multi-document.mdx

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Multi-Document Retrieval

Query across multiple indexed documents using the cross-document strategy with graph-based score boosting.

Python

import asyncio
from vectorless import (
    Engine, IndexContext, QueryContext,
    IndexOptions, StrategyPreference
)

async def main():
    engine = Engine(
        workspace="./workspace",
        api_key="sk-...",
        model="gpt-4o",
    )

    # Index multiple documents
    docs = ["./report-q1.pdf", "./report-q2.pdf", "./report-q3.pdf"]
    doc_ids = []

    for path in docs:
        result = await engine.index(IndexContext.from_path(path))
        doc_ids.append(result.doc_id)
        print(f"Indexed: {path} → {result.doc_id}")

    # Check the cross-document graph
    graph = await engine.get_graph()
    if graph:
        print(f"\nGraph: {graph.node_count()} docs, {graph.edge_count()} edges")
        for doc_id in doc_ids:
            neighbors = graph.get_neighbors(doc_id)
            for edge in neighbors:
                print(f"  {doc_id[:8]}... → {edge.target_doc_id[:8]}... ({edge.weight:.2f})")

    # Query across all documents
    result = await engine.query(
        QueryContext("Compare quarterly revenue trends")
        .with_doc_ids(doc_ids)
        .with_strategy(StrategyPreference.CROSS_DOCUMENT)
    )

    for item in result.items:
        print(f"\n[{item.doc_id[:8]}...] Score: {item.score:.2f}")
        print(item.content[:300])

    # Or query entire workspace
    result = await engine.query(
        QueryContext("What documents discuss risk factors?")
        .with_workspace()
    )

    print(f"\nFound in {len(result.items)} document(s)")

    # Cleanup
    for doc_id in doc_ids:
        await engine.remove(doc_id)

asyncio.run(main())

Key Concepts

Document Graph

After indexing, documents are connected in a graph based on shared keywords. The graph enables:

• Score boosting — High-confidence results in one document boost neighbor documents
• Relationship discovery — Automatically find related documents
• Cross-referencing — Results from connected documents are surfaced together

Merge Strategies

The cross-document strategy supports multiple merge modes:

Strategy	Description
TopK	Return top-K results across all documents
BestPerDocument	Best result from each document
WeightedByRelevance	Weight by each document's best score
GraphBoosted	Use graph connections to boost scores