- Move from OpenAI to Mistral Embeddings
- Move embedding model settings from tenant to catalog - BUG: error processing configuration for chunking patterns in HTML_PROCESSOR - Removed eveai_chat from docker-files and nginx configuration, as it is now obsolete - BUG: error in Library Operations when creating a new default RAG library - BUG: Added public type in migration scripts - Removed SocketIO from all code and requirements.txt
This commit is contained in:
Josako
@@ -652,12 +652,15 @@ def json_to_patterns(json_content: str) -> str:
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def json_to_pattern_list(json_content: str) -> list:
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"""Convert JSON patterns list to text area content"""
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try:
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patterns = json.loads(json_content)
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if not isinstance(patterns, list):
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raise ValueError("JSON must contain a list of patterns")
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# Unescape if needed
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patterns = [pattern.replace('\\\\', '\\') for pattern in patterns]
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return patterns
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if json_content:
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patterns = json.loads(json_content)
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if not isinstance(patterns, list):
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raise ValueError("JSON must contain a list of patterns")
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# Unescape if needed
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patterns = [pattern.replace('\\\\', '\\') for pattern in patterns]
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return patterns
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else:
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return []
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except json.JSONDecodeError as e:
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raise ValueError(f"Invalid JSON format: {e}")
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@@ -124,4 +124,15 @@ class EveAISocketInputException(EveAIException):
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"""Raised when a socket call receives an invalid payload"""
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def __init__(self, message, status_code=400, payload=None):
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super.__init__(message, status_code, payload)
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super.__init__(message, status_code, payload)
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class EveAIInvalidEmbeddingModel(EveAIException):
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"""Raised when no or an invalid embedding model is provided in the catalog"""
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def __init__(self, tenant_id, catalog_id, status_code=400, payload=None):
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self.tenant_id = tenant_id
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self.catalog_id = catalog_id
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# Construct the message dynamically
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message = f"Tenant with ID '{tenant_id}' has no or an invalid embedding model in Catalog {catalog_id}."
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super().__init__(message, status_code, payload)
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@@ -1,23 +1,25 @@
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import os
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from typing import Dict, Any, Optional
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from typing import Dict, Any, Optional, Tuple
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import langcodes
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from langchain_core.language_models import BaseChatModel
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from common.langchain.llm_metrics_handler import LLMMetricsHandler
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from common.langchain.templates.template_manager import TemplateManager
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from langchain_openai import OpenAIEmbeddings, ChatOpenAI, OpenAI
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from langchain_openai import ChatOpenAI
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from langchain_anthropic import ChatAnthropic
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from langchain_mistralai import ChatMistralAI
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from flask import current_app
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from datetime import datetime as dt, timezone as tz
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from common.langchain.tracked_openai_embeddings import TrackedOpenAIEmbeddings
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from common.eveai_model.tracked_mistral_embeddings import TrackedMistralAIEmbeddings
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from common.langchain.tracked_transcription import TrackedOpenAITranscription
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from common.models.user import Tenant
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from common.utils.cache.base import CacheHandler
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from config.model_config import MODEL_CONFIG
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from common.extensions import template_manager, cache_manager
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from common.models.document import EmbeddingLargeOpenAI, EmbeddingSmallOpenAI
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from common.utils.eveai_exceptions import EveAITenantNotFound
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from common.extensions import template_manager
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from common.models.document import EmbeddingMistral
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from common.utils.eveai_exceptions import EveAITenantNotFound, EveAIInvalidEmbeddingModel
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llm_model_cache: Dict[Tuple[str, float], BaseChatModel] = {}
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llm_metrics_handler = LLMMetricsHandler()
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def create_language_template(template: str, language: str) -> str:
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@@ -55,6 +57,63 @@ def replace_variable_in_template(template: str, variable: str, value: str) -> st
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return template.replace(variable, value or "")
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def get_embedding_model_and_class(tenant_id, catalog_id, full_embedding_name):
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"""
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Retrieve the embedding model and embedding model class to store Embeddings
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Args:
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tenant_id: ID of the tenant
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catalog_id: ID of the catalog
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full_embedding_name: The full name of the embedding model: <provider>.<model>
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Returns:
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embedding_model, embedding_model_class
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"""
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embedding_provider, embedding_model_name = full_embedding_name.split('.')
