qdrant-analytics

name	qdrant-analytics
description	Build spend analytics and dashboards from Qdrant vector data. Covers bulk scroll extraction, aggregation patterns, vendor grouping, and Chart.js visualization. Use when building analytics features, aggregating procurement data, or creating dashboards.
metadata	{"author":"cognee-hackathon","version":"1.0"}

Qdrant Analytics Patterns

Patterns for extracting and aggregating data from Qdrant collections for analytics dashboards.

Bulk Data Extraction via Scroll

Iterate all records for aggregation:

all_records = []
next_offset = None

while True:
    records, next_offset = client.scroll(
        collection_name="DocumentChunk_text",
        limit=100,
        offset=next_offset,
        with_payload=True,
        scroll_filter=models.Filter(
            must=[
                models.FieldCondition(
                    key="doc_type",
                    match=models.MatchValue(value="invoice"),
                )
            ]
        ),
    )
    all_records.extend(records)
    if next_offset is None:
        break

Vendor-Grouped Search

Group search results by vendor for comparison:

results = client.query_points_groups(
    collection_name="DocumentChunk_text",
    query=embedding_vector,
    group_by="vendor_name",
    limit=10,      # number of groups
    group_size=5,   # results per group
)

for group in results.groups:
    vendor = group.id
    hits = group.hits

Aggregation Pattern

Extract payloads and aggregate in Python:

from collections import defaultdict

vendor_spend = defaultdict(float)
for record in all_records:
    payload = record.payload
    vendor = payload.get("vendor_name", "Unknown")
    amount = float(payload.get("amount", 0))
    vendor_spend[vendor] += amount

# Sort by spend
top_vendors = sorted(vendor_spend.items(), key=lambda x: x[1], reverse=True)

FastAPI Analytics Endpoint

Return aggregated data as JSON for Chart.js:

@app.get("/api/analytics")
async def analytics():
    records = scroll_all_records("DocumentChunk_text", doc_type="invoice")
    return {
        "total_spend": sum(r.payload.get("amount", 0) for r in records),
        "invoice_count": len(records),
        "by_vendor": aggregate_by_field(records, "vendor_name", "amount"),
        "by_month": aggregate_by_field(records, "month", "amount"),
    }

Decision Table

Need	Pattern
Total spend across all invoices	Scroll all + sum amounts
Top vendors by spend	Scroll + groupby vendor + sort
Monthly trends	Scroll + groupby month + sort by date
Semantic search within analytics	Query API + filters
Vendor-grouped search results	Group API with group_by="vendor_name"