Business event tracing completed for both eveai_workers tasks and eveai_chat_workers tasks
This commit is contained in:
Josako
@@ -46,7 +46,7 @@ class BusinessEvent:
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parent_span_id = self.span_id
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self.span_counter += 1
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new_span_id = f"{self.trace_id}-{self.span_counter}"
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new_span_id = str(uuid.uuid4())
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# Save the current span info
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self.spans.append((self.span_id, self.span_name, self.parent_span_id))
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@@ -56,9 +56,12 @@ class BusinessEvent:
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self.span_name = span_name
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self.parent_span_id = parent_span_id
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self.log(f"Starting span {span_name}")
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try:
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yield
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finally:
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self.log(f"Ending span {span_name}")
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# Restore the previous span info
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if self.spans:
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self.span_id, self.span_name, self.parent_span_id = self.spans.pop()
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@@ -103,7 +106,9 @@ class BusinessEvent:
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db.session.commit()
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def __enter__(self):
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self.log(f'Starting Trace for {self.event_type}')
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return BusinessEventContext(self).__enter__()
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.log(f'Ending Trace for {self.event_type}')
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return BusinessEventContext(self).__exit__(exc_type, exc_val, exc_tb)
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@@ -9,10 +9,12 @@ from typing import List, Any, Iterator
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from collections.abc import MutableMapping
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from openai import OpenAI
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from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
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from portkey_ai.langchain.portkey_langchain_callback_handler import LangchainCallbackHandler
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from common.models.document import EmbeddingSmallOpenAI, EmbeddingLargeOpenAI
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from common.models.user import Tenant
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from config.model_config import MODEL_CONFIG
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from common.utils.business_event_context import current_event
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class CitedAnswer(BaseModel):
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@@ -91,87 +93,115 @@ class ModelVariables(MutableMapping):
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@property
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def embedding_model(self):
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if self._embedding_model is None:
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
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portkey_metadata = self.get_portkey_metadata()
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if self._variables['embedding_provider'] == 'openai':
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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provider='openai',
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metadata=portkey_metadata)
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api_key = os.getenv('OPENAI_API_KEY')
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model = self._variables['embedding_model']
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self._embedding_model = OpenAIEmbeddings(api_key=api_key,
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model=model,
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._embedding_db_model = EmbeddingSmallOpenAI \
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if model == 'text-embedding-3-small' \
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else EmbeddingLargeOpenAI
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else:
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raise ValueError(f"Invalid embedding provider: {self._variables['embedding_provider']}")
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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provider=self._variables['embedding_provider'],
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metadata=portkey_metadata)
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api_key = os.getenv('OPENAI_API_KEY')
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model = self._variables['embedding_model']
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self._embedding_model = OpenAIEmbeddings(api_key=api_key,
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model=model,
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._embedding_db_model = EmbeddingSmallOpenAI \
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if model == 'text-embedding-3-small' \
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else EmbeddingLargeOpenAI
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return self._embedding_model
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@property
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def llm(self):
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if self._llm is None:
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self._initialize_llm()
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portkey_headers = self.get_portkey_headers_for_llm()
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api_key = self.get_api_key_for_llm()
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self._llm = ChatOpenAI(api_key=api_key,
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model=self._variables['llm_model'],
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temperature=self._variables['RAG_temperature'],
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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return self._llm
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@property
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def llm_no_rag(self):
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if self._llm_no_rag is None:
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self._initialize_llm()
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portkey_headers = self.get_portkey_headers_for_llm()
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api_key = self.get_api_key_for_llm()
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self._llm_no_rag = ChatOpenAI(api_key=api_key,
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model=self._variables['llm_model'],
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temperature=self._variables['RAG_temperature'],
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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return self._llm_no_rag
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def _initialize_llm(self):
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
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def get_portkey_headers_for_llm(self):
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portkey_metadata = self.get_portkey_metadata()
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider=self._variables['llm_provider'])
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return portkey_headers
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def get_portkey_metadata(self):
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = {'tenant_id': str(self.tenant.id),
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'environment': environment,
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'trace_id': current_event.trace_id,
