feat: add support to pass custom parameters in llm adapters during cognify (#1802)

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## Description
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this PR.
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A user raised this issue/feature request:
https://github.com/topoteretes/cognee/issues/1784
This PR implements this in a way, but I may have misunderstood the
request. If I'm correct, I think it makes sense to support this, if our
users need it.

## Type of Change
<!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify):

## Screenshots/Videos (if applicable)
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## Pre-submission Checklist
<!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR**
- [ ] **This PR contains minimal changes necessary to address the
issue/feature**
- [ ] My code follows the project's coding standards and style
guidelines
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have added necessary documentation (if applicable)
- [ ] All new and existing tests pass
- [ ] I have searched existing PRs to ensure this change hasn't been
submitted already
- [ ] I have linked any relevant issues in the description
- [ ] My commits have clear and descriptive messages

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

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.
@@ -223,6 +224,7 @@ 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()
@@ -288,6 +291,7 @@ 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/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py

@@ -52,7 +52,7 @@ class AnthropicAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py

@@ -80,7 +80,7 @@ class GeminiAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py

@@ -80,7 +80,7 @@ class GenericAPIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py

@@ -69,7 +69,7 @@ class MistralAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from the user query.

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py

@@ -76,7 +76,7 @@ class OllamaAPIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a structured output from the LLM using the provided text and system prompt.
@@ -123,7 +123,7 @@ class OllamaAPIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def create_transcript(self, input_file: str) -> str:
+    async def create_transcript(self, input_file: str, **kwargs) -> str:
         """
         Generate an audio transcript from a user query.
 
@@ -162,7 +162,7 @@ class OllamaAPIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def transcribe_image(self, input_file: str) -> str:
+    async def transcribe_image(self, input_file: str, **kwargs) -> str:
         """
         Transcribe content from an image using base64 encoding.
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py

@@ -112,7 +112,7 @@ class OpenAIAdapter(LLMInterface):
         reraise=True,
     )
     async def acreate_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.
@@ -154,6 +154,7 @@ class OpenAIAdapter(LLMInterface):
                     api_version=self.api_version,
                     response_model=response_model,
                     max_retries=self.MAX_RETRIES,
+                    **kwargs,
                 )
         except (
             ContentFilterFinishReasonError,
@@ -180,6 +181,7 @@ class OpenAIAdapter(LLMInterface):
                         # api_base=self.fallback_endpoint,
                         response_model=response_model,
                         max_retries=self.MAX_RETRIES,
+                        **kwargs,
                     )
             except (
                 ContentFilterFinishReasonError,
@@ -205,7 +207,7 @@ class OpenAIAdapter(LLMInterface):
         reraise=True,
     )
     def create_structured_output(
-        self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
+        self, text_input: str, system_prompt: str, response_model: Type[BaseModel], **kwargs
     ) -> BaseModel:
         """
         Generate a response from a user query.
@@ -245,6 +247,7 @@ class OpenAIAdapter(LLMInterface):
             api_version=self.api_version,
             response_model=response_model,
             max_retries=self.MAX_RETRIES,
+            **kwargs,
         )
 
     @retry(
@@ -254,7 +257,7 @@ class OpenAIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def create_transcript(self, input):
+    async def create_transcript(self, input, **kwargs):
         """
         Generate an audio transcript from a user query.
 
@@ -281,6 +284,7 @@ class OpenAIAdapter(LLMInterface):
                 api_base=self.endpoint,
                 api_version=self.api_version,
                 max_retries=self.MAX_RETRIES,
+                **kwargs,
             )
 
         return transcription
@@ -292,7 +296,7 @@ class OpenAIAdapter(LLMInterface):
         before_sleep=before_sleep_log(logger, logging.DEBUG),
         reraise=True,
     )
-    async def transcribe_image(self, input) -> BaseModel:
+    async def transcribe_image(self, input, **kwargs) -> BaseModel:
         """
         Generate a transcription of an image from a user query.
 
@@ -337,4 +341,5 @@ class OpenAIAdapter(LLMInterface):
             api_version=self.api_version,
             max_completion_tokens=300,
             max_retries=self.MAX_RETRIES,
+            **kwargs,
         )

cognee/tasks/graph/extract_graph_from_data.py

@@ -97,6 +97,7 @@ 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.
@@ -111,7 +112,7 @@ 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
         ]
     )