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https://github.com/Significant-Gravitas/AutoGPT.git
synced 2026-04-08 03:00:28 -04:00
fix(copilot): add retry user feedback, move constant to module level, improve docstrings
- Add StreamStatus response type and emit "Optimizing conversation context..." before compaction retries so users get feedback during the 10-30s wait - _MAX_STREAM_ATTEMPTS already at module level alongside _PROMPT_TOO_LONG_PATTERNS (reviewer item addressed) - Expand docstrings for _messages_to_transcript and _transcript_to_messages in transcript.py
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@@ -43,6 +43,7 @@ class ResponseType(str, Enum):
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ERROR = "error"
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USAGE = "usage"
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HEARTBEAT = "heartbeat"
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STATUS = "status"
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class StreamBaseResponse(BaseModel):
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@@ -232,3 +233,15 @@ class StreamHeartbeat(StreamBaseResponse):
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def to_sse(self) -> str:
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"""Convert to SSE comment format to keep connection alive."""
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return ": heartbeat\n\n"
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class StreamStatus(StreamBaseResponse):
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"""Transient status notification shown to the user during long operations.
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Used to provide feedback when the backend performs behind-the-scenes work
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(e.g., compacting conversation context on a retry) that would otherwise
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leave the user staring at an unexplained pause.
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"""
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type: ResponseType = ResponseType.STATUS
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message: str = Field(..., description="Human-readable status message")
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@@ -52,6 +52,7 @@ from ..response_model import (
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StreamFinish,
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StreamHeartbeat,
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StreamStart,
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StreamStatus,
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StreamTextDelta,
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StreamToolInputAvailable,
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StreamToolOutputAvailable,
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@@ -1478,6 +1479,7 @@ async def stream_chat_completion_sdk(
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_attempt + 1,
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_MAX_STREAM_ATTEMPTS,
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)
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yield StreamStatus(message="Optimizing conversation context\u2026")
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ctx = await _reduce_context(
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transcript_content,
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@@ -656,7 +656,21 @@ def _flatten_tool_result_content(blocks: list) -> str:
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def _transcript_to_messages(content: str) -> list[dict]:
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"""Convert JSONL transcript entries to message dicts for compress_context."""
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"""Convert JSONL transcript entries to plain message dicts for compression.
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Parses each line of the JSONL *content*, skips strippable metadata entries
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(progress, file-history-snapshot, etc.), and extracts the ``role`` and
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flattened ``content`` from the ``message`` field of each remaining entry.
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Structured content blocks (``tool_use``, ``tool_result``, images) are
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flattened to plain text via ``_flatten_assistant_content`` and
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``_flatten_tool_result_content`` so that ``compress_context`` can
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perform token counting and LLM summarization on uniform strings.
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Returns:
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A list of ``{"role": str, "content": str}`` dicts suitable for
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``compress_context``.
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"""
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messages: list[dict] = []
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for line in content.strip().split("\n"):
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if not line.strip():
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@@ -685,7 +699,22 @@ def _transcript_to_messages(content: str) -> list[dict]:
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def _messages_to_transcript(messages: list[dict]) -> str:
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"""Convert compressed message dicts back to JSONL transcript format."""
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"""Convert compressed message dicts back to JSONL transcript format.
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Rebuilds a minimal JSONL transcript from the ``{"role", "content"}``
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dicts returned by ``compress_context``. Each message becomes one JSONL
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line with a fresh ``uuid`` / ``parentUuid`` chain so the CLI's
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``--resume`` flag can reconstruct a valid conversation tree.
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Assistant messages are wrapped in the full ``message`` envelope
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(``id``, ``model``, ``stop_reason``, structured ``content`` blocks)
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that the CLI expects. User messages use the simpler ``{role, content}``
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form.
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Returns:
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A newline-terminated JSONL string, or an empty string if *messages*
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is empty.
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"""
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lines: list[str] = []
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last_uuid: str = "" # root entry uses empty string, not null
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for msg in messages:
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