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'span_id': current_event.span_id,
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'span_name': current_event.span_name,
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'parent_span_id': current_event.parent_span_id,
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}
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return portkey_metadata
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def get_api_key_for_llm(self):
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if self._variables['llm_provider'] == 'openai':
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider='openai')
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api_key = os.getenv('OPENAI_API_KEY')
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self._llm = ChatOpenAI(api_key=api_key,
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model=self._variables['llm_model'],
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temperature=self._variables['RAG_temperature'],
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._llm_no_rag = ChatOpenAI(api_key=api_key,
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model=self._variables['llm_model'],
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temperature=self._variables['no_RAG_temperature'],
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._variables['tool_calling_supported'] = self._variables['llm_model'] in ['gpt-4o', 'gpt-4o-mini']
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elif self._variables['llm_provider'] == 'anthropic':
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else: # self._variables['llm_provider'] == 'anthropic'
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api_key = os.getenv('ANTHROPIC_API_KEY')
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llm_model_ext = os.getenv('ANTHROPIC_LLM_VERSIONS', {}).get(self._variables['llm_model'])
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self._llm = ChatAnthropic(api_key=api_key,
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model=llm_model_ext,
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temperature=self._variables['RAG_temperature'])
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self._llm_no_rag = ChatAnthropic(api_key=api_key,
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model=llm_model_ext,
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temperature=self._variables['RAG_temperature'])
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self._variables['tool_calling_supported'] = True
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else:
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raise ValueError(f"Invalid chat provider: {self._variables['llm_provider']}")
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return api_key
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# def _initialize_llm(self):
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#
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#
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# if self._variables['llm_provider'] == 'openai':
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# portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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# metadata=portkey_metadata,
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# provider='openai')
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#
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# self._llm = ChatOpenAI(api_key=api_key,
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# model=self._variables['llm_model'],
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# temperature=self._variables['RAG_temperature'],
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# base_url=PORTKEY_GATEWAY_URL,
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# default_headers=portkey_headers)
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# self._llm_no_rag = ChatOpenAI(api_key=api_key,
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# model=self._variables['llm_model'],
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# temperature=self._variables['no_RAG_temperature'],
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# base_url=PORTKEY_GATEWAY_URL,
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# default_headers=portkey_headers)
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# self._variables['tool_calling_supported'] = self._variables['llm_model'] in ['gpt-4o', 'gpt-4o-mini']
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# elif self._variables['llm_provider'] == 'anthropic':
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# api_key = os.getenv('ANTHROPIC_API_KEY')
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# llm_model_ext = os.getenv('ANTHROPIC_LLM_VERSIONS', {}).get(self._variables['llm_model'])
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# self._llm = ChatAnthropic(api_key=api_key,
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# model=llm_model_ext,
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# temperature=self._variables['RAG_temperature'])
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# self._llm_no_rag = ChatAnthropic(api_key=api_key,
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# model=llm_model_ext,
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# temperature=self._variables['RAG_temperature'])
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# self._variables['tool_calling_supported'] = True
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# else:
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# raise ValueError(f"Invalid chat provider: {self._variables['llm_provider']}")
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@property
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def transcription_client(self):
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if self._transcription_client is None:
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = {'tenant_id': str(self.tenant.id), 'environment': environment}
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider='openai')
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api_key = os.getenv('OPENAI_API_KEY')
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self._transcription_client = OpenAI(api_key=api_key,
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._variables['transcription_model'] = 'whisper-1'
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = self.get_portkey_metadata()
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portkey_headers = createHeaders(api_key=os.getenv('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider='openai')
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api_key = os.getenv('OPENAI_API_KEY')
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self._transcription_client = OpenAI(api_key=api_key,
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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self._variables['transcription_model'] = 'whisper-1'
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return self._transcription_client
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@property
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@@ -24,6 +24,8 @@ from common.utils.celery_utils import current_celery
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from common.utils.model_utils import select_model_variables, create_language_template, replace_variable_in_template
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from common.langchain.eveai_retriever import EveAIRetriever
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from common.langchain.eveai_history_retriever import EveAIHistoryRetriever
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from common.utils.business_event import BusinessEvent
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from common.utils.business_event_context import current_event
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# Healthcheck task
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@@ -65,53 +67,56 @@ def ask_question(tenant_id, question, language, session_id, user_timezone, room)
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'interaction_id': 'interaction_id_value'
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}
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"""
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current_app.logger.info(f'ask_question: Received question for tenant {tenant_id}: {question}. Processing...')
