Merge pull request #234 from codelion/codelion-patch-1

Add conversation logging

Author: codelion

README.md

@@ -287,7 +287,7 @@ and then use the same in your OpenAI client. You can pass any HuggingFace model
 with your HuggingFace key. We also support adding any number of LoRAs on top of the model by using the `+` separator.
 
 E.g. The following code loads the base model `meta-llama/Llama-3.2-1B-Instruct` and then adds two LoRAs on top - `patched-codes/Llama-3.2-1B-FixVulns` and `patched-codes/Llama-3.2-1B-FastApply`.
-You can specify which LoRA to use using the `active_adapter` param in `extra_args` field of OpenAI SDK client. By default we will load the last specified adapter.
+You can specify which LoRA to use using the `active_adapter` param in `extra_body` field of OpenAI SDK client. By default we will load the last specified adapter.
 
 ```python
 OPENAI_BASE_URL = "http://localhost:8000/v1"
@@ -748,4 +748,4 @@ If you use this library in your research, please cite:
 
 <p align="center">
   ⭐ <a href="https://github.com/codelion/optillm">Star us on GitHub</a> if you find OptiLLM useful!
-</p>
+</p>

optillm.py

@@ -2,7 +2,7 @@
 import os
 
 # Version information
-__version__ = "0.1.28"
+__version__ = "0.2.0"
 
 # Get the path to the root optillm.py
 spec = util.spec_from_file_location(

optillm/__init__.py

@@ -1,8 +1,10 @@
 import logging
+import optillm
+from optillm import conversation_logger
 
 logger = logging.getLogger(__name__)
 
-def best_of_n_sampling(system_prompt: str, initial_query: str, client, model: str, n: int = 3) -> str:
+def best_of_n_sampling(system_prompt: str, initial_query: str, client, model: str, n: int = 3, request_id: str = None) -> str:
     bon_completion_tokens = 0
 
     messages = [{"role": "system", "content": system_prompt},
@@ -12,13 +14,20 @@ def best_of_n_sampling(system_prompt: str, initial_query: str, client, model: st
 
     try:
         # Try to generate n completions in a single API call using n parameter
-        response = client.chat.completions.create(
-            model=model,
-            messages=messages,
-            max_tokens=4096,
-            n=n,
-            temperature=1
-        )
+        provider_request = {
+            "model": model,
+            "messages": messages,
+            "max_tokens": 4096,
+            "n": n,
+            "temperature": 1
+        }
+        response = client.chat.completions.create(**provider_request)
+        
+        # Log provider call
+        if request_id:
+            response_dict = response.model_dump() if hasattr(response, 'model_dump') else response
+            conversation_logger.log_provider_call(request_id, provider_request, response_dict)
+        
         completions = [choice.message.content for choice in response.choices]
         logger.info(f"Generated {len(completions)} initial completions using n parameter. Tokens used: {response.usage.completion_tokens}")
         bon_completion_tokens += response.usage.completion_tokens
@@ -30,12 +39,19 @@ def best_of_n_sampling(system_prompt: str, initial_query: str, client, model: st
         # Fallback: Generate completions one by one in a loop
         for i in range(n):
             try:
-                response = client.chat.completions.create(
-                    model=model,
-                    messages=messages,
-                    max_tokens=4096,
-                    temperature=1
-                )
+                provider_request = {
+                    "model": model,
+                    "messages": messages,
+                    "max_tokens": 4096,
+                    "temperature": 1
+                }
+                response = client.chat.completions.create(**provider_request)
+                
+                # Log provider call
+                if request_id:
+                    response_dict = response.model_dump() if hasattr(response, 'model_dump') else response
+                    conversation_logger.log_provider_call(request_id, provider_request, response_dict)
+                
                 completions.append(response.choices[0].message.content)
                 bon_completion_tokens += response.usage.completion_tokens
                 logger.debug(f"Generated completion {i+1}/{n}")
@@ -59,13 +75,20 @@ def best_of_n_sampling(system_prompt: str, initial_query: str, client, model: st
         rating_messages.append({"role": "assistant", "content": completion})
         rating_messages.append({"role": "user", "content": "Rate the above response:"})
 
-        rating_response = client.chat.completions.create(
-            model=model,
-            messages=rating_messages,
-            max_tokens=256,
-            n=1,
-            temperature=0.1
-        )
+        provider_request = {
+            "model": model,
+            "messages": rating_messages,
+            "max_tokens": 256,
+            "n": 1,
+            "temperature": 0.1
+        }
+        rating_response = client.chat.completions.create(**provider_request)
+        
+        # Log provider call
+        if request_id:
+            response_dict = rating_response.model_dump() if hasattr(rating_response, 'model_dump') else rating_response
+            conversation_logger.log_provider_call(request_id, provider_request, response_dict)
+        
         bon_completion_tokens += rating_response.usage.completion_tokens
         try:
             rating = float(rating_response.choices[0].message.content.strip())