Context Compression
DocsGPT implements a smart context compression system to manage long conversations effectively. This feature prevents conversations from hitting the LLM’s context window limit while preserving critical information and continuity.
How It Works
The compression system operates on a “summarize and truncate” principle:
- Threshold Check: Before each request, the system calculates the total token count of the conversation history.
- Trigger: If the token count exceeds a configured threshold (default: 80% of the model’s context limit), compression is triggered.
- Summarization: An LLM (potentially a different, cheaper/faster one) processes the older part of the conversation—including previous summaries, user messages, agent responses, and tool outputs.
- Context Replacement: The system generates a comprehensive summary of the older history. For subsequent requests, the LLM receives this Summary + Recent Messages instead of the full raw history.
Key Features
- Recursive Summarization: New summaries incorporate previous summaries, ensuring that information from the very beginning of a long chat is not lost.
- Tool Call Support: The compression logic explicitly handles tool calls and their outputs (e.g., file readings, search results), summarizing their results so the agent retains knowledge of what it has already done.
- “Needle in a Haystack” Preservation: The prompts are designed to identify and preserve specific, critical details (like passwords, keys, or specific user instructions) even when compressing large amounts of text.
Configuration
You can configure the compression behavior in your .env file or docsgpt/core/settings/agents.py:
| Setting | Default | Description |
|---|---|---|
ENABLE_CONVERSATION_COMPRESSION | True | Master switch to enable/disable the feature. |
COMPRESSION_THRESHOLD_PERCENTAGE | 0.8 | The fraction of the context window (0.0 to 1.0) that triggers compression. |
COMPRESSION_MODEL_OVERRIDE | None | (Optional) A different model to write the summaries, for example a cheaper one such as gpt-5.4-mini while gpt-5.5 answers. It must be a registered model id (from the model catalog or a custom model YAML); an unknown id makes compression fail, and the failure is only logged. |
COMPRESSION_MAX_HISTORY_POINTS | 3 | The number of past compression points to keep in the database (older ones are discarded as they are incorporated into newer summaries). |
COMPRESSION_RECENT_FIELD_MAX_TOKENS | 8000 | Cap, in tokens, on each prompt, response and tool result in the messages after the last compression point, which are otherwise kept verbatim. One very large message there can undo the compression. 0 turns the cap off. |
COMPRESSION_PROMPT_VERSION | v1.0 | Which summarization prompt to use, read from docsgpt/prompts/compression/<version>.txt. |
Architecture
The system is modularized into several components in docsgpt/api/answer/services/compression/:
CompressionOrchestrator(orchestrator.py): Entry point. Checks the threshold before each request, and also during a tool loop, when tool results fill the context mid-answer; then it runs the compression and hands back the rebuilt context.CompressionThresholdChecker(threshold_checker.py): Calculates token usage and decides when to compress.TokenCounter(token_counter.py): Counts the tokens of a conversation or message list, including an estimate for attached images.CompressionService(service.py): Runs the summarization, manages DB updates, and reconstructs the context (Summary + Recent Messages) for the LLM.MessageBuilder(message_builder.py): Builds the message list from a summary and the recent messages, including after a mid-loop compression so tool execution can continue.CompressionPromptBuilder(prompt_builder.py): Constructs the specific prompts used to instruct the LLM to summarize the conversation effectively.