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with BusinessEvent("Ask Question", tenant_id=tenant_id, session_id=session_id):
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current_app.logger.info(f'ask_question: Received question for tenant {tenant_id}: {question}. Processing...')
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try:
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# Retrieve the tenant
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tenant = Tenant.query.get(tenant_id)
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if not tenant:
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raise Exception(f'Tenant {tenant_id} not found.')
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try:
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# Retrieve the tenant
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tenant = Tenant.query.get(tenant_id)
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if not tenant:
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raise Exception(f'Tenant {tenant_id} not found.')
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# Ensure we are working in the correct database schema
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Database(tenant_id).switch_schema()
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# Ensure we are working in the correct database schema
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Database(tenant_id).switch_schema()
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# Ensure we have a session to story history
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chat_session = ChatSession.query.filter_by(session_id=session_id).first()
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if not chat_session:
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try:
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chat_session = ChatSession()
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chat_session.session_id = session_id
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chat_session.session_start = dt.now(tz.utc)
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chat_session.timezone = user_timezone
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db.session.add(chat_session)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error initializing chat session in database: {e}')
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raise
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# Ensure we have a session to story history
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chat_session = ChatSession.query.filter_by(session_id=session_id).first()
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if not chat_session:
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try:
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chat_session = ChatSession()
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chat_session.session_id = session_id
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chat_session.session_start = dt.now(tz.utc)
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chat_session.timezone = user_timezone
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db.session.add(chat_session)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error initializing chat session in database: {e}')
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raise
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if tenant.rag_tuning:
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current_app.rag_tuning_logger.debug(f'Received question for tenant {tenant_id}:\n{question}. Processing...')
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current_app.rag_tuning_logger.debug(f'Tenant Information: \n{tenant.to_dict()}')
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current_app.rag_tuning_logger.debug(f'===================================================================')
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current_app.rag_tuning_logger.debug(f'===================================================================')
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if tenant.rag_tuning:
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current_app.rag_tuning_logger.debug(f'Received question for tenant {tenant_id}:\n{question}. Processing...')
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current_app.rag_tuning_logger.debug(f'Tenant Information: \n{tenant.to_dict()}')
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current_app.rag_tuning_logger.debug(f'===================================================================')
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current_app.rag_tuning_logger.debug(f'===================================================================')
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result, interaction = answer_using_tenant_rag(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
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result['interaction_id'] = interaction.id
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result['room'] = room # Include the room in the result
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if result['insufficient_info']:
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if 'LLM' in tenant.fallback_algorithms:
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result, interaction = answer_using_llm(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['LLM']['name']
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with current_event.create_span("RAG Answer"):
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result, interaction = answer_using_tenant_rag(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
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result['interaction_id'] = interaction.id
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result['room'] = room # Include the room in the result
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return result
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except Exception as e:
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current_app.logger.error(f'ask_question: Error processing question: {e}')
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raise
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if result['insufficient_info']:
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if 'LLM' in tenant.fallback_algorithms:
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with current_event.create_span("Fallback Algorithm LLM"):
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result, interaction = answer_using_llm(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['LLM']['name']
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result['interaction_id'] = interaction.id
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result['room'] = room # Include the room in the result
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return result
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except Exception as e:
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current_app.logger.error(f'ask_question: Error processing question: {e}')
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raise
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def answer_using_tenant_rag(question, language, tenant, chat_session):
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@@ -131,92 +136,94 @@ def answer_using_tenant_rag(question, language, tenant, chat_session):
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# Langchain debugging if required
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# set_debug(True)
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detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
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current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
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if tenant.rag_tuning:
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current_app.rag_tuning_logger.debug(f'Detailed Question for tenant {tenant.id}:\n{question}.')
