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Business event tracing completed for both eveai_workers tasks and eveai_chat_workers tasks

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This commit is contained in:
Josako
2024-09-27 10:53:42 +02:00
parent ee1b0f1cfa
commit d9cb00fcdc
9 changed files with 306 additions and 253 deletions
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7
common/utils/business_event.py
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@@ -46,7 +46,7 @@ class BusinessEvent:
parent_span_id = self.span_id
self.span_counter += 1
new_span_id = f"{self.trace_id}-{self.span_counter}"
new_span_id = str(uuid.uuid4())
# Save the current span info
self.spans.append((self.span_id, self.span_name, self.parent_span_id))
@@ -56,9 +56,12 @@ class BusinessEvent:
self.span_name = span_name
self.parent_span_id = parent_span_id
self.log(f"Starting span {span_name}")
try:
yield
finally:
self.log(f"Ending span {span_name}")
# Restore the previous span info
if self.spans:
self.span_id, self.span_name, self.parent_span_id = self.spans.pop()
@@ -103,7 +106,9 @@ class BusinessEvent:
db.session.commit()
def __enter__(self):
self.log(f'Starting Trace for {self.event_type}')
return BusinessEventContext(self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.log(f'Ending Trace for {self.event_type}')
return BusinessEventContext(self).__exit__(exc_type, exc_val, exc_tb)

154
common/utils/model_utils.py
View File

@@ -9,10 +9,12 @@ from typing import List, Any, Iterator
from collections.abc import MutableMapping
from openai import OpenAI
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from portkey_ai.langchain.portkey_langchain_callback_handler import LangchainCallbackHandler
from common.models.document import EmbeddingSmallOpenAI, EmbeddingLargeOpenAI
from common.models.user import Tenant
from config.model_config import MODEL_CONFIG
from common.utils.business_event_context import current_event
class CitedAnswer(BaseModel):
@@ -91,87 +93,115 @@ class ModelVariables(MutableMapping):
@property
def embedding_model(self):
if self._embedding_model is None:
environment = os.getenv('FLASK_ENV', 'development')
portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
portkey_metadata = self.get_portkey_metadata()
if self._variables['embedding_provider'] == 'openai':
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
provider='openai',
metadata=portkey_metadata)
api_key = os.getenv('OPENAI_API_KEY')
model = self._variables['embedding_model']
self._embedding_model = OpenAIEmbeddings(api_key=api_key,
model=model,
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._embedding_db_model = EmbeddingSmallOpenAI \
if model == 'text-embedding-3-small' \
else EmbeddingLargeOpenAI
else:
raise ValueError(f"Invalid embedding provider: {self._variables['embedding_provider']}")
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
provider=self._variables['embedding_provider'],
metadata=portkey_metadata)
api_key = os.getenv('OPENAI_API_KEY')
model = self._variables['embedding_model']
self._embedding_model = OpenAIEmbeddings(api_key=api_key,
model=model,
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._embedding_db_model = EmbeddingSmallOpenAI \
if model == 'text-embedding-3-small' \
else EmbeddingLargeOpenAI
return self._embedding_model
@property
def llm(self):
if self._llm is None:
self._initialize_llm()
portkey_headers = self.get_portkey_headers_for_llm()
api_key = self.get_api_key_for_llm()
self._llm = ChatOpenAI(api_key=api_key,
model=self._variables['llm_model'],
temperature=self._variables['RAG_temperature'],
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
return self._llm
@property
def llm_no_rag(self):
if self._llm_no_rag is None:
self._initialize_llm()
portkey_headers = self.get_portkey_headers_for_llm()
api_key = self.get_api_key_for_llm()
self._llm_no_rag = ChatOpenAI(api_key=api_key,
model=self._variables['llm_model'],
temperature=self._variables['RAG_temperature'],
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
return self._llm_no_rag
def _initialize_llm(self):
environment = os.getenv('FLASK_ENV', 'development')
portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
def get_portkey_headers_for_llm(self):
portkey_metadata = self.get_portkey_metadata()
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
metadata=portkey_metadata,
provider=self._variables['llm_provider'])
return portkey_headers
def get_portkey_metadata(self):
environment = os.getenv('FLASK_ENV', 'development')
portkey_metadata = {'tenant_id': str(self.tenant.id),
'environment': environment,
'trace_id': current_event.trace_id,
'span_id': current_event.span_id,
'span_name': current_event.span_name,
'parent_span_id': current_event.parent_span_id,
}
return portkey_metadata
def get_api_key_for_llm(self):
if self._variables['llm_provider'] == 'openai':
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
metadata=portkey_metadata,
provider='openai')
api_key = os.getenv('OPENAI_API_KEY')
self._llm = ChatOpenAI(api_key=api_key,
model=self._variables['llm_model'],
temperature=self._variables['RAG_temperature'],
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._llm_no_rag = ChatOpenAI(api_key=api_key,
model=self._variables['llm_model'],
temperature=self._variables['no_RAG_temperature'],
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._variables['tool_calling_supported'] = self._variables['llm_model'] in ['gpt-4o', 'gpt-4o-mini']
elif self._variables['llm_provider'] == 'anthropic':
else: # self._variables['llm_provider'] == 'anthropic'
api_key = os.getenv('ANTHROPIC_API_KEY')
llm_model_ext = os.getenv('ANTHROPIC_LLM_VERSIONS', {}).get(self._variables['llm_model'])
self._llm = ChatAnthropic(api_key=api_key,
model=llm_model_ext,
temperature=self._variables['RAG_temperature'])
self._llm_no_rag = ChatAnthropic(api_key=api_key,
model=llm_model_ext,
temperature=self._variables['RAG_temperature'])
self._variables['tool_calling_supported'] = True
else:
raise ValueError(f"Invalid chat provider: {self._variables['llm_provider']}")
return api_key
# def _initialize_llm(self):
#
#
# if self._variables['llm_provider'] == 'openai':
# portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
# metadata=portkey_metadata,
# provider='openai')
#
# self._llm = ChatOpenAI(api_key=api_key,
# model=self._variables['llm_model'],
# temperature=self._variables['RAG_temperature'],
# base_url=PORTKEY_GATEWAY_URL,
# default_headers=portkey_headers)
# self._llm_no_rag = ChatOpenAI(api_key=api_key,
# model=self._variables['llm_model'],
# temperature=self._variables['no_RAG_temperature'],
# base_url=PORTKEY_GATEWAY_URL,
# default_headers=portkey_headers)
# self._variables['tool_calling_supported'] = self._variables['llm_model'] in ['gpt-4o', 'gpt-4o-mini']
# elif self._variables['llm_provider'] == 'anthropic':
# api_key = os.getenv('ANTHROPIC_API_KEY')
# llm_model_ext = os.getenv('ANTHROPIC_LLM_VERSIONS', {}).get(self._variables['llm_model'])
# self._llm = ChatAnthropic(api_key=api_key,
# model=llm_model_ext,
# temperature=self._variables['RAG_temperature'])
# self._llm_no_rag = ChatAnthropic(api_key=api_key,
# model=llm_model_ext,
# temperature=self._variables['RAG_temperature'])
# self._variables['tool_calling_supported'] = True
# else:
# raise ValueError(f"Invalid chat provider: {self._variables['llm_provider']}")
@property
def transcription_client(self):
if self._transcription_client is None:
environment = os.getenv('FLASK_ENV', 'development')
portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
metadata=portkey_metadata,
provider='openai')
api_key = os.getenv('OPENAI_API_KEY')
self._transcription_client = OpenAI(api_key=api_key,
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._variables['transcription_model'] = 'whisper-1'
environment = os.getenv('FLASK_ENV', 'development')
portkey_metadata = self.get_portkey_metadata()
portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
metadata=portkey_metadata,
provider='openai')
api_key = os.getenv('OPENAI_API_KEY')
self._transcription_client = OpenAI(api_key=api_key,
base_url=PORTKEY_GATEWAY_URL,
default_headers=portkey_headers)
self._variables['transcription_model'] = 'whisper-1'
return self._transcription_client
@property