Asistente de Configuración RAG

A RAG Pipeline Configurator is a system architecture utility used to design and optimize Retrieval-Augmented Generation workflows. This tool is essential for AI engineers, data scientists, and backend developers tuning chunk sizes, calculating overlap percentages, estimating token costs for vector databases, and ensuring that the context fed into the LLM is both relevant and cost-effective.

The configuration engine handles pipeline logic through a rigorous optimization pipeline:

1. Ingestion Strategy (Chunking): The tool calculates the optimal breakdown of your source text:
   • Fixed Size: "Split every 512 tokens."
   • Semantic: "Split by paragraph or header."
   • Overlap: "Keep 50 tokens from the previous chunk to maintain context."
2. Retrieval Math: The engine estimates the Search Density:
   • Top-K: How many chunks to send to the LLM (e.g., "Retrieve the top 5 matches").
   • Context Window Usage: Checks if (Chunk Size * Top-K) fits in your model's memory.
3. Cost Estimation: The tool projects the Embedding and Generation Costs based on your document volume (e.g., "Indexing 1M words with OpenAI text-3-large will cost $X").
4. Reactive Real-time Simulation: Your "Pipeline Stats" and "Token Usage" update instantly as you adjust the slider for chunk size or change the embedding model.

The History of RAG: From Hallucinations to Grounding

How we fix AI's memory problems has evolved from "Fine-tuning" to "Context Injection."

• The Hallucination Era (2022): Early ChatGPT would make up facts. We couldn't "teach" it new things easily.
• The Vector Database (2023): Engineers realized they could turn text into numbers (Vectors) and search them instantly. This allowed AIs to "Look up" answers.
• The Tuning Problem (2024): Getting RAG right is hard. "Retrieved context" is often irrelevant if the chunks are wrong. This tool Automates the math of context window management.

Technical Comparison: Chunking Strategies

Understanding how to "Slice" your data is vital for Answer Quality and Accuracy.

Strategy	Logic	Best For	Workflow Impact
Fixed	strict token count	Simple Text	Speed
Recursive	Separators (\n, .)	Articles	Coherence
Semantic	Meaning-based	Complex Docs	Precision
Agentic	AI decides	Unstructured	Intelligence
Parent-Child	Small chunks -> Big block	Tables/Charts	Context

By using this tool, you ensure your Knowledge Base is readable by your AI.

Security and Privacy Considerations

Your pipeline design is performed in a secure, local environment:

• Local Logical Execution: All calcualtions and estimates are performed locally in your browser. Your internal documentation strategies—which reveal your IP handling metrics—never touch our servers.
• Zero Log Policy: We do not store or track your inputs. Your RAG Architecture and Cost Data remain entirely confidential.
• W3C Security Compliance: The tool operates within the standard browser sandbox, ensuring no interaction with your local file system or Private Metadata.
• Privacy First: To maintain absolute Data Privacy, the tool functions as an anonymous utility.