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current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
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new_interaction.detailed_question = detailed_question
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new_interaction.detailed_question_at = dt.now(tz.utc)
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retriever = EveAIRetriever(model_variables, tenant_info)
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llm = model_variables['llm']
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template = model_variables['rag_template']
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language_template = create_language_template(template, language)
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full_template = replace_variable_in_template(language_template, "{tenant_context}", model_variables['rag_context'])
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rag_prompt = ChatPromptTemplate.from_template(full_template)
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setup_and_retrieval = RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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if tenant.rag_tuning:
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current_app.rag_tuning_logger.debug(f'Full prompt for tenant {tenant.id}:\n{full_template}.')
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current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
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new_interaction_embeddings = []
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if not model_variables['cited_answer_cls']: # The model doesn't support structured feedback
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output_parser = StrOutputParser()
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chain = setup_and_retrieval | rag_prompt | llm | output_parser
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# Invoke the chain with the actual question
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answer = chain.invoke(detailed_question)
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new_interaction.answer = answer
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result = {
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'answer': answer,
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'citations': [],
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'insufficient_info': False
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}
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else: # The model supports structured feedback
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structured_llm = llm.with_structured_output(model_variables['cited_answer_cls'])
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chain = setup_and_retrieval | rag_prompt | structured_llm
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result = chain.invoke(detailed_question).dict()
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current_app.logger.debug(f'ask_question: result answer: {result['answer']}')
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current_app.logger.debug(f'ask_question: result citations: {result["citations"]}')
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current_app.logger.debug(f'ask_question: insufficient information: {result["insufficient_info"]}')
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with current_event.create_span("Detail Question"):
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detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
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current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
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if tenant.rag_tuning:
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current_app.rag_tuning_logger.debug(f'ask_question: result answer: {result['answer']}')
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current_app.rag_tuning_logger.debug(f'ask_question: result citations: {result["citations"]}')
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current_app.rag_tuning_logger.debug(f'ask_question: insufficient information: {result["insufficient_info"]}')
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current_app.rag_tuning_logger.debug(f'Detailed Question for tenant {tenant.id}:\n{question}.')
|
||||
current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
|
||||
new_interaction.answer = result['answer']
|
||||
new_interaction.detailed_question = detailed_question
|
||||
new_interaction.detailed_question_at = dt.now(tz.utc)
|
||||
|
||||
# Filter out the existing Embedding IDs
|
||||
given_embedding_ids = [int(emb_id) for emb_id in result['citations']]
|
||||
embeddings = (
|
||||
db.session.query(Embedding)
|
||||
.filter(Embedding.id.in_(given_embedding_ids))
|
||||
.all()
|
||||
)
|
||||
existing_embedding_ids = [emb.id for emb in embeddings]
|
||||
urls = list(set(emb.document_version.url for emb in embeddings))
|
||||
with current_event.create_span("Generate Answer using RAG"):
|
||||
retriever = EveAIRetriever(model_variables, tenant_info)
|
||||
llm = model_variables['llm']
|
||||
template = model_variables['rag_template']
|
||||
language_template = create_language_template(template, language)
|
||||
full_template = replace_variable_in_template(language_template, "{tenant_context}", model_variables['rag_context'])
|
||||
rag_prompt = ChatPromptTemplate.from_template(full_template)
|
||||
setup_and_retrieval = RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
|
||||
if tenant.rag_tuning:
|
||||
current_app.rag_tuning_logger.debug(f'Referenced documents for answer for tenant {tenant.id}:\n')
|
||||
current_app.rag_tuning_logger.debug(f'{urls}')
|
||||
current_app.rag_tuning_logger.debug(f'Full prompt for tenant {tenant.id}:\n{full_template}.')
|
||||
current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
|
||||
|
||||
for emb_id in existing_embedding_ids:
|
||||
new_interaction_embedding = InteractionEmbedding(embedding_id=emb_id)
|
||||
new_interaction_embedding.interaction = new_interaction
|
||||
new_interaction_embeddings.append(new_interaction_embedding)
|
||||
new_interaction_embeddings = []
|
||||
if not model_variables['cited_answer_cls']: # The model doesn't support structured feedback
|
||||
output_parser = StrOutputParser()
