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.
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Patterns for extracting and aggregating data from Qdrant collections for analytics dashboards.
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
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
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)
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"),
}
| 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" |
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Use at a cognee hackathon. Watches the participant's experience with cognee throughout the session — errors, slowness, confusion, workarounds, and signs of frustration in how they work with you — and keeps one private feedback file they send to the cognee team at the end.
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Use at the Mem Off hackathon. Keeps a running, evidence-backed report on how the participant's memory system performed for their coding agent during the day — what the harness measured, what cognee-report's scorecard says, what was recalled and whether it helped — in ./memoff-report.md and ./memoff-report.json. The report is submitted together with the action results; it never contains quotes, keys, data or names.
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Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.
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Detect anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.
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Semantic search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.
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