Remove ModelVariables (model_utils) from application & optimize Tenant

common/models/user.py

@@ -24,17 +24,13 @@ class Tenant(db.Model):
     name = db.Column(db.String(80), unique=True, nullable=False)
     website = db.Column(db.String(255), nullable=True)
     timezone = db.Column(db.String(50), nullable=True, default='UTC')
-    rag_context = db.Column(db.Text, nullable=True)
     type = db.Column(db.String(20), nullable=True, server_default='Active')
 
     # language information
     default_language = db.Column(db.String(2), nullable=True)
     allowed_languages = db.Column(ARRAY(sa.String(2)), nullable=True)
 
-    # LLM specific choices
+   # Entitlements
-    llm_model = db.Column(db.String(50), nullable=True)
-
-    # Entitlements
     currency = db.Column(db.String(20), nullable=True)
     storage_dirty = db.Column(db.Boolean, nullable=True, default=False)
 
@@ -62,11 +58,9 @@ class Tenant(db.Model):
             'name': self.name,
             'website': self.website,
             'timezone': self.timezone,
-            'rag_context': self.rag_context,
             'type': self.type,
             'default_language': self.default_language,
             'allowed_languages': self.allowed_languages,
-            'llm_model': self.llm_model,
             'currency': self.currency,
         }
 

common/utils/model_utils.py

@@ -139,142 +139,26 @@ def process_pdf():
     full_model_name = 'mistral-ocr-latest'
 
 
-def get_template(template_name: str, version: Optional[str] = "1.0") -> tuple[
+def get_template(template_name: str, version: Optional[str] = "1.0", temperature: float = 0.3) -> tuple[
     Any, BaseChatModel | None | ChatOpenAI | ChatMistralAI]:
     """
     Get a prompt template
     """
     prompt = cache_manager.prompts_config_cache.get_config(template_name, version)
     if "llm_model" in prompt:
-        llm = get_embedding_llm(full_model_name=prompt["llm_model"])
+        llm = get_embedding_llm(full_model_name=prompt["llm_model"], temperature=temperature)
     else:
-        llm = get_embedding_llm()
+        llm = get_embedding_llm(temperature=temperature)
 
