Flow temperature parameter (#533)

* Add temperature parameter to LlmService and roll out to all LLMs

trustgraph-flow/trustgraph/model/text_completion/azure/llm.py

@@ -55,7 +55,9 @@ class Processor(LlmService):
         self.max_output = max_output
         self.default_model = model
 
-    def build_prompt(self, system, content):
+    def build_prompt(self, system, content, temperature=None):
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         data =  {
             "messages": [
@@ -67,7 +69,7 @@ class Processor(LlmService):
                 }
             ],
             "max_tokens": self.max_output,
-            "temperature": self.temperature,
+            "temperature": effective_temperature,
             "top_p": 1
         }
 
@@ -100,18 +102,22 @@ class Processor(LlmService):
 
         return result
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         try:
 
             prompt = self.build_prompt(
                 system,
-                prompt
+                prompt,
+                effective_temperature
             )
 
             response = self.call_llm(prompt)

trustgraph-flow/trustgraph/model/text_completion/azure_openai/llm.py

@@ -62,12 +62,15 @@ class Processor(LlmService):
             azure_endpoint = endpoint,
         )
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
@@ -86,7 +89,7 @@ class Processor(LlmService):
                         ]
                     }
                 ],
-                temperature=self.temperature,
+                temperature=effective_temperature,
                 max_tokens=self.max_output,
                 top_p=1,
             )

trustgraph-flow/trustgraph/model/text_completion/claude/llm.py

@@ -48,19 +48,22 @@ class Processor(LlmService):
 
         logger.info("Claude LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         try:
 
             response = message = self.claude.messages.create(
                 model=model_name,
                 max_tokens=self.max_output,
-                temperature=self.temperature,
+                temperature=effective_temperature,
                 system = system,
                 messages=[
                     {

trustgraph-flow/trustgraph/model/text_completion/cohere/llm.py

@@ -45,12 +45,15 @@ class Processor(LlmService):
 
         logger.info("Cohere LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         try:
 
@@ -58,7 +61,7 @@ class Processor(LlmService):
                 model=model_name,
                 message=prompt,
                 preamble = system,
-                temperature=self.temperature,
+                temperature=effective_temperature,
                 chat_history=[],
                 prompt_truncation='auto',
                 connectors=[]

trustgraph-flow/trustgraph/model/text_completion/googleaistudio/llm.py

@@ -86,12 +86,18 @@ class Processor(LlmService):
 
         logger.info("GoogleAIStudio LLM service initialized")
 
-    def _get_or_create_config(self, model_name):
-        """Get cached generation config or create new one"""
-        if model_name not in self.generation_configs:
-            logger.info(f"Creating generation config for '{model_name}'")
-            self.generation_configs[model_name] = types.GenerateContentConfig(
-                temperature = self.temperature,
+    def _get_or_create_config(self, model_name, temperature=None):
+        """Get or create generation config with dynamic temperature"""
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
+
+        # Create cache key that includes temperature to avoid conflicts
+        cache_key = f"{model_name}:{effective_temperature}"
+
+        if cache_key not in self.generation_configs:
+            logger.info(f"Creating generation config for '{model_name}' with temperature {effective_temperature}")
+            self.generation_configs[cache_key] = types.GenerateContentConfig(
+                temperature = effective_temperature,
                 top_p = 1,
                 top_k = 40,
                 max_output_tokens = self.max_output,
@@ -99,16 +105,19 @@ class Processor(LlmService):
                 safety_settings = self.safety_settings,
             )
 
-        return self.generation_configs[model_name]
+        return self.generation_configs[cache_key]
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
-        generation_config = self._get_or_create_config(model_name)
+        generation_config = self._get_or_create_config(model_name, effective_temperature)
         # Set system instruction per request (can't be cached)
         generation_config.system_instruction = system
 

trustgraph-flow/trustgraph/model/text_completion/llamafile/llm.py

@@ -50,12 +50,15 @@ class Processor(LlmService):
 
         logger.info("Llamafile LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
@@ -65,15 +68,15 @@ class Processor(LlmService):
                 model=model_name,
                 messages=[
                     {"role": "user", "content": prompt}
-                ]
-                #temperature=self.temperature,
-                #max_tokens=self.max_output,
-                #top_p=1,
-                #frequency_penalty=0,
-                #presence_penalty=0,
-                #response_format={
-                #    "type": "text"
-                #}
+                ],
+                temperature=effective_temperature,
+                max_tokens=self.max_output,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+                response_format={
+                    "type": "text"
+                }
             )
 
