updated the context calculation

app/api/endpoints/gemini.py

@@ -54,11 +54,15 @@ async def gemini_chat(
                 "response": response_text
             }
             
+        prompt_content = chat_data.prompt
+        if chat_data.context:
+            prompt_content = f"Context: {chat_data.context}\n\nPrompt: {chat_data.prompt}"
+
         # Using the async generation method provided by the new google-genai library
         # We use await to ensure we don't block the event loop
         response = await client.aio.models.generate_content(
             model="gemini-2.0-flash", 
-            contents=chat_data.prompt
+            contents=prompt_content
         )
         
         # Track usage if valid module
@@ -67,7 +71,7 @@ async def gemini_chat(
             # 1 char ~= 0.25 tokens (rough estimate if exact count not returned)
             # Gemini response usually has usage_metadata
             usage = response.usage_metadata
-            prompt_tokens = usage.prompt_token_count if usage else len(chat_data.prompt) // 4
+            prompt_tokens = usage.prompt_token_count if usage else len(prompt_content) // 4
             completion_tokens = usage.candidates_token_count if usage else len(response.text) // 4
             
             module.ingress_tokens += prompt_tokens

app/api/endpoints/openai.py

@@ -49,9 +49,14 @@ async def openai_chat(
             }
 
         # Perform Async call to OpenAI
+        messages = []
+        if chat_data.context:
+            messages.append({"role": "system", "content": chat_data.context})
+        messages.append({"role": "user", "content": chat_data.prompt})
+
         response = await client.chat.completions.create(
             model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": chat_data.prompt}]
+            messages=messages
         )
         
         # Track usage
@@ -61,6 +66,15 @@ async def openai_chat(
                 module.ingress_tokens += usage.prompt_tokens
                 module.egress_tokens += usage.completion_tokens
                 module.total_tokens += usage.total_tokens
+            else:
+                # Fallback estimation
+                total_content = "".join([m["content"] for m in messages])
+                prompt_tokens = len(total_content) // 4
+                completion_tokens = len(response.choices[0].message.content) // 4
+                module.ingress_tokens += prompt_tokens
+                module.egress_tokens += completion_tokens
+                module.total_tokens += (prompt_tokens + completion_tokens)
+            
             db.commit()
 
         return {