|
||||
|
||||
result['citations'] = urls
|
||||
chain = setup_and_retrieval | rag_prompt | llm | output_parser
|
||||
|
||||
# Disable langchain debugging if set above.
|
||||
# set_debug(False)
|
||||
# Invoke the chain with the actual question
|
||||
answer = chain.invoke(detailed_question)
|
||||
new_interaction.answer = answer
|
||||
result = {
|
||||
'answer': answer,
|
||||
'citations': [],
|
||||
'insufficient_info': False
|
||||
}
|
||||
|
||||
new_interaction.answer_at = dt.now(tz.utc)
|
||||
chat_session.session_end = dt.now(tz.utc)
|
||||
else: # The model supports structured feedback
|
||||
structured_llm = llm.with_structured_output(model_variables['cited_answer_cls'])
|
||||
|
||||
try:
|
||||
db.session.add(chat_session)
|
||||
db.session.add(new_interaction)
|
||||
db.session.add_all(new_interaction_embeddings)
|
||||
db.session.commit()
|
||||
return result, new_interaction
|
||||
except SQLAlchemyError as e:
|
||||
current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
|
||||
raise
|
||||
chain = setup_and_retrieval | rag_prompt | structured_llm
|
||||
|
||||
result = chain.invoke(detailed_question).dict()
|
||||
current_app.logger.debug(f'ask_question: result answer: {result['answer']}')
|
||||
current_app.logger.debug(f'ask_question: result citations: {result["citations"]}')
|
||||
current_app.logger.debug(f'ask_question: insufficient information: {result["insufficient_info"]}')
|
||||
if tenant.rag_tuning:
|
||||
current_app.rag_tuning_logger.debug(f'ask_question: result answer: {result['answer']}')
|
||||
current_app.rag_tuning_logger.debug(f'ask_question: result citations: {result["citations"]}')
|
||||
current_app.rag_tuning_logger.debug(f'ask_question: insufficient information: {result["insufficient_info"]}')
|
||||
current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
|
||||
new_interaction.answer = result['answer']
|
||||
|
||||
# Filter out the existing Embedding IDs
|
||||
given_embedding_ids = [int(emb_id) for emb_id in result['citations']]
|
||||
embeddings = (
|
||||
db.session.query(Embedding)
|
||||
.filter(Embedding.id.in_(given_embedding_ids))
|
||||
.all()
|
||||
)
|
||||
existing_embedding_ids = [emb.id for emb in embeddings]
|
||||
urls = list(set(emb.document_version.url for emb in embeddings))
|
||||
if tenant.rag_tuning:
|
||||
current_app.rag_tuning_logger.debug(f'Referenced documents for answer for tenant {tenant.id}:\n')
|
||||
current_app.rag_tuning_logger.debug(f'{urls}')
|
||||
current_app.rag_tuning_logger.debug(f'-------------------------------------------------------------------')
|
||||
|
||||
for emb_id in existing_embedding_ids:
|
||||
new_interaction_embedding = InteractionEmbedding(embedding_id=emb_id)
|
||||
new_interaction_embedding.interaction = new_interaction
|
||||
new_interaction_embeddings.append(new_interaction_embedding)