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# Calculate the embedding model to be used
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if embedding_provider == "mistral":
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api_key = current_app.config['MISTRAL_API_KEY']
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embedding_model = TrackedMistralAIEmbeddings(
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model=embedding_model_name
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)
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else:
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raise EveAIInvalidEmbeddingModel(tenant_id, catalog_id)
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# Calculate the Embedding Model Class to be used to store embeddings
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if embedding_model_name == "mistral-embed":
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embedding_model_class = EmbeddingMistral
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else:
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raise EveAIInvalidEmbeddingModel(tenant_id, catalog_id)
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return embedding_model, embedding_model_class
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def get_llm(full_model_name, temperature):
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if not full_model_name:
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full_model_name = 'openai.gpt-4o' # Default to gpt-4o for now, as this is the original model developed against
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llm = llm_model_cache.get((full_model_name, temperature))
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if not llm:
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llm_provider, llm_model_name = full_model_name.split('.')
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if llm_provider == "openai":
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llm = ChatOpenAI(
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api_key=current_app.config['OPENAI_API_KEY'],
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model=llm_model_name,
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temperature=temperature,
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callbacks=[llm_metrics_handler]
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)
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elif llm_provider == "mistral":
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llm = ChatMistralAI(
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api_key=current_app.config['MISTRAL_API_KEY'],
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model=llm_model_name,
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temperature=temperature,
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callbacks=[llm_metrics_handler]
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)
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llm_model_cache[(full_model_name, temperature)] = llm
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class ModelVariables:
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"""Manages model-related variables and configurations"""
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@@ -63,15 +122,13 @@ class ModelVariables:
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Initialize ModelVariables with tenant and optional template manager
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Args:
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tenant: Tenant instance
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template_manager: Optional TemplateManager instance
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tenant_id: Tenant instance
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variables: Optional variables
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"""
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current_app.logger.info(f'Model variables initialized with tenant {tenant_id} and variables \n{variables}')
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self.tenant_id = tenant_id
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self._variables = variables if variables is not None else self._initialize_variables()
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current_app.logger.info(f'Model _variables initialized to {self._variables}')
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self._embedding_model = None
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self._embedding_model_class = None
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self._llm_instances = {}
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self.llm_metrics_handler = LLMMetricsHandler()
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self._transcription_model = None
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@@ -85,7 +142,6 @@ class ModelVariables:
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raise EveAITenantNotFound(self.tenant_id)
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# Set model providers
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variables['embedding_provider'], variables['embedding_model'] = tenant.embedding_model.split('.')
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variables['llm_provider'], variables['llm_model'] = tenant.llm_model.split('.')
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variables['llm_full_model'] = tenant.llm_model
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@@ -102,28 +158,6 @@ class ModelVariables:
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return variables
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@property
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def embedding_model(self):
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"""Get the embedding model instance"""
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if self._embedding_model is None:
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api_key = os.getenv('OPENAI_API_KEY')
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self._embedding_model = TrackedOpenAIEmbeddings(
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api_key=api_key,
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model=self._variables['embedding_model']
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)
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return self._embedding_model
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@property
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def embedding_model_class(self):
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"""Get the embedding model class"""
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if self._embedding_model_class is None:
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if self._variables['embedding_model'] == 'text-embedding-3-large':
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self._embedding_model_class = EmbeddingLargeOpenAI
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else: # text-embedding-3-small
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self._embedding_model_class = EmbeddingSmallOpenAI
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return self._embedding_model_class
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@property
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def annotation_chunk_length(self):
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return self._variables['annotation_chunk_length']
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@@ -13,14 +13,12 @@ def set_tenant_session_data(sender, user, **kwargs):
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tenant = Tenant.query.filter_by(id=user.tenant_id).first()
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session['tenant'] = tenant.to_dict()
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session['default_language'] = tenant.default_language
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session['default_embedding_model'] = tenant.embedding_model
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session['default_llm_model'] = tenant.llm_model
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def clear_tenant_session_data(sender, user, **kwargs):
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session.pop('tenant', None)
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session.pop('default_language', None)
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session.pop('default_embedding_model', None)
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session.pop('default_llm_model', None)
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