122d1a18df
- Allow URLs to contain other information than just HTML information. It can alose refer to e.g. PDF-files.
228 lines
10 KiB
Python
228 lines
10 KiB
Python
import os
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import langcodes
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from flask import current_app
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from langchain_openai import OpenAIEmbeddings, ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langchain.prompts import ChatPromptTemplate
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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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class CitedAnswer(BaseModel):
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"""Default docstring - to be replaced with actual prompt"""
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answer: str = Field(
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...,
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description="The answer to the user question, based on the given sources",
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)
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citations: List[int] = Field(
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...,
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description="The integer IDs of the SPECIFIC sources that were used to generate the answer"
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)
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insufficient_info: bool = Field(
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False, # Default value is set to False
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description="A boolean indicating wether given sources were sufficient or not to generate the answer"
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)
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def set_language_prompt_template(cls, language_prompt):
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cls.__doc__ = language_prompt
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def select_model_variables(tenant):
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embedding_provider = tenant.embedding_model.rsplit('.', 1)[0]
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embedding_model = tenant.embedding_model.rsplit('.', 1)[1]
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llm_provider = tenant.llm_model.rsplit('.', 1)[0]
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llm_model = tenant.llm_model.rsplit('.', 1)[1]
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# Set model variables
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model_variables = {}
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if tenant.es_k:
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model_variables['k'] = tenant.es_k
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else:
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model_variables['k'] = 5
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if tenant.es_similarity_threshold:
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model_variables['similarity_threshold'] = tenant.es_similarity_threshold
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else:
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model_variables['similarity_threshold'] = 0.7
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if tenant.chat_RAG_temperature:
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model_variables['RAG_temperature'] = tenant.chat_RAG_temperature
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else:
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model_variables['RAG_temperature'] = 0.3
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if tenant.chat_no_RAG_temperature:
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model_variables['no_RAG_temperature'] = tenant.chat_no_RAG_temperature
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else:
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model_variables['no_RAG_temperature'] = 0.5
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# Set Tuning variables
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if tenant.embed_tuning:
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model_variables['embed_tuning'] = tenant.embed_tuning
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else:
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model_variables['embed_tuning'] = False
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if tenant.rag_tuning:
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model_variables['rag_tuning'] = tenant.rag_tuning
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else:
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model_variables['rag_tuning'] = False
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if tenant.rag_context:
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model_variables['rag_context'] = tenant.rag_context
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else:
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model_variables['rag_context'] = " "
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# Set HTML Chunking Variables
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model_variables['html_tags'] = tenant.html_tags
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model_variables['html_end_tags'] = tenant.html_end_tags
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model_variables['html_included_elements'] = tenant.html_included_elements
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model_variables['html_excluded_elements'] = tenant.html_excluded_elements
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model_variables['html_excluded_classes'] = tenant.html_excluded_classes
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# Set Chunk Size variables
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model_variables['min_chunk_size'] = tenant.min_chunk_size
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model_variables['max_chunk_size'] = tenant.max_chunk_size
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environment = os.getenv('FLASK_ENV', 'development')
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portkey_metadata = {'tenant_id': str(tenant.id), 'environment': environment}
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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_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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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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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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f'error: Invalid embedding model')
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case _:
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raise Exception(f'Error setting model variables for tenant {tenant.id} '
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f'error: Invalid embedding provider')
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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_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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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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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-4o' | 'gpt-4o-mini':
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tool_calling_supported = True
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PDF_chunk_size = 10000
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PDF_chunk_overlap = 200
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PDF_min_chunk_size = 8000
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PDF_max_chunk_size = 12000
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case _:
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raise Exception(f'Error setting model variables for tenant {tenant.id} '
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f'error: Invalid chat model')
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case 'anthropic':
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api_key = current_app.config.get('ANTHROPIC_API_KEY')
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# Anthropic does not have the same 'generic' model names as OpenAI
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llm_model_ext = current_app.config.get('ANTHROPIC_LLM_VERSIONS').get(llm_model)
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model_variables['llm'] = ChatAnthropic(api_key=api_key,
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model=llm_model_ext,
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temperature=model_variables['RAG_temperature'])
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model_variables['llm_no_rag'] = ChatAnthropic(api_key=api_key,
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model=llm_model_ext,
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temperature=model_variables['RAG_temperature'])
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tool_calling_supported = True
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PDF_chunk_size = 10000
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PDF_chunk_overlap = 200
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PDF_min_chunk_size = 8000
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PDF_max_chunk_size = 12000
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case _:
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raise Exception(f'Error setting model variables for tenant {tenant.id} '
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f'error: Invalid chat provider')
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model_variables['PDF_chunk_size'] = PDF_chunk_size
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model_variables['PDF_chunk_overlap'] = PDF_chunk_overlap
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model_variables['PDF_min_chunk_size'] = PDF_min_chunk_size
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model_variables['PDF_max_chunk_size'] = PDF_max_chunk_size
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if tool_calling_supported:
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model_variables['cited_answer_cls'] = CitedAnswer
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templates = current_app.config['PROMPT_TEMPLATES'][f'{llm_provider}.{llm_model}']
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model_variables['summary_template'] = templates['summary']
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model_variables['rag_template'] = templates['rag']
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model_variables['history_template'] = templates['history']
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model_variables['encyclopedia_template'] = templates['encyclopedia']
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model_variables['transcript_template'] = templates['transcript']
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model_variables['html_parse_template'] = templates['html_parse']
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model_variables['pdf_parse_template'] = templates['pdf_parse']
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model_variables['annotation_chunk_length'] = current_app.config['ANNOTATION_TEXT_CHUNK_LENGTH'][tenant.llm_model]
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# Transcription Client Variables.
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# Using Groq
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# api_key = current_app.config.get('GROQ_API_KEY')
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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 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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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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def create_language_template(template, language):
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try:
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full_language = langcodes.Language.make(language=language)
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language_template = template.replace('{language}', full_language.display_name())
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except ValueError:
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language_template = template.replace('{language}', language)
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return language_template
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def replace_variable_in_template(template, variable, value):
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return template.replace(variable, value)
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