|
||||
|
||||
result['citations'] = urls
|
||||
|
||||
# Disable langchain debugging if set above.
|
||||
# set_debug(False)
|
||||
|
||||
new_interaction.answer_at = dt.now(tz.utc)
|
||||
chat_session.session_end = dt.now(tz.utc)
|
||||
|
||||
try:
|
||||
db.session.add(chat_session)
|
||||
db.session.add(new_interaction)
|
||||
db.session.add_all(new_interaction_embeddings)
|
||||
db.session.commit()
|
||||
return result, new_interaction
|
||||
except SQLAlchemyError as e:
|
||||
current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
|
||||
raise
|
||||
|
||||
|
||||
def answer_using_llm(question, language, tenant, chat_session):
|
||||
@@ -236,47 +243,49 @@ def answer_using_llm(question, language, tenant, chat_session):
|
||||
# Langchain debugging if required
|
||||
# set_debug(True)
|
||||
|
||||
detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
|
||||
current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
|
||||
new_interaction.detailed_question = detailed_question
|
||||
new_interaction.detailed_question_at = dt.now(tz.utc)
|
||||
with current_event.create_span("Detail Question"):
|
||||
detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
|
||||
current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
|
||||
new_interaction.detailed_question = detailed_question
|
||||
new_interaction.detailed_question_at = dt.now(tz.utc)
|
||||
|
||||
retriever = EveAIRetriever(model_variables, tenant_info)
|
||||
llm = model_variables['llm_no_rag']
|
||||
template = model_variables['encyclopedia_template']
|
||||
language_template = create_language_template(template, language)
|
||||
rag_prompt = ChatPromptTemplate.from_template(language_template)
|
||||
setup = RunnablePassthrough()
|
||||
output_parser = StrOutputParser()
|
||||
with current_event.create_span("Detail Answer using LLM"):
|
||||
retriever = EveAIRetriever(model_variables, tenant_info)
|
||||
llm = model_variables['llm_no_rag']
|
||||
template = model_variables['encyclopedia_template']
|
||||
language_template = create_language_template(template, language)
|
||||
rag_prompt = ChatPromptTemplate.from_template(language_template)
|
||||
setup = RunnablePassthrough()
|
||||
output_parser = StrOutputParser()
|
||||
|
||||
new_interaction_embeddings = []
|
||||
new_interaction_embeddings = []
|
||||
|
||||
chain = setup | rag_prompt | llm | output_parser
|
||||
input_question = {"question": detailed_question}
|
||||
chain = setup | rag_prompt | llm | output_parser
|
||||
input_question = {"question": detailed_question}
|
||||
|
||||
# Invoke the chain with the actual question
|
||||
answer = chain.invoke(input_question)
|
||||
new_interaction.answer = answer
|
||||
result = {
|
||||
'answer': answer,
|
||||
'citations': [],
|
||||
'insufficient_info': False
|
||||
}
|
||||
# Invoke the chain with the actual question
|
||||
answer = chain.invoke(input_question)
|
||||
new_interaction.answer = answer
|
||||
result = {
|
||||
'answer': answer,
|
||||
'citations': [],
|
||||
'insufficient_info': False
|
||||
}
|
||||
|
||||
# Disable langchain debugging if set above.
|
||||
# set_debug(False)
|
||||
# Disable langchain debugging if set above.
|
||||
# set_debug(False)
|
||||
|
||||
new_interaction.answer_at = dt.now(tz.utc)
|
||||
chat_session.session_end = dt.now(tz.utc)
|
||||
new_interaction.answer_at = dt.now(tz.utc)
|
||||
chat_session.session_end = dt.now(tz.utc)
|
||||
|
||||
try:
|
||||
db.session.add(chat_session)
|
||||
db.session.add(new_interaction)
|
||||
db.session.commit()
|
||||
return result, new_interaction
|
||||
except SQLAlchemyError as e:
|
||||
current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
|
||||
raise
|
||||
try:
|
||||
db.session.add(chat_session)
|
||||
db.session.add(new_interaction)
|
||||
db.session.commit()
|
||||
return result, new_interaction
|
||||
except SQLAlchemyError as e:
|
||||
current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
|
||||
raise
|
||||
|
||||
|
||||
def tasks_ping():
|
||||
|
||||
@@ -7,6 +7,7 @@ from common.extensions import minio_client
|
||||
import subprocess
|
||||
|
||||
from .transcription_processor import TranscriptionProcessor
|
||||
from common.utils.business_event_context import current_event
|
||||
|
||||
|
||||
class AudioProcessor(TranscriptionProcessor):
|
||||
@@ -24,8 +25,13 @@ class AudioProcessor(TranscriptionProcessor):
|
||||
self.document_version.id,
|
||||
self.document_version.file_name
|
||||
)
|
