- 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

CHANGELOG.md

@@ -25,6 +25,29 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Security
 - In case of vulnerabilities.
 
+## [2.1.0-alfa]
+
+### Added
+- Zapier Refresh Document
+- SPIN Specialist definition - from start to finish
+- Introduction of startup scripts in eveai_app
+- Caching for all configurations added
+- Caching for processed specialist configurations
+- Caching for specialist history
+- Augmented Specialist Editor, including Specialist graphic presentation
+- Introduction of specialist_execution_api, introducting SSE
+- Introduction of crewai framework for specialist implementation
+- Test app for testing specialists - also serves as a sample client application for SSE
+- 
+
+### Changed
+- Improvement of startup of applications using gevent, and better handling and scaling of multiple connections
+- STANDARD_RAG Specialist improvement
+- 
+
+### Deprecated
+- eveai_chat - using sockets - will be replaced with new specialist_execution_api and SSE
+
 ## [2.0.1-alfa]
 
 ### Added

common/eveai_model/eveai_embedding_base.py

@@ -0,0 +1,11 @@
+from abc import abstractmethod
+from typing import List
+
+
+class EveAIEmbeddings:
+    @abstractmethod
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        pass
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.embed_documents([text])[0]

common/eveai_model/tracked_mistral_embeddings.py

@@ -0,0 +1,40 @@
+from flask import current_app
+from langchain_mistralai import MistralAIEmbeddings
+from typing import List, Any
+import time
+
+from common.eveai_model.eveai_embedding_base import EveAIEmbeddings
+from common.utils.business_event_context import current_event
+from mistralai import Mistral
+
+
+class TrackedMistralAIEmbeddings(EveAIEmbeddings):
+    def __init__(self, model: str = "mistral_embed"):
+        api_key = current_app.config['MISTRAL_API_KEY']
+        self.client = Mistral(
+            api_key=api_key
+        )
+        self.model = model
+        super().__init__()
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        start_time = time.time()
+        result = self.client.embeddings.create(
+            model=self.model,
+            inputs=texts
+        )
+        end_time = time.time()
+
+        metrics = {
+            'total_tokens': result.usage.total_tokens,
+            'prompt_tokens': result.usage.prompt_tokens,  # For embeddings, all tokens are prompt tokens
+            'completion_tokens': result.usage.completion_tokens,
+            'time_elapsed': end_time - start_time,
+            'interaction_type': 'Embedding',
+        }
+        current_event.log_llm_metrics(metrics)
+
+        embeddings = [embedding.embedding for embedding in result.data]
+
+        return embeddings
+

common/extensions.py

@@ -5,7 +5,6 @@ from flask_security import Security
 from flask_mailman import Mail
 from flask_login import LoginManager
 from flask_cors import CORS
-from flask_socketio import SocketIO
 from flask_jwt_extended import JWTManager
 from flask_session import Session
 from flask_wtf import CSRFProtect
@@ -27,7 +26,6 @@ security = Security()
 mail = Mail()
 login_manager = LoginManager()
 cors = CORS()
-socketio = SocketIO()
 jwt = JWTManager()
 session = Session()
 api_rest = Api()

common/models/document.py

@@ -12,8 +12,10 @@ class Catalog(db.Model):
     description = db.Column(db.Text, nullable=True)
     type = db.Column(db.String(50), nullable=False, default="STANDARD_CATALOG")
 
-    min_chunk_size = db.Column(db.Integer, nullable=True, default=2000)
-    max_chunk_size = db.Column(db.Integer, nullable=True, default=3000)
+    embedding_model = db.Column(db.String(50), nullable=True)
+
+    min_chunk_size = db.Column(db.Integer, nullable=True, default=1500)
+    max_chunk_size = db.Column(db.Integer, nullable=True, default=2500)
 
     # Meta Data
     user_metadata = db.Column(JSONB, nullable=True)

common/models/user.py

@@ -31,7 +31,6 @@ class Tenant(db.Model):
     allowed_languages = db.Column(ARRAY(sa.String(2)), nullable=True)
 
     # LLM specific choices
-    embedding_model = db.Column(db.String(50), nullable=True)
     llm_model = db.Column(db.String(50), nullable=True)
 
     # Entitlements
@@ -66,7 +65,6 @@ class Tenant(db.Model):
             'type': self.type,
             'default_language': self.default_language,
             'allowed_languages': self.allowed_languages,
-            'embedding_model': self.embedding_model,
             'llm_model': self.llm_model,
             'currency': self.currency,
         }

common/utils/config_field_types.py

@@ -652,12 +652,15 @@ def json_to_patterns(json_content: str) -> str:
 def json_to_pattern_list(json_content: str) -> list:
     """Convert JSON patterns list to text area content"""
     try:
-        patterns = json.loads(json_content)
-        if not isinstance(patterns, list):
-            raise ValueError("JSON must contain a list of patterns")
-        # Unescape if needed
-        patterns = [pattern.replace('\\\\', '\\') for pattern in patterns]
-        return patterns
+        if json_content:
+            patterns = json.loads(json_content)
+            if not isinstance(patterns, list):
+                raise ValueError("JSON must contain a list of patterns")
+            # Unescape if needed
+            patterns = [pattern.replace('\\\\', '\\') for pattern in patterns]
+            return patterns
+        else:
+            return []
     except json.JSONDecodeError as e:
         raise ValueError(f"Invalid JSON format: {e}")
 

common/utils/eveai_exceptions.py

@@ -124,4 +124,15 @@ class EveAISocketInputException(EveAIException):
     """Raised when a socket call receives an invalid payload"""
 
     def __init__(self, message, status_code=400, payload=None):
-        super.__init__(message, status_code, payload)
+        super.__init__(message, status_code, payload)
+
+
+class EveAIInvalidEmbeddingModel(EveAIException):
+    """Raised when no or an invalid embedding model is provided in the catalog"""
+
+    def __init__(self, tenant_id, catalog_id, status_code=400, payload=None):
+        self.tenant_id = tenant_id
+        self.catalog_id = catalog_id
+        # Construct the message dynamically
+        message = f"Tenant with ID '{tenant_id}' has no or an invalid embedding model in Catalog {catalog_id}."
+        super().__init__(message, status_code, payload)

common/utils/model_utils.py

@@ -1,23 +1,25 @@
 import os
-from typing import Dict, Any, Optional
+from typing import Dict, Any, Optional, Tuple
 
 import langcodes
+from langchain_core.language_models import BaseChatModel
 
 from common.langchain.llm_metrics_handler import LLMMetricsHandler
-from common.langchain.templates.template_manager import TemplateManager
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI, OpenAI
+from langchain_openai import ChatOpenAI
 from langchain_anthropic import ChatAnthropic
+from langchain_mistralai import ChatMistralAI
 from flask import current_app
-from datetime import datetime as dt, timezone as tz
 