     return prompt["content"], llm
 
 
-class ModelVariables:
+def get_transcription_model(model_name: str = "whisper-1") -> TrackedOpenAITranscription:
-    """Manages model-related variables and configurations"""
+    """
-
+    Get a transcription model instance
-    def __init__(self, tenant_id: int, variables: Dict[str, Any] = None):
+    """
-        """
+    api_key = os.getenv('OPENAI_API_KEY')
-        Initialize ModelVariables with tenant and optional template manager
+    return TrackedOpenAITranscription(
-
+        api_key=api_key,
-        Args:
+        model=model_name
-            tenant_id: Tenant instance
+    )
-            variables: Optional variables
-        """
-        current_app.logger.info(f'Model variables initialized with tenant {tenant_id} and variables \n{variables}')
-        self.tenant_id = tenant_id
-        self._variables = variables if variables is not None else self._initialize_variables()
-        current_app.logger.info(f'Model _variables initialized to {self._variables}')
-        self._llm_instances = {}
-        self.llm_metrics_handler = LLMMetricsHandler()
-        self._transcription_model = None
-
-    def _initialize_variables(self) -> Dict[str, Any]:
-        """Initialize the variables dictionary"""
-        variables = {}
-
-        tenant = Tenant.query.get(self.tenant_id)
-        if not tenant:
-            raise EveAITenantNotFound(self.tenant_id)
-
-        # Set model providers
-        variables['llm_provider'], variables['llm_model'] = tenant.llm_model.split('.')
-        variables['llm_full_model'] = tenant.llm_model
-
-        # Set model-specific configurations
-        model_config = MODEL_CONFIG.get(variables['llm_provider'], {}).get(variables['llm_model'], {})
-        variables.update(model_config)
-
-        # Additional configurations
-        variables['annotation_chunk_length'] = current_app.config['ANNOTATION_TEXT_CHUNK_LENGTH'][tenant.llm_model]
-        variables['max_compression_duration'] = current_app.config['MAX_COMPRESSION_DURATION']
-        variables['max_transcription_duration'] = current_app.config['MAX_TRANSCRIPTION_DURATION']
-        variables['compression_cpu_limit'] = current_app.config['COMPRESSION_CPU_LIMIT']
-        variables['compression_process_delay'] = current_app.config['COMPRESSION_PROCESS_DELAY']
-
-        return variables
-
-    @property
-    def annotation_chunk_length(self):
-        return self._variables['annotation_chunk_length']
-
-    @property
-    def max_compression_duration(self):
-        return self._variables['max_compression_duration']
-
-    @property
-    def max_transcription_duration(self):
-        return self._variables['max_transcription_duration']
-
-    @property
-    def compression_cpu_limit(self):
-        return self._variables['compression_cpu_limit']
-
-    @property
-    def compression_process_delay(self):
-        return self._variables['compression_process_delay']
-
-    def get_llm(self, temperature: float = 0.3, **kwargs) -> Any:
-        """
-        Get an LLM instance with specific configuration
-
-        Args:
-            temperature: The temperature for the LLM
-            **kwargs: Additional configuration parameters
-
-        Returns:
-            An instance of the configured LLM
-        """
-        cache_key = f"{temperature}_{hash(frozenset(kwargs.items()))}"
-
-        if cache_key not in self._llm_instances:
-            provider = self._variables['llm_provider']
-            model = self._variables['llm_model']
-
-            if provider == 'openai':
-                self._llm_instances[cache_key] = ChatOpenAI(
-                    api_key=os.getenv('OPENAI_API_KEY'),
-                    model=model,
-                    temperature=temperature,
-                    callbacks=[self.llm_metrics_handler],
-                    **kwargs
-                )
-            elif provider == 'anthropic':
-                self._llm_instances[cache_key] = ChatAnthropic(
-                    api_key=os.getenv('ANTHROPIC_API_KEY'),
-                    model=current_app.config['ANTHROPIC_LLM_VERSIONS'][model],
-                    temperature=temperature,
-                    callbacks=[self.llm_metrics_handler],
-                    **kwargs
-                )
-            else:
-                raise ValueError(f"Unsupported LLM provider: {provider}")
-
-        return self._llm_instances[cache_key]
-
-    @property
-    def transcription_model(self) -> TrackedOpenAITranscription:
-        """Get the transcription model instance"""
-        if self._transcription_model is None:
-            api_key = os.getenv('OPENAI_API_KEY')
-            self._transcription_model = TrackedOpenAITranscription(
-                api_key=api_key,
-                model='whisper-1'
-            )
-        return self._transcription_model
-
-    # Remove the old transcription-related methods since they're now handled by TrackedOpenAITranscription
-    @property
-    def transcription_client(self):
-        raise DeprecationWarning("Use transcription_model instead")
-
-    def transcribe(self, *args, **kwargs):
-        raise DeprecationWarning("Use transcription_model.transcribe() instead")
-
-
-# Helper function to get cached model variables
-def get_model_variables(tenant_id: int) -> ModelVariables:
-    return ModelVariables(tenant_id=tenant_id)

config/config.py

@@ -81,11 +81,7 @@ class Config(object):
     ANTHROPIC_LLM_VERSIONS = {'claude-3-5-sonnet': 'claude-3-5-sonnet-20240620', }
 
     # Annotation text chunk length
-    ANNOTATION_TEXT_CHUNK_LENGTH = {
+    ANNOTATION_TEXT_CHUNK_LENGTH = 10000
-        'openai.gpt-4o': 10000,
-        'openai.gpt-4o-mini': 10000,
-        'anthropic.claude-3-5-sonnet': 8000
-    }
 
     # Environemnt Loaders
     OPENAI_API_KEY = environ.get('OPENAI_API_KEY')

eveai_app/templates/user/tenant_overview.html

@@ -10,7 +10,7 @@
     <form method="post">
         {{ form.hidden_tag() }}
         <!-- Main Tenant Information -->
-        {% set main_fields = ['name', 'code', 'website', 'default_language', 'allowed_languages', 'rag_context', 'type'] %}
+        {% set main_fields = ['name', 'code', 'website', 'default_language', 'allowed_languages', 'type'] %}
         {% for field in form %}
             {{ render_included_field(field, disabled_fields=main_fields, include_fields=main_fields) }}
         {% endfor %}
@@ -20,11 +20,6 @@
             <div class="col-lg-12">
                 <div class="nav-wrapper position-relative end-0">
                     <ul class="nav nav-pills nav-fill p-1" role="tablist">
-                        <li class="nav-item" role="presentation">
-                            <a class="nav-link mb-0 px-0 py-1 active" data-toggle="tab" href="#model-info-tab" role="tab" aria-controls="model-info" aria-selected="true">
-                                Model Information
-                            </a>
-                        </li>
                         <li class="nav-item">
                             <a class="nav-link mb-0 px-0 py-1" data-toggle="tab" href="#license-info-tab" role="tab" aria-controls="license-info" aria-selected="false">
                                 License Information
@@ -33,13 +28,6 @@
                     </ul>
                 </div>
                 <div class="tab-content tab-space">
-                    <!-- Model Information Tab -->
-                    <div class="tab-pane fade show active" id="model-info-tab" role="tabpanel">
-                        {% set model_fields = ['llm_model'] %}
-                        {% for field in form %}
-                            {{ render_included_field(field, disabled_fields=model_fields, include_fields=model_fields) }}
-                        {% endfor %}
-                    </div>
                     <!-- License Information Tab -->
                     <div class="tab-pane fade" id="license-info-tab" role="tabpanel">
                         {% set license_fields = ['currency', 'usage_email', ] %}

