Refactor code to add support for generating custom temperature text

examples/custom_temperature.py

@@ -0,0 +1,11 @@
+from _context import sm
+
+text = sm.generate_text(
+    prompt="Write a short summary of 'Pride and Prejudice'.",
+    llm_provider="openai",
+    llm_model="gpt-4o",
+    temperature=0.5,
+    max_tokens=150,
+)
+
+print(text)

simplemind/__init__.py

@@ -23,11 +23,11 @@ def generate_data(prompt, *, llm_model=None, llm_provider=None, response_model=N
     )
 
 
-def generate_text(prompt, *, llm_model=None, llm_provider=None):
+def generate_text(prompt, *, llm_model=None, llm_provider=None, **kwargs):
     """Generate text from a given prompt."""
     provider = find_provider(llm_provider or settings.DEFAULT_LLM_PROVIDER)
 
-    return provider.generate_text(prompt=prompt, llm_model=llm_model)
+    return provider.generate_text(prompt=prompt, llm_model=llm_model, **kwargs)
 
 
 __all__ = [

simplemind/providers/anthropic.py

@@ -30,7 +30,7 @@ class Anthropic(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_anthropic(self.client)
 
-    def send_conversation(self, conversation: "Conversation"):
+    def send_conversation(self, conversation: "Conversation", **kwargs):
         """Send a conversation to the Anthropic API."""
         from ..models import Message
 
@@ -42,6 +42,7 @@ class Anthropic(BaseProvider):
             model=conversation.llm_model or DEFAULT_MODEL,
             messages=messages,
             max_tokens=DEFAULT_MAX_TOKENS,
+            **kwargs,
         )
 
         # Get the response content from the Anthropic response
@@ -62,13 +63,16 @@ class Anthropic(BaseProvider):
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model):
+    def generate_text(self, prompt, *, llm_model, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.client.messages.create(
-            model=llm_model, messages=messages, max_tokens=DEFAULT_MAX_TOKENS
+            model=llm_model,
+            messages=messages,
+            max_tokens=DEFAULT_MAX_TOKENS,
+            **kwargs,
         )
 
         return response.content[0].text

simplemind/providers/groq.py

@@ -29,7 +29,11 @@ class Groq(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_groq(self.client)
 
-    def send_conversation(self, conversation: "Conversation") -> "Message":
+    def send_conversation(
+        self,
+        conversation: "Conversation",
+        **kwargs,
+    ) -> "Message":
         """Send a conversation to the Groq API."""
         from ..models import Message
 
@@ -38,7 +42,9 @@ class Groq(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages
+            model=conversation.llm_model or DEFAULT_MODEL,
+            messages=messages,
+            **kwargs,
         )
 
         # Get the response content from the Groq response
@@ -53,7 +59,7 @@ class Groq(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt: str, response_model):
+    def structured_response(self, prompt: str, response_model, **kwargs):
         # Ensure messages are provided in kwargs
         messages = [
             {"role": "user", "content": prompt},
@@ -62,16 +68,25 @@ class Groq(BaseProvider):
         response = self.structured_client.chat.completions.create(
             messages=messages,
             response_model=response_model,
+            **kwargs,
         )
         return response
 
-    def generate_text(self, prompt: str, *, llm_model: str):
+    def generate_text(
+        self,
+        prompt: str,
+        *,
+        llm_model: str,
+        **kwargs,
+    ):
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.structured_client.chat.completions.create(
-            messages=messages, model=llm_model
+            messages=messages,
+            model=llm_model,
+            **kwargs,
         )
 
         return response.choices[0].message.content

simplemind/providers/openai.py

@@ -29,7 +29,7 @@ class OpenAI(BaseProvider):
         """A OpenAI client with Instructor."""
         return instructor.from_openai(self.client)
 
-    def send_conversation(self, conversation: "Conversation"):
+    def send_conversation(self, conversation: "Conversation", **kwargs):
         """Send a conversation to the OpenAI API."""
         from ..models import Message
 
@@ -38,7 +38,7 @@ class OpenAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages
+            model=conversation.llm_model or DEFAULT_MODEL, messages=messages, **kwargs
         )
 
         # Get the response content from the OpenAI response
@@ -53,24 +53,24 @@ class OpenAI(BaseProvider):
             llm_provider=PROVIDER_NAME,
         )
 
-    def structured_response(self, prompt, response_model, *, llm_model: str):
+    def structured_response(self, prompt, response_model, *, llm_model: str, **kwargs):
         # Ensure messages are provided in kwargs
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.structured_client.chat.completions.create(
-            messages=messages, model=llm_model, response_model=response_model
+            messages=messages, model=llm_model, response_model=response_model, **kwargs
         )
         return response
 
-    def generate_text(self, prompt, *, llm_model):
+    def generate_text(self, prompt, *, llm_model, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.client.chat.completions.create(
-            messages=messages, model=llm_model
+            messages=messages, model=llm_model, **kwargs
         )
 
         return response.choices[0].message.content

simplemind/providers/xai.py

@@ -34,7 +34,7 @@ class XAI(BaseProvider):
         """A client patched with Instructor."""
         return instructor.from_openai(self.client)
 
-    def send_conversation(self, conversation: "Conversation"):
+    def send_conversation(self, conversation: "Conversation", **kwargs):
         """Send a conversation to the OpenAI API."""
         from ..models import Message
 
@@ -43,7 +43,9 @@ class XAI(BaseProvider):
         ]
 
         response = self.client.chat.completions.create(
-            model=conversation.llm_model or DEFAULT_MODEL, messages=messages
+            model=conversation.llm_model or DEFAULT_MODEL,
+            messages=messages,
+            **kwargs,
         )
 
         # Get the response content from the OpenAI response
@@ -61,13 +63,15 @@ class XAI(BaseProvider):
     def structured_response(self, prompt: str, response_model, *, llm_model):
         raise NotImplementedError("XAI does not support structured responses")
 
-    def generate_text(self, prompt, *, llm_model):
+    def generate_text(self, prompt, *, llm_model, **kwargs):
         messages = [
             {"role": "user", "content": prompt},
         ]
 
         response = self.client.chat.completions.create(
-            messages=messages, model=llm_model
+            messages=messages,
+            model=llm_model,
+            **kwargs,
         )
 
         return response.choices[0].message.content