Merge pull request #9 from jonnyjohnson1/jonny/llmclient_class

Jonny/llmclient class

topos/api/api_routes.py

@@ -13,9 +13,9 @@
 from collections import Counter, OrderedDict, defaultdict
 from pydantic import BaseModel
 
-from ..generations.ollama_chat import generate_response
+from ..generations.chat_gens import LLMChatGens
 from ..utilities.utils import create_conversation_string
-from ..services.ontology_service.mermaid_chart import get_mermaid_chart
+from ..services.ontology_service.mermaid_chart import MermaidCreator
 
 cache_manager = ConversationCacheManager()
 class ConversationIDRequest(BaseModel):
@@ -129,16 +129,23 @@ async def conv_to_image(request: ConversationIDRequest):
     if conv_data is None:
         raise HTTPException(status_code=404, detail="Conversation not found in cache")
 
-
+
+    # model specifications
+    # TODO UPDATE SO ITS NOT HARDCODED
     model = "dolphin-llama3"
+    provider = 'ollama' # defaults to ollama right now
+    api_key = 'ollama'
+
+    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+
     context = create_conversation_string(conv_data, 6)
     print(context)
     print(f"\t[ converting conversation to image to text prompt: using model {model}]")
     conv_to_text_img_prompt = "Create an interesting, and compelling image-to-text prompt that can be used in a diffussor model. Be concise and convey more with the use of metaphor. Steer the image style towards Slavador Dali's fantastic, atmospheric, heroesque paintings that appeal to everyman themes."
-    txt_to_img_prompt = generate_response(context, conv_to_text_img_prompt, model=model, temperature=0)
+    txt_to_img_prompt = llm_client.generate_response(context, conv_to_text_img_prompt, temperature=0)
     # print(txt_to_img_prompt)
     print(f"\t[ generating a file name {model} ]")
-    txt_to_img_filename = generate_response(txt_to_img_prompt, "Based on the context create an appropriate, and BRIEF, filename with no spaces. Do not use any file extensions in your name, that will be added in a later step.", model=model, temperature=0)
+    txt_to_img_filename = llm_client.generate_response(txt_to_img_prompt, "Based on the context create an appropriate, and BRIEF, filename with no spaces. Do not use any file extensions in your name, that will be added in a later step.", temperature=0)
 
     # run huggingface comic diffusion
     pipeline = DiffusionPipeline.from_pretrained("ogkalu/Comic-Diffusion")
@@ -180,7 +187,15 @@ class GenNextMessageOptions(BaseModel):
 async def create_next_messages(request: GenNextMessageOptions):
     conversation_id = request.conversation_id
     query = request.query
+
+    # 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'
+
+    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+
     voice_settings = request.voice_settings  if request.voice_settings != None else """{
     "tone": "analytical",
     "distance": "distant",
@@ -211,7 +226,7 @@ async def create_next_messages(request: GenNextMessageOptions):
     system_prompt += conv_json
 
 
-    next_message_options = generate_response(system_prompt, query, model=model, temperature=0)
+    next_message_options = llm_client.generate_response(system_prompt, query, temperature=0)
     print(next_message_options)
 
     # return the options
@@ -225,36 +240,65 @@ class ConversationTopicsRequest(BaseModel):
 @router.post("/gen_conversation_topics")
 async def create_next_messages(request: ConversationTopicsRequest):
     conversation_id = request.conversation_id
+    # 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'
+
+    llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
 
     # load conversation
     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")
 
     context = create_conversation_string(conv_data, 12)
-    print(f"\t[ generating summary :: model {model} :: subject {subject}]")
+    # print(f"\t[ generating summary :: model {model} :: subject {subject}]")
 
     query = f""
     # topic list first pass
     system_prompt = "PRESENT CONVERSATION:\n-------<context>" + context + "\n-------\n"
     query += """List the topics and those closely related to what this conversation traverses."""
-    topic_list = generate_response(system_prompt, query, model=model, temperature=0)
+    topic_list = llm_client.generate_response(system_prompt, query, temperature=0)
     print(topic_list)
 
