Triplet Embeddings

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When to use this

• Complete Quickstart to understand basic operations
• Ensure you have LLM Providers configured
• Have an existing knowledge graph created with remember()

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Code in Action

import asyncio
import cognee
from cognee import SearchType
from cognee.memify_pipelines.create_triplet_embeddings import create_triplet_embeddings
from cognee.modules.users.methods import get_default_user

DATASET = "triplet_demo"

async def main():
    await cognee.remember(
        "GraphRAG combines vector search with graph traversal for better context.",
        dataset_name=DATASET,
        self_improvement=False,
    )

    user = await get_default_user()
    await create_triplet_embeddings(user=user, dataset=DATASET)

    results = await cognee.recall(
        query_type=SearchType.TRIPLET_COMPLETION,
        query_text="How does GraphRAG work?",
        datasets=[DATASET],
    )
    for result in results:
        print(result)

asyncio.run(main())

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What Just Happened

1. Remember — builds a knowledge graph with entities and relationships from your text.
2. get_default_user() — retrieves the authenticated user. This pipeline requires a User object with write access to the dataset.
3. create_triplet_embeddings(user, dataset) — iterates over all graph triplets, converts each to a text representation, and indexes them in the vector DB.
4. Recall with TRIPLET_COMPLETION — queries the new Triplet_text collection by semantic similarity.

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What Changed in Your Graph

• The Triplet_text collection is populated in the vector DB. Each entry is a text representation of a graph triplet (source → relationship → target).
• recall(..., query_type=SearchType.TRIPLET_COMPLETION) queries now return results by matching your query against these triplet embeddings.

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Additional Information