eveai_app/views/user_forms.py

@@ -22,8 +22,6 @@ class TenantForm(FlaskForm):
     currency = SelectField('Currency', choices=[], validators=[DataRequired()])
     # Timezone
     timezone = SelectField('Timezone', choices=[], validators=[DataRequired()])
-    # LLM fields
-    llm_model = SelectField('Large Language Model', choices=[], validators=[DataRequired()])
 
     # For Super Users only - Allow to assign the tenant to the partner
     assign_to_partner = BooleanField('Assign to Partner', default=False)

eveai_chat_workers/chat_session_cache.py

@@ -177,7 +177,6 @@ def register_chat_session_cache_handlers(cache_manager):
     cache_manager.register_handler(ChatSessionCacheHandler, 'eveai_chat_workers')
 
 
-# Helper function similar to get_model_variables
 def get_chat_history(session_id: str) -> CachedSession:
     """
     Get cached chat history for a session, loading from database if needed

eveai_chat_workers/specialists/STANDARD_RAG_SPECIALIST/1_0.py

@@ -12,7 +12,8 @@ from common.utils.business_event_context import current_event
 from eveai_chat_workers.specialists.specialist_typing import SpecialistArguments, SpecialistResult
 from eveai_chat_workers.chat_session_cache import get_chat_history
 from common.models.interaction import Specialist
-from common.utils.model_utils import get_model_variables, create_language_template, replace_variable_in_template
+from common.utils.model_utils import create_language_template, replace_variable_in_template, \
+    get_template
 from eveai_chat_workers.specialists.base_specialist import BaseSpecialistExecutor
 from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments
 
@@ -38,9 +39,6 @@ class SpecialistExecutor(BaseSpecialistExecutor):
         # Initialize retrievers
         self.retrievers = self._initialize_retrievers()
 
-        # Initialize model variables
-        self.model_variables = get_model_variables(tenant_id)
-
     @property
     def type(self) -> str:
         return "STANDARD_RAG_SPECIALIST"
@@ -68,8 +66,7 @@ class SpecialistExecutor(BaseSpecialistExecutor):
             ])
 
             # Get LLM and template
-            llm = self.model_variables.get_llm(temperature=0.3)
+            template, llm = get_template("history", temperature=0.3)
-            template = self.model_variables.get_template('history')
             language_template = create_language_template(template, language)
 
             # Create prompt
@@ -179,11 +176,7 @@ class SpecialistExecutor(BaseSpecialistExecutor):
             with current_event.create_span("Specialist RAG invocation"):
                 try:
                     self.update_progress(self.task_id, "EveAI Chain Start", {})
-                    # Get LLM with specified temperature
+                    template, llm = get_template("rag", self.temperature)
-                    llm = self.model_variables.get_llm(temperature=self.temperature)
-
-                    # Get template
-                    template = self.model_variables.get_template('rag')
                     language_template = create_language_template(template, language)
                     full_template = replace_variable_in_template(
                         language_template,

eveai_chat_workers/specialists/crewai_base_specialist.py

@@ -8,7 +8,7 @@ from langchain_core.prompts import ChatPromptTemplate
 
 from common.models.interaction import Specialist
 from common.utils.business_event_context import current_event
-from common.utils.model_utils import get_model_variables, get_crewai_llm, create_language_template
+from common.utils.model_utils import get_crewai_llm, create_language_template, get_template
 from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments
 from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAIAgent, EveAICrewAITask
 from crewai.tools import BaseTool
@@ -41,9 +41,6 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
         # Initialize retrievers
         self.retrievers = self._initialize_retrievers()
 
-        # Initialize model variables
-        self.model_variables = get_model_variables(tenant_id)
-
         # initialize the Flow
         self.flow = None
 