-from common.langchain.tracked_openai_embeddings import TrackedOpenAIEmbeddings
+from common.eveai_model.tracked_mistral_embeddings import TrackedMistralAIEmbeddings
 from common.langchain.tracked_transcription import TrackedOpenAITranscription
 from common.models.user import Tenant
-from common.utils.cache.base import CacheHandler
 from config.model_config import MODEL_CONFIG
-from common.extensions import template_manager, cache_manager
-from common.models.document import EmbeddingLargeOpenAI, EmbeddingSmallOpenAI
-from common.utils.eveai_exceptions import EveAITenantNotFound
+from common.extensions import template_manager
+from common.models.document import EmbeddingMistral
+from common.utils.eveai_exceptions import EveAITenantNotFound, EveAIInvalidEmbeddingModel
+
+llm_model_cache: Dict[Tuple[str, float], BaseChatModel] = {}
+llm_metrics_handler = LLMMetricsHandler()
 
 
 def create_language_template(template: str, language: str) -> str:
@@ -55,6 +57,63 @@ def replace_variable_in_template(template: str, variable: str, value: str) -> st
     return template.replace(variable, value or "")
 
 
+def get_embedding_model_and_class(tenant_id, catalog_id, full_embedding_name):
+    """
+        Retrieve the embedding model and embedding model class to store Embeddings
+
+        Args:
+            tenant_id: ID of the tenant
+            catalog_id: ID of the catalog
+            full_embedding_name: The full name of the embedding model: <provider>.<model>
+
+        Returns:
+            embedding_model, embedding_model_class
+        """
+    embedding_provider, embedding_model_name = full_embedding_name.split('.')
+
+    # Calculate the embedding model to be used
+    if embedding_provider == "mistral":
+        api_key = current_app.config['MISTRAL_API_KEY']
+        embedding_model = TrackedMistralAIEmbeddings(
+            model=embedding_model_name
+        )
+    else:
+        raise EveAIInvalidEmbeddingModel(tenant_id, catalog_id)
+
+    # Calculate the Embedding Model Class to be used to store embeddings
+    if embedding_model_name == "mistral-embed":
+        embedding_model_class = EmbeddingMistral
+    else:
+        raise EveAIInvalidEmbeddingModel(tenant_id, catalog_id)
+
+    return embedding_model, embedding_model_class
+
+
+def get_llm(full_model_name, temperature):
+    if not full_model_name:
+        full_model_name = 'openai.gpt-4o'  # Default to gpt-4o for now, as this is the original model developed against
+
+    llm = llm_model_cache.get((full_model_name, temperature))
+    if not llm:
+        llm_provider, llm_model_name = full_model_name.split('.')
+        if llm_provider == "openai":
+            llm = ChatOpenAI(
+                api_key=current_app.config['OPENAI_API_KEY'],
+                model=llm_model_name,
+                temperature=temperature,
+                callbacks=[llm_metrics_handler]
+            )
+        elif llm_provider == "mistral":
+            llm = ChatMistralAI(
+                api_key=current_app.config['MISTRAL_API_KEY'],
+                model=llm_model_name,
+                temperature=temperature,
+                callbacks=[llm_metrics_handler]
+            )
+
+        llm_model_cache[(full_model_name, temperature)] = llm
+
+
 class ModelVariables:
     """Manages model-related variables and configurations"""
 
@@ -63,15 +122,13 @@ class ModelVariables:
         Initialize ModelVariables with tenant and optional template manager
 
         Args:
-            tenant: Tenant instance
-            template_manager: Optional TemplateManager instance
+            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._embedding_model = None
-        self._embedding_model_class = None
         self._llm_instances = {}
         self.llm_metrics_handler = LLMMetricsHandler()
         self._transcription_model = None
@@ -85,7 +142,6 @@ class ModelVariables:
             raise EveAITenantNotFound(self.tenant_id)
 
         # Set model providers
-        variables['embedding_provider'], variables['embedding_model'] = tenant.embedding_model.split('.')
         variables['llm_provider'], variables['llm_model'] = tenant.llm_model.split('.')
         variables['llm_full_model'] = tenant.llm_model
 
@@ -102,28 +158,6 @@ class ModelVariables:
 
         return variables
 
-    @property
-    def embedding_model(self):
-        """Get the embedding model instance"""
-        if self._embedding_model is None:
-            api_key = os.getenv('OPENAI_API_KEY')
-            self._embedding_model = TrackedOpenAIEmbeddings(
-                api_key=api_key,
-                model=self._variables['embedding_model']
-            )
-        return self._embedding_model
-
-    @property
-    def embedding_model_class(self):
-        """Get the embedding model class"""
-        if self._embedding_model_class is None:
-            if self._variables['embedding_model'] == 'text-embedding-3-large':
-                self._embedding_model_class = EmbeddingLargeOpenAI
-            else:   # text-embedding-3-small
-                self._embedding_model_class = EmbeddingSmallOpenAI
-
-        return self._embedding_model_class
-
     @property
     def annotation_chunk_length(self):
         return self._variables['annotation_chunk_length']

common/utils/security.py

@@ -13,14 +13,12 @@ def set_tenant_session_data(sender, user, **kwargs):
     tenant = Tenant.query.filter_by(id=user.tenant_id).first()
     session['tenant'] = tenant.to_dict()
     session['default_language'] = tenant.default_language
-    session['default_embedding_model'] = tenant.embedding_model
     session['default_llm_model'] = tenant.llm_model
 
 
 def clear_tenant_session_data(sender, user, **kwargs):
     session.pop('tenant', None)
     session.pop('default_language', None)
-    session.pop('default_embedding_model', None)
     session.pop('default_llm_model', None)
 
 

config/config.py

@@ -63,8 +63,10 @@ class Config(object):
     SUPPORTED_CURRENCIES = ['€', '$']
 
     # supported LLMs
-    SUPPORTED_EMBEDDINGS = ['openai.text-embedding-3-small', 'openai.text-embedding-3-large', 'mistral.mistral-embed']
-    SUPPORTED_LLMS = ['openai.gpt-4o', 'anthropic.claude-3-5-sonnet', 'openai.gpt-4o-mini']
+    # SUPPORTED_EMBEDDINGS = ['openai.text-embedding-3-small', 'openai.text-embedding-3-large', 'mistral.mistral-embed']
+    SUPPORTED_EMBEDDINGS = ['mistral.mistral-embed']
+    SUPPORTED_LLMS = ['openai.gpt-4o', 'anthropic.claude-3-5-sonnet', 'openai.gpt-4o-mini',
+                      'mistral.mistral-large-latest', 'mistral.mistral-small-latest']
 
     ANTHROPIC_LLM_VERSIONS = {'claude-3-5-sonnet': 'claude-3-5-sonnet-20240620', }
 
@@ -75,13 +77,10 @@ class Config(object):
         'anthropic.claude-3-5-sonnet': 8000
     }
 
-    # OpenAI API Keys
+    # Environemnt Loaders
     OPENAI_API_KEY = environ.get('OPENAI_API_KEY')
-
-    # Groq API Keys
+    MISTRAL_API_KEY = environ.get('MISTRAL_API_KEY')
     GROQ_API_KEY = environ.get('GROQ_API_KEY')
-
-    # Anthropic API Keys
     ANTHROPIC_API_KEY = environ.get('ANTHROPIC_API_KEY')
 