             inputtokens = resp.usage.prompt_tokens

trustgraph-flow/trustgraph/model/text_completion/lmstudio/llm.py

@@ -50,12 +50,15 @@ class Processor(LlmService):
 
         logger.info("LMStudio LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
@@ -67,15 +70,15 @@ class Processor(LlmService):
                 model=model_name,
                 messages=[
                     {"role": "user", "content": prompt}
-                ]
-                #temperature=self.temperature,
-                #max_tokens=self.max_output,
-                #top_p=1,
-                #frequency_penalty=0,
-                #presence_penalty=0,
-                #response_format={
-                #    "type": "text"
-                #}
+                ],
+                temperature=effective_temperature,
+                max_tokens=self.max_output,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+                response_format={
+                    "type": "text"
+                }
             )
 
             logger.debug(f"Full response: {resp}")

trustgraph-flow/trustgraph/model/text_completion/mistral/llm.py

@@ -48,12 +48,15 @@ class Processor(LlmService):
 
         logger.info("Mistral LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
@@ -72,7 +75,7 @@ class Processor(LlmService):
                         ]
                     }
                 ],
-                temperature=self.temperature,
+                temperature=effective_temperature,
                 max_tokens=self.max_output,
                 top_p=1,
                 frequency_penalty=0,

trustgraph-flow/trustgraph/model/text_completion/ollama/llm.py

@@ -17,6 +17,7 @@ from .... base import LlmService, LlmResult
 default_ident = "text-completion"
 
 default_model = 'gemma2:9b'
+default_temperature = 0.0
 default_ollama = os.getenv("OLLAMA_HOST", 'http://localhost:11434')
 
 class Processor(LlmService):
@@ -24,30 +25,36 @@ class Processor(LlmService):
     def __init__(self, **params):
 
         model = params.get("model", default_model)
+        temperature = params.get("temperature", default_temperature)
         ollama = params.get("ollama", default_ollama)
 
         super(Processor, self).__init__(
             **params | {
                 "model": model,
+                "temperature": temperature,
                 "ollama": ollama,
             }
         )
 
         self.default_model = model
+        self.temperature = temperature
         self.llm = Client(host=ollama)
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
         try:
 
-            response = self.llm.generate(model_name, prompt)
+            response = self.llm.generate(model_name, prompt, options={'temperature': effective_temperature})
 
             response_text = response['response']
             logger.debug("Sending response...")
@@ -89,6 +96,13 @@ class Processor(LlmService):
             help=f'ollama (default: {default_ollama})'
         )
 
+        parser.add_argument(
+            '-t', '--temperature',
+            type=float,
+            default=default_temperature,
+            help=f'LLM temperature parameter (default: {default_temperature})'
+        )
+
 def run():
 
     Processor.launch(default_ident, __doc__)

trustgraph-flow/trustgraph/model/text_completion/openai/llm.py

@@ -58,12 +58,15 @@ class Processor(LlmService):
 
         logger.info("OpenAI LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         prompt = system + "\n\n" + prompt
 
@@ -82,7 +85,7 @@ class Processor(LlmService):
                         ]
                     }
                 ],
-                temperature=self.temperature,
+                temperature=effective_temperature,
                 max_tokens=self.max_output,
                 top_p=1,
                 frequency_penalty=0,

trustgraph-flow/trustgraph/model/text_completion/tgi/llm.py

@@ -51,12 +51,15 @@ class Processor(LlmService):
         logger.info(f"Using TGI service at {base_url}")
         logger.info("TGI LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         headers = {
             "Content-Type": "application/json",
@@ -75,7 +78,7 @@ class Processor(LlmService):
                 }
             ],
             "max_tokens": self.max_output,
-            "temperature": self.temperature,
+            "temperature": effective_temperature,
         }            
 
         try:

trustgraph-flow/trustgraph/model/text_completion/vllm/llm.py

@@ -52,12 +52,15 @@ class Processor(LlmService):
         logger.info(f"Using vLLM service at {base_url}")
         logger.info("vLLM LLM service initialized")
 
-    async def generate_content(self, system, prompt, model=None):
+    async def generate_content(self, system, prompt, model=None, temperature=None):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
+        # Use provided temperature or fall back to default
+        effective_temperature = temperature if temperature is not None else self.temperature
 
         logger.debug(f"Using model: {model_name}")
+        logger.debug(f"Using temperature: {effective_temperature}")
 
         headers = {
             "Content-Type": "application/json",
@@ -67,7 +70,7 @@ class Processor(LlmService):
             "model": model_name,
             "prompt": system + "\n\n" + prompt,
             "max_tokens": self.max_output,
-            "temperature": self.temperature,
+            "temperature": effective_temperature,
         }            
 
         try: