updates to controller and pydantic BaseModels

topos/api/api_routes.py

@@ -13,7 +13,7 @@
 from collections import Counter, OrderedDict, defaultdict
 from pydantic import BaseModel
 
-from ..generations.chat_gens import LLMChatGens
+from ..generations.chat_gens import LLMController
 from ..utilities.utils import create_conversation_string
 from ..services.ontology_service.mermaid_chart import MermaidCreator
 
@@ -136,7 +136,7 @@ async def conv_to_image(request: ConversationIDRequest):
     provider = 'ollama' # defaults to ollama right now
     api_key = 'ollama'
 
-    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+    llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
     context = create_conversation_string(conv_data, 6)
     print(context)
@@ -180,21 +180,23 @@ async def conv_to_image(request: ConversationIDRequest):
 class GenNextMessageOptions(BaseModel):
     conversation_id: str
     query: str
+    provider: str
+    api_key: str
     model: str
     voice_settings: dict
 
 @router.post("/gen_next_message_options")
 async def create_next_messages(request: GenNextMessageOptions):
     conversation_id = request.conversation_id
     query = request.query
-
+    print(request.provider, "/", request.model)
+    print(request.api_key)
     # model specifications
-    # TODO UPDATE SO ITS NOT HARDCODED
     model = request.model if request.model != None else "dolphin-llama3"
-    provider = 'ollama' # defaults to ollama right now
-    api_key = 'ollama'
+    provider = request.provider if request.provider != None else 'ollama' # defaults to ollama right now
+    api_key = request.api_key if request.api_key != None else 'ollama'
 
-    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+    llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
     voice_settings = request.voice_settings  if request.voice_settings != None else """{
     "tone": "analytical",
@@ -246,7 +248,7 @@ async def create_next_messages(request: ConversationTopicsRequest):
     provider = 'ollama' # defaults to ollama right now
     api_key = 'ollama'
 
-    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+    llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
     # load conversation
     conv_data = cache_manager.load_from_cache(conversation_id)
@@ -341,6 +343,8 @@ class MermaidChartPayload(BaseModel):
     conversation_id: str
     full_conversation: bool = False
     model: str = "dolphin-llama3"
+    provider: str = "ollama"
+    api_key: str = "ollama"
     temperature: float = 0.04
 
 @router.post("/generate_mermaid_chart")
@@ -350,11 +354,11 @@ async def generate_mermaid_chart(payload: MermaidChartPayload):
         full_conversation = payload.full_conversation
         # model specifications
         model = payload.model
-        provider = payload.get('provider', 'ollama') # defaults to ollama right now
-        api_key = payload.get('api_key', 'ollama')
+        provider = payload.provider# defaults to ollama right now
+        api_key = payload.api_key
         temperature = payload.temperature
-
-        llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+        
+        llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
         mermaid_generator = MermaidCreator(llm_client)
 
@@ -365,7 +369,7 @@ async def generate_mermaid_chart(payload: MermaidChartPayload):
             conv_data = cache_manager.load_from_cache(conversation_id)
             if conv_data is None:
                 raise HTTPException(status_code=404, detail="Conversation not found in cache")
-            print(f"\t[ generating mermaid chart :: using model {model} :: full conversation ]")
+            print(f"\t[ generating mermaid chart :: {provider}/{model} :: full conversation ]")
             return {"status": "generating", "response": "generating mermaid chart", 'completed': False}
             # TODO: Complete this branch if needed
 
@@ -375,6 +379,7 @@ async def generate_mermaid_chart(payload: MermaidChartPayload):
                 print(f"\t[ generating mermaid chart :: using model {model} ]")
                 try:
                     mermaid_string = await mermaid_generator.get_mermaid_chart(message)
+                    print(mermaid_string)
                     if mermaid_string == "Failed to generate mermaid":
                         return {"status": "error", "response": mermaid_string, 'completed': True}
                     else:

topos/api/websocket_handlers.py

@@ -4,7 +4,7 @@
 import traceback
 import pprint
 
-from ..generations.chat_gens import LLMChatGens
+from ..generations.chat_gens import LLMController
 # from topos.FC.semantic_compression import SemanticCompression
 # from ..config import get_openai_api_key
 from ..models.llm_classes import vision_models
@@ -49,6 +49,7 @@ async def chat(websocket: WebSocket):
         while True:
             data = await websocket.receive_text()
             payload = json.loads(data)
+            print(payload)
             conversation_id = payload["conversation_id"]
             message_id = payload["message_id"]
             chatbot_msg_id = payload["chatbot_msg_id"]
@@ -72,8 +73,8 @@ async def chat(websocket: WebSocket):
             model = payload.get("model", "solar")
             provider = payload.get('provider', 'ollama') # defaults to ollama right now
             api_key = payload.get('api_key', 'ollama')
-
-            llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+            print("inputs", provider, api_key)
+            llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
 
             # Update default_config with provided processing_config, if any
@@ -289,13 +290,14 @@ async def meta_chat(websocket: WebSocket):
             temperature = float(payload.get("temperature", 0.04))
             current_topic = payload.get("topic", "Unknown")
 
-
             # model specifications
             model = payload.get("model", "solar")
             provider = payload.get('provider', 'ollama') # defaults to ollama right now
             api_key = payload.get('api_key', 'ollama')
+            print(provider,"/",model)
+            print(api_key)
 
-            llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+            llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
             # Set system prompt
             system_prompt = f"""You are a highly skilled conversationalist, adept at communicating strategies and tactics. Help the user navigate their current conversation to determine what to say next. 
@@ -363,7 +365,7 @@ async def meta_chat(websocket: WebSocket):
             provider = payload.get('provider', 'ollama') # defaults to ollama right now
             api_key = payload.get('api_key', 'ollama')
 
-            llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+            llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
 
             # load conversation
@@ -427,7 +429,7 @@ async def meta_chat(websocket: WebSocket):
             api_key = payload.get('api_key', 'ollama')
             temperature = float(payload.get("temperature", 0.04))
 
-            llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+            llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
             mermaid_generator = MermaidCreator(llm_client)
             # load conversation

topos/channel/experimental/debatesim_experimental_think.py

@@ -29,7 +29,7 @@
 from ..FC.argument_detection import ArgumentDetection
 from ..config import get_openai_api_key
 from ..models.llm_classes import vision_models
-from ..generations.chat_gens import LLMChatGens
+from ..generations.chat_gens import LLMController
 from ..services.database.app_state import AppState
 from ..utilities.utils import create_conversation_string
 from ..services.classification_service.base_analysis import base_text_classifier, base_token_classifier
@@ -344,7 +344,7 @@ async def debate_step(self, websocket: WebSocket, data, app_state):
         provider = payload.get('provider', 'ollama') # defaults to ollama right now
         api_key = payload.get('api_key', 'ollama')
 
-        llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+        llm_client = LLMController(model_name=model, provider=provider, api_key=api_key)
 
         temperature = float(payload.get("temperature", 0.04))
         current_topic = payload.get("topic", "Unknown")

topos/generations/chat_gens.py

@@ -10,7 +10,7 @@
     "ollama": "dolphin-llama3"
     }
 
-class LLMChatGens:
+class LLMController:
     def __init__(self, model_name: str, provider: str, api_key: str):
         self.provier = provider
         self.api_key = api_key

topos/generations/llm_client.py

@@ -13,7 +13,7 @@ def __init__(self, provider: str, api_key: str):
         self.provider = provider.lower()
         self.api_key = api_key
         self.client = self._init_client()
-        print(f"Init client:: {self.provider}")
+        print(f"Init client :: {self.provider}")
 
     def _init_client(self):
         if self.provider == "openai":

topos/services/ontology_service/mermaid_chart.py

@@ -2,11 +2,11 @@
 import re
 
 from topos.FC.ontological_feature_detection import OntologicalFeatureDetection
-from topos.generations.chat_gens import LLMChatGens
+from topos.generations.chat_gens import LLMController
 
 class MermaidCreator:
-    def __init__(self, llmChatGens: LLMChatGens):
-        self.client = llmChatGens
+    def __init__(self, LLMController: LLMController):
+        self.client = LLMController
 
     def get_ontology_old_method(self, message):
         user_id = "jonny"
@@ -163,7 +163,7 @@ async def get_mermaid_chart(self, message, websocket = None):
                 if websocket:
                     await websocket.send_json({"status": "generating", "response": f"generating mermaid_chart_from_triples :: try {attempt + 1}", 'completed': False})
             response = self.client.generate_response_messages(message_history)
-            mermaid_chart = self.client.extract_mermaid_chart(response)
+            mermaid_chart = self.extract_mermaid_chart(response)
             if mermaid_chart:
                 # refined_mermaid_chart = refine_mermaid_lines(mermaid_chart)
                 return mermaid_chart