     # Celery settings
@@ -93,7 +92,7 @@ class Config(object):
 
     # SocketIO settings
     # SOCKETIO_ASYNC_MODE = 'threading'
-    SOCKETIO_ASYNC_MODE = 'gevent'
+    # SOCKETIO_ASYNC_MODE = 'gevent'
 
     # Session Settings
     SESSION_TYPE = 'redis'
@@ -207,13 +206,13 @@ class DevConfig(Config):
     # UNSTRUCTURED_FULL_URL = 'https://flowitbv-16c4us0m.api.unstructuredapp.io/general/v0/general'
 
     # SocketIO settings
-    SOCKETIO_MESSAGE_QUEUE = f'{REDIS_BASE_URI}/1'
-    SOCKETIO_CORS_ALLOWED_ORIGINS = '*'
-    SOCKETIO_LOGGER = True
-    SOCKETIO_ENGINEIO_LOGGER = True
-    SOCKETIO_PING_TIMEOUT = 20000
-    SOCKETIO_PING_INTERVAL = 25000
-    SOCKETIO_MAX_IDLE_TIME = timedelta(minutes=60)  # Changing this value ==> change maxConnectionDuration value in
+    # SOCKETIO_MESSAGE_QUEUE = f'{REDIS_BASE_URI}/1'
+    # SOCKETIO_CORS_ALLOWED_ORIGINS = '*'
+    # SOCKETIO_LOGGER = True
+    # SOCKETIO_ENGINEIO_LOGGER = True
+    # SOCKETIO_PING_TIMEOUT = 20000
+    # SOCKETIO_PING_INTERVAL = 25000
+    # SOCKETIO_MAX_IDLE_TIME = timedelta(minutes=60)  # Changing this value ==> change maxConnectionDuration value in
                                                     # eveai-chat-widget.js
 
     # Google Cloud settings
@@ -299,13 +298,13 @@ class ProdConfig(Config):
     SESSION_REDIS = redis.from_url(f'{REDIS_BASE_URI}/2')
 
     # SocketIO settings
-    SOCKETIO_MESSAGE_QUEUE = f'{REDIS_BASE_URI}/1'
-    SOCKETIO_CORS_ALLOWED_ORIGINS = '*'
-    SOCKETIO_LOGGER = True
-    SOCKETIO_ENGINEIO_LOGGER = True
-    SOCKETIO_PING_TIMEOUT = 20000
-    SOCKETIO_PING_INTERVAL = 25000
-    SOCKETIO_MAX_IDLE_TIME = timedelta(minutes=60)  # Changing this value ==> change maxConnectionDuration value in
+    # SOCKETIO_MESSAGE_QUEUE = f'{REDIS_BASE_URI}/1'
+    # SOCKETIO_CORS_ALLOWED_ORIGINS = '*'
+    # SOCKETIO_LOGGER = True
+    # SOCKETIO_ENGINEIO_LOGGER = True
+    # SOCKETIO_PING_TIMEOUT = 20000
+    # SOCKETIO_PING_INTERVAL = 25000
+    # SOCKETIO_MAX_IDLE_TIME = timedelta(minutes=60)  # Changing this value ==> change maxConnectionDuration value in
     # eveai-chat-widget.js
 
     # Google Cloud settings

config/prompts/html_parse/1.0.0.yaml

@@ -13,6 +13,7 @@ content: |
   HTML is between triple backquotes.
 
