Multi message conversation support

backend/context.py

@@ -0,0 +1,89 @@
+"""Context management for multi-message conversations with smart summarization."""
+
+from typing import List, Dict, Any
+from .openrouter import query_model
+
+
+async def summarize_older_messages(messages: List[Dict[str, str]]) -> str:
+    """Summarize older messages into a concise conversation summary."""
+    conversation_text = ""
+    for msg in messages:
+        role = msg["role"].capitalize()
+        if msg["role"] == "user":
+            conversation_text += f"{role}: {msg['content']}\n\n"
+        else:
+            if "stage3" in msg and "response" in msg["stage3"]:
+                conversation_text += f"{role}: {msg['stage3']['response']}\n\n"
+            elif "content" in msg:
+                conversation_text += f"{role}: {msg['content']}\n\n"
+
+    summary_prompt = f"""Summarize the following conversation concisely in 2-3 sentences. Focus on key topics, questions asked, and important context that would be needed to understand follow-up questions.
+
+Conversation:
+{conversation_text}
+
+Concise summary:"""
+
+    messages_for_api = [{"role": "user", "content": summary_prompt}]
+    response = await query_model("google/gemini-2.0-flash-exp:free", messages_for_api, timeout=30.0)
+
+    if response is None:
+        return "Previous conversation: " + conversation_text[:200] + "..."
+
+    return response.get('content', '').strip()
+
+
+def format_assistant_message(assistant_msg: Dict[str, Any]) -> str:
+    """Convert council's 3-stage output into clean text for context."""
+    if "stage3" in assistant_msg and "response" in assistant_msg["stage3"]:
+        return assistant_msg["stage3"]["response"]
+
+    if "content" in assistant_msg:
+        return assistant_msg["content"]
+
+    return "[Assistant response]"
+
+
+async def build_context_messages(
+    conversation_messages: List[Dict[str, Any]],
+    current_query: str,
+    recent_message_limit: int = 5
+) -> List[Dict[str, str]]:
+    """Build message history with smart summarization for long conversations."""
+    if len(conversation_messages) == 0:
+        return [{"role": "user", "content": current_query}]
+
+    num_recent_messages = recent_message_limit * 2
+
+    if len(conversation_messages) <= num_recent_messages:
+        formatted_messages = []
+        for msg in conversation_messages:
+            if msg["role"] == "user":
+                formatted_messages.append({"role": "user", "content": msg["content"]})
+            else:
+                content = format_assistant_message(msg)
+                formatted_messages.append({"role": "assistant", "content": content})
+
+        formatted_messages.append({"role": "user", "content": current_query})
+        return formatted_messages
+
+    older_messages = conversation_messages[:-num_recent_messages]
+    recent_messages = conversation_messages[-num_recent_messages:]
+
+    summary = await summarize_older_messages(older_messages)
+
+    formatted_messages = [
+        {"role": "user", "content": f"[Previous conversation summary: {summary}]"},
+        {"role": "assistant", "content": "I understand the previous conversation context."}
+    ]
+
+    for msg in recent_messages:
+        if msg["role"] == "user":
+            formatted_messages.append({"role": "user", "content": msg["content"]})
+        else:
+            content = format_assistant_message(msg)
+            formatted_messages.append({"role": "assistant", "content": content})
+
+    formatted_messages.append({"role": "user", "content": current_query})
+    return formatted_messages
+

backend/council.py

@@ -5,19 +5,16 @@
 from .config import COUNCIL_MODELS, CHAIRMAN_MODEL
 
 
-async def stage1_collect_responses(user_query: str) -> List[Dict[str, Any]]:
+async def stage1_collect_responses(messages: List[Dict[str, str]]) -> List[Dict[str, Any]]:
     """
     Stage 1: Collect individual responses from all council models.
 
     Args:
-        user_query: The user's question
+        messages: Full message history including current query
 
     Returns:
         List of dicts with 'model' and 'response' keys
     """
-    messages = [{"role": "user", "content": user_query}]
-
-    # Query all models in parallel
     responses = await query_models_parallel(COUNCIL_MODELS, messages)
 
     # Format results
@@ -293,35 +290,34 @@ async def generate_conversation_title(user_query: str) -> str:
     return title
 
 
-async def run_full_council(user_query: str) -> Tuple[List, List, Dict, Dict]:
+async def run_full_council(messages: List[Dict[str, str]]) -> Tuple[List, List, Dict, Dict]:
     """
-    Run the complete 3-stage council process.
+    Run the complete 3-stage council process with conversation context.
 
