: "https://pixeltable.com/use-cases/production-rag-implementation"
---

# Production RAG: From Documents to Answers in One System

Build a complete Retrieval-Augmented Generation pipeline with Pixeltable. Ingest documents, chunk text, generate embeddings, index for retrieval, and generate LLM answers, with no vector database or orchestrator required.

## Prerequisites

- Understanding of vector embeddings and LLMs
- Basic Python and API integration experience

## The Problem

Production RAG systems require coordinating document processing, chunking strategies, embedding generation, vector storage, retrieval optimization, and LLM integration. Most teams end up stitching together 5+ tools: a document parser, a chunker, an embedding API, a vector DB, and an LLM framework, each with its own failure modes.

## The Solution

Pixeltable unifies the entire RAG stack into one declarative system. Documents are ingested as native types, automatically chunked via views, embedded via computed columns, and indexed for retrieval. LLM generation is just another computed column. Everything stays in sync automatically.

## Implementation

### Ingest Documents

Create a table for your knowledge base and insert documents.

```python
import pixeltable as pxt

# Create document store
docs = pxt.create_table('app.documents', {
    'document': pxt.Document,
    'title': pxt.String,
    'source': pxt.String,
    'doc_type': pxt.String,
})

# Insert documents: PDF, DOCX, HTML, Markdown
docs.insert([
    {'document': '/data/product_guide.pdf',
     'title': 'Product Guide v3', 'source': 'internal', 'doc_type': 'pdf'},
    {'document': 'https://example.com/api-docs.html',
     'title': 'API Reference', 'source': 'docs-site', 'doc_type': 'html'},
])
```

Pixeltable handles PDF extraction, HTML parsing, and text extraction automatically via the Document type.


### Chunk & Embed

Create chunks with overlaps and add embedding indexes for retrieval.

```python
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer

# Automatic chunking with configurable overlap
chunks = pxt.create_view(
    'app.chunks',
    docs,
    iterator=document_splitter(
        document=docs.document,
        separators='sentence',
        limit=512,
        overlap=50
    )
)

# Embedding index for semantic retrieval
chunks.add_embedding_index(
    'text',
    string_embed=sentence_transformer.using(
        model_id='sentence-transformers/all-MiniLM-L6-v2'
    )
)

print(f"Created {chunks.count()} chunks with embedding index")
```

Chunking parameters are declarative. Change chunk_size or overlap and Pixeltable recomputes only what changed.


### Retrieve & Generate

Build a retrieval query and wire it to an LLM for answer generation.

```python
from pixeltable.functions import openai

# Define a reusable retrieval query
@pxt.query
def get_context(question: str, n: int = 5):
    return chunks.select(
        chunks.text, chunks.title
    ).order_by(
        chunks.text.similarity(string=question), asc=False
    ).limit(n)

# Create a queries table with automatic RAG
queries = pxt.create_table('app.queries', {
    'question': pxt.String,
    'user_id': pxt.String,
})

# Context retrieval as a computed column
queries.add_computed_column(
    context=get_context(queries.question)
)

# LLM answer generation: grounded in retrieved context
queries.add_computed_column(
    answer=openai.chat_completions(
        model='gpt-4o',
        messages=[{
            'role': 'system',
            'content': 'Answer based on the provided context. Cite sources.'
        }, {
            'role': 'user',
            'content': queries.question.apply(
                lambda q: f"Context: {queries.context}\nQuestion: {q}"
            )
        }]
    ).choices[0].message.content
)
```

Every query you insert triggers retrieval + generation automatically. Results are cached: the same question returns instantly.


### Serve via API

Expose your RAG pipeline as a FastAPI endpoint.

```python
from fastapi import FastAPI

app = FastAPI()

@app.post("/ask")
def ask(question: str, user_id: str = "anonymous"):
    # Insert triggers the full RAG pipeline
    queries.insert([{
        'question': question,
        'user_id': user_id,
    }])

    # Return the computed answer
    result = queries.select(
        queries.answer, queries.context
    ).where(
        queries.question == question
    ).collect()

    return {"answer": result[0]['answer'], "sources": result[0]['context']}

@app.post("/ingest")
def ingest(title: str, url: str):
    docs.insert([{'document': url, 'title': title, 'source': 'api'}])
    return {"status": "indexed", "chunks": chunks.count()}
```

New documents are automatically chunked, embedded, and indexed. The RAG pipeline stays current without batch jobs.


## Benefits

- Complete RAG stack in one system, no vector DB, no orchestrator
- Automatic embedding synchronization when documents change
- Built-in caching reduces LLM API costs by up to 60%
- Incremental updates: add a document and only its chunks are processed
- Full lineage: trace any answer back to its source chunks and documents

## Use Cases

- Enterprise knowledge bases and internal search
- Customer support chatbots with grounded answers
- Research question-answering over large document collections
- Legal and compliance document analysis
- Product documentation assistants

## Performance


| Metric | Value | Description |

| --- | --- | --- |

| Development Time | 10x faster | vs building from separate components |

| API Cost Savings | 60% | With built-in caching and deduplication |

## Requirements

- Python 3.9+
- OpenAI API key for embeddings and LLM generation
- 8GB+ RAM recommended for large document collections

## Resources

- [AI Automation Workflow: The Pipeline Is the Table](https://pixeltable.com/blog/ai-automation-workflow) - Document RAG as a table, not a vector-DB stitch
- [Production RAG: Data-Centric Approach](https://pixeltable.com/blog/production-rag-data-centric) - Building reliable RAG with data-centric principles
- [Multimodal RAG: Dev to Production](https://pixeltable.com/blog/multimodal-rag-production) - End-to-end multimodal RAG deployment guide
- [Embedding Management Guide](https://pixeltable.com/blog/embedding-management-guide) - Production-ready embedding management patterns