feat: add kwargs to cognify and related tasks

cognee/api/v1/cognify/cognify.py

@@ -53,6 +53,7 @@ async def cognify(
     custom_prompt: Optional[str] = None,
     temporal_cognify: bool = False,
     data_per_batch: int = 20,
+    **kwargs
 ):
     """
     Transform ingested data into a structured knowledge graph.
@@ -224,6 +225,7 @@ async def cognify(
             config=config,
             custom_prompt=custom_prompt,
             chunks_per_batch=chunks_per_batch,
+            **kwargs,
         )
 
     # By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for
@@ -251,6 +253,7 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
     config: Config = None,
     custom_prompt: Optional[str] = None,
     chunks_per_batch: int = 100,
+    **kwargs,
 ) -> list[Task]:
     if config is None:
         ontology_config = get_ontology_env_config()
@@ -286,6 +289,7 @@ async def get_default_tasks(  # TODO: Find out a better way to do this (Boris's
             config=config,
             custom_prompt=custom_prompt,
             task_config={"batch_size": chunks_per_batch},
+            **kwargs,
         ),  # Generate knowledge graphs from the document chunks.
         Task(
             summarize_text,

cognee/infrastructure/llm/LLMGateway.py

@@ -11,7 +11,7 @@ class LLMGateway:
 
     @staticmethod
     def acreate_structured_output(
-        text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> Coroutine:
         llm_config = get_llm_config()
         if llm_config.structured_output_framework.upper() == "BAML":
@@ -31,7 +31,7 @@ class LLMGateway:
 
             llm_client = get_llm_client()
             return llm_client.acreate_structured_output(
-                text_input=text_input, system_prompt=system_prompt, response_model=response_model
+                text_input=text_input, system_prompt=system_prompt, response_model=response_model, **kwargs
             )
 
     @staticmethod

cognee/infrastructure/llm/extraction/knowledge_graph/extract_content_graph.py

@@ -10,7 +10,7 @@ from cognee.infrastructure.llm.config import (
 
 
 async def extract_content_graph(
-    content: str, response_model: Type[BaseModel], custom_prompt: Optional[str] = None
+    content: str, response_model: Type[BaseModel], custom_prompt: Optional[str] = None, **kwargs
 ):
     if custom_prompt:
         system_prompt = custom_prompt
@@ -30,7 +30,7 @@ async def extract_content_graph(
         system_prompt = render_prompt(prompt_path, {}, base_directory=base_directory)
 
     content_graph = await LLMGateway.acreate_structured_output(
-        content, system_prompt, response_model
+        content, system_prompt, response_model, **kwargs
     )
 
     return content_graph

cognee/tasks/graph/extract_graph_from_data.py

@@ -99,6 +99,7 @@ async def extract_graph_from_data(
     graph_model: Type[BaseModel],
     config: Config = None,
     custom_prompt: Optional[str] = None,
+    **kwargs,
 ) -> List[DocumentChunk]:
     """
     Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model.
@@ -113,7 +114,7 @@ async def extract_graph_from_data(
 
     chunk_graphs = await asyncio.gather(
         *[
-            extract_content_graph(chunk.text, graph_model, custom_prompt=custom_prompt)
+            extract_content_graph(chunk.text, graph_model, custom_prompt=custom_prompt, **kwargs)
             for chunk in data_chunks
         ]
     )