@@ -212,8 +209,7 @@ class CrewAIBaseSpecialistExecutor(BaseSpecialistExecutor):
         try:
             with current_event.create_span("Specialist Detail Question"):
                 # Get LLM and template
-                llm = self.model_variables.get_llm(temperature=0.3)
+                template, llm = get_template("history", temperature=0.3)
-                template = cache_manager.prompts_config_cache.get_config('history').get('content', '')
                 language_template = create_language_template(template, language)
 
                 # Create prompt

eveai_workers/processors/audio_processor.py

@@ -8,20 +8,23 @@ import tempfile
 from common.extensions import minio_client
 import subprocess
 
+from flask import current_app
+
+from common.utils.model_utils import get_transcription_model
 from .processor_registry import ProcessorRegistry
 from .transcription_processor import TranscriptionBaseProcessor
 from common.utils.business_event_context import current_event
 
 
 class AudioProcessor(TranscriptionBaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
-        self.transcription_model = model_variables.transcription_model
+        self.transcription_model = get_transcription_model()
         self.ffmpeg_path = 'ffmpeg'
-        self.max_compression_duration = model_variables.max_compression_duration
+        self.max_compression_duration = current_app.config['MAX_COMPRESSION_DURATION']
-        self.max_transcription_duration = model_variables.max_transcription_duration
+        self.max_transcription_duration = current_app.config['MAX_TRANSCRIPTION_DURATION']
-        self.compression_cpu_limit = model_variables.compression_cpu_limit  # CPU usage limit in percentage
+        self.compression_cpu_limit = current_app.config['COMPRESSION_CPU_LIMIT']  # CPU usage limit in percentage
-        self.compression_process_delay = model_variables.compression_process_delay  # Delay between processing chunks in seconds
+        self.compression_process_delay = current_app.config['COMPRESSION_PROCESS_DELAY']  # Delay between processing chunks in seconds
         self.file_type = document_version.file_type
 
     def _get_transcription(self):
@@ -154,7 +157,7 @@ class AudioProcessor(TranscriptionBaseProcessor):
                     file_size = os.path.getsize(temp_audio.name)
 
                     with open(temp_audio.name, 'rb') as audio_file:
-                        transcription = self.model_variables.transcription_model.transcribe(
+                        transcription = self.transcription_model.transcribe(
                             file=audio_file,
                             language=self.document_version.language,
                             response_format='verbose_json',

eveai_workers/processors/base_processor.py

@@ -7,9 +7,8 @@ from config.logging_config import TuningLogger
 
 
 class BaseProcessor(ABC):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
         self.tenant = tenant
-        self.model_variables = model_variables
         self.document_version = document_version
         self.catalog = catalog
         self.processor = processor

eveai_workers/processors/docx_processor.py

@@ -7,8 +7,8 @@ import re
 
 
 class DocxProcessor(BaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
         self.config = processor.configuration
         self.extract_comments = self.config.get('extract_comments', False)
         self.extract_headers_footers = self.config.get('extract_headers_footers', False)

eveai_workers/processors/html_processor.py

@@ -11,8 +11,8 @@ from common.utils.string_list_converter import StringListConverter as SLC
 
 
 class HTMLProcessor(BaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
         cat_conf = catalog.configuration
         proc_conf = processor.configuration
         self.html_tags = SLC.string_to_list(proc_conf['html_tags'])

eveai_workers/processors/markdown_processor.py

@@ -18,8 +18,8 @@ def _find_first_h1(markdown: str) -> str:
 
 
 class MarkdownProcessor(BaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
 
         self.chunk_size = catalog.max_chunk_size
         self.chunk_overlap = 0

eveai_workers/processors/pdf_processor.py

@@ -16,8 +16,8 @@ from .processor_registry import ProcessorRegistry
 
 
 class PDFProcessor(BaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
 
         self.chunk_size = catalog.max_chunk_size
         self.chunk_overlap = 0

eveai_workers/processors/transcription_processor.py

@@ -3,6 +3,7 @@ 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 flask import current_app
 
 from common.utils.model_utils import create_language_template, get_embedding_llm, get_template
 from .base_processor import BaseProcessor
@@ -10,9 +11,9 @@ from common.utils.business_event_context import current_event
 
 
 class TranscriptionBaseProcessor(BaseProcessor):
-    def __init__(self, tenant, model_variables, document_version, catalog, processor):
+    def __init__(self, tenant, document_version, catalog, processor):
-        super().__init__(tenant, model_variables, document_version, catalog, processor)
+        super().__init__(tenant, document_version, catalog, processor)
-        self.annotation_chunk_size = model_variables.annotation_chunk_length
+        self.annotation_chunk_size = current_app.config['ANNOTATION_TEXT_CHUNK_LENGTH']
         self.annotation_chunk_overlap = 0
 