   ```{html}```
+model: "mistral.mistral-small-latest"
 metadata:
   author: "Josako"
   date_added: "2024-11-10"

docker/compose_dev.yaml

@@ -28,6 +28,7 @@ x-common-variables: &common-variables
   FLOWER_PASSWORD: 'Jungles'
   OPENAI_API_KEY: 'sk-proj-8R0jWzwjL7PeoPyMhJTZT3BlbkFJLb6HfRB2Hr9cEVFWEhU7'
   GROQ_API_KEY: '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'
+  MISTRAL_API_KEY: 'jGDc6fkCbt0iOC0jQsbuZhcjLWBPGc2b'
   ANTHROPIC_API_KEY: 'sk-ant-api03-c2TmkzbReeGhXBO5JxNH6BJNylRDonc9GmZd0eRbrvyekec2'
   JWT_SECRET_KEY: 'bsdMkmQ8ObfMD52yAFg4trrvjgjMhuIqg2fjDpD/JqvgY0ccCcmlsEnVFmR79WPiLKEA3i8a5zmejwLZKl4v9Q=='
   API_ENCRYPTION_KEY: 'xfF5369IsredSrlrYZqkM9ZNrfUASYYS6TCcAR9UKj4='
@@ -65,7 +66,7 @@ services:
       - ./logs/nginx:/var/log/nginx
     depends_on:
       - eveai_app
-      - eveai_chat
+      - eveai_api
     networks:
       - eveai-network
 
@@ -134,39 +135,39 @@ services:
     networks:
       - eveai-network
 
-  eveai_chat:
-    image: josakola/eveai_chat:latest
-    build:
-      context: ..
-      dockerfile: ./docker/eveai_chat/Dockerfile
-      platforms:
-        - linux/amd64
-        - linux/arm64
-    ports:
-      - 5002:5002
-    environment:
-      <<: *common-variables
-      COMPONENT_NAME: eveai_chat
-    volumes:
-      - ../eveai_chat:/app/eveai_chat
-      - ../common:/app/common
-      - ../config:/app/config
-      - ../scripts:/app/scripts
-      - ../patched_packages:/app/patched_packages
-      - ./eveai_logs:/app/logs
-    depends_on:
-      db:
-        condition: service_healthy
-      redis:
-        condition: service_healthy
-    healthcheck:
-      test: [ "CMD", "curl", "-f", "http://localhost:5002/healthz/ready" ]  # Adjust based on your health endpoint
-      interval: 30s
-      timeout: 1s
-      retries: 3
-      start_period: 30s
-    networks:
-      - eveai-network
+#  eveai_chat:
+#    image: josakola/eveai_chat:latest
+#    build:
+#      context: ..
+#      dockerfile: ./docker/eveai_chat/Dockerfile
+#      platforms:
+#        - linux/amd64
+#        - linux/arm64
+#    ports:
+#      - 5002:5002
+#    environment:
+#      <<: *common-variables
+#      COMPONENT_NAME: eveai_chat
+#    volumes:
+#      - ../eveai_chat:/app/eveai_chat
+#      - ../common:/app/common
+#      - ../config:/app/config
+#      - ../scripts:/app/scripts
+#      - ../patched_packages:/app/patched_packages
+#      - ./eveai_logs:/app/logs
+#    depends_on:
+#      db:
+#        condition: service_healthy
+#      redis:
+#        condition: service_healthy
+#    healthcheck:
+#      test: [ "CMD", "curl", "-f", "http://localhost:5002/healthz/ready" ]  # Adjust based on your health endpoint
+#      interval: 30s
+#      timeout: 1s
+#      retries: 3
+#      start_period: 30s
+#    networks:
+#      - eveai-network
 
   eveai_chat_workers:
     image: josakola/eveai_chat_workers:latest

docker/compose_stackhero.yaml

@@ -31,6 +31,7 @@ x-common-variables: &common-variables
   OPENAI_API_KEY: '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'
   GROQ_API_KEY: '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'
   ANTHROPIC_API_KEY: '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'
+  MISTRAL_API_KEY: 'PjnUeDRPD7B144wdHlH0CzR7m0z8RHXi'
   JWT_SECRET_KEY: '0d99e810e686ea567ef305d8e9b06195c4db482952e19276590a726cde60a408'
   API_ENCRYPTION_KEY: 'Ly5XYWwEKiasfAwEqdEMdwR-k0vhrq6QPYd4whEROB0='
   GRAYLOG_HOST: de4zvu.stackhero-network.com
@@ -66,7 +67,7 @@ services:
       - "traefik.http.services.nginx.loadbalancer.server.port=80"
     depends_on:
       - eveai_app
-      - eveai_chat
+      - eveai_api
     networks:
       - eveai-network
 
@@ -99,23 +100,23 @@ services:
     networks:
       - eveai-network
 
-  eveai_chat:
-    platform: linux/amd64
-    image: josakola/eveai_chat:latest
-    ports:
-      - 5002:5002
-    environment:
-      <<: *common-variables
-      COMPONENT_NAME: eveai_chat
-    volumes:
-      - eveai_logs:/app/logs
-    healthcheck:
-      test: [ "CMD", "curl", "-f", "http://localhost:5002/healthz/ready" ]  # Adjust based on your health endpoint
-      interval: 10s
-      timeout: 5s
-      retries: 5
-    networks:
-      - eveai-network
+#  eveai_chat:
+#    platform: linux/amd64
+#    image: josakola/eveai_chat:latest
+#    ports:
+#      - 5002:5002
+#    environment:
+#      <<: *common-variables
+#      COMPONENT_NAME: eveai_chat
+#    volumes:
+#      - eveai_logs:/app/logs
+#    healthcheck:
+#      test: [ "CMD", "curl", "-f", "http://localhost:5002/healthz/ready" ]  # Adjust based on your health endpoint
+#      interval: 10s
+#      timeout: 5s
+#      retries: 5
+#    networks:
+#      - eveai-network
 
   eveai_chat_workers:
     platform: linux/amd64

eveai_api/__init__.py

@@ -5,7 +5,7 @@ from flask_jwt_extended import get_jwt_identity, verify_jwt_in_request
 from sqlalchemy.exc import SQLAlchemyError
 from werkzeug.exceptions import HTTPException
 
-from common.extensions import db, api_rest, jwt, minio_client, simple_encryption, cors
+from common.extensions import db, api_rest, jwt, minio_client, simple_encryption, cors, cache_manager
 import os
 import logging.config
 
@@ -31,7 +31,7 @@ def create_app(config_file=None):
         case 'development':
             app.config.from_object(get_config('dev'))
         case 'production':
-            app.config.from_object(get_config('prod'))  
+            app.config.from_object(get_config('prod'))
         case _:
             app.config.from_object(get_config('dev'))
 
@@ -60,6 +60,9 @@ def create_app(config_file=None):
     # Register Request Debugger
     register_request_debugger(app)
 
+    # Register Cache Handlers
+    register_cache_handlers(app)
+
     @app.before_request
     def check_cors():
         if request.method == 'OPTIONS':
@@ -120,6 +123,7 @@ def register_extensions(app):
     jwt.init_app(app)
     minio_client.init_app(app)
     simple_encryption.init_app(app)
+    cache_manager.init_app(app)
     cors.init_app(app, resources={
         r"/api/v1/*": {
             "origins": "*",
@@ -201,3 +205,8 @@ def register_error_handlers(app):
             "message": str(e),
             "type": "BadRequestError"
         }), 400
+
+
+def register_cache_handlers(app):
+    from common.utils.cache.config_cache import register_config_cache_handlers
+    register_config_cache_handlers(cache_manager)

eveai_api/api/specialist_execution_api.py

@@ -5,9 +5,11 @@ from flask import Response, stream_with_context, current_app
 from flask_restx import Namespace, Resource, fields
 from flask_jwt_extended import jwt_required, get_jwt_identity
 
+from common.extensions import cache_manager
 from common.utils.celery_utils import current_celery
 from common.utils.execution_progress import ExecutionProgressTracker
 from eveai_api.api.auth import requires_service
+from common.models.interaction import Specialist
 
 specialist_execution_ns = Namespace('specialist-execution', description='Specialist execution operations')
 
@@ -87,3 +89,47 @@ class ExecutionStream(Resource):
                 'Connection': 'keep-alive'
             }
         )
+
+
+specialist_arguments_input = specialist_execution_ns.model('SpecialistArgumentsInput', {
+    'specialist_id': fields.Integer(required=True, description='ID of the specialist to use'),
+})
+
+specialist_arguments_response = specialist_execution_ns.model('SpecialistArgumentsResponse', {
+    'arguments': fields.Raw(description='Dynamic list of attributes for the specialist.'),