     Args:
-        user_query: The user's question
+        messages: Full message history in OpenAI format
 
     Returns:
         Tuple of (stage1_results, stage2_results, stage3_result, metadata)
     """
-    # Stage 1: Collect individual responses
-    stage1_results = await stage1_collect_responses(user_query)
+    current_query = messages[-1]["content"]
+
+    stage1_results = await stage1_collect_responses(messages)
 
-    # If no models responded successfully, return error
     if not stage1_results:
         return [], [], {
             "model": "error",
             "response": "All models failed to respond. Please try again."
         }, {}
 
-    # Stage 2: Collect rankings
-    stage2_results, label_to_model = await stage2_collect_rankings(user_query, stage1_results)
+    stage2_results, label_to_model = await stage2_collect_rankings(current_query, stage1_results)
 
     # Calculate aggregate rankings
     aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
 
     # Stage 3: Synthesize final answer
     stage3_result = await stage3_synthesize_final(
-        user_query,
+        current_query,
         stage1_results,
         stage2_results
     )

backend/main.py

@@ -11,6 +11,7 @@
 
 from . import storage
 from .council import run_full_council, generate_conversation_title, stage1_collect_responses, stage2_collect_rankings, stage3_synthesize_final, calculate_aggregate_rankings
+from .context import build_context_messages
 
 app = FastAPI(title="LLM Council API")
 
@@ -101,11 +102,13 @@ async def send_message(conversation_id: str, request: SendMessageRequest):
         title = await generate_conversation_title(request.content)
         storage.update_conversation_title(conversation_id, title)
 
-    # Run the 3-stage council process
-    stage1_results, stage2_results, stage3_result, metadata = await run_full_council(
+    messages = await build_context_messages(
+        conversation["messages"][:-1],
         request.content
     )
 
+    stage1_results, stage2_results, stage3_result, metadata = await run_full_council(messages)
+
     # Add assistant message with all stages
     storage.add_assistant_message(
         conversation_id,
@@ -147,9 +150,14 @@ async def event_generator():
             if is_first_message:
                 title_task = asyncio.create_task(generate_conversation_title(request.content))
 
+            messages = await build_context_messages(
+                conversation["messages"][:-1],
+                request.content
+            )
+
             # Stage 1: Collect responses
             yield f"data: {json.dumps({'type': 'stage1_start'})}\n\n"
-            stage1_results = await stage1_collect_responses(request.content)
+            stage1_results = await stage1_collect_responses(messages)
             yield f"data: {json.dumps({'type': 'stage1_complete', 'data': stage1_results})}\n\n"
 
             # Stage 2: Collect rankings

frontend/.nvmrc

@@ -0,0 +1,9 @@
+20
+
+
+
+
+
+
+
+

frontend/src/components/ChatInterface.css

@@ -33,6 +33,21 @@
   font-size: 16px;
 }
 
+.context-indicator {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 16px;
+  margin-bottom: 20px;
+  background: linear-gradient(135deg, #f8f9ff 0%, #f0f7ff 100%);
+  border: 1px solid #d0e7ff;
+  border-radius: 8px;
+  color: #4a90e2;
+  font-size: 13px;
+  font-weight: 500;
+  box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+}
+
 .message-group {
   margin-bottom: 32px;
 }

frontend/src/components/ChatInterface.jsx

@@ -57,7 +57,13 @@ export default function ChatInterface({
             <p>Ask a question to consult the LLM Council</p>
           </div>
         ) : (
-          conversation.messages.map((msg, index) => (
+          <>
+            {conversation.messages.length > 6 && (
+              <div className="context-indicator">
+                💭 Using conversation context ({conversation.messages.length} messages)
+              </div>
+            )}
+            {conversation.messages.map((msg, index) => (
             <div key={index} className="message-group">
               {msg.role === 'user' ? (
                 <div className="user-message">
@@ -107,7 +113,8 @@ export default function ChatInterface({
                 </div>
               )}
             </div>
-          ))
+            ))}
+          </>
         )}
 
         {isLoading && (
@@ -120,26 +127,28 @@ export default function ChatInterface({
         <div ref={messagesEndRef} />
       </div>
 
-      {conversation.messages.length === 0 && (
-        <form className="input-form" onSubmit={handleSubmit}>
-          <textarea
-            className="message-input"
-            placeholder="Ask your question... (Shift+Enter for new line, Enter to send)"
-            value={input}
-            onChange={(e) => setInput(e.target.value)}
-            onKeyDown={handleKeyDown}
-            disabled={isLoading}
-            rows={3}
-          />
-          <button
-            type="submit"
-            className="send-button"
-            disabled={!input.trim() || isLoading}
-          >
-            Send
-          </button>
-        </form>
-      )}
+      <form className="input-form" onSubmit={handleSubmit}>
+        <textarea
+          className="message-input"
+          placeholder={
+            conversation.messages.length === 0
+              ? "Ask your question... (Shift+Enter for new line, Enter to send)"
+              : "Ask a follow-up question... (Shift+Enter for new line, Enter to send)"
+          }
+          value={input}
+          onChange={(e) => setInput(e.target.value)}
+          onKeyDown={handleKeyDown}
+          disabled={isLoading}
+          rows={3}
+        />
+        <button
+          type="submit"
+          className="send-button"
+          disabled={!input.trim() || isLoading}
+        >
+          Send
+        </button>
+      </form>
     </div>
   );
 }