refactor: move graph_prompt_path to llm config

cognee/api/v1/cognify/cognify.py

@@ -111,7 +111,6 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
     chunker=TextChunker,
     chunk_size: int = None,
     ontology_file_path: Optional[str] = None,
-    graph_prompt_path: Optional[str] = None,
 ) -> list[Task]:
     if user is None:
         user = await get_default_user()
@@ -132,7 +131,6 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
             extract_graph_from_data,
             graph_model=graph_model,
             ontology_adapter=ontology_adapter,
-            graph_prompt_path=graph_prompt_path,
             task_config={"batch_size": 10},
         ),  # Generate knowledge graphs from the document chunks.
         Task(

cognee/infrastructure/llm/config.py

@@ -15,6 +15,7 @@ class LLMConfig(BaseSettings):
     llm_streaming: bool = False
     llm_max_tokens: int = 16384
     transcription_model: str = "whisper-1"
+    graph_prompt_path: str = "generate_graph_prompt.txt"
 
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
@@ -83,6 +84,7 @@ def to_dict(self) -> dict:
             "streaming": self.llm_streaming,
             "max_tokens": self.llm_max_tokens,
             "transcription_model": self.transcription_model,
+            "graph_prompt_path": self.graph_prompt_path,
         }
 
 

cognee/modules/data/extraction/knowledge_graph/extract_content_graph.py

@@ -2,14 +2,14 @@
 from pydantic import BaseModel
 from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import render_prompt
+from cognee.infrastructure.llm.config import get_llm_config
 
 
-async def extract_content_graph(
-    content: str, response_model: Type[BaseModel], graph_prompt_path: Optional[str] = None
-):
+async def extract_content_graph(content: str, response_model: Type[BaseModel]):
     llm_client = get_llm_client()
+    llm_config = get_llm_config()
 
-    prompt_path = graph_prompt_path or "generate_graph_prompt.txt"
+    prompt_path = llm_config.graph_prompt_path
     system_prompt = render_prompt(prompt_path, {})
     content_graph = await llm_client.acreate_structured_output(
         content, system_prompt, response_model

cognee/tasks/graph/extract_graph_from_data.py

@@ -54,13 +54,9 @@ async def extract_graph_from_data(
     data_chunks: list[DocumentChunk],
     graph_model: Type[BaseModel],
     ontology_adapter: OntologyResolver = OntologyResolver(),
-    graph_prompt_path: Optional[str] = None,
 ) -> List[DocumentChunk]:
     """Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model."""
     chunk_graphs = await asyncio.gather(
-        *[
-            extract_content_graph(chunk.text, graph_model, graph_prompt_path)
-            for chunk in data_chunks
-        ]
+        *[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
     )
     return await integrate_chunk_graphs(data_chunks, chunk_graphs, graph_model, ontology_adapter)