     # return the image
     return {"response" : topic_list}
 
 
 @router.post("/list_models")
-async def list_models():
-    url = "http://localhost:11434/api/tags"
+async def list_models(provider: str = 'ollama', api_key: str = 'ollama'):
+    # Define the URLs for different providers
+
+    list_models_urls = {
+        'ollama': "http://localhost:11434/api/tags",
+        'openai': "https://api.openai.com/v1/models",
+        'groq': "https://api.groq.com/openai/v1/models"
+    }
+
+    if provider not in list_models_urls:
+        raise HTTPException(status_code=400, detail="Unsupported provider")
+
+    # Get the appropriate URL based on the provider
+    url = list_models_urls.get(provider.lower())
+
+    if provider.lower() == 'ollama':
+        # No need for headers with Ollama
+        headers = {}
+    else:
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
     try:
-        result = requests.get(url)
+        # Make the request with the appropriate headers
+        result = requests.get(url, headers=headers)
         if result.status_code == 200:
             return {"result": result.json()}
         else:
-            raise HTTPException(status_code=404, detail="Models not found")
+            raise HTTPException(status_code=result.status_code, detail="Models not found")
     except requests.ConnectionError:
         raise HTTPException(status_code=500, detail="Server connection error")
 
@@ -304,9 +348,18 @@ async def generate_mermaid_chart(payload: MermaidChartPayload):
     try:
         conversation_id = payload.conversation_id
         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')
         temperature = payload.temperature
 
+        llm_client = LLMChatGens(model_name=model, provider=provider, api_key=api_key)
+
+        mermaid_generator = MermaidCreator(llm_client)
+
+
+
         if full_conversation:
             cache_manager = ConversationCacheManager()
             conv_data = cache_manager.load_from_cache(conversation_id)
@@ -321,7 +374,7 @@ async def generate_mermaid_chart(payload: MermaidChartPayload):
             if message:
                 print(f"\t[ generating mermaid chart :: using model {model} ]")
                 try:
-                    mermaid_string = await get_mermaid_chart(message)
+                    mermaid_string = await mermaid_generator.get_mermaid_chart(message)
                     if mermaid_string == "Failed to generate mermaid":
                         return {"status": "error", "response": mermaid_string, 'completed': True}
                     else:

topos/api/websocket_handlers.py

@@ -1,10 +1,10 @@
-from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect
 from datetime import datetime
 import time
 import traceback
 import pprint
 
-from ..generations.ollama_chat import stream_chat
+from ..generations.chat_gens import LLMChatGens
 # from topos.FC.semantic_compression import SemanticCompression
 # from ..config import get_openai_api_key
 from ..models.llm_classes import vision_models
@@ -13,7 +13,7 @@
 from ..utilities.utils import create_conversation_string
 from ..services.classification_service.base_analysis import base_text_classifier, base_token_classifier
 from ..services.loggers.process_logger import ProcessLogger
-from ..services.ontology_service.mermaid_chart import get_mermaid_chart
+from ..services.ontology_service.mermaid_chart import MermaidCreator
 
 # cache database
 from topos.FC.conversation_cache_manager import ConversationCacheManager
@@ -55,11 +55,11 @@ async def chat(websocket: WebSocket):
             chatbot_msg_id = payload["chatbot_msg_id"]
             message = payload["message"]
             message_history = payload["message_history"]
-            model = payload.get("model", "solar")
             temperature = float(payload.get("temperature", 0.04))
             current_topic = payload.get("topic", "Unknown")
             processing_config = payload.get("processing_config", {})
-
+
+
             # Set default values if any key is missing or if processing_config is None
             default_config = {
                 "showInMessageNER": True,
@@ -68,6 +68,14 @@ async def chat(websocket: WebSocket):
                 "calculateModerationTags": True,
                 "showSidebarBaseAnalytics": True
             }
+
+            # model specifications
+            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)
+
 
             # Update default_config with provided processing_config, if any
             config = {**default_config, **processing_config}
@@ -171,9 +179,9 @@ async def chat(websocket: WebSocket):
             is_first_token = True
             total_tokens = 0  # Initialize token counter
             ttfs = 0 # init time to first token value
-            await process_logger.start("llm_generation_stream_chat", provider="ollama", model=model, len_msg_hist=len(simp_msg_history))
+            await process_logger.start("llm_generation_stream_chat", provider=provider, model=model, len_msg_hist=len(simp_msg_history))
             start_time = time.time()  # Track the start time for the whole process
-            for chunk in stream_chat(simp_msg_history, model=model, temperature=temperature):
+            for chunk in llm_client.stream_chat(simp_msg_history, temperature=temperature):
                 if len(chunk) > 0:
                     if is_first_token:
                         ttfs_end_time = time.time()
@@ -283,6 +291,14 @@ 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')
+
+            llm_client = LLMChatGens(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. 
             You possess a private, unmentioned expertise: PhDs in CBT and DBT, an elegant, smart, provocative speech style, extensive world travel, and deep literary theory knowledge à la Terry Eagleton. Demonstrate your expertise through your guidance, without directly stating it."""
@@ -308,7 +324,7 @@ async def meta_chat(websocket: WebSocket):
 
             # Processing the chat
             output_combined = ""
-            for chunk in stream_chat(simp_msg_history, model=model, temperature=temperature):
+            for chunk in llm_client.stream_chat(simp_msg_history, temperature=temperature):
                 try:
                     output_combined += chunk
                     await websocket.send_json({"status": "generating", "response": output_combined, 'completed': False})
@@ -342,8 +358,15 @@ async def meta_chat(websocket: WebSocket):
 
             conversation_id = payload["conversation_id"]
             subject = payload.get("subject", "knowledge")
-            model = payload.get("model", "solar")
             temperature = float(payload.get("temperature", 0.04))
+
+            # model specifications
+            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)
+
 
             # load conversation
             cache_manager = ConversationCacheManager()
@@ -367,7 +390,7 @@ async def meta_chat(websocket: WebSocket):
 
             # Processing the chat
             output_combined = ""
-            for chunk in stream_chat(msg_history, model=model, temperature=temperature):
+            for chunk in llm_client.stream_chat(msg_history, temperature=temperature):
                 try:
                     output_combined += chunk
                     await websocket.send_json({"status": "generating", "response": output_combined, 'completed': False})
@@ -400,9 +423,15 @@ async def meta_chat(websocket: WebSocket):
             message = payload.get("message", None)
             conversation_id = payload["conversation_id"]
             full_conversation = payload.get("full_conversation", False)
+            # model specifications
             model = payload.get("model", "dolphin-llama3")
+            provider = payload.get('provider', 'ollama') # defaults to ollama right now
+            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)
+
+            mermaid_generator = MermaidCreator(llm_client)
             # load conversation
             if full_conversation:
                 cache_manager = ConversationCacheManager()
@@ -412,13 +441,13 @@ async def meta_chat(websocket: WebSocket):
                 print(f"\t[ generating mermaid chart :: using model {model} :: full conversation ]")
                 await websocket.send_json({"status": "generating", "response": "generating mermaid chart", 'completed': False})
                 context = create_conversation_string(conv_data, 12)
-                # TODO Coomplete this branch
+                # TODO Complete this branch
             else:
                 if message:
                     print(f"\t[ generating mermaid chart :: using model {model} ]")
                     await websocket.send_json({"status": "generating", "response": "generating mermaid chart", 'completed': False})
                     try:
-                        mermaid_string = await get_mermaid_chart(message, websocket = websocket)
+                        mermaid_string = await mermaid_generator.get_mermaid_chart(message, websocket = websocket)
                         if mermaid_string == "Failed to generate mermaid":
                             await websocket.send_json({"status": "error", "response": mermaid_string, 'completed': True})
                         else:

topos/channel/debatesim.py

@@ -33,7 +33,6 @@
 from ..FC.argument_detection import ArgumentDetection
 from ..config import get_openai_api_key
 from ..models.llm_classes import vision_models
-from ..generations.ollama_chat import stream_chat
 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

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.ollama_chat import stream_chat
+from ..generations.chat_gens import LLMChatGens
 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
@@ -338,7 +338,14 @@ async def debate_step(self, websocket: WebSocket, data, app_state):
             user_id = app_state.get_value("user_id", "")
 
         message_history = payload["message_history"]
+
+        # model specifications
         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)
+
         temperature = float(payload.get("temperature", 0.04))
         current_topic = payload.get("topic", "Unknown")
 
@@ -456,7 +463,7 @@ async def debate_step(self, websocket: WebSocket, data, app_state):
 
         # Processing the chat
         output_combined = ""
-        for chunk in stream_chat(simp_msg_history, model=model, temperature=temperature):
+        for chunk in llm_client.stream_chat(simp_msg_history, model=model, temperature=temperature):
             output_combined += chunk
             await websocket.send_json({"status": "generating", "response": output_combined, 'completed': False})