# Context Compaction Logic Update Plan ## Objective Modify the context compaction logic in `crates/forge_domain/src/compaction.rs` to identify and compress only continuous sequences of assistant messages, rather than compressing the entire context. This will provide more targeted and effective context compression while preserving the structure of conversations. ## Current Implementation Analysis The current implementation in `compaction.rs` performs compaction on the entire context as a single unit: 1. It checks if compaction is needed via `should_perform_compaction` 2. If needed, it generates a summary of the entire context 3. It then builds a new compacted context with just the summary This approach doesn't distinguish between different message roles or sequences, treating all messages equally in the compaction process. ## Implementation Plan ### 1. Identify Compressible Sequences Add functionality to identify sequences of assistant messages that qualify for compression: ```rust /// Identifies sequences of assistant messages that can be compressed (2+ consecutive messages) fn identify_compressible_sequences(&self, context: &Context) -> Vec<(usize, usize)> { let messages = context.messages(); let mut sequences = Vec::new(); let mut current_sequence_start: Option = None; for (i, message) in messages.iter().enumerate() { if message.is_assistant() { // Start a new sequence or continue current one if current_sequence_start.is_none() { current_sequence_start = Some(i); } } else { // End of a potential sequence if let Some(start) = current_sequence_start { // Only compress sequences with more than 1 assistant message if i - start > 1 { sequences.push((start, i - 1)); } current_sequence_start = None; } } } // Check for a sequence at the end if let Some(start) = current_sequence_start { let end = messages.len() - 1; if end - start > 0 { // More than 1 message sequences.push((start, end)); } } sequences } ``` ### 2. Update Main Compaction Method Modify the main compaction method to use the sequence identification: ```rust pub async fn compact_context(&self, agent: &Agent, context: Context) -> Result { if !self.should_perform_compaction(agent, &context) { return Ok(context); } debug!( agent_id = %agent.id, "Context compaction triggered" ); // Identify compressible sequences let sequences = self.identify_compressible_sequences(&context); if sequences.is_empty() { debug!(agent_id = %agent.id, "No compressible sequences found"); return Ok(context); } // Process the compressible sequences and build new context let compacted_context = self.compress_context_sequences(agent, context, sequences).await?; Ok(compacted_context) } ``` ### 3. Implement Sequence Compression Create a method to handle the compression of identified sequences: ```rust async fn compress_context_sequences( &self, agent: &Agent, original_context: Context, sequences: Vec<(usize, usize)> ) -> Result { let messages = original_context.messages(); let mut compacted_messages = Vec::new(); let mut next_index = 0; // Process each sequence for (start, end) in sequences { // Add any messages before this sequence compacted_messages.extend(messages[next_index..start].to_vec()); // Extract the sequence to summarize let sequence = &messages[start..=end]; // Only process if we have multiple assistant messages (safety check) if sequence.len() > 1 && sequence.iter().all(|m| m.is_assistant()) { // Generate summary for this sequence let summary = self.generate_summary_for_sequence(agent, sequence).await?; // Add the summary as a single assistant message compacted_messages.push(ContextMessage::assistant(summary, None)); } else { // If not eligible for compression, keep original messages compacted_messages.extend(sequence.to_vec()); } next_index = end + 1; } // Add any remaining messages if next_index < messages.len() { compacted_messages.extend(messages[next_index..].to_vec()); } // Build the new context let mut compacted_context = Context::default(); // Add system message if present in original context if let Some(system_msg) = original_context.system_message() { compacted_context = compacted_context.set_first_system_message(system_msg.clone()); } // Add all the processed messages for msg in compacted_messages { compacted_context = compacted_context.add_message(msg); } Ok(compacted_context) } ``` ### 4. Update Summary Generation Create a method to generate summaries for specific sequences: ```rust async fn generate_summary_for_sequence( &self, agent: &Agent, messages: &[ContextMessage] ) -> Result { let compact = agent.compact.as_ref().unwrap(); // Create a temporary context with just the sequence for summarization let mut sequence_context = Context::default(); for msg in messages { sequence_context = sequence_context.add_message(msg.clone()); } // Render the summarization prompt let prompt = self .services .template_service() .render_summarization(agent, &sequence_context) .await?; let message = ContextMessage::user(prompt); let summary_context = Context::default().add_message(message); // Get summary from the provider let response = self .services .provider_service() .chat(&compact.model, summary_context) .await?; self.collect_completion_stream_content(response).await } ``` ### 5. Update Helper Methods The `should_perform_compaction` method remains unchanged since it's still applicable. ### 6. Remove Unused Methods The original `generate_summary` and `build_compacted_context` methods will be replaced by our new sequence-based methods, so they can be removed or repurposed. ## Verification Criteria 1. **Correctness**: The compacted context should: - Preserve all user messages in their original positions - Replace only sequences of 2+ assistant messages with summaries - Keep single assistant messages unchanged 2. **Functionality**: - The compaction should trigger under the same conditions as before - Summary generation should work properly for each sequence - The context structure should be preserved 3. **Edge Cases**: - Empty contexts should be handled properly - Contexts with no compressible sequences should be returned unchanged - Boundary conditions (sequences at start/end of context) should work correctly 4. **Performance**: - Any performance impact should be minimal, especially for large contexts - The number of API calls for summarization should be proportional to the number of compressible sequences