||||
compressed_audio = self._compress_audio(file_data)
|
||||
return self._transcribe_audio(compressed_audio)
|
||||
|
||||
with current_event.create_span("Audio Processing"):
|
||||
compressed_audio = self._compress_audio(file_data)
|
||||
with current_event.create_span("Transcription Generation"):
|
||||
transcription = self._transcribe_audio(compressed_audio)
|
||||
|
||||
return transcription
|
||||
|
||||
def _compress_audio(self, audio_data):
|
||||
self._log("Compressing audio")
|
||||
|
||||
@@ -31,8 +31,10 @@ class HTMLProcessor(Processor):
|
||||
)
|
||||
html_content = file_data.decode('utf-8')
|
||||
|
||||
extracted_html, title = self._parse_html(html_content)
|
||||
markdown = self._generate_markdown_from_html(extracted_html)
|
||||
with current_event.create_span("HTML Content Extraction"):
|
||||
extracted_html, title = self._parse_html(html_content)
|
||||
with current_event.create_span("Markdown Generation"):
|
||||
markdown = self._generate_markdown_from_html(extracted_html)
|
||||
|
||||
self._save_markdown(markdown)
|
||||
self._log("Finished processing HTML")
|
||||
|
||||
@@ -10,6 +10,7 @@ from langchain_core.runnables import RunnablePassthrough
|
||||
from common.extensions import minio_client
|
||||
from common.utils.model_utils import create_language_template
|
||||
from .processor import Processor
|
||||
from common.utils.business_event_context import current_event
|
||||
|
||||
|
||||
class PDFProcessor(Processor):
|
||||
@@ -32,13 +33,14 @@ class PDFProcessor(Processor):
|
||||
self.document_version.file_name
|
||||
)
|
||||
|
||||
extracted_content = self._extract_content(file_data)
|
||||
structured_content, title = self._structure_content(extracted_content)
|
||||
with current_event.create_span("PDF Extraction"):
|
||||
extracted_content = self._extract_content(file_data)
|
||||
structured_content, title = self._structure_content(extracted_content)
|
||||
|
||||
llm_chunks = self._split_content_for_llm(structured_content)
|
||||
markdown = self._process_chunks_with_llm(llm_chunks)
|
||||
|
||||
self._save_markdown(markdown)
|
||||
with current_event.create_span("Markdown Generation"):
|
||||
llm_chunks = self._split_content_for_llm(structured_content)
|
||||
markdown = self._process_chunks_with_llm(llm_chunks)
|
||||
self._save_markdown(markdown)
|
||||
self._log("Finished processing PDF")
|
||||
return markdown, title
|
||||
except Exception as e:
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
# transcription_processor.py
|
||||
from common.utils.model_utils import create_language_template
|
||||
from .processor import Processor
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
|
||||
from common.utils.model_utils import create_language_template
|
||||
from .processor import Processor
|
||||
from common.utils.business_event_context import current_event
|
||||
|
||||
|
||||
class TranscriptionProcessor(Processor):
|
||||
def __init__(self, tenant, model_variables, document_version):
|
||||
@@ -16,12 +18,14 @@ class TranscriptionProcessor(Processor):
|
||||
def process(self):
|
||||
self._log("Starting Transcription processing")
|
||||
try:
|
||||
transcription = self._get_transcription()
|
||||
chunks = self._chunk_transcription(transcription)
|
||||
markdown_chunks = self._process_chunks(chunks)
|
||||
full_markdown = self._combine_markdown_chunks(markdown_chunks)
|
||||
self._save_markdown(full_markdown)
|
||||
self._log("Finished processing Transcription")
|
||||
with current_event.create_span("Transcription Generation"):
|
||||
transcription = self._get_transcription()
|
||||
with current_event.create_span("Markdown Generation"):
|
||||
chunks = self._chunk_transcription(transcription)
|
||||
markdown_chunks = self._process_chunks(chunks)
|
||||
full_markdown = self._combine_markdown_chunks(markdown_chunks)
|
||||
self._save_markdown(full_markdown)
|
||||
self._log("Finished processing Transcription")
|
||||
return full_markdown, self._extract_title_from_markdown(full_markdown)
|
||||
except Exception as e:
|
||||
self._log(f"Error processing Transcription: {str(e)}", level='error')
|
||||
|
||||
@@ -39,8 +39,6 @@ def create_embeddings(tenant_id, document_version_id):
|
||||
# BusinessEvent creates a context, which is why we need to use it with a with block
|
||||
with BusinessEvent('Create Embeddings', tenant_id, document_version_id=document_version_id):
|
||||
current_app.logger.info(f'Creating embeddings for tenant {tenant_id} on document version {document_version_id}')
