Fix langfuse

cognee/api/v1/cognify/code_graph_pipeline.py

@@ -29,8 +29,17 @@
                                          expand_dependency_graph,
                                          get_repo_file_dependencies)
 from cognee.tasks.storage import add_data_points
+
+from cognee.base_config import get_base_config
+from cognee.shared.data_models import MonitoringTool
+
+monitoring = get_base_config().monitoring_tool
+if monitoring == MonitoringTool.LANGFUSE:
+    from langfuse.decorators import observe
+
 from cognee.tasks.summarization import summarize_code
 
+
 logger = logging.getLogger("code_graph_pipeline")
 
 update_status_lock = asyncio.Lock()
@@ -62,7 +71,7 @@ async def code_graph_pipeline(datasets: Union[str, list[str]] = None, user: User
 
     return await asyncio.gather(*awaitables)
 
-
+@observe
 async def run_pipeline(dataset: Dataset, user: User):
     '''DEPRECATED: Use `run_code_graph_pipeline` instead. This function will be removed.'''
     data_documents: list[Data] = await get_dataset_data(dataset_id = dataset.id)

cognee/base_config.py

@@ -10,7 +10,9 @@ class BaseConfig(BaseSettings):
     monitoring_tool: object = MonitoringTool.LANGFUSE
     graphistry_username: Optional[str] = os.getenv("GRAPHISTRY_USERNAME")
     graphistry_password: Optional[str] = os.getenv("GRAPHISTRY_PASSWORD")
-
+    langfuse_public_key: Optional[str] = os.getenv("LANGFUSE_PUBLIC_KEY")
+    langfuse_secret_key: Optional[str] = os.getenv("LANGFUSE_SECRET_KEY")
+    langfuse_host: Optional[str] = os.getenv("LANGFUSE_HOST")
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 
     def to_dict(self) -> dict:

cognee/infrastructure/llm/openai/adapter.py

@@ -6,26 +6,31 @@
 import litellm
 import instructor
 from pydantic import BaseModel
-
+from cognee.shared.data_models import MonitoringTool
 from cognee.exceptions import InvalidValueError
 from cognee.infrastructure.llm.llm_interface import LLMInterface
 from cognee.infrastructure.llm.prompts import read_query_prompt
+from cognee.base_config import get_base_config
+
+if MonitoringTool.LANGFUSE:
+    from langfuse.decorators import observe
 
 class OpenAIAdapter(LLMInterface):
     name = "OpenAI"
     model: str
     api_key: str
     api_version: str
-  
+
     """Adapter for OpenAI's GPT-3, GPT=4 API"""
+
     def __init__(
-        self,
-        api_key: str,
-        endpoint: str,
-        api_version: str,
-        model: str,
-        transcription_model: str,
-        streaming: bool = False,
+            self,
+            api_key: str,
+            endpoint: str,
+            api_version: str,
+            model: str,
+            transcription_model: str,
+            streaming: bool = False,
     ):
         self.aclient = instructor.from_litellm(litellm.acompletion)
         self.client = instructor.from_litellm(litellm.completion)
@@ -35,45 +40,52 @@ def __init__(
         self.endpoint = endpoint
         self.api_version = api_version
         self.streaming = streaming
+        base_config = get_base_config()
+
+
+    @observe()
+    async def acreate_structured_output(self, text_input: str, system_prompt: str,
+                                        response_model: Type[BaseModel]) -> BaseModel:
 
-    async def acreate_structured_output(self, text_input: str, system_prompt: str, response_model: Type[BaseModel]) -> BaseModel:
         """Generate a response from a user query."""
 
         return await self.aclient.chat.completions.create(
-            model = self.model,
-            messages = [{
+            model=self.model,
+            messages=[{
                 "role": "user",
                 "content": f"""Use the given format to
                 extract information from the following input: {text_input}. """,
             }, {
                 "role": "system",
                 "content": system_prompt,
             }],
-            api_key = self.api_key,
-            api_base = self.endpoint,
-            api_version = self.api_version,
-            response_model = response_model,
-            max_retries = 5,
+            api_key=self.api_key,
+            api_base=self.endpoint,
+            api_version=self.api_version,
+            response_model=response_model,
+            max_retries=5,
         )
 
-    def create_structured_output(self, text_input: str, system_prompt: str, response_model: Type[BaseModel]) -> BaseModel:
+    @observe
+    def create_structured_output(self, text_input: str, system_prompt: str,
+                                 response_model: Type[BaseModel]) -> BaseModel:
         """Generate a response from a user query."""
 
         return self.client.chat.completions.create(
-            model = self.model,
-            messages = [{
+            model=self.model,
+            messages=[{
                 "role": "user",
                 "content": f"""Use the given format to
                 extract information from the following input: {text_input}. """,
             }, {
                 "role": "system",
                 "content": system_prompt,
             }],
-            api_key = self.api_key,
-            api_base = self.endpoint,
-            api_version = self.api_version,
-            response_model = response_model,
-            max_retries = 5,
+            api_key=self.api_key,
+            api_base=self.endpoint,
+            api_version=self.api_version,
+            response_model=response_model,
+            max_retries=5,
         )
 
     def create_transcript(self, input):
@@ -86,12 +98,12 @@ def create_transcript(self, input):
         #     audio_data = audio_file.read()
 
         transcription = litellm.transcription(
-            model = self.transcription_model,
-            file = Path(input),
+            model=self.transcription_model,
+            file=Path(input),
             api_key=self.api_key,
             api_base=self.endpoint,
             api_version=self.api_version,
-            max_retries = 5,
+            max_retries=5,
         )
 
         return transcription
@@ -101,8 +113,8 @@ def transcribe_image(self, input) -> BaseModel:
             encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
 
         return litellm.completion(
-            model = self.model,
-            messages = [{
+            model=self.model,
+            messages=[{
                 "role": "user",
                 "content": [
                     {
@@ -119,8 +131,8 @@ def transcribe_image(self, input) -> BaseModel:
             api_key=self.api_key,
             api_base=self.endpoint,
             api_version=self.api_version,
-            max_tokens = 300,
-            max_retries = 5,
+            max_tokens=300,
+            max_retries=5,
         )
 
     def show_prompt(self, text_input: str, system_prompt: str) -> str:
@@ -132,4 +144,4 @@ def show_prompt(self, text_input: str, system_prompt: str) -> str:
         system_prompt = read_query_prompt(system_prompt)
 
         formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None
-        return formatted_prompt
+        return formatted_prompt