Why Build an Orchestrator?
Standard RAG (Retrieval-Augmented Generation) systems often fail when faced with complex, multi-faceted questions or broad documentation. A Fine-Grained Multi-Query Orchestrator solves this by decomposing complex user prompts into specific, atomic sub-queries, retrieving targeted granular chunks, and synthesizing the results. This interactive guide explores the architecture, performance benefits, and implementation strategy.
Performance Impact Analysis
Comparing Standard RAG vs. Multi-Query Orchestration on complex reasoning tasks (Simulated Data).
Finds hidden details by asking multiple variations.
Retrieves specific snippets, not just large blobs.
Slightly slower due to multi-step processing.
System Architecture
The orchestrator is not a single retrieval call. It is a workflow. Click on the nodes in the diagram below to understand the responsibility of each component in the pipeline, from the Query Decomposer to the Final Synthesis.
Select a Component
Click on any block in the diagram to view its technical specification, input/output requirements, and implementation strategy.
Key Tech Stack
- LangChain / LlamaIndex
- Vector DB (Pinecone/Weaviate)
- Small LLM (Fast) for Routing
- Large LLM (Smart) for Synthesis
When to Use It?
Fine-grained orchestration isn't for every query. It shines when questions are ambiguous, comparative, or require multi-hop reasoning. Use the interactive simulator below to see how the system handles different types of requests.
Choose a Scenario
> System Ready.
Waiting for user input...
How to Build
Implementing this architecture requires a distinct shift from standard "Chunk & Embed" workflows. Here is the step-by-step implementation guide.
The Decomposer (Query Transformation)
You need a "Router" LLM call before retrieval. Do not pass the raw query to the Vector DB.
Fine-Grained Indexing (Parent-Child)
Standard RAG retrieves large chunks (500+ tokens). Fine-grained retrieval uses Small-to-Big retrieval.
- Child Chunks: 128 tokens. Used for *search* (high semantic match).
- Parent Chunks: 512-1024 tokens. Used for *context* (what is actually sent to LLM).
- Linkage: Store Parent ID in Child metadata.
Parallel Execution & Fusion
Execute sub-queries asynchronously.
Combine results from multiple sub-queries into a single ranked list.
Remove identical parent chunks retrieved by different sub-queries.