|
||||
current_event.log("Starting Embedding Creation Task")
|
||||
|
||||
try:
|
||||
# Retrieve Tenant for which we are processing
|
||||
tenant = Tenant.query.get(tenant_id)
|
||||
@@ -125,13 +123,13 @@ def delete_embeddings_for_document_version(document_version):
|
||||
|
||||
|
||||
def process_pdf(tenant, model_variables, document_version):
|
||||
current_event.log("Starting PDF Processing")
|
||||
processor = PDFProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
with current_event.create_span("PDF Processing"):
|
||||
processor = PDFProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
|
||||
# Process markdown and embed
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
current_event.log("Finished PDF Processing")
|
||||
with current_event.create_span("Embedding"):
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
|
||||
|
||||
def process_html(tenant, model_variables, document_version):
|
||||
@@ -144,29 +142,27 @@ def process_html(tenant, model_variables, document_version):
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
|
||||
|
||||
|
||||
def process_audio(tenant, model_variables, document_version):
|
||||
current_event.log("Starting Audio Processing")
|
||||
processor = AudioProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
with current_event.create_span("Audio Processing"):
|
||||
processor = AudioProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
|
||||
# Process markdown and embed
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
current_event.log("Finished Audio Processing")
|
||||
with current_event.create_span("Embedding"):
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
|
||||
|
||||
def process_srt(tenant, model_variables, document_version):
|
||||
current_event.log("Starting SRT Processing")
|
||||
processor = SRTProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
with current_event.create_span("SRT Processing"):
|
||||
processor = SRTProcessor(tenant, model_variables, document_version)
|
||||
markdown, title = processor.process()
|
||||
|
||||
# Process markdown and embed
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
current_event.log("Finished SRT Processing")
|
||||
with current_event.create_span("Embedding"):
|
||||
embed_markdown(tenant, model_variables, document_version, markdown, title)
|
||||
|
||||
|
||||
def embed_markdown(tenant, model_variables, document_version, markdown, title):
|
||||
current_event.log("Starting Embedding Markdown Processing")
|
||||
# Create potential chunks
|
||||
potential_chunks = create_potential_chunks_for_markdown(tenant.id, document_version, f"{document_version.id}.md")
|
||||
|
||||
@@ -195,7 +191,6 @@ def embed_markdown(tenant, model_variables, document_version, markdown, title):
|
||||
|
||||
current_app.logger.info(f'Embeddings created successfully for tenant {tenant.id} '
|
||||
f'on document version {document_version.id} :-)')
|
||||
current_event.log("Finished Embedding Markdown Processing")
|
||||
|
||||
|
||||
def enrich_chunks(tenant, model_variables, document_version, title, chunks):
|
||||
@@ -238,7 +233,7 @@ def enrich_chunks(tenant, model_variables, document_version, title, chunks):
|
||||
|
||||
|
||||
def summarize_chunk(tenant, model_variables, document_version, chunk):
|
||||
current_event.log("Starting Summarizing Chunk Processing")
|
||||
current_event.log("Starting Summarizing Chunk")
|
||||
current_app.logger.debug(f'Summarizing chunk for tenant {tenant.id} '
|
||||
f'on document version {document_version.id}')
|
||||
llm = model_variables['llm']
|
||||
@@ -256,7 +251,7 @@ def summarize_chunk(tenant, model_variables, document_version, chunk):
|
||||
summary = chain.invoke({"text": chunk})
|
||||
current_app.logger.debug(f'Finished summarizing chunk for tenant {tenant.id} '
|
||||
f'on document version {document_version.id}.')
|
||||
current_event.log("Finished summarizing chunk for tenant ")
|
||||
current_event.log("Finished Summarizing Chunk")
|
||||
return summary
|
||||
except LangChainException as e:
|
||||
current_app.logger.error(f'Error creating summary for chunk enrichment for tenant {tenant.id} '
|
||||
|
||||
@@ -63,7 +63,7 @@ zxcvbn~=4.4.28
|
||||
groq~=0.9.0
|
||||
pydub~=0.25.1
|
||||
argparse~=1.4.0
|
||||
portkey_ai~=1.8.2
|
||||
portkey_ai~=1.8.7
|
||||
minio~=7.2.7
|
||||
Werkzeug~=3.0.3
|
||||
itsdangerous~=2.2.0
|
||||
|
||||
Reference in New Issue
Block a user