+})
+
+
+@specialist_execution_ns.route('/specialist_arguments', methods=['GET'])
+class SpecialistArgument(Resource):
+    @jwt_required()
+    @requires_service('SPECIALIST_API')
+    @specialist_execution_ns.expect(specialist_arguments_input)
+    @specialist_execution_ns.response(200, 'Specialist configuration fetched.', specialist_arguments_response)
+    @specialist_execution_ns.response(404, 'Specialist configuration not found.')
+    @specialist_execution_ns.response(500, 'Internal Server Error')
+    def get(self):
+        """Start execution of a specialist"""
+        tenant_id = get_jwt_identity()
+        data = specialist_execution_ns.payload
+        specialist_id = data['specialist_id']
+        try:
+            specialist = Specialist.query.get(specialist_id)
+            if specialist:
+                configuration = cache_manager.specialists_config_cache.get_config(specialist.type,
+                                                                                  specialist.type_version)
+                current_app.logger.debug(f"Configuration returned: {configuration}")
+                if configuration:
+                    if 'arguments' in configuration:
+                        return {
+                            'arguments': configuration['arguments'],
+                        }, 200
+                    else:
+                        specialist_execution_ns.abort(404, 'No arguments found in specialist configuration.')
+                else:
+                    specialist_execution_ns.abort(404, 'Error fetching Specialist configuration.')
+            else:
+                specialist_execution_ns.abort(404, 'Error fetching Specialist')
+        except Exception as e:
+            current_app.logger.error(f"Error while retrieving Specialist configuration: {str(e)}")
+            specialist_execution_ns.abort(500, 'Unexpected Error while fetching Specialist configuration.')

eveai_app/__init__.py

@@ -1,7 +1,7 @@
 import logging
 import os
-from flask import Flask, render_template, jsonify, flash, redirect, request
-from flask_security import SQLAlchemyUserDatastore, LoginForm
+from flask import Flask, jsonify
+from flask_security import SQLAlchemyUserDatastore
 from flask_security.signals import user_authenticated
 from werkzeug.middleware.proxy_fix import ProxyFix
 import logging.config
@@ -12,7 +12,6 @@ from common.models.user import User, Role, Tenant, TenantDomain
 import common.models.interaction
 import common.models.entitlements
 import common.models.document
-from common.utils.nginx_utils import prefixed_url_for
 from common.utils.startup_eveai import perform_startup_actions
 from config.logging_config import LOGGING
 from common.utils.security import set_tenant_session_data

eveai_app/templates/document/edit_catalog.html

@@ -11,7 +11,7 @@ When you change chunking of embedding information, you'll need to manually refre
 {% block content %}
     <form method="post">
         {{ form.hidden_tag() }}
-        {% set disabled_fields = ['type'] %}
+        {% set disabled_fields = ['type', 'embedding_model'] %}
         {% set exclude_fields = [] %}
         <!-- Render Static Fields -->
         {% for field in form.get_static_fields() %}

eveai_app/templates/user/edit_tenant.html

@@ -9,7 +9,7 @@
 {% block content %}
     <form method="post">
         {{ form.hidden_tag() }}
-        {%  set disabled_fields = ['name', 'embedding_model', 'llm_model'] %}
+        {%  set disabled_fields = ['name', 'llm_model'] %}
         {%  set exclude_fields = [] %}
         {% for field in form %}
             {{ render_field(field, disabled_fields, exclude_fields) }}

eveai_app/templates/user/tenant_overview.html

@@ -35,7 +35,7 @@
                 <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 = ['embedding_model', 'llm_model'] %}
+                        {% set model_fields = ['llm_model'] %}
                         {% for field in form %}
                             {{ render_included_field(field, disabled_fields=model_fields, include_fields=model_fields) }}
                         {% endfor %}

eveai_app/views/document_forms.py

@@ -1,6 +1,7 @@
 from flask import session, current_app
 from flask_wtf import FlaskForm
-from wtforms import (StringField, BooleanField, SubmitField, DateField, IntegerField, SelectField, TextAreaField, URLField)
+from wtforms import (StringField, BooleanField, SubmitField, DateField, IntegerField, SelectField, TextAreaField,
+                     URLField)
 from wtforms.validators import DataRequired, Length, Optional, URL, ValidationError, NumberRange
 from flask_wtf.file import FileField, FileRequired
 import json
@@ -30,10 +31,13 @@ class CatalogForm(FlaskForm):
     # Select Field for Catalog Type (Uses the CATALOG_TYPES defined in config)
     type = SelectField('Catalog Type', validators=[DataRequired()])
 
-    min_chunk_size = IntegerField('Minimum Chunk Size (2000)', validators=[NumberRange(min=0), Optional()],
-                                  default=2000)
-    max_chunk_size = IntegerField('Maximum Chunk Size (3000)', validators=[NumberRange(min=0), Optional()],
-                                  default=3000)
+    # Selection fields for processing & creating embeddings
+    embedding_model = SelectField('Embedding Model', choices=[], validators=[DataRequired()])
+
+    min_chunk_size = IntegerField('Minimum Chunk Size (1500)', validators=[NumberRange(min=0), Optional()],
+                                  default=1500)
+    max_chunk_size = IntegerField('Maximum Chunk Size (2500)', validators=[NumberRange(min=0), Optional()],
+                                  default=2500)
 
     # Metadata fields
     user_metadata = TextAreaField('User Metadata', validators=[Optional(), validate_json])
@@ -43,6 +47,7 @@ class CatalogForm(FlaskForm):
         super().__init__(*args, **kwargs)
         # Dynamically populate the 'type' field using the constructor
         self.type.choices = [(key, value['name']) for key, value in CATALOG_TYPES.items()]
+        self.embedding_model.choices = [(model, model) for model in current_app.config['SUPPORTED_EMBEDDINGS']]
 
 
 class EditCatalogForm(DynamicFormBase):
@@ -52,6 +57,9 @@ class EditCatalogForm(DynamicFormBase):
     # Select Field for Catalog Type (Uses the CATALOG_TYPES defined in config)
     type = StringField('Catalog Type', validators=[DataRequired()], render_kw={'readonly': True})
 
+    # Selection fields for processing & creating embeddings
+    embedding_model = StringField('Embedding Model', validators=[DataRequired()], render_kw={'readonly': True})
+
     min_chunk_size = IntegerField('Minimum Chunk Size (2000)', validators=[NumberRange(min=0), Optional()],
                                   default=2000)
     max_chunk_size = IntegerField('Maximum Chunk Size (3000)', validators=[NumberRange(min=0), Optional()],
@@ -59,7 +67,7 @@ class EditCatalogForm(DynamicFormBase):
 
     # Metadata fields
     user_metadata = TextAreaField('User Metadata', validators=[Optional(), validate_json])
-    system_metadata = TextAreaField('System Metadata', validators=[Optional(), validate_json],)
+    system_metadata = TextAreaField('System Metadata', validators=[Optional(), validate_json], )
 
 
 class ProcessorForm(FlaskForm):

eveai_app/views/document_views.py

@@ -684,8 +684,9 @@ def create_default_rag_library():
             name='Default RAG Catalog',
             description='Default RAG Catalog',