     def process(self):

eveai_workers/tasks.py

@@ -17,8 +17,7 @@ from common.models.document import DocumentVersion, Embedding, Document, Process
 from common.models.user import Tenant
 from common.utils.celery_utils import current_celery
 from common.utils.database import Database
-from common.utils.model_utils import create_language_template, get_model_variables, get_embedding_model_and_class, \
+from common.utils.model_utils import create_language_template, get_embedding_model_and_class, get_template
-    get_embedding_llm, get_template
 
 from common.utils.business_event import BusinessEvent
 from common.utils.business_event_context import current_event
@@ -58,9 +57,6 @@ def create_embeddings(tenant_id, document_version_id):
         catalog_id = doc.catalog_id
         catalog = Catalog.query.get_or_404(catalog_id)
 
-        # Select variables to work with depending on tenant and model
-        model_variables = get_model_variables(tenant_id)
-
         # Define processor related information
         processor_type, processor_class = ProcessorRegistry.get_processor_for_file_type(document_version.file_type)
         processor = get_processor_for_document(catalog_id, document_version.file_type, document_version.sub_file_type)
@@ -102,7 +98,6 @@ def create_embeddings(tenant_id, document_version_id):
             with current_event.create_span(f"{processor_type} Processing"):
                 document_processor = processor_class(
                     tenant=tenant,
-                    model_variables=model_variables,
                     document_version=document_version,
                     catalog=catalog,
                     processor=processor
@@ -114,7 +109,7 @@ def create_embeddings(tenant_id, document_version_id):
                 })
 
             with current_event.create_span("Embedding"):
-                embed_markdown(tenant, model_variables, document_version, catalog, document_processor, markdown, title)
+                embed_markdown(tenant, document_version, catalog, document_processor, markdown, title)
 
             current_event.log("Finished Embedding Creation Task")
 
@@ -142,7 +137,7 @@ def delete_embeddings_for_document_version(document_version):
         raise
 
 
-def embed_markdown(tenant, model_variables, document_version, catalog, processor, markdown, title):
+def embed_markdown(tenant, document_version, catalog, processor, markdown, title):
     # Create potential chunks
     potential_chunks = create_potential_chunks_for_markdown(tenant.id, document_version, processor, markdown,
                                                             catalog.max_chunk_size)
@@ -154,7 +149,7 @@ def embed_markdown(tenant, model_variables, document_version, catalog, processor
 
     # Enrich chunks
     with current_event.create_span("Enrich Chunks"):
-        enriched_chunks = enrich_chunks(tenant, model_variables, document_version, title, chunks)
+        enriched_chunks = enrich_chunks(tenant, document_version, title, chunks)
         processor.log_tuning("Enriched Chunks: ", {'enriched_chunks': enriched_chunks})
 
     # Create embeddings
@@ -178,10 +173,10 @@ def embed_markdown(tenant, model_variables, document_version, catalog, processor
                             f'on document version {document_version.id} :-)')
 
 
-def enrich_chunks(tenant, model_variables, document_version, title, chunks):
+def enrich_chunks(tenant, document_version, title, chunks):
     summary = ''
     if len(chunks) > 1:
-        summary = summarize_chunk(tenant, model_variables, document_version, chunks[0])
+        summary = summarize_chunk(tenant, document_version, chunks[0])
 
     chunk_total_context = (f'Filename: {document_version.object_name}\n'
                            f'User Context:\n{document_version.user_context}\n\n'
@@ -209,7 +204,7 @@ def enrich_chunks(tenant, model_variables, document_version, title, chunks):
     return enriched_chunks
 
 
-def summarize_chunk(tenant, model_variables, document_version, chunk):
+def summarize_chunk(tenant, document_version, chunk):
     current_event.log("Starting Summarizing Chunk")
 
     template, llm = get_template("summary")

migrations/public/versions/7d3c6f48735c_remove_rag_context_and_llm_model_from_.py

@@ -0,0 +1,31 @@
+"""Remove rag_context and llm_model from Tenant
+
+Revision ID: 7d3c6f48735c
+Revises: 4eae969dcac2
+Create Date: 2025-05-20 08:09:23.673137
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = '7d3c6f48735c'
+down_revision = '4eae969dcac2'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('tenant', schema=None) as batch_op:
+        batch_op.drop_column('rag_context')
+        batch_op.drop_column('llm_model')
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    pass
+    # ### end Alembic commands ###