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A minimal guide to shaping graph extraction with a custom LLM prompt. You’ll pass your prompt via custom_prompt to cognee.remember() to control entity types, relationship labels, and extraction rules.
For the built-in graph extraction prompts selected through GRAPH_PROMPT_PATH, see Cognify.
Before you start:
Complete Quickstart to understand basic operations
custom_prompt = """Extract only people and cities as entities.Connect people to cities with the relationship "lives_in".Ignore all other entities."""
The custom prompt overrides the default system prompt used during entity/relationship extraction. It constrains node types, enforces relationship naming, and reduces noise.
custom_prompt is ignored when temporal_cognify=True.
await cognee.forget(everything=True)await cognee.remember( [ "Alice moved to Paris in 2010, while Bob has always lived in New York.", "Andreas was born in Venice, but later settled in Lisbon.", "Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.", ], custom_prompt=custom_prompt, self_improvement=False,)
This resets the local state and then uses remember() to ingest the text and build the graph in one pass. The same approach works with multiple documents, files, or entire datasets.
custom_prompt vs system_prompt: which prompt goes where
Cognee uses two distinct prompts at two different stages, and they are not interchangeable. Passing one where the other is expected will silently have no effect.
Entity & relationship extraction — remember() runs add() + cognify() under the hood, so this overrides the graph extraction prompt used during graph build.
Answer generation — instructs the LLM how to compose the final answer from retrieved context. Only applies to completion-style retrieval (GRAPH_COMPLETION, RAG_COMPLETION, TRIPLET_COMPLETION, etc.); ignored by only_context=True and by non-completion retrieval paths.
Answer generation — instructs the LLM how to compose the final answer from retrieved context. Only applies to completion-style search types; ignored by CHUNKS, SUMMARIES, CYPHER, and when only_context=True.
import asyncioimport cogneefrom cognee.api.v1.search import SearchTypecustom_prompt = """Extract only people and cities as entities.Connect people to cities with the relationship "lives_in".Ignore all other entities."""async def main(): await cognee.forget(everything=True) await cognee.remember( [ "Alice moved to Paris in 2010, while Bob has always lived in New York.", "Andreas was born in Venice, but later settled in Lisbon.", "Diana and Tom were born and raised in Helsinki. Diana currently resides in Berlin, while Tom never moved.", ], custom_prompt=custom_prompt, self_improvement=False, ) res = await cognee.recall( query_type=SearchType.GRAPH_COMPLETION, query_text="Where does Alice live?", ) print(res)if __name__ == "__main__": asyncio.run(main())
Legacy guide
import asyncioimport cogneefrom cognee.api.v1.search import SearchTypecustom_prompt = """Extract only people and cities as entities.Connect people to cities with the relationship "lives_in".Ignore all other entities."""async def main(): await cognee.add([ "Alice moved to Paris in 2010, while Bob has always lived in New York.", "Andreas was born in Venice, but later settled in Lisbon.", "Diana and Tom were born and raised in Helsingy. Diana currently resides in Berlin, while Tom never moved." ]) await cognee.cognify(custom_prompt=custom_prompt) res = await cognee.recall( query_type=SearchType.GRAPH_COMPLETION, query_text="Where does Alice live?", ) print(res)if __name__ == "__main__": asyncio.run(main())
This simple example uses a few strings for demonstration. In practice, you can add multiple documents, files, or entire datasets - the custom prompt processing works the same way across all your data.
If you are running Cognee as a server and want to infer a schema or generate a prompt through HTTP instead of writing it by hand, see the LLM Utility Endpoints examples in Deploy REST API Server.