             type="STANDARD_CATALOG",
-            min_chunk_size=2000,
-            max_chunk_size=3000,
+            min_chunk_size=1500,
+            max_chunk_size=2500,
+            embedding_model="mistral.mistral-embed"
         )
         set_logging_information(cat, timestamp)
 
@@ -696,7 +697,7 @@ def create_default_rag_library():
             name='Default HTML Processor',
             description='Default HTML Processor',
             catalog_id=cat.id,
-            type="HTML Processor",
+            type="HTML_PROCESSOR",
             configuration={
                 "html_tags": "p, h1, h2, h3, h4, h5, h6, li, table, thead, tbody, tr, td",
                 "html_end_tags": "p, li, table",

eveai_app/views/user_forms.py

@@ -21,7 +21,6 @@ class TenantForm(FlaskForm):
     # Timezone
     timezone = SelectField('Timezone', choices=[], validators=[DataRequired()])
     # LLM fields
-    embedding_model = SelectField('Embedding Model', choices=[], validators=[DataRequired()])
     llm_model = SelectField('Large Language Model', choices=[], validators=[DataRequired()])
     # Embedding variables
     submit = SubmitField('Submit')
@@ -36,7 +35,6 @@ class TenantForm(FlaskForm):
         # initialise timezone
         self.timezone.choices = [(tz, tz) for tz in pytz.all_timezones]
         # initialise LLM fields
-        self.embedding_model.choices = [(model, model) for model in current_app.config['SUPPORTED_EMBEDDINGS']]
         self.llm_model.choices = [(model, model) for model in current_app.config['SUPPORTED_LLMS']]
         # Initialize fallback algorithms
         self.type.choices = [(t, t) for t in current_app.config['TENANT_TYPES']]

eveai_app/views/user_views.py

@@ -228,7 +228,6 @@ def handle_tenant_selection():
     # set tenant information in the session
     session['tenant'] = the_tenant.to_dict()
     session['default_language'] = the_tenant.default_language
-    session['embedding_model'] = the_tenant.embedding_model
     session['llm_model'] = the_tenant.llm_model
     # remove catalog-related items from the session
     session.pop('catalog_id', None)

eveai_chat_workers/retrievers/standard_rag.py

@@ -10,7 +10,7 @@ from common.extensions import db
 from common.models.document import Document, DocumentVersion, Catalog, Retriever
 from common.models.user import Tenant
 from common.utils.datetime_utils import get_date_in_timezone
-from common.utils.model_utils import get_model_variables
+from common.utils.model_utils import get_embedding_model_and_class
 from .base import BaseRetriever
 
 from .registry import RetrieverRegistry
@@ -25,10 +25,10 @@ class StandardRAGRetriever(BaseRetriever):
 
         retriever = Retriever.query.get_or_404(retriever_id)
         self.catalog_id = retriever.catalog_id
+        self.tenant_id = tenant_id
         self.similarity_threshold = retriever.configuration.get('es_similarity_threshold', 0.3)
         self.k = retriever.configuration.get('es_k', 8)
         self.tuning = retriever.tuning
-        self.model_variables = get_model_variables(self.tenant_id)
 
         self.log_tuning("Standard RAG retriever initialized")
 
@@ -161,8 +161,9 @@ class StandardRAGRetriever(BaseRetriever):
 
     def _get_query_embedding(self, query: str):
         """Get embedding for the query text"""
-        embedding_model = self.model_variables.embedding_model
-        return embedding_model.embed_query(query)
+        catalog = Catalog.query.get_or_404(self.catalog_id)
+        embedding_model, embedding_model_class = get_embedding_model_and_class(self.tenant_id, self.catalog_id,
+                                                                               catalog.embedding_model)
 
 
 # Register the retriever type

eveai_workers/tasks.py

@@ -12,18 +12,20 @@ from langchain_core.runnables import RunnablePassthrough
 from sqlalchemy import or_
 from sqlalchemy.exc import SQLAlchemyError
 
-from common.extensions import db, minio_client
+from common.extensions import db
 from common.models.document import DocumentVersion, Embedding, Document, Processor, Catalog
 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
+from common.utils.model_utils import create_language_template, get_model_variables, get_embedding_model_and_class
 
 from common.utils.business_event import BusinessEvent
 from common.utils.business_event_context import current_event
 from config.type_defs.processor_types import PROCESSOR_TYPES
 from eveai_workers.processors.processor_registry import ProcessorRegistry
 
+from common.utils.eveai_exceptions import EveAIInvalidEmbeddingModel
+
 from common.utils.config_field_types import json_to_pattern_list
 
 
@@ -155,7 +157,7 @@ def embed_markdown(tenant, model_variables, document_version, catalog, processor
 
     # Create embeddings
     with current_event.create_span("Create Embeddings"):
-        embeddings = embed_chunks(tenant, model_variables, document_version, enriched_chunks)
+        embeddings = embed_chunks(tenant, catalog, document_version, enriched_chunks)
 
     # Update document version and save embeddings
     try:
@@ -227,9 +229,14 @@ def summarize_chunk(tenant, model_variables, document_version, chunk):
         raise
 
 
-def embed_chunks(tenant, model_variables, document_version, chunks):
-    embedding_model = model_variables.embedding_model
+def embed_chunks(tenant, catalog, document_version, chunks):
+    if catalog.embedding_model:
+        embedding_model, embedding_model_class = get_embedding_model_and_class(tenant.id, catalog.id,
+                                                                               catalog.embedding_model)
+    else:
+        raise EveAIInvalidEmbeddingModel(tenant.id, catalog.id)
 
+    # Actually embed
     try:
         embeddings = embedding_model.embed_documents(chunks)
     except LangChainException as e:
@@ -241,7 +248,7 @@ def embed_chunks(tenant, model_variables, document_version, chunks):
     # Add embeddings to the database
     new_embeddings = []
     for chunk, embedding in zip(chunks, embeddings):
-        new_embedding = model_variables.embedding_model_class()
+        new_embedding = embedding_model_class()
         new_embedding.document_version = document_version
         new_embedding.active = True
         new_embedding.chunk = chunk
@@ -309,7 +316,7 @@ def combine_chunks_for_markdown(potential_chunks, min_chars, max_chars, processo
 
         return False
 
-    chunking_patterns = json_to_pattern_list(processor.configuration.get('chunking_patterns', []))
+    chunking_patterns = json_to_pattern_list(processor.configuration.get('chunking_patterns', ""))
 
     processor.log_tuning(f'Chunking Patterns Extraction: ', {
         'Full Configuration': processor.configuration,

migrations/public/versions/b02d9ad000f4_move_embedding_model_settings_to_catalog.py

@@ -0,0 +1,32 @@
+"""Move embedding model settings to Catalog
+
+Revision ID: b02d9ad000f4
+Revises: f0ab991a6411
+Create Date: 2025-02-21 22:11:10.313148
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = 'b02d9ad000f4'
+down_revision = 'f0ab991a6411'
+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('embedding_model')
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('tenant', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('embedding_model', sa.VARCHAR(length=50), autoincrement=False, nullable=True))
+
+    # ### end Alembic commands ###

migrations/tenant/env.py

@@ -71,7 +71,7 @@ target_db = current_app.extensions['migrate'].db
