Portkey Integration
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@@ -8,6 +8,7 @@ import ast
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from typing import List
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from openai import OpenAI
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# from groq import Groq
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from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
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from common.models.document import EmbeddingSmallOpenAI, EmbeddingLargeOpenAI
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@@ -91,16 +92,26 @@ def select_model_variables(tenant):
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# Set Embedding variables
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match embedding_provider:
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case 'openai':
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portkey_metadata = {'tenant_id': str(tenant.id)}
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portkey_headers = createHeaders(api_key=current_app.config.get('PORTKEY_API_KEY'),
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provider='openai',
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metadata=portkey_metadata)
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match embedding_model:
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case 'text-embedding-3-small':
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api_key = current_app.config.get('OPENAI_API_KEY')
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model_variables['embedding_model'] = OpenAIEmbeddings(api_key=api_key,
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model='text-embedding-3-small')
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model='text-embedding-3-small',
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers
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)
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model_variables['embedding_db_model'] = EmbeddingSmallOpenAI
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case 'text-embedding-3-large':
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api_key = current_app.config.get('OPENAI_API_KEY')
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model_variables['embedding_model'] = OpenAIEmbeddings(api_key=api_key,
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model='text-embedding-3-large')
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model='text-embedding-3-large',
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers
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)
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model_variables['embedding_db_model'] = EmbeddingLargeOpenAI
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case _:
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raise Exception(f'Error setting model variables for tenant {tenant.id} '
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@@ -112,13 +123,22 @@ def select_model_variables(tenant):
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# Set Chat model variables
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match llm_provider:
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case 'openai':
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portkey_metadata = {'tenant_id': str(tenant.id)}
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portkey_headers = createHeaders(api_key=current_app.config.get('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider='openai')
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tool_calling_supported = False
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api_key = current_app.config.get('OPENAI_API_KEY')
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model_variables['llm'] = ChatOpenAI(api_key=api_key,
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model=llm_model,
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temperature=model_variables['RAG_temperature'])
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temperature=model_variables['RAG_temperature'],
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base_url=PORTKEY_GATEWAY_URL,
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default_headers=portkey_headers)
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model_variables['llm_no_rag'] = ChatOpenAI(api_key=api_key,
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model=llm_model,
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temperature=model_variables['no_RAG_temperature'])
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temperature=model_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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tool_calling_supported = False
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match llm_model:
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case 'gpt-4-turbo' | 'gpt-4o':
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@@ -161,9 +181,16 @@ def select_model_variables(tenant):
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# model_variables['transcription_client'] = Groq(api_key=api_key)
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# model_variables['transcription_model'] = 'whisper-large-v3'
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# Using OpenAI
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# Using OpenAI for transcriptions
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portkey_metadata = {'tenant_id': str(tenant.id)}
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portkey_headers = createHeaders(api_key=current_app.config.get('PORTKEY_API_KEY'),
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metadata=portkey_metadata,
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provider='openai'
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)
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api_key = current_app.config.get('OPENAI_API_KEY')
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model_variables['transcription_client'] = OpenAI(api_key=api_key)
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model_variables['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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model_variables['transcription_model'] = 'whisper-1'
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return model_variables
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