cognee-community-graph-adapter-turbopuffer 0.1.0

TurboPuffer graph adapter for cognee (graph emulated on TurboPuffer namespaces + attributes)

cognee-community-graph-adapter-turbopuffer

A community graph database adapter for cognee backed by TurboPuffer.

TurboPuffer is a vector/document store with no native graph traversal or query language, so this adapter emulates a graph on two TurboPuffer namespaces per dataset — exactly as the core Postgres graph adapter emulates one on relational tables. All traversal is done client-side with single-hop edge queries.

Storage model

Per dataset, namespace-prefixed by database_name:

• {database_name}_graph_node — one doc per node: id, name, type (the DataPoint class name), belongs_to_set (string[]), and a non-filterable properties JSON blob holding all remaining fields (so large text is kept in full).
• {database_name}_graph_edge — one doc per edge: a deterministic uuid5 id (TurboPuffer caps ids at 64 bytes), source_id, target_id, relationship_name, denormalized endpoint identity (source_name/source_type/target_name/target_type), and the full edge properties JSON blob.

Install

uv pip install -e cognee-community/packages/graph/turbopuffer
# or, from the package directory:
uv pip install -e .

Configure & use

import cognee
from cognee_community_graph_adapter_turbopuffer import register

register()  # registers the "turbopuffer" graph provider + dataset handler
cognee.config.set_graph_database_provider("turbopuffer")

# Environment:
#   TURBOPUFFER_API_KEY   (required)
#   TURBOPUFFER_REGION    (required by the SDK; e.g. gcp-us-central1)

await cognee.add("Alice followed the White Rabbit.")
await cognee.cognify()
results = await cognee.search("Who does Alice meet?")

For multi-tenant backend access control, also select the graph dataset handler:

GRAPH_DATABASE_PROVIDER=turbopuffer
GRAPH_DATASET_DATABASE_HANDLER=turbopuffer_graph

(The handler is registered under turbopuffer_graph so it does not collide with the TurboPuffer vector adapter's turbopuffer handler.)

Supported vs. not (v1)

Supported: CRUD, single-hop reads (get_neighbors, get_connections, get_edges), get_neighborhood(depth=1), get_graph_data, get_filtered_graph_data, get_nodeset_subgraph (OR + client-side AND), get_graph_metrics (counts + mean degree), get_triplets_batch (offset pagination via deterministic cursor scan), remove_belongs_to_set_tags, delete_graph, per-dataset isolation.

Not in v1: multi-hop BFS (get_neighborhood(depth>=2)) and raw Cypher (query) raise NotImplementedError. Connectivity-dependent metrics (connected components, diameter, clustering) return the -1 sentinel, matching the Postgres adapter.

Examples & tests

# build the Alice graph and query it
GRAPH_DATABASE_PROVIDER=turbopuffer python examples/example.py
python examples/inspect_graph.py --raw       # inspect node/edge namespaces
python examples/export_graph.py               # dump nodes.json / edges.json

# tests (see tests/README.md for tiers)
./run_tests.sh integration                    # live per-method + isolation
./run_tests.sh e2e                            # full add->cognify->search on Alice