 def get_public_table_names():
     # TODO: This function should include the necessary functionality to automatically retrieve table names
     return ['role', 'roles_users', 'tenant', 'user', 'tenant_domain','license_tier', 'license', 'license_usage',
-            'business_event_log']
+            'business_event_log', 'tenant_project']
 
 
 PUBLIC_TABLES = get_public_table_names()

migrations/tenant/versions/2b04e961eee4_link_embedding_models_to_catalog.py

@@ -0,0 +1,29 @@
+"""Link embedding models to Catalog
+
+Revision ID: 2b04e961eee4
+Revises: e58835fadd96
+Create Date: 2025-02-21 22:06:43.527013
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import pgvector
+
+
+# revision identifiers, used by Alembic.
+revision = '2b04e961eee4'
+down_revision = 'e58835fadd96'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('catalog', sa.Column('embedding_model', sa.String(length=50), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('catalog', 'embedding_model')
+    # ### end Alembic commands ###

nginx/nginx.conf

@@ -74,24 +74,24 @@ http {
             root   /etc/nginx/public;
         }
 
-        location /chat/ {
-            proxy_pass http://eveai_chat:5002/;
-
-            proxy_set_header Host $host;
-            proxy_set_header X-Real-IP $remote_addr;
-            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-            proxy_set_header X-Forwarded-Proto $scheme;
-            proxy_http_version 1.1;
-            proxy_set_header Upgrade $http_upgrade;
-            proxy_set_header Connection "upgrade";
-            proxy_buffering off;
-
-            # Add CORS headers
-            add_header 'Access-Control-Allow-Origin' '*' always;
-            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS' always;
-            add_header 'Access-Control-Allow-Headers' 'DNT,X-CustomHeader,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Authorization' always;
-            add_header 'Access-Control-Allow-Credentials' 'true' always;
-        }
+#         location /chat/ {
+#             proxy_pass http://eveai_chat:5002/;
+#
+#             proxy_set_header Host $host;
+#             proxy_set_header X-Real-IP $remote_addr;
+#             proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+#             proxy_set_header X-Forwarded-Proto $scheme;
+#             proxy_http_version 1.1;
+#             proxy_set_header Upgrade $http_upgrade;
+#             proxy_set_header Connection "upgrade";
+#             proxy_buffering off;
+#
+#             # Add CORS headers
+#             add_header 'Access-Control-Allow-Origin' '*' always;
+#             add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS' always;
+#             add_header 'Access-Control-Allow-Headers' 'DNT,X-CustomHeader,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Authorization' always;
+#             add_header 'Access-Control-Allow-Credentials' 'true' always;
+#         }
 
         location /admin/ {
 #             include uwsgi_params;

requirements.txt

@@ -1,4 +1,4 @@
-alembic~=1.13.2
+alembic~=1.14.1
 annotated-types~=0.7.0
 bcrypt~=4.1.3
 beautifulsoup4~=4.12.3
@@ -6,18 +6,17 @@ celery~=5.4.0
 certifi~=2024.7.4
 chardet~=5.2.0
 cors~=1.0.1
-Flask~=3.0.3
+Flask~=3.1.0
 Flask-BabelEx~=0.9.4
 Flask-Bootstrap~=3.3.7.1
 Flask-Cors~=5.0.0
-Flask-JWT-Extended~=4.6.0
+Flask-JWT-Extended~=4.7.1
 Flask-Login~=0.6.3
 flask-mailman~=1.1.1
-Flask-Migrate~=4.0.7
+Flask-Migrate~=4.1.0
 Flask-Principal~=0.4.0
-Flask-Security-Too~=5.5.2
+Flask-Security-Too~=5.6.0
 Flask-Session~=0.8.0
-Flask-SocketIO~=5.3.6
 Flask-SQLAlchemy~=3.1.1
 Flask-WTF~=1.2.1
 gevent~=24.2.1
@@ -43,12 +42,10 @@ pgvector~=0.2.5
 pycryptodome~=3.20.0
 pydantic~=2.9.1
 PyJWT~=2.8.0
-PySocks~=1.7.1
 python-dateutil~=2.9.0.post0
 python-engineio~=4.9.1
 python-iso639~=2024.4.27
 python-magic~=0.4.27
-python-socketio~=5.11.3
 pytz~=2024.1
 PyYAML~=6.0.2
 redis~=5.0.4
@@ -64,7 +61,7 @@ groq~=0.9.0
 pydub~=0.25.1
 argparse~=1.4.0
 minio~=7.2.7
-Werkzeug~=3.0.3
+Werkzeug~=3.1.3
 itsdangerous~=2.2.0
 cryptography~=43.0.0
 graypy~=2.1.0
@@ -91,4 +88,6 @@ dogpile.cache~=1.3.3
 python-docx~=1.1.2
 crewai~=0.102.0
 sseclient~=0.0.27
-termcolor~=2.5.0
+termcolor~=2.5.0
+mistral-common~=1.5.3
+mistralai~=1.5.0

tests/interactive_client/specialist_client.py

@@ -1,247 +0,0 @@
-#!/usr/bin/env python3
-import json
-import logging
-import sys
-import time
-import requests  # Used for calling the auth API
-from datetime import datetime
-import yaml      # For loading the YAML configuration
-from urllib.parse import urlparse
-
-import socketio  # Official python-socketio client
-
-# ----------------------------
-# Constants for authentication and specialist selection
-# ----------------------------
-API_KEY = "EveAI-8342-2966-4731-6578-1010-8903-4230-4378"
-TENANT_ID = 2
-SPECIALIST_ID = 2
-BASE_API_URL = "http://macstudio.ask-eve-ai-local.com:8080/api/api/v1"
-BASE_SOCKET_URL = "http://macstudio.ask-eve-ai-local.com:8080"
-CONFIG_FILE = "config/specialists/SPIN_SPECIALIST/1.0.0.yaml"  # Path to specialist configuration
-
-# ----------------------------
-# Logging Configuration
-# ----------------------------
-LOG_FILENAME = "specialist_client.log"
-logging.basicConfig(
-    filename=LOG_FILENAME,
-    level=logging.DEBUG,
-    format="%(asctime)s %(levelname)s: %(message)s"
-)
-console_handler = logging.StreamHandler(sys.stdout)
-console_handler.setLevel(logging.INFO)
-logging.getLogger('').addHandler(console_handler)
-
-# ----------------------------
-# Create the Socket.IO client using the official python-socketio client
-# ----------------------------
-sio = socketio.Client(logger=True, engineio_logger=True)
-room = None  # Global variable to store the assigned room
-
-# ----------------------------
-# Event Handlers
-# ----------------------------
-@sio.event
-def connect():
-    logging.info("Connected to Socket.IO server.")
-    print("Connected to server.")
-
-@sio.event
-def disconnect():
-    logging.info("Disconnected from Socket.IO server.")
-    print("Disconnected from server.")
-
-@sio.on("connect_error")
-def on_connect_error(data):
-    logging.error("Connect error: %s", data)
-    print("Connect error:", data)
-
-@sio.on("authenticated")
-def on_authenticated(data):
-    global room
-    room = data.get("room")
-    logging.info("Authenticated. Room: %s", room)
-    print("Authenticated. Room:", room)
-
-@sio.on("room_join")
-def on_room_join(data):
-    global room
-    room = data.get("room")
-    logging.info("Room join event received. Room: %s", room)
-    print("Joined room:", room)
-
-@sio.on("token_expired")
-def on_token_expired(data):
-    logging.warning("Token expired.")
-    print("Token expired. Please refresh your session.")
-
-@sio.on("reconnect_attempt")
-def on_reconnect_attempt(attempt):
-    logging.info("Reconnect attempt #%s", attempt)
-    print(f"Reconnect attempt #{attempt}")
-
-@sio.on("reconnect")
-def on_reconnect():
-    logging.info("Reconnected successfully.")
-    print("Reconnected to server.")
-
-@sio.on("reconnect_failed")
-def on_reconnect_failed():
-    logging.error("Reconnection failed.")
-    print("Reconnection failed. Please refresh.")
-
-@sio.on("room_rejoin_result")
-def on_room_rejoin_result(data):
-    if data.get("success"):
-        global room
-        room = data.get("room")
-        logging.info("Successfully rejoined room: %s", room)
-        print("Rejoined room:", room)
-    else:
-        logging.error("Failed to rejoin room.")
-        print("Failed to rejoin room.")
-
-@sio.on("bot_response")
-def on_bot_response(data):
-    logging.info("Received bot response: %s", data)
-    print("Bot response received:")
-    print(json.dumps(data, indent=2))
-
-@sio.on("task_status")
-def on_task_status(data):
-    logging.info("Received task status: %s", data)
-    print("Task status:")
-    print(json.dumps(data, indent=2))
-
-# ----------------------------
-# Helper: Retrieve token from REST API
-# ----------------------------
-def retrieve_token(api_url: str) -> str:
-    payload = {
-        "tenant_id": TENANT_ID,
-        "api_key": API_KEY
-    }
-    try:
-        logging.info("Requesting token from %s with payload: %s", api_url, payload)
-        response = requests.post(api_url, json=payload)
-        response.raise_for_status()
-        token = response.json()["access_token"]
-        logging.info("Token retrieved successfully.")
-        return token
-    except Exception as e:
-        logging.error("Failed to retrieve token: %s", e)
-        raise e
-
-# ----------------------------
-# Main Interactive UI Function
-# ----------------------------
-def main():
-    global room
-
-    # Retrieve the token
-    auth_url = f"{BASE_API_URL}/auth/token"
-    try:
-        token = retrieve_token(auth_url)
-        print("Token retrieved successfully.")
-    except Exception as e:
-        print("Error retrieving token. Check logs for details.")
-        sys.exit(1)
-
-    # Parse the BASE_SOCKET_URL
-    parsed_url = urlparse(BASE_SOCKET_URL)
-    host_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
-
-    # Connect to the Socket.IO server.
-    # Note: Use `auth` instead of `query_string` (the official client uses the `auth` parameter)
-    try:
-        sio.connect(
-            host_url,
-            socketio_path='/chat/socket.io',
-            auth={"token": token},
-        )
-    except Exception as e:
-        logging.error("Failed to connect to Socket.IO server: %s", e)
-        print("Failed to connect to Socket.IO server:", e)
-        sys.exit(1)
-
-    # Allow time for authentication and room assignment.
-    time.sleep(2)
-    if not room:
-        logging.warning("No room assigned. Exiting.")
-        print("No room assigned by the server. Exiting.")
-        sio.disconnect()
-        sys.exit(1)
-
-    # Load specialist configuration from YAML.
-    try:
-        with open(CONFIG_FILE, "r") as f:
-            specialist_config = yaml.safe_load(f)
-        arg_config = specialist_config.get("arguments", {})
-        logging.info("Loaded specialist argument configuration: %s", arg_config)
-    except Exception as e:
-        logging.error("Failed to load specialist configuration: %s", e)
-        print("Failed to load specialist configuration. Exiting.")
-        sys.exit(1)
-
-    # Dictionary to store default values for static arguments (except "query")
-    static_defaults = {}
-
-    print("\nInteractive Specialist Client")
-    print("For each iteration, you will be prompted for the following arguments:")
-    for key, details in arg_config.items():
-        print(f" - {details.get('name', key)}: {details.get('description', '')}")
-    print("Type 'quit' or 'exit' as the query to end the session.\n")
-
-    # Interactive loop: prompt for arguments and send user message.
-    while True:
-        current_arguments = {}
-        for arg_key, arg_details in arg_config.items():
-            prompt_msg = f"Enter {arg_details.get('name', arg_key)}"
-            desc = arg_details.get("description", "")
-            if desc:
-                prompt_msg += f" ({desc})"
-            if arg_key != "query":
-                default_value = static_defaults.get(arg_key, "")
-                if default_value:
-                    prompt_msg += f" [default: {default_value}]"
-                prompt_msg += ": "
-                value = input(prompt_msg).strip()
-                if not value:
-                    value = default_value
-                static_defaults[arg_key] = value
-            else:
-                prompt_msg += " (required): "
-                value = input(prompt_msg).strip()
-                while not value:
-                    print("Query is required. Please enter a value.")
-                    value = input(prompt_msg).strip()
-            current_arguments[arg_key] = value
-
-        if current_arguments.get("query", "").lower() in ["quit", "exit"]:
-            break
-
-        try:
-            timezone = datetime.now().astimezone().tzname()
-        except Exception:
-            timezone = "UTC"
-
-        payload = {
-            "token": token,
-            "tenant_id": TENANT_ID,
-            "specialist_id": SPECIALIST_ID,
-            "arguments": current_arguments,
-            "timezone": timezone,
-            "room": room
-        }
-
-        logging.info("Sending user_message with payload: %s", payload)
-        print("Sending message to specialist...")
-        sio.emit("user_message", payload)
-        time.sleep(1)
-
-    print("Exiting interactive session.")
-    sio.disconnect()
-
-if __name__ == "__main__":
-    main()

tests/specialist_execution/test_specialist_client.py

@@ -18,9 +18,7 @@ sys.path.append(project_root)
 API_BASE_URL = "http://macstudio.ask-eve-ai-local.com:8080/api/api/v1"
 TENANT_ID = 2  # Replace with your tenant ID
 API_KEY = "EveAI-5096-5466-6143-1487-8085-4174-2080-7208"  # Replace with your API key
-SPECIALIST_TYPE = "SPIN_SPECIALIST"  # Replace with your specialist type
 SPECIALIST_ID = 5  # Replace with your specialist ID
-ROOT_FOLDER = "../.."
 
 
 def get_auth_token() -> str:
@@ -52,15 +50,27 @@ def get_session_id(auth_token: str) -> str:
     return response.json()["session_id"]
 
 
-def load_specialist_config() -> Dict[str, Any]:
-    """Load specialist configuration from YAML file"""
-    config_path = f"{ROOT_FOLDER}/config/specialists/{SPECIALIST_TYPE}/1.0.0.yaml"
-    if not os.path.exists(config_path):
-        print(colored(f"Error: Configuration file not found: {config_path}", "red"))
-        sys.exit(1)
+def get_specialist_config(auth_token: str, specialist_id: int) -> Dict[str, Any]:
+    """Get specialist configuration from API"""
+    headers = {
+        'Authorization': f'Bearer {auth_token}',
+        'Content-Type': 'application/json'
+    }
 
-    with open(config_path, 'r') as f:
-        return yaml.safe_load(f)
+    response = requests.get(
+        f"{API_BASE_URL}/specialist-execution/specialist_arguments",
+        headers=headers,
+        json={
+            'specialist_id': specialist_id
+        }
+    )
+
+    print(colored(f"Status Code: {response.status_code}", "cyan"))
+    if response.status_code == 200:
+        config_data = response.json()
+        return config_data.get('arguments', {})
+    else:
+        raise Exception(f"Failed to get specialist configuration: {response.text}")
 
 
 def get_argument_value(arg_name: str, arg_config: Dict[str, Any], previous_value: Any = None) -> Any:
@@ -163,8 +173,10 @@ def main():
         auth_token = get_auth_token()
 
         # Load specialist configuration
-        print(colored(f"Loading specialist configuration {SPECIALIST_TYPE}", "cyan"))
-        config = load_specialist_config()
+        print(colored(f"Loading specialist configuration", "cyan"))
+        config = {
+            'arguments': get_specialist_config(auth_token, SPECIALIST_ID)
+        }
         previous_args = None
 
         while True: