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feat: v0.4.0 — rich content support with typed blocks and loss visibility

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Extracts per-message content into a typed `blocks` list (text, code,
thinking, tool_use, tool_result, image_placeholder, file_placeholder,
unknown) and renders them at exporter write time. Voice transcripts,
Custom Instructions, and image references now appear in exports
instead of being silently dropped.

Foundation:
- src/blocks.py: pure block constructors, _safe_fence (fence-corruption
  defense, verified live in Joplin), _blockquote_prefix, render
- src/loss_report.py: per-run tally surfaced as INFO summary at end of
  export so silently-dropped data becomes visible

Providers:
- ChatGPT: dispatch on content_type produces typed blocks; voice shapes
  (audio_transcription, audio_asset_pointer, real_time_user_audio_video_
  asset_pointer) locked from live DevTools capture; Custom Instructions
  bug fix (parts-vs-direct-fields); role filter lifted; hidden-context
  marker driven by is_visually_hidden_from_conversation flag
- Claude: defensive dispatch for text/thinking/tool_use/tool_result/image
  with recursive nested-block flattening; untested against real rich-
  content data — fix-forward in v0.4.1

Exporter:
- Markdown renders from blocks at write time via render_blocks_to_markdown;
  backward-compat fallback to content for any pre-v0.4.0 cached data

Tests:
- 27 new tests across providers, exporters, CLI; fixtures rebuilt with
  real-shape ChatGPT voice + Custom Instructions cases
- 181/181 pass

Behavior changes (intentional):
- JSON output omits content; consumers should read blocks
- Per-conversation message counts increase (Custom Instructions, image-
  only, tool-only messages now appear)
- Existing exports not auto-re-rendered; users wanting fresh output run
  cache --clear then export

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
JesseMarkowitz
2026-05-04 23:17:18 -04:00
parent 4798edcea7
commit 473d02f71a
16 changed files with 1786 additions and 232 deletions
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35
CHANGELOG.md
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@@ -3,6 +3,41 @@
All notable changes to this project will be documented here.
Format follows [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
## [0.4.0] - Unreleased
### Added
- Rich content support: messages now carry an ordered `blocks` list (text, code, thinking, tool_use, tool_result, citation, image_placeholder, file_placeholder, unknown)
- ChatGPT voice mode: `audio_transcription` parts render as text blocks; `audio_asset_pointer` and `real_time_user_audio_video_asset_pointer` render as `📎 File attached` placeholders with size and duration metadata
- ChatGPT Custom Instructions: `user_editable_context` and `model_editable_context` messages now appear in exports (were silently dropped — pre-existing bug fixed); rendered with a `> ℹ️ Hidden context` marker driven by the `is_visually_hidden_from_conversation` flag
- Image placeholders for `image_asset_pointer` parts (uploads + DALL-E) inside `multimodal_text` and at message level
- Defensive Claude block extraction: `text`, `thinking`, `tool_use`, `tool_result` (including nested-block flattening), `image` blocks (untested against real data; will fix-forward in v0.4.1 if real shapes diverge)
- `LossReport` summary table emitted at end of every `export` run, breaking down `unknown blocks` and `extraction failures` by raw type so silently-dropped data becomes visible
- `_safe_fence` helper picks a fence longer than any backtick run in extracted content, preventing embedded triple-backticks from corrupting downstream rendering (verified live in Joplin during planning)
- `unknown` blocks render as `> ⚠️ Unsupported content` with the raw type, observed top-level keys, and reason — so future API additions are visible rather than silent
### Changed
- ChatGPT role filter (previously dropped `tool` and `system` messages) is **lifted**: all roles now route through normal extraction; truly empty messages skip via the existing empty-content guard
- Markdown rendering moves from provider-time to exporter-write-time. Providers produce blocks; exporters call `render_blocks_to_markdown` at write time. This unblocks future Obsidian/HTML exporters
- `BaseProvider.normalize_conversation` signature now accepts an optional `LossReport` parameter (breaking change for any future custom subclass; FileProvider hasn't shipped yet)
- `o1`/`o3` reasoning subparts inside `text` content_type messages remain rendered as plain text (defensive; reclassification to `thinking` block deferred until live shape is captured)
### Fixed
- `user_editable_context` / `model_editable_context` extraction (parts-vs-direct-fields mismatch) — Custom Instructions are no longer silently dropped from every conversation
### Migration
- Existing exports are not re-rendered automatically. To pick up v0.4.0 rendering for previously exported conversations:
```
python -m src.main cache --clear
python -m src.main export --provider all
```
- JSON exports: messages now contain `blocks` (typed structured content) and may omit the legacy `content` field. External consumers reading JSON should prefer `blocks`.
- Per-conversation message counts may increase: previously-dropped Custom Instructions, image-only user turns, and tool-only assistant turns now appear.
### Out of scope (deferred to v0.5.0+)
- Binary downloads of images and audio assets (placeholders show metadata only; `content not preserved in this export`)
- Joplin resource upload for embedded media
- Filename resolution for `file-XYZ` / `sediment://` references
- Speculative ChatGPT types (`tether_browsing_display`, `tether_quote`) and DALL-E assistant images — fall through to `unknown` blocks if encountered
## [0.2.0] - Unreleased
### Added
- Joplin import automation: `joplin` command syncs exported Markdown files to Joplin as notes

50
FUTURE.md
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@@ -7,6 +7,7 @@ of these additions straightforward.
**Completed:**
- v0.1.0 — Core export: ChatGPT + Claude, incremental sync, Markdown + JSON output
- v0.2.0 — Joplin import automation (`joplin` command, create/update notes, notebook auto-creation)
- v0.4.0 — Rich content support: typed message blocks (text, code, thinking, tool_use, tool_result, image_placeholder, file_placeholder, unknown); ChatGPT voice transcripts as text + audio placeholders; Custom Instructions extraction; data-loss visibility via `LossReport` summary and visible `unknown` blocks
---
@@ -58,26 +59,43 @@ export command to accept a pre-downloaded export ZIP or JSON.
---
## Rich Content Support (v0.4.0)
## Binary Content Downloads (v0.5.0)
Currently only text content is exported. Future versions should handle:
v0.4.0 ships placeholders for images and audio assets but does not download
the binary content. The `_safe_fence`-wrapped placeholders include the asset
reference (`sediment://...` or `file-service://...`), MIME type, size, and
duration where available; the actual bytes are not preserved.
### Claude
- Artifacts (code, documents, HTML) — export as separate files, link from Markdown
- Uploaded images — download and embed or link
- Extended thinking/reasoning blocks — include as collapsible sections
- Tool call results and web search citations — include as footnotes or appendices
Next steps:
- Download attached images alongside the Markdown export, save under a
`media/` sibling directory with a stable filename derived from the asset
reference.
- Replace `image_placeholder` rendering with an inline `![](relative/path)`
reference once the file is on disk.
- Joplin integration: upload binaries as Joplin resources via `POST /resources`,
rewrite the rendered Markdown to use `:/resourceId` references, and track
the resource ID in the cache manifest so re-syncs stay idempotent.
- DALL-E images on the assistant side: not observed in this user's data; the
code path exists (`source = "model_generated"`) but is untested.
### ChatGPT
- DALL-E generated images — download and embed or link
- Code Interpreter outputs — export code and results
- File attachments — download and reference
- Voice transcripts — include as text
The block-level schema is already in place — only the file-fetch + rewrite
layer needs to be added. See the `image_placeholder` and `file_placeholder`
block definitions in `src/blocks.py`.
Implementation note: the normalized message schema already includes a
`content_type` field placeholder. When this work begins, extend the schema
rather than replacing it. Non-text content already logs a WARNING when
encountered so users can see what was skipped.
## Reclassify o1/o3 Reasoning Subparts (v0.4.1)
v0.4.0 leaves dict parts inside `text` content_type messages with shape
`{"summary": ..., "content": ...}` rendered as plain text (defensive — the
shape was inferred from a code comment, not captured live). Once a real
reasoning conversation is captured, reclassify these as `thinking` blocks.
## Suppress Hidden Context (v0.4.x)
If Custom Instructions duplication across conversations becomes a storage
problem, add `EXPORTER_INCLUDE_HIDDEN_CONTEXT=false` env var. The toggle is
a single `os.getenv()` check at the start of
`_extract_editable_context_blocks` in `src/providers/chatgpt.py` — return
empty list if disabled.
---

4
README.md
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@@ -426,7 +426,7 @@ Make sure you've added the project IDs to `CHATGPT_PROJECT_IDS` in your `.env`.
The provider's internal API may have changed. Run with `--debug`, sanitize the output (remove any personal content), and check the project's GitHub Issues for known fixes.
### Non-text content warnings
Images, code interpreter outputs, DALL-E generations, and Claude artifacts are not exported in v0.2.0. A WARNING is logged for each skipped item. See `FUTURE.md` for the roadmap.
Since v0.4.0, rich content is preserved as typed blocks in the export. ChatGPT voice transcripts render as text and audio assets as `📎 File attached` placeholders with size and duration metadata. Images render as `🖼️ Image attached` placeholders showing the asset reference. Custom Instructions appear under a `> ℹ️ Hidden context` marker. Anything the extractor doesn't recognise renders as a visible `> ⚠️ Unsupported content` block naming the type and observed keys, *and* increments a counter in the post-export summary so you can tell whether real content is being silently skipped. Binary downloads (the actual image/audio bytes) are still deferred — see `FUTURE.md` v0.5.0.
### Empty export / all conversations skipped
No new or updated conversations since your last run. To verify: `ai-chat-exporter cache --show`. To force a full re-export: `ai-chat-exporter cache --clear`.
@@ -444,7 +444,7 @@ See `FUTURE.md` for planned features:
- **v0.2.x** — `export --force` flag; `joplin --force` flag; per-conversation cache reset
- **v0.3.0** — Official API fallback: parse export ZIP files from ChatGPT/Claude settings
- **v0.4.0** — Rich content: images, artifacts, code interpreter output, extended thinking
- **v0.4.x / v0.5.0** — Binary content downloads (images, audio bytes) and Joplin resource upload; reclassify o1/o3 reasoning subparts; optional `EXPORTER_INCLUDE_HIDDEN_CONTEXT` toggle
- **v0.5.0** — Watch/scheduled mode; Obsidian vault output
---

2
pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "ai-chat-exporter"
version = "0.2.1"
version = "0.4.0"
description = "Export ChatGPT and Claude conversation history to Markdown for personal archival in Joplin"
requires-python = ">=3.11"
dependencies = [

322
src/blocks.py Normal file
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@@ -0,0 +1,322 @@
"""Typed content blocks for normalized messages.
Providers produce ordered lists of blocks; exporters render them. Living outside
``src/providers/`` deliberately — blocks are a separate concern from extraction
or rendering, shared by both layers.
Block dicts always have ``type`` set to one of the BLOCK_TYPE_* constants.
Construct via the ``make_*`` helpers; never build dicts by hand. The ``unknown``
block constructor REQUIRES a corresponding WARNING log + ``LossReport`` tally
at the call site — see plan §Data-loss visibility.
"""
import json
from typing import Any
BLOCK_TYPE_TEXT = "text"
BLOCK_TYPE_CODE = "code"
BLOCK_TYPE_THINKING = "thinking"
BLOCK_TYPE_TOOL_USE = "tool_use"
BLOCK_TYPE_TOOL_RESULT = "tool_result"
BLOCK_TYPE_CITATION = "citation"
BLOCK_TYPE_IMAGE_PLACEHOLDER = "image_placeholder"
BLOCK_TYPE_FILE_PLACEHOLDER = "file_placeholder"
BLOCK_TYPE_UNKNOWN = "unknown"
BLOCK_TYPE_HIDDEN_CONTEXT_MARKER = "hidden_context_marker"
UNKNOWN_REASON_UNKNOWN_TYPE = "unknown_type"
UNKNOWN_REASON_EXTRACTION_FAILED = "extraction_failed"
UNKNOWN_REASON_ALL_BLOCKS_FAILED = "all_blocks_failed"
UNKNOWN_REASON_UNKNOWN_FIELD_IN_KNOWN_TYPE = "unknown_field_in_known_type"
_OBSERVED_KEYS_LIMIT = 10
# ---------------------------------------------------------------------------
# Constructors
# ---------------------------------------------------------------------------
def make_text_block(text: str) -> dict | None:
"""Return a text block, or None if the text is empty/whitespace-only.
Returning None lets callers do ``if block: blocks.append(block)`` and prune
empty blocks at construction time. See plan §Finalizing the message dict.
"""
if not isinstance(text, str) or not text.strip():
return None
return {"type": BLOCK_TYPE_TEXT, "text": text}
def make_code_block(code: str, language: str = "") -> dict | None:
"""Return a code block, or None if code is empty."""
if not isinstance(code, str) or not code.strip():
return None
return {"type": BLOCK_TYPE_CODE, "language": language or "", "code": code}
def make_thinking_block(text: str) -> dict | None:
"""Return a thinking block, or None if empty."""
if not isinstance(text, str) or not text.strip():
return None
return {"type": BLOCK_TYPE_THINKING, "text": text}
def make_tool_use_block(name: str, input_data: Any, tool_id: str | None = None) -> dict:
"""Return a tool_use block.
Always returns a block (no None) — tool calls are meaningful even with
empty inputs.
"""
return {
"type": BLOCK_TYPE_TOOL_USE,
"name": name or "",
"input": input_data if input_data is not None else {},
"tool_id": tool_id,
}
def make_tool_result_block(
output: str,
tool_name: str | None = None,
is_error: bool = False,
) -> dict:
"""Return a tool_result block."""
return {
"type": BLOCK_TYPE_TOOL_RESULT,
"tool_name": tool_name,
"output": output if isinstance(output, str) else str(output),
"is_error": bool(is_error),
}
def make_citation_block(
url: str,
title: str | None = None,
snippet: str | None = None,
) -> dict | None:
if not url:
return None
return {
"type": BLOCK_TYPE_CITATION,
"url": url,
"title": title,
"snippet": snippet,
}
def make_image_placeholder(
ref: str,
source: str = "unknown",
mime: str | None = None,
) -> dict:
"""source ∈ {'user_upload', 'model_generated', 'unknown'}."""
return {
"type": BLOCK_TYPE_IMAGE_PLACEHOLDER,
"ref": ref or "",
"source": source,
"mime": mime,
}
def make_file_placeholder(
ref: str,
filename: str | None = None,
mime: str | None = None,
size_bytes: int | None = None,
duration_seconds: float | None = None,
) -> dict:
return {
"type": BLOCK_TYPE_FILE_PLACEHOLDER,
"ref": ref or "",
"filename": filename,
"mime": mime,
"size_bytes": size_bytes,
"duration_seconds": duration_seconds,
}
def make_unknown_block(
raw_type: str,
observed_keys: list[str] | None = None,
reason: str = UNKNOWN_REASON_UNKNOWN_TYPE,
summary: str | None = None,
) -> dict:
"""Construct an unknown block.
Every call site MUST also emit a WARNING log and increment a LossReport
tally — see plan §Data-loss visibility. The block surfaces the loss at
read time; the WARNING surfaces it at run time. Both signals matter.
"""
keys = list(observed_keys or [])[:_OBSERVED_KEYS_LIMIT]
return {
"type": BLOCK_TYPE_UNKNOWN,
"raw_type": raw_type,
"observed_keys": keys,
"reason": reason,
"summary": summary,
}
def make_hidden_context_marker(content_type: str) -> dict:
"""A short prepend block that flags the surrounding message as hidden context.
Driven by the ``metadata.is_visually_hidden_from_conversation`` flag, not by
content_type matching. The marker tells the reader "this message is
hidden in the source UI; we're showing it here for archival fidelity."
"""
return {
"type": BLOCK_TYPE_HIDDEN_CONTEXT_MARKER,
"content_type": content_type or "",
}
# ---------------------------------------------------------------------------
# Rendering
# ---------------------------------------------------------------------------
def render_blocks_to_markdown(blocks: list[dict]) -> str:
"""Render an ordered list of blocks to a single Markdown string.
Blocks are joined with one blank line between them. Pure function; no I/O.
"""
if not blocks:
return ""
rendered: list[str] = []
for block in blocks:
chunk = _render_one(block)
if chunk:
rendered.append(chunk)
return "\n\n".join(rendered)
def _render_one(block: dict) -> str:
btype = block.get("type", "")
if btype == BLOCK_TYPE_TEXT:
return block.get("text", "")
if btype == BLOCK_TYPE_CODE:
lang = block.get("language") or ""
code = block.get("code", "")
fence = _safe_fence(code)
return f"{fence}{lang}\n{code}\n{fence}"
if btype == BLOCK_TYPE_THINKING:
text = block.get("text", "")
quoted = _blockquote_prefix(text)
return f"**💭 Reasoning**\n\n{quoted}"
if btype == BLOCK_TYPE_TOOL_USE:
name = block.get("name", "")
input_data = block.get("input", {})
body_json = json.dumps(input_data, indent=2, sort_keys=False, default=str, ensure_ascii=False)
fence = _safe_fence(body_json)
body = f"{fence}json\n{body_json}\n{fence}"
quoted = _blockquote_prefix(f"🔧 **Tool: {name}**\n{body}")
return quoted
if btype == BLOCK_TYPE_TOOL_RESULT:
output = block.get("output", "")
is_error = bool(block.get("is_error"))
header = "❌ **Result (error)**" if is_error else "📤 **Result**"
fence = _safe_fence(output)
body = f"{fence}\n{output}\n{fence}"
return _blockquote_prefix(f"{header}\n{body}")
if btype == BLOCK_TYPE_CITATION:
url = block.get("url", "")
title = block.get("title") or url
return f"[{title}]({url})"
if btype == BLOCK_TYPE_IMAGE_PLACEHOLDER:
ref = block.get("ref", "")
source = block.get("source", "unknown")
mime = block.get("mime")
meta_parts = [source] if source else []
if mime:
meta_parts.append(mime)
meta_parts.append("content not preserved in this export")
meta = ", ".join(meta_parts)
return f"> 🖼️ **Image attached** — `{ref}` ({meta})"
if btype == BLOCK_TYPE_FILE_PLACEHOLDER:
ref = block.get("ref", "")
filename = block.get("filename")
label = filename or ref
mime = block.get("mime")
size_bytes = block.get("size_bytes")
duration = block.get("duration_seconds")
meta_parts: list[str] = []
if mime:
meta_parts.append(mime)
if isinstance(size_bytes, int) and size_bytes > 0:
kb = size_bytes / 1024
meta_parts.append(f"{kb:.1f} KB" if kb < 1024 else f"{kb / 1024:.2f} MB")
if isinstance(duration, (int, float)) and duration > 0:
meta_parts.append(f"{duration:.2f}s")
meta_parts.append("content not preserved in this export")
meta = ", ".join(meta_parts)
return f"> 📎 **File attached** — `{label}` ({meta})"
if btype == BLOCK_TYPE_UNKNOWN:
raw_type = block.get("raw_type", "?")
reason = block.get("reason", UNKNOWN_REASON_UNKNOWN_TYPE)
keys = block.get("observed_keys") or []
summary = block.get("summary")
first_line = f"⚠️ **Unsupported content** — type `{raw_type}` ({reason})"
lines = [first_line]
if summary:
lines.append(summary)
if keys:
keys_str = ", ".join(f"`{k}`" for k in keys)
lines.append(f"Keys observed: {keys_str}")
return _blockquote_prefix("\n".join(lines))
if btype == BLOCK_TYPE_HIDDEN_CONTEXT_MARKER:
ctype = block.get("content_type", "")
return f"> ℹ️ **Hidden context** — `{ctype}`"
# Defensive: a block of unrecognised local type (shouldn't happen if
# constructors are used). Render as visible warning rather than dropping.
return f"> ⚠️ **Internal: unrecognised block type** — `{btype}`"
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _safe_fence(text: str) -> str:
"""Return a backtick fence longer than the longest run of backticks in ``text``.
CommonMark requires the closing fence to be at least as long as the opening
fence. Picking N+1 (where N = longest run in content) ensures the content's
own backticks are inert. Minimum is 3.
Verified live against Joplin during planning — see plan
§Backtick-corruption defense.
"""
if not isinstance(text, str):
return "```"
longest_run = 0
current_run = 0
for ch in text:
if ch == "`":
current_run += 1
if current_run > longest_run:
longest_run = current_run
else:
current_run = 0
fence_len = max(3, longest_run + 1)
return "`" * fence_len
def _blockquote_prefix(text: str) -> str:
"""Prefix every line of ``text`` with ``> `` so the whole block renders as a quote.
Empty source lines become ``>`` (no trailing space) so blockquote continuity
is preserved without trailing-whitespace noise.
"""
if not isinstance(text, str):
return ""
out_lines: list[str] = []
for line in text.split("\n"):
if line == "":
out_lines.append(">")
else:
out_lines.append(f"> {line}")
return "\n".join(out_lines)

14
src/exporters/markdown.py
View File

@@ -6,6 +6,7 @@ from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from src.blocks import render_blocks_to_markdown
from src.utils import build_export_path, generate_filename
logger = logging.getLogger(__name__)
@@ -125,10 +126,17 @@ class MarkdownExporter:
# Messages
for msg in messages:
role = msg.get("role", "user")
content = msg.get("content", "")
blocks = msg.get("blocks") or []
timestamp = msg.get("timestamp")
if not content or not content.strip():
# Prefer rendering from blocks (v0.4.0+). Backward-compat fallback:
# if blocks is missing/empty AND content exists, render content as-is.
if blocks:
body = render_blocks_to_markdown(blocks)
else:
body = msg.get("content", "") or ""
if not body or not body.strip():
logger.warning(
"[markdown] Skipping empty/whitespace message in conversation %s",
conv_id[:8],
@@ -143,7 +151,7 @@ class MarkdownExporter:
else:
lines.append("")
lines.append(content)
lines.append(body)
lines.append("")
lines.append("---")
lines.append("")

85
src/loss_report.py Normal file
View File

@@ -0,0 +1,85 @@
"""Per-export-run tally for content that was dropped or partially extracted.
Surfaces the loss visibility that the rest of the system promises in its
output (visible ``unknown`` blocks). The summary emitted at the end of
each export is the load-bearing operator-facing signal: if a real content
type starts being silently dropped, this is where it shows up.
Pass a single instance through ``BaseProvider.normalize_conversation`` and
read it back in ``src/main.py`` after the export loop. No global state.
"""
from collections import Counter
from dataclasses import dataclass, field
_TOP_N_BREAKDOWN = 5
@dataclass
class LossReport:
"""Counters for things that didn't render cleanly in an export run."""
# Type-keyed counters. Values are int counts.
unknown_blocks: Counter = field(default_factory=Counter)
extraction_failures: Counter = field(default_factory=Counter)
filtered_roles: Counter = field(default_factory=Counter)
# Aggregate counters
messages_rendered: int = 0
conversations: int = 0
# Recording -------------------------------------------------------------
def record_unknown(self, raw_type: str) -> None:
self.unknown_blocks[raw_type or "?"] += 1
def record_extraction_failure(self, raw_type: str) -> None:
self.extraction_failures[raw_type or "?"] += 1
def record_filtered_role(self, role: str) -> None:
self.filtered_roles[role or "?"] += 1
def record_message(self) -> None:
self.messages_rendered += 1
def record_conversation(self) -> None:
self.conversations += 1
# Summary ---------------------------------------------------------------
def format_summary(self) -> str:
"""Return a multi-line summary table suitable for INFO logging.
Format pinned by plan §Post-export summary — "(none)" sentinel when a
counter is empty, top-5 breakdown with "+ N more types" overflow.
"""
lines: list[str] = ["[export] Run summary:"]
lines.append(f" conversations: {self.conversations}")
lines.append(f" messages rendered: {self.messages_rendered}")
lines.extend(_format_section("unknown blocks: ", self.unknown_blocks))
lines.extend(_format_section("extraction failures: ", self.extraction_failures))
lines.append(
" filtered roles: "
"(filter lifted in v0.4.0 — counter retained for future use, expected 0)"
)
if self.filtered_roles:
for role, count in self.filtered_roles.most_common(_TOP_N_BREAKDOWN):
lines.append(f" {role}={count}")
return "\n".join(lines)
def _format_section(label: str, counter: Counter) -> list[str]:
"""Render one counter section: header line + indented breakdown lines."""
total = sum(counter.values())
header = f" {label} {total}"
if total == 0:
return [header, " (none)"]
lines = [header]
most_common = counter.most_common()
for raw_type, count in most_common[:_TOP_N_BREAKDOWN]:
lines.append(f" {raw_type}={count}")
if len(most_common) > _TOP_N_BREAKDOWN:
remainder = len(most_common) - _TOP_N_BREAKDOWN
lines.append(f" + {remainder} more types")
return lines

10
src/main.py
View File

@@ -16,6 +16,7 @@ from rich.table import Table
from src.cache import Cache, CacheError
from src.config import ConfigError
from src.logging_config import setup_logging
from src.loss_report import LossReport
from src.providers.base import ProviderError
console = Console()
@@ -554,6 +555,9 @@ def export(
# Summary counters
summary: dict[str, dict[str, int]] = {}
# Single LossReport tracks data-loss visibility across all providers in this run.
loss_report = LossReport()
for prov_name, prov_instance in providers_to_run:
summary[prov_name] = {"exported": 0, "skipped": 0, "failed": 0}
@@ -611,7 +615,7 @@ def export(
for key, val in raw_conv.items():
if (key.startswith("_") or key in _PROPAGATE_KEYS) and key not in full_raw:
full_raw[key] = val
normalized = prov_instance.normalize_conversation(full_raw)
normalized = prov_instance.normalize_conversation(full_raw, loss_report)
exported_path: Path | None = None
if md_exporter:
@@ -642,6 +646,10 @@ def export(
if not dry_run:
_print_export_summary(summary)
# Emit the data-loss summary at INFO level so it lands in the log file
# AND the operator's console (default level is INFO).
for line in loss_report.format_summary().split("\n"):
logger.info(line)
def _resolve_providers(provider: str, cfg) -> list[tuple[str, object]]:

11
src/providers/base.py
View File

@@ -9,6 +9,7 @@ from typing import Any
import requests
from src.loss_report import LossReport
from src.utils import redact_secrets
# curl_cffi has its own exception hierarchy (rooted at CurlError → OSError),
@@ -89,8 +90,14 @@ class BaseProvider(ABC):
"""Return the full conversation detail for a single ID."""
@abstractmethod
def normalize_conversation(self, raw: dict) -> dict:
"""Transform provider-specific schema to the common normalized schema."""
def normalize_conversation(self, raw: dict, loss_report: LossReport | None = None) -> dict:
"""Transform provider-specific schema to the common normalized schema.
``loss_report`` accumulates counts of dropped/unhandled content so the
export loop can surface a single summary at the end. When None, providers
construct a throwaway local report (so calling normalize_conversation in
isolation, e.g. from tests, doesn't crash).
"""
# ------------------------------------------------------------------
# Concrete helpers

500
src/providers/chatgpt.py
View File

@@ -25,6 +25,19 @@ from typing import Any
from curl_cffi import requests as curl_requests
from src.blocks import (
UNKNOWN_REASON_EXTRACTION_FAILED,
UNKNOWN_REASON_UNKNOWN_FIELD_IN_KNOWN_TYPE,
UNKNOWN_REASON_UNKNOWN_TYPE,
make_code_block,
make_file_placeholder,
make_hidden_context_marker,
make_image_placeholder,
make_text_block,
make_thinking_block,
make_unknown_block,
)
from src.loss_report import LossReport
from src.providers.base import BaseProvider, ProviderError, REQUEST_TIMEOUT
logger = logging.getLogger(__name__)
@@ -551,7 +564,7 @@ class ChatGPTProvider(BaseProvider):
# Normalization
# ------------------------------------------------------------------
def normalize_conversation(self, raw: dict) -> dict:
def normalize_conversation(self, raw: dict, loss_report: LossReport | None = None) -> dict:
"""Transform ChatGPT raw schema to the common normalized schema.
ChatGPT stores messages in a nested ``mapping`` dict where each node
@@ -562,6 +575,7 @@ class ChatGPTProvider(BaseProvider):
fetch_all_conversations). The conversation detail endpoint does not
include project information.
"""
report = loss_report if loss_report is not None else LossReport()
conv_id = raw.get("id", "")
title = raw.get("title") or "Untitled"
created_at = _ts_to_iso(raw.get("create_time"))
@@ -580,7 +594,10 @@ class ChatGPTProvider(BaseProvider):
)
mapping: dict = raw.get("mapping", {})
messages = _extract_messages(mapping, raw, conv_id)
messages = _extract_messages(mapping, raw, conv_id, report)
for _ in messages:
report.record_message()
report.record_conversation()
return {
"id": conv_id,
@@ -610,14 +627,18 @@ def _ts_to_iso(ts: float | int | str | None) -> str:
def _extract_messages(
mapping: dict[str, Any], raw: dict, conv_id: str
mapping: dict[str, Any], raw: dict, conv_id: str, report: LossReport
) -> list[dict]:
"""Walk the ChatGPT conversation mapping tree to produce an ordered message list."""
"""Walk the ChatGPT conversation mapping tree to produce an ordered message list.
All roles (user/assistant/system/tool) are processed; the prior filter that
dropped non-user/assistant messages is lifted in v0.4.0 — truly empty
messages skip via the empty-content guard, anything with content renders.
"""
if not mapping:
logger.warning("[chatgpt] Conversation %s has empty mapping", conv_id[:8])
return []
# Find the root node (the one that has no parent, or whose parent is None/not in mapping)
root_id = _find_root(mapping)
if root_id is None:
logger.warning(
@@ -635,68 +656,12 @@ def _extract_messages(
node = mapping.get(node_id, {})
msg_data = node.get("message")
if msg_data:
role = msg_data.get("author", {}).get("role", "")
# Skip system/tool messages silently unless they have visible content
if role in ("user", "assistant"):
content_obj = msg_data.get("content", {})
content_type = content_obj.get("content_type", "text")
ts = msg_data.get("create_time")
built = _build_message(msg_data, conv_id, node_id, report)
if built is not None:
messages.append(built)
# Content types whose parts[] contain plain text strings.
# model_editable_context / user_editable_context = project instructions
# thoughts / reasoning_recap = o1/o3 reasoning traces
_TEXT_PARTS_TYPES = {
"text",
"model_editable_context",
"user_editable_context",
"thoughts",
"reasoning_recap",
}
if content_type in _TEXT_PARTS_TYPES:
text = _extract_text(content_obj, conv_id, node_id)
if text:
messages.append(
{
"role": role,
"content": text,
"content_type": "text",
"timestamp": _ts_to_iso(ts) if ts else None,
}
)
else:
logger.debug(
"[chatgpt] Skipping empty %s message in conversation %s",
content_type,
conv_id[:8],
)
elif content_type == "code":
# Inline code response — extract and wrap in a fenced code block
code_text = content_obj.get("text") or "\n".join(
p for p in content_obj.get("parts", []) if isinstance(p, str)
)
language = content_obj.get("language", "")
if code_text:
messages.append(
{
"role": role,
"content": f"```{language}\n{code_text}\n```",
"content_type": "code",
"timestamp": _ts_to_iso(ts) if ts else None,
}
)
else:
logger.warning(
"[chatgpt] Skipping %s content in conversation %s message %s "
"— rich content not yet supported (see FUTURE.md)",
content_type,
conv_id[:8],
node_id[:8],
)
# Walk children in order (ChatGPT typically has one child per node in a linear chat)
# Walk children in order (linear in typical conversations)
for child_id in node.get("children", []):
walk(child_id)
@@ -718,36 +683,405 @@ def _find_root(mapping: dict[str, Any]) -> str | None:
return None
def _extract_text(content_obj: dict, conv_id: str, node_id: str) -> str:
"""Extract plain text from a ChatGPT content object.
def _build_message(
msg_data: dict, conv_id: str, node_id: str, report: LossReport
) -> dict | None:
"""Construct a normalized message dict (with ``blocks``) for one ChatGPT node.
Handles three part shapes:
- str — plain text (most messages)
- dict with content_type="text" — wrapped text part
- dict with "content" key — o1/o3 thoughts/reasoning parts
Returns None for messages that should be skipped (truly empty). Otherwise
returns a dict with ``role``, ``content_type``, ``timestamp``, ``blocks``.
"""
parts = content_obj.get("parts", [])
if not parts:
return ""
author = msg_data.get("author") or {}
role = author.get("role", "") or ""
if role not in ("user", "assistant", "system", "tool"):
# Unrecognised role — log and surface, but pass through so role metadata
# is preserved for the reader.
logger.debug(
"[chatgpt] Unrecognised role %r in conversation %s message %s",
role,
conv_id[:8],
node_id[:8],
)
content_obj = msg_data.get("content") or {}
content_type = content_obj.get("content_type", "text")
ts = msg_data.get("create_time")
metadata = msg_data.get("metadata") or {}
is_hidden = bool(metadata.get("is_visually_hidden_from_conversation"))
blocks = _extract_blocks_for_content(
content_type, content_obj, role, conv_id, node_id, report
)
if not blocks:
logger.debug(
"[chatgpt] Skipping empty %s message in conversation %s",
content_type,
conv_id[:8],
)
return None
if is_hidden:
# Prepend a marker so the reader knows this message is hidden in the
# source UI. The marker is content-type-agnostic.
blocks = [make_hidden_context_marker(content_type)] + blocks
# Vestigial content_type: "code" for code-only messages, otherwise "text"
msg_content_type = "code" if (
len(blocks) == 1 and blocks[0].get("type") == "code"
) else "text"
return {
"role": role or "user",
"content_type": msg_content_type,
"timestamp": _ts_to_iso(ts) if ts else None,
"blocks": blocks,
}
# Content types whose ``parts`` are plain text strings.
_PLAIN_TEXT_PARTS_TYPES = {"text"}
# Content types that carry inline reasoning/thoughts.
_THINKING_TYPES = {"thoughts", "reasoning_recap"}
# Custom-Instructions / model-context types — direct fields, NOT parts.
_DIRECT_FIELD_CONTEXT_TYPES = {
"user_editable_context",
"model_editable_context",
}
# Known direct fields per context type. Anything not listed but non-null
# becomes an `unknown` block per the no-silent-drop-of-non-null-fields rule.
_USER_EDITABLE_CONTEXT_KNOWN_FIELDS = ("user_profile", "user_instructions")
_MODEL_EDITABLE_CONTEXT_KNOWN_FIELDS = (
"model_set_context",
"repository",
"repo_summary",
"structured_context",
)
def _extract_blocks_for_content(
content_type: str,
content_obj: dict,
role: str,
conv_id: str,
node_id: str,
report: LossReport,
) -> list[dict]:
"""Dispatch on content_type and return a list of blocks for one message."""
if content_type in _PLAIN_TEXT_PARTS_TYPES:
return _extract_text_content_type_blocks(content_obj, conv_id, node_id, report)
if content_type == "multimodal_text":
return _extract_multimodal_blocks(content_obj, role, conv_id, node_id, report)
if content_type == "code":
code_text = content_obj.get("text") or "\n".join(
p for p in content_obj.get("parts", []) if isinstance(p, str)
)
language = content_obj.get("language", "") or ""
block = make_code_block(code_text, language)
return [block] if block else []
if content_type in _THINKING_TYPES:
text = _join_string_parts(content_obj)
block = make_thinking_block(text)
return [block] if block else []
if content_type in _DIRECT_FIELD_CONTEXT_TYPES:
return _extract_editable_context_blocks(
content_type, content_obj, conv_id, node_id, report
)
if content_type == "image_asset_pointer":
# Top-level image (rare — usually nested inside multimodal_text).
ref = content_obj.get("asset_pointer", "")
source = "user_upload" if role == "user" else "model_generated"
return [make_image_placeholder(ref=ref, source=source)]
# Unknown content_type → visible unknown block + WARNING + tally
keys = list(content_obj.keys())
logger.warning(
"[chatgpt] Unknown content_type %r in conversation %s message %s "
"— see plan §Data-loss visibility (rendering as unknown block)",
content_type,
conv_id[:8],
node_id[:8],
)
report.record_unknown(content_type or "?")
return [
make_unknown_block(
raw_type=content_type or "?",
observed_keys=keys,
reason=UNKNOWN_REASON_UNKNOWN_TYPE,
)
]
def _extract_text_content_type_blocks(
content_obj: dict, conv_id: str, node_id: str, report: LossReport
) -> list[dict]:
"""Extract blocks for ``content_type == "text"``.
Plural-parts rule: emit ONE text block per message with all string parts
joined by ``\\n``. Don't emit one block per part.
Dict parts inside a text content_type message (the suspected o1/o3 reasoning
subpart shape ``{"summary": ..., "content": ...}``) are preserved as text
today — defensive behavior pending real-data capture in v0.4.1.
"""
parts = content_obj.get("parts", []) or []
string_chunks: list[str] = []
text_parts = []
for part in parts:
if isinstance(part, str):
text_parts.append(part)
string_chunks.append(part)
elif isinstance(part, dict):
part_type = part.get("content_type", "")
if part_type == "text":
text_parts.append(part.get("text", ""))
txt = part.get("text", "") or ""
if txt:
string_chunks.append(txt)
elif "content" in part:
# o1/o3 thoughts parts: {"summary": "...", "content": "..."}
text_parts.append(part["content"])
# Suspected o1/o3 reasoning subpart. Defensive: preserve as text
# block (matches current behavior). v0.4.1 reclassifies once
# the real shape is captured live.
content_val = part.get("content", "") or ""
if content_val:
string_chunks.append(content_val)
elif part_type:
# Image, file, or other binary attachment — skip and warn
# Non-text dict part inside a text content_type — surface it.
logger.warning(
"[chatgpt] Skipping %s attachment in conversation %s "
"— rich content not yet supported (see FUTURE.md)",
"[chatgpt] Unexpected %s part inside text content_type "
"in conversation %s message %s — rendering as unknown block",
part_type,
conv_id[:8],
node_id[:8],
)
report.record_unknown(part_type)
# Inline mark in the joined text so order is preserved.
string_chunks.append(
f"\n[Unknown part: type={part_type}; "
f"keys={list(part.keys())[:10]}]\n"
)
return "\n".join(t for t in text_parts if t)
joined = "\n".join(c for c in string_chunks if c)
block = make_text_block(joined)
return [block] if block else []
def _join_string_parts(content_obj: dict) -> str:
"""Helper: join all string parts in ``parts`` with newlines."""
parts = content_obj.get("parts", []) or []
return "\n".join(p for p in parts if isinstance(p, str) and p)
def _extract_multimodal_blocks(
content_obj: dict, role: str, conv_id: str, node_id: str, report: LossReport
) -> list[dict]:
"""Extract blocks from a ``multimodal_text`` content object.
Walks ``parts`` in array order — order varies between user and assistant
turns, and the extractor preserves source ordering. Emits text +
image_placeholder + file_placeholder blocks per part.
"""
parts = content_obj.get("parts", []) or []
blocks: list[dict] = []
for part in parts:
if isinstance(part, str):
block = make_text_block(part)
if block:
blocks.append(block)
continue
if not isinstance(part, dict):
continue
part_type = part.get("content_type", "")
if part_type == "audio_transcription":
txt = part.get("text", "") or ""
block = make_text_block(txt)
if block:
blocks.append(block)
elif "text" not in part:
logger.warning(
"[chatgpt] audio_transcription part missing 'text' key "
"in conversation %s message %s",
conv_id[:8],
node_id[:8],
)
report.record_extraction_failure("audio_transcription")
blocks.append(
make_unknown_block(
raw_type="audio_transcription",
observed_keys=list(part.keys()),
reason=UNKNOWN_REASON_EXTRACTION_FAILED,
summary="expected key 'text' not found",
)
)
continue
if part_type == "image_asset_pointer":
ref = part.get("asset_pointer", "")
source = "user_upload" if role == "user" else "model_generated"
mime = None
blocks.append(make_image_placeholder(ref=ref, source=source, mime=mime))
continue
if part_type == "audio_asset_pointer":
blocks.append(_audio_asset_placeholder(part))
continue
if part_type == "real_time_user_audio_video_asset_pointer":
# Wrapper carrying a nested audio_asset_pointer + optional video frames.
nested_audio = part.get("audio_asset_pointer")
if isinstance(nested_audio, dict):
blocks.append(_audio_asset_placeholder(nested_audio))
else:
logger.warning(
"[chatgpt] real_time_user_audio_video_asset_pointer missing "
"nested audio_asset_pointer in conversation %s message %s",
conv_id[:8],
node_id[:8],
)
report.record_extraction_failure(
"real_time_user_audio_video_asset_pointer"
)
blocks.append(
make_unknown_block(
raw_type="real_time_user_audio_video_asset_pointer",
observed_keys=list(part.keys()),
reason=UNKNOWN_REASON_EXTRACTION_FAILED,
summary="expected nested 'audio_asset_pointer' not found",
)
)
frames = part.get("frames_asset_pointers") or []
if frames:
# Defensive: empty in all observed cases, but if non-empty
# surface as a separate file placeholder.
video_ref = part.get("video_container_asset_pointer") or "(video frames)"
blocks.append(
make_file_placeholder(
ref=str(video_ref),
mime="video/unknown",
)
)
continue
# Anything else inside multimodal_text — visible unknown block
logger.warning(
"[chatgpt] Unknown multimodal_text part type %r in conversation %s message %s",
part_type,
conv_id[:8],
node_id[:8],
)
report.record_unknown(part_type or "?")
blocks.append(
make_unknown_block(
raw_type=part_type or "?",
observed_keys=list(part.keys()),
reason=UNKNOWN_REASON_UNKNOWN_TYPE,
)
)
return blocks
def _audio_asset_placeholder(audio_part: dict) -> dict:
"""Build a file_placeholder for an audio_asset_pointer dict.
Handles missing/zero metadata defensively.
"""
ref = audio_part.get("asset_pointer", "") or ""
fmt = audio_part.get("format") or "unknown"
size_bytes = audio_part.get("size_bytes")
if not isinstance(size_bytes, int) or size_bytes <= 0:
size_bytes = None
metadata = audio_part.get("metadata") or {}
start = metadata.get("start") if isinstance(metadata, dict) else None
end = metadata.get("end") if isinstance(metadata, dict) else None
duration: float | None = None
if isinstance(start, (int, float)) and isinstance(end, (int, float)):
diff = float(end) - float(start)
if diff > 0:
duration = diff
return make_file_placeholder(
ref=ref,
mime=f"audio/{fmt}" if fmt else "audio/unknown",
size_bytes=size_bytes,
duration_seconds=duration,
)
def _extract_editable_context_blocks(
content_type: str, content_obj: dict, conv_id: str, node_id: str, report: LossReport
) -> list[dict]:
"""Extract blocks from user_editable_context / model_editable_context messages.
These have no ``parts`` field — they carry direct keys. Read all known
fields, emit one labeled fenced block per non-null known field, and emit an
``unknown`` block for any unrecognised non-null direct field (no-silent-drop
rule).
"""
if content_type == "user_editable_context":
known_fields: tuple[str, ...] = _USER_EDITABLE_CONTEXT_KNOWN_FIELDS
elif content_type == "model_editable_context":
known_fields = _MODEL_EDITABLE_CONTEXT_KNOWN_FIELDS
else:
known_fields = ()
blocks: list[dict] = []
label_kind = "Custom Instructions" if content_type == "user_editable_context" else "Model Context"
for field in known_fields:
value = content_obj.get(field)
if value is None or (isinstance(value, str) and not value.strip()):
continue
if isinstance(value, (dict, list)):
# Render as a JSON-rendered text block. _safe_fence will wrap it.
import json as _json
rendered = _json.dumps(value, indent=2, default=str, ensure_ascii=False)
else:
rendered = str(value)
label = f"**{label_kind} — {field}:**"
# Emit as text block; the renderer's _safe_fence wraps the raw value.
# We use a "labeled fenced block" pattern: header line + raw content
# joined inside one text block, where the renderer will leave it alone.
# To get the safe-fence wrap we use a code block (which calls _safe_fence
# internally and renders without language-hint corruption risk).
blocks.append(make_text_block(label))
code_block = make_code_block(rendered, language="")
if code_block:
blocks.append(code_block)
# Catch unknown non-null direct fields (no-silent-drop rule).
structural_keys = {"content_type", "parts"}
for key, value in content_obj.items():
if key in structural_keys or key in known_fields:
continue
if value is None:
continue
# Reject null/empty containers.
if isinstance(value, (str, list, dict)) and not value:
continue
logger.warning(
"[chatgpt] Unknown non-null field %r in %s message %s/%s",
key,
content_type,
conv_id[:8],
node_id[:8],
)
report.record_unknown(f"{content_type}.{key}")
blocks.append(
make_unknown_block(
raw_type=f"{content_type}.{key}",
observed_keys=list(content_obj.keys()),
reason=UNKNOWN_REASON_UNKNOWN_FIELD_IN_KNOWN_TYPE,
summary=f"unknown non-null field '{key}' in {content_type}",
)
)
return blocks

194
src/providers/claude.py
View File

@@ -5,6 +5,17 @@ import os
from curl_cffi import requests as curl_requests
from src.blocks import (
UNKNOWN_REASON_EXTRACTION_FAILED,
UNKNOWN_REASON_UNKNOWN_TYPE,
make_image_placeholder,
make_text_block,
make_thinking_block,
make_tool_result_block,
make_tool_use_block,
make_unknown_block,
)
from src.loss_report import LossReport
from src.providers.base import BaseProvider, ProviderError
logger = logging.getLogger(__name__)
@@ -161,8 +172,9 @@ class ClaudeProvider(BaseProvider):
return data
def normalize_conversation(self, raw: dict) -> dict:
def normalize_conversation(self, raw: dict, loss_report: LossReport | None = None) -> dict:
"""Transform Claude raw schema to the common normalized schema."""
report = loss_report if loss_report is not None else LossReport()
conv_id = raw.get("uuid") or raw.get("id", "")
title = raw.get("name") or raw.get("title") or "Untitled"
created_at = raw.get("created_at") or raw.get("create_time") or ""
@@ -178,40 +190,37 @@ class ClaudeProvider(BaseProvider):
# Messages
raw_messages = raw.get("chat_messages") or raw.get("messages") or []
messages = []
messages: list[dict] = []
for msg in raw_messages:
role = _map_role(msg.get("sender") or msg.get("role", ""))
if not role:
continue
# Content can be a string or a list of content blocks
content_raw = msg.get("content") or msg.get("text") or ""
content, skipped_types = _extract_claude_text(content_raw, conv_id)
for ctype in skipped_types:
logger.warning(
"[claude] Skipping %s content in conversation %s "
"— rich content not yet supported (see FUTURE.md)",
ctype,
conv_id[:8],
)
content_raw = msg.get("content") if "content" in msg else msg.get("text", "")
blocks = _extract_claude_blocks(content_raw, conv_id, report)
timestamp = msg.get("created_at") or msg.get("timestamp") or None
if content is None:
if not blocks:
logger.debug("[claude] Skipping empty message in conversation %s", conv_id[:8])
continue
content_type = "text"
messages.append(
{
"role": role,
"content": content,
"content_type": "text",
"content_type": content_type,
"timestamp": timestamp,
"blocks": blocks,
}
)
for _ in messages:
report.record_message()
report.record_conversation()
return {
"id": conv_id,
"title": title,
@@ -242,43 +251,134 @@ def _map_role(sender: str) -> str | None:
return mapping.get(sender.lower()) if sender else None
def _extract_claude_text(
content: str | list | dict, conv_id: str
) -> tuple[str | None, list[str]]:
"""Extract plain text from a Claude content field.
def _extract_claude_blocks(
content: str | list | dict | None, conv_id: str, report: LossReport
) -> list[dict]:
"""Extract typed blocks from a Claude content field.
Returns:
(text_or_None, list_of_skipped_content_types)
Defensive dispatch — zero observed cases of rich Claude content in the
user's archive at planning time, so this is theory-only. Real shapes
will be locked in v0.4.1 once captured. Any unrecognised block type
surfaces as an `unknown` block + WARNING + tally.
"""
skipped: list[str] = []
if content is None:
return []
if isinstance(content, str):
text = content.strip()
return (text if text else None), skipped
block = make_text_block(content)
return [block] if block else []
if isinstance(content, list):
parts: list[str] = []
for block in content:
if isinstance(block, str):
parts.append(block)
elif isinstance(block, dict):
btype = block.get("type", "text")
if btype == "text":
t = block.get("text", "").strip()
if t:
parts.append(t)
else:
skipped.append(btype)
text = "\n".join(parts).strip()
return (text if text else None), skipped
blocks: list[dict] = []
for item in content:
if isinstance(item, str):
block = make_text_block(item)
if block:
blocks.append(block)
elif isinstance(item, dict):
blocks.extend(_dispatch_claude_block(item, conv_id, report))
return blocks
if isinstance(content, dict):
btype = content.get("type", "text")
if btype == "text":
text = content.get("text", "").strip()
return (text if text else None), skipped
else:
skipped.append(btype)
return None, skipped
return _dispatch_claude_block(content, conv_id, report)
return None, skipped
return []
def _dispatch_claude_block(block: dict, conv_id: str, report: LossReport) -> list[dict]:
"""Translate one raw Claude content block into normalized blocks."""
btype = block.get("type", "text")
if btype == "text":
block_obj = make_text_block(block.get("text", "") or "")
return [block_obj] if block_obj else []
if btype == "thinking":
# Claude extended-thinking blocks may use 'thinking' or 'text' field.
text = block.get("thinking") or block.get("text") or ""
block_obj = make_thinking_block(text)
return [block_obj] if block_obj else []
if btype == "tool_use":
return [
make_tool_use_block(
name=block.get("name", "") or "",
input_data=block.get("input"),
tool_id=block.get("id"),
)
]
if btype == "tool_result":
# ``content`` may be a string or a list of nested blocks (recursive).
nested = block.get("content")
output = _flatten_tool_result_content(nested, conv_id, report)
return [
make_tool_result_block(
output=output,
tool_name=None,
is_error=bool(block.get("is_error")),
)
]
if btype == "image":
# Source shape is unverified; try the most likely fields.
source = block.get("source") or {}
ref = ""
if isinstance(source, dict):
ref = (
source.get("file_uuid")
or source.get("media_type")
or source.get("url")
or ""
)
return [make_image_placeholder(ref=ref or "(unknown)", source="user_upload")]
# Unknown block type
keys = list(block.keys())
logger.warning(
"[claude] Unknown block type %r in conversation %s "
"— see plan §Data-loss visibility (rendering as unknown block)",
btype,
conv_id[:8],
)
report.record_unknown(btype or "?")
return [
make_unknown_block(
raw_type=btype or "?",
observed_keys=keys,
reason=UNKNOWN_REASON_UNKNOWN_TYPE,
)
]
def _flatten_tool_result_content(
nested: object, conv_id: str, report: LossReport
) -> str:
"""Flatten Claude tool_result content (string OR list of nested blocks) to text.
Recurses into nested text blocks; any non-text nested block becomes a
visible inline marker so non-text content isn't silently dropped.
"""
if nested is None:
return ""
if isinstance(nested, str):
return nested
if isinstance(nested, list):
chunks: list[str] = []
for item in nested:
if isinstance(item, str):
chunks.append(item)
elif isinstance(item, dict):
btype = item.get("type", "text")
if btype == "text":
chunks.append(item.get("text", "") or "")
else:
keys = list(item.keys())[:10]
report.record_extraction_failure(f"tool_result.{btype}")
chunks.append(
f"[Unsupported nested {btype} block; keys={keys}]"
)
return "\n".join(c for c in chunks if c)
if isinstance(nested, dict):
return _flatten_tool_result_content([nested], conv_id, report)
return str(nested)

90
tests/fixtures/chatgpt_conversation.json vendored
View File

@@ -8,12 +8,30 @@
"node-root": {
"id": "node-root",
"parent": null,
"children": ["node-1"],
"children": ["node-uec"],
"message": null
},
"node-uec": {
"id": "node-uec",
"parent": "node-root",
"children": ["node-1"],
"message": {
"id": "node-uec",
"author": {"role": "user"},
"create_time": null,
"content": {
"content_type": "user_editable_context",
"user_profile": "Preferred name: Jesse",
"user_instructions": "The user provided the additional info about how they would like you to respond:\n```Always cite sources.```"
},
"metadata": {
"is_visually_hidden_from_conversation": true
}
}
},
"node-1": {
"id": "node-1",
"parent": "node-root",
"parent": "node-uec",
"children": ["node-2"],
"message": {
"id": "node-1",
@@ -28,7 +46,7 @@
"node-2": {
"id": "node-2",
"parent": "node-1",
"children": ["node-3"],
"children": ["node-mm-user"],
"message": {
"id": "node-2",
"author": {"role": "assistant"},
@@ -39,17 +57,71 @@
}
}
},
"node-3": {
"id": "node-3",
"node-mm-user": {
"id": "node-mm-user",
"parent": "node-2",
"children": [],
"children": ["node-mm-assistant"],
"message": {
"id": "node-3",
"id": "node-mm-user",
"author": {"role": "user"},
"create_time": 1704067300.0,
"content": {
"content_type": "image_asset_pointer",
"parts": [{"content_type": "image_asset_pointer", "asset_pointer": "file://some-image"}]
"content_type": "multimodal_text",
"parts": [
{"content_type": "audio_transcription", "text": "What is the capital of France?", "direction": "in", "decoding_id": null},
{"content_type": "real_time_user_audio_video_asset_pointer", "frames_asset_pointers": [], "video_container_asset_pointer": null, "audio_asset_pointer": {"content_type": "audio_asset_pointer", "asset_pointer": "sediment://file_user001", "size_bytes": 50000, "format": "wav", "metadata": {"start": 0.0, "end": 2.5}}, "audio_start_timestamp": 1.0}
]
},
"metadata": {"voice_mode_message": true}
}
},
"node-mm-assistant": {
"id": "node-mm-assistant",
"parent": "node-mm-user",
"children": ["node-mm-user-rev"],
"message": {
"id": "node-mm-assistant",
"author": {"role": "assistant"},
"create_time": 1704067305.0,
"content": {
"content_type": "multimodal_text",
"parts": [
{"content_type": "audio_transcription", "text": "The capital of France is Paris.", "direction": "out", "decoding_id": null},
{"content_type": "audio_asset_pointer", "asset_pointer": "sediment://file_assistant001", "size_bytes": 80000, "format": "wav", "metadata": {"start": 0.0, "end": 3.2}}
]
}
}
},
"node-mm-user-rev": {
"id": "node-mm-user-rev",
"parent": "node-mm-assistant",
"children": ["node-image-only"],
"message": {
"id": "node-mm-user-rev",
"author": {"role": "user"},
"create_time": 1704067400.0,
"content": {
"content_type": "multimodal_text",
"parts": [
{"content_type": "real_time_user_audio_video_asset_pointer", "frames_asset_pointers": [], "video_container_asset_pointer": null, "audio_asset_pointer": {"content_type": "audio_asset_pointer", "asset_pointer": "sediment://file_user002", "size_bytes": 30000, "format": "wav", "metadata": {"start": 0.0, "end": 1.5}}, "audio_start_timestamp": 5.0},
{"content_type": "audio_transcription", "text": "Tell me more please.", "direction": "in", "decoding_id": null}
]
}
}
},
"node-image-only": {
"id": "node-image-only",
"parent": "node-mm-user-rev",
"children": [],
"message": {
"id": "node-image-only",
"author": {"role": "user"},
"create_time": 1704067500.0,
"content": {
"content_type": "multimodal_text",
"parts": [
{"content_type": "image_asset_pointer", "asset_pointer": "file-service://image001"}
]
}
}
}

9
tests/fixtures/claude_conversation.json vendored
View File

@@ -30,6 +30,15 @@
"sender": "human",
"created_at": "2024-06-10T14:45:00.000Z",
"content": "Thank you, that helped!"
},
{
"uuid": "msg-004",
"sender": "human",
"created_at": "2024-06-10T14:50:00.000Z",
"content": [
{"type": "text", "text": "What about this image?"},
{"type": "image", "source": {"file_uuid": "claude-image-uuid-1", "media_type": "image/png"}}
]
}
]
}

47
tests/test_cli.py
View File

@@ -127,3 +127,50 @@ class TestExportSinceValidation:
},
)
assert "Invalid --since date" not in result.output
# ---------------------------------------------------------------------------
# LossReport summary
# ---------------------------------------------------------------------------
class TestLossReportSummary:
"""The LossReport's format_summary() pinned format covers zero, top-5, and overflow cases."""
def test_zero_summary_uses_none_sentinel(self):
from src.loss_report import LossReport
report = LossReport()
out = report.format_summary()
assert "[export] Run summary:" in out
assert "conversations: 0" in out
assert "messages rendered: 0" in out
# Both "(none)" sentinels present — never empty parens
assert out.count("(none)") == 2
def test_top_5_breakdown(self):
from src.loss_report import LossReport
report = LossReport()
for raw_type in ("a", "b", "c", "d", "e", "f", "g"):
report.record_unknown(raw_type)
if raw_type == "a":
# Make 'a' the most common
for _ in range(4):
report.record_unknown("a")
out = report.format_summary()
# Top entry shown
assert "a=5" in out
# Overflow line present (7 types, top 5 + 2 more)
assert "+ 2 more types" in out
def test_messages_and_conversations_recorded(self):
from src.loss_report import LossReport
report = LossReport()
report.record_conversation()
report.record_message()
report.record_message()
out = report.format_summary()
assert "conversations: 1" in out
assert "messages rendered: 2" in out

256
tests/test_exporters.py
View File

@@ -1,4 +1,4 @@
"""Unit tests for src/exporters/."""
"""Unit tests for src/exporters/ and src/blocks.py."""
import json
import os
@@ -7,6 +7,23 @@ from pathlib import Path
import pytest
from src.blocks import (
BLOCK_TYPE_TEXT,
UNKNOWN_REASON_EXTRACTION_FAILED,
UNKNOWN_REASON_UNKNOWN_TYPE,
_blockquote_prefix,
_safe_fence,
make_code_block,
make_file_placeholder,
make_hidden_context_marker,
make_image_placeholder,
make_text_block,
make_thinking_block,
make_tool_result_block,
make_tool_use_block,
make_unknown_block,
render_blocks_to_markdown,
)
from src.exporters.markdown import MarkdownExporter, _yaml_escape, _format_timestamp
from src.exporters.json_export import JSONExporter
@@ -250,3 +267,240 @@ class TestFormatTimestamp:
def test_empty_string(self):
assert _format_timestamp("") == ""
# ---------------------------------------------------------------------------
# Block helpers and rendering
# ---------------------------------------------------------------------------
class TestSafeFence:
def test_minimum_three_backticks(self):
assert _safe_fence("plain text") == "```"
def test_four_backticks_when_three_in_content(self):
assert _safe_fence("here ``` is a fence") == "````"
def test_five_backticks_when_four_in_content(self):
assert _safe_fence("here ```` is four") == "`````"
def test_handles_empty_string(self):
assert _safe_fence("") == "```"
def test_handles_run_at_end(self):
# Trailing run still counted
assert _safe_fence("text ending in ```") == "````"
class TestBlockquotePrefix:
def test_single_line(self):
assert _blockquote_prefix("hello") == "> hello"
def test_multi_line(self):
assert _blockquote_prefix("a\nb\nc") == "> a\n> b\n> c"
def test_empty_lines_become_naked_quote_marker(self):
assert _blockquote_prefix("a\n\nb") == "> a\n>\n> b"
def test_empty_string(self):
assert _blockquote_prefix("") == ">"
class TestBlockConstructors:
def test_make_text_block_returns_none_for_empty(self):
assert make_text_block("") is None
assert make_text_block(" ") is None
def test_make_text_block_returns_dict(self):
b = make_text_block("hello")
assert b == {"type": "text", "text": "hello"}
def test_make_code_block_returns_none_for_empty(self):
assert make_code_block("") is None
def test_make_thinking_block_returns_none_for_empty(self):
assert make_thinking_block("") is None
class TestRenderBlocks:
def test_text_block_renders_as_paragraph(self):
out = render_blocks_to_markdown([make_text_block("Hello world")])
assert out == "Hello world"
def test_blocks_separated_by_blank_line(self):
out = render_blocks_to_markdown(
[make_text_block("first"), make_text_block("second")]
)
assert out == "first\n\nsecond"
def test_code_block_with_language(self):
out = render_blocks_to_markdown([make_code_block("print(1)", language="python")])
assert "```python" in out
assert "print(1)" in out
def test_thinking_block_uses_blockquote(self):
out = render_blocks_to_markdown([make_thinking_block("step 1\nstep 2")])
assert "**💭 Reasoning**" in out
assert "> step 1" in out
assert "> step 2" in out
def test_tool_use_renders_as_blockquote_with_safe_fence(self):
out = render_blocks_to_markdown(
[make_tool_use_block("search", {"query": "test"})]
)
assert "> 🔧 **Tool: search**" in out
# Every line of the body is blockquote-prefixed
assert "> ```json" in out
assert "> }" in out
def test_tool_use_with_multiline_input(self):
out = render_blocks_to_markdown(
[make_tool_use_block("complex", {"a": 1, "b": [{"x": "y"}]})]
)
# Prefix every line of multi-line JSON
for line in out.split("\n"):
assert line.startswith(">") or line == ""
def test_tool_result_success_uses_outbox_icon(self):
out = render_blocks_to_markdown([make_tool_result_block("OK")])
assert "📤 **Result**" in out
assert "❌" not in out
def test_tool_result_error_uses_x_icon(self):
out = render_blocks_to_markdown([make_tool_result_block("oops", is_error=True)])
assert "❌ **Result (error)**" in out
assert "📤" not in out
def test_image_placeholder_rendering(self):
out = render_blocks_to_markdown(
[make_image_placeholder(ref="file-123", source="user_upload")]
)
assert "🖼️ **Image attached**" in out
assert "`file-123`" in out
assert "user_upload" in out
assert "content not preserved" in out
def test_file_placeholder_with_metadata(self):
out = render_blocks_to_markdown(
[make_file_placeholder(ref="sediment://x", mime="audio/wav", size_bytes=10240, duration_seconds=2.5)]
)
assert "📎 **File attached**" in out
assert "audio/wav" in out
assert "KB" in out
assert "2.50s" in out
def test_unknown_block_renders_with_keys(self):
out = render_blocks_to_markdown(
[
make_unknown_block(
raw_type="future_x",
observed_keys=["foo", "bar"],
reason=UNKNOWN_REASON_UNKNOWN_TYPE,
)
]
)
assert "⚠️ **Unsupported content**" in out
assert "future_x" in out
assert "`foo`" in out
assert "`bar`" in out
def test_unknown_extraction_failed_includes_summary(self):
out = render_blocks_to_markdown(
[
make_unknown_block(
raw_type="audio_transcription",
observed_keys=["asset_pointer"],
reason=UNKNOWN_REASON_EXTRACTION_FAILED,
summary="expected key 'text' not found",
)
]
)
assert "extraction_failed" in out
assert "expected key 'text' not found" in out
def test_hidden_context_marker(self):
out = render_blocks_to_markdown(
[make_hidden_context_marker("user_editable_context")]
)
assert "ℹ️ **Hidden context**" in out
assert "`user_editable_context`" in out
def test_safe_fence_prevents_runaway_code_block(self):
# Content contains an unbalanced opening fence — without _safe_fence
# this would corrupt downstream rendering.
evil_content = "before\n```Follow\ntext\nraw is: \"```"
block = make_code_block(evil_content)
out = render_blocks_to_markdown([block, make_text_block("after")])
# The 4-backtick wrap should be present
assert "````" in out
# The "after" text should appear OUTSIDE any code block — it follows
# the closing ```` fence.
assert out.endswith("after")
def test_block_order_preserved(self):
blocks = [
make_text_block("a"),
make_image_placeholder(ref="r1", source="user_upload"),
make_text_block("b"),
]
out = render_blocks_to_markdown(blocks)
assert out.index("a") < out.index("Image attached")
assert out.index("Image attached") < out.index("b")
# ---------------------------------------------------------------------------
# Markdown exporter with blocks
# ---------------------------------------------------------------------------
SAMPLE_CONV_BLOCKS = {
"id": "blocks12345",
"title": "Blocks Conversation",
"provider": "claude",
"project": None,
"created_at": "2024-06-10T14:32:00Z",
"updated_at": "2024-06-10T15:00:00Z",
"message_count": 1,
"messages": [
{
"role": "assistant",
"content_type": "text",
"timestamp": None,
"blocks": [
{"type": "text", "text": "Here is the answer."},
{"type": "tool_use", "name": "search", "input": {"q": "x"}, "tool_id": "t1"},
],
}
],
}
class TestMarkdownExporterWithBlocks:
def test_renders_blocks(self, tmp_path):
exp = MarkdownExporter(tmp_path)
path = exp.export(SAMPLE_CONV_BLOCKS)
body = path.read_text()
assert "Here is the answer." in body
assert "🔧 **Tool: search**" in body
def test_falls_back_to_content_when_blocks_missing(self, tmp_path):
# Backward-compat: messages with `content` only (no `blocks`) still render.
exp = MarkdownExporter(tmp_path)
path = exp.export(SAMPLE_CONV) # SAMPLE_CONV has content only, no blocks
body = path.read_text()
assert "Hello, how are you?" in body
def test_skips_messages_with_neither_blocks_nor_content(self, tmp_path):
conv = {
**SAMPLE_CONV_BLOCKS,
"messages": [
{"role": "user", "content_type": "text", "timestamp": None, "blocks": []},
{"role": "assistant", "content_type": "text", "timestamp": None, "blocks": [
{"type": "text", "text": "I am here."}
]},
],
}
exp = MarkdownExporter(tmp_path)
path = exp.export(conv)
body = path.read_text()
assert "I am here." in body

389
tests/test_providers.py
View File

@@ -1,19 +1,52 @@
"""Unit tests for src/providers/ using fixture files."""
import json
import logging
from pathlib import Path
import pytest
from src.blocks import (
BLOCK_TYPE_FILE_PLACEHOLDER,
BLOCK_TYPE_HIDDEN_CONTEXT_MARKER,
BLOCK_TYPE_IMAGE_PLACEHOLDER,
BLOCK_TYPE_TEXT,
BLOCK_TYPE_THINKING,
BLOCK_TYPE_TOOL_RESULT,
BLOCK_TYPE_TOOL_USE,
BLOCK_TYPE_UNKNOWN,
)
from src.loss_report import LossReport
FIXTURES = Path(__file__).parent / "fixtures"
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _block_types(message: dict) -> list[str]:
return [b.get("type") for b in (message.get("blocks") or [])]
def _first_block(message: dict, block_type: str) -> dict | None:
for b in message.get("blocks") or []:
if b.get("type") == block_type:
return b
return None
# ---------------------------------------------------------------------------
# ChatGPT
# ---------------------------------------------------------------------------
class TestChatGPTNormalization:
"""Test ChatGPTProvider.normalize_conversation() using fixture data."""
"""ChatGPT normalize_conversation block-extraction behavior."""
def _get_provider(self):
from src.providers.chatgpt import ChatGPTProvider
# Bypass __init__ token check
p = ChatGPTProvider.__new__(ChatGPTProvider)
import requests
p._session = requests.Session()
@@ -31,7 +64,6 @@ class TestChatGPTNormalization:
assert result["id"] == "chatgpt-conv-001"
assert result["title"] == "Python Async Tutorial"
assert result["provider"] == "chatgpt"
# No entry in _project_map → project is None
assert result["project"] is None
assert result["created_at"] != ""
assert result["updated_at"] != ""
@@ -46,7 +78,6 @@ class TestChatGPTNormalization:
assert result["id"] == "chatgpt-conv-002"
def test_normalizes_with_project_from_map(self):
"""Project name from _project_map (populated by fetch_all_conversations) flows through."""
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
p._project_map["chatgpt-conv-001"] = "My Research Project"
@@ -54,32 +85,167 @@ class TestChatGPTNormalization:
assert result["project"] == "My Research Project"
def test_extracts_text_messages(self):
def test_text_message_emits_text_block(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assert len(result["messages"]) >= 2
user_msgs = [m for m in result["messages"] if m["role"] == "user"]
assert any("async" in m["content"].lower() for m in user_msgs)
# The "How does async/await..." message
async_msgs = [
m for m in user_msgs
if any(
"async" in (b.get("text") or "").lower()
for b in (m.get("blocks") or [])
)
]
assert async_msgs, "expected a user message about async/await"
assert _block_types(async_msgs[0]) == [BLOCK_TYPE_TEXT]
def test_skips_non_text_content_with_warning(self, caplog):
import logging
def test_code_block_preserved_with_language(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
with caplog.at_level(logging.WARNING):
result = p.normalize_conversation(raw)
# The fixture has an image_asset_pointer node — should be warned about
assert any(
"image_asset_pointer" in r.message or "rich content" in r.message
for r in caplog.records
)
result = p.normalize_conversation(raw)
def test_model_editable_context_included_without_warning(self, caplog):
"""model_editable_context messages (project instructions) should be included, not warned about."""
import logging
conv = {
"id": "test-conv-mec",
assistant_msgs = [m for m in result["messages"] if m["role"] == "assistant"]
# The first assistant message is the async/await answer with a python fence
text_block = _first_block(assistant_msgs[0], BLOCK_TYPE_TEXT)
assert text_block is not None
assert "```python" in text_block["text"]
def test_multimodal_voice_user_message(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
# node-mm-user: audio_transcription "What is the capital of France?"
# + real_time_user_audio_video_asset_pointer wrapping a sediment:// URL
capital_msgs = [
m for m in result["messages"]
if any(
"capital of france" in (b.get("text") or "").lower()
for b in (m.get("blocks") or [])
)
]
assert capital_msgs, "expected the audio_transcription text to surface"
types = _block_types(capital_msgs[0])
assert BLOCK_TYPE_TEXT in types
assert BLOCK_TYPE_FILE_PLACEHOLDER in types
file_block = _first_block(capital_msgs[0], BLOCK_TYPE_FILE_PLACEHOLDER)
assert file_block["ref"].startswith("sediment://")
assert file_block["mime"] == "audio/wav"
assert file_block["size_bytes"] == 50000
assert file_block["duration_seconds"] == pytest.approx(2.5)
def test_multimodal_voice_reverse_order_preserved(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
# node-mm-user-rev has parts in REVERSE order: asset first, transcription second.
rev_msgs = [
m for m in result["messages"]
if any(
"tell me more" in (b.get("text") or "").lower()
for b in (m.get("blocks") or [])
)
]
assert rev_msgs, "expected the reverse-order voice message"
types = _block_types(rev_msgs[0])
# Order preserved: file_placeholder before text
assert types == [BLOCK_TYPE_FILE_PLACEHOLDER, BLOCK_TYPE_TEXT]
def test_image_only_user_message_renders(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
image_msgs = [
m for m in result["messages"]
if any(b.get("type") == BLOCK_TYPE_IMAGE_PLACEHOLDER for b in (m.get("blocks") or []))
]
assert image_msgs, "image-only user message should now render"
def test_user_editable_context_emits_blocks(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
# The user_editable_context message has user_profile + user_instructions.
# It should now appear (was silently dropped pre-v0.4.0).
uec_msgs = [
m for m in result["messages"]
if any(
"Custom Instructions" in (b.get("text") or "")
for b in (m.get("blocks") or [])
)
]
assert uec_msgs, "user_editable_context should be visible in output"
# Hidden context marker should be prepended.
assert uec_msgs[0]["blocks"][0]["type"] == BLOCK_TYPE_HIDDEN_CONTEXT_MARKER
def test_user_editable_context_uses_safe_fence(self):
"""The user_instructions value contains embedded triple-backticks; the rendered
Markdown must use a fence longer than 3 backticks so embedded fences are inert.
"""
from src.blocks import render_blocks_to_markdown
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
uec_msgs = [
m for m in result["messages"]
if any(
"Custom Instructions" in (b.get("text") or "")
for b in (m.get("blocks") or [])
)
]
assert uec_msgs
rendered = render_blocks_to_markdown(uec_msgs[0]["blocks"])
# Content has ``` inside, so the wrap fence must be at least 4 backticks.
assert "````" in rendered, "expected a 4+ backtick safe-fence wrap"
def test_message_roles_are_valid(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
for msg in result["messages"]:
assert msg["role"] in ("user", "assistant", "system", "tool")
def test_message_count_matches(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assert result["message_count"] == len(result["messages"])
def test_loss_report_records_messages(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
report = LossReport()
result = p.normalize_conversation(raw, report)
assert report.messages_rendered == len(result["messages"])
assert report.conversations == 1
class TestChatGPTUnknownContent:
"""Unrecognised content types should produce visible unknown blocks + WARNING + tally."""
def _get_provider(self):
from src.providers.chatgpt import ChatGPTProvider
p = ChatGPTProvider.__new__(ChatGPTProvider)
import requests
p._session = requests.Session()
p._org_id = None
p._project_ids = []
p._project_map = {}
p._project_name_cache = {}
return p
def _make_unknown_conv(self):
return {
"id": "test-unknown",
"title": "Test",
"create_time": 1700000000.0,
"update_time": 1700000001.0,
@@ -91,46 +257,45 @@ class TestChatGPTNormalization:
"id": "msg1",
"author": {"role": "user"},
"content": {
"content_type": "model_editable_context",
"parts": ["These are the project instructions."],
"content_type": "future_unknown_type_xyz",
"some_field": "value",
},
"create_time": 1700000001.0,
"status": "finished_successfully",
},
"parent": "root",
"children": [],
},
},
}
def test_unknown_content_type_produces_unknown_block(self):
p = self._get_provider()
result = p.normalize_conversation(self._make_unknown_conv())
assert any(
b.get("type") == BLOCK_TYPE_UNKNOWN
for m in result["messages"]
for b in (m.get("blocks") or [])
)
def test_unknown_content_type_logs_warning(self, caplog):
p = self._get_provider()
with caplog.at_level(logging.WARNING):
result = p.normalize_conversation(conv)
assert any(m["content"] == "These are the project instructions." for m in result["messages"])
assert not any("model_editable_context" in r.message for r in caplog.records)
p.normalize_conversation(self._make_unknown_conv())
assert any("future_unknown_type_xyz" in r.message for r in caplog.records)
def test_message_roles_are_valid(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
def test_unknown_content_type_increments_loss_report(self):
p = self._get_provider()
result = p.normalize_conversation(raw)
for msg in result["messages"]:
assert msg["role"] in ("user", "assistant", "system")
report = LossReport()
p.normalize_conversation(self._make_unknown_conv(), report)
assert report.unknown_blocks["future_unknown_type_xyz"] == 1
def test_message_count_matches(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assert result["message_count"] == len(result["messages"])
def test_code_fence_preserved(self):
raw = json.loads((FIXTURES / "chatgpt_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
all_content = " ".join(m["content"] for m in result["messages"])
assert "```python" in all_content
# ---------------------------------------------------------------------------
# Claude
# ---------------------------------------------------------------------------
class TestClaudeNormalization:
"""Test ClaudeProvider.normalize_conversation() using fixture data."""
"""Claude normalize_conversation block-extraction behavior."""
def _get_provider(self):
from src.providers.claude import ClaudeProvider
@@ -150,55 +315,138 @@ class TestClaudeNormalization:
assert result["provider"] == "claude"
assert result["project"] == "StarTOS Packaging"
assert result["created_at"] == "2024-06-10T14:32:00.000Z"
assert isinstance(result["messages"], list)
def test_normalizes_without_project(self):
raw = json.loads((FIXTURES / "claude_no_project.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assert result["project"] is None
assert result["id"] == "claude-conv-002"
def test_string_content_extracted(self):
raw = json.loads((FIXTURES / "claude_no_project.json").read_text())
def test_string_content_emits_text_block(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assert any("Docker" in m["content"] for m in result["messages"])
thanks_msgs = [
m for m in result["messages"]
if any(
"thank you" in (b.get("text") or "").lower()
for b in (m.get("blocks") or [])
)
]
assert thanks_msgs
def test_list_content_extracted(self):
def test_list_content_emits_blocks_in_order(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
assistant_msgs = [m for m in result["messages"] if m["role"] == "assistant"]
assert any("manifest" in m["content"].lower() for m in assistant_msgs)
# msg-002 has text + tool_use, in that order.
assert assistant_msgs
types = _block_types(assistant_msgs[0])
assert BLOCK_TYPE_TEXT in types
assert BLOCK_TYPE_TOOL_USE in types
# Order preserved
assert types.index(BLOCK_TYPE_TEXT) < types.index(BLOCK_TYPE_TOOL_USE)
def test_non_text_blocks_skipped_with_warning(self, caplog):
import logging
def test_tool_use_block_fields(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
p = self._get_provider()
with caplog.at_level(logging.WARNING):
result = p.normalize_conversation(raw)
# The fixture has a tool_use block — should warn
result = p.normalize_conversation(raw)
assistant_msgs = [m for m in result["messages"] if m["role"] == "assistant"]
tool_block = _first_block(assistant_msgs[0], BLOCK_TYPE_TOOL_USE)
assert tool_block["name"] == "search"
assert tool_block["input"] == {"query": "startOS docs"}
assert tool_block["tool_id"] == "tool-001"
def test_image_block_emits_image_placeholder(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
p = self._get_provider()
result = p.normalize_conversation(raw)
msg004 = [
m for m in result["messages"]
if any(b.get("type") == BLOCK_TYPE_IMAGE_PLACEHOLDER for b in (m.get("blocks") or []))
]
assert msg004
img = _first_block(msg004[0], BLOCK_TYPE_IMAGE_PLACEHOLDER)
assert img["ref"] == "claude-image-uuid-1"
def test_unknown_block_type_records_loss(self):
from src.blocks import BLOCK_TYPE_UNKNOWN as _UNK
raw = {
"uuid": "test-unknown",
"name": "T",
"chat_messages": [
{
"uuid": "m1",
"sender": "human",
"content": [{"type": "future_block_xyz", "data": "..."}],
}
],
}
p = self._get_provider()
report = LossReport()
result = p.normalize_conversation(raw, report)
assert any(
"tool_use" in r.message or "rich content" in r.message
for r in caplog.records
b.get("type") == _UNK
for m in result["messages"]
for b in (m.get("blocks") or [])
)
assert report.unknown_blocks["future_block_xyz"] == 1
def test_message_count_matches(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
def test_thinking_block(self):
raw = {
"uuid": "thinking-test",
"name": "T",
"chat_messages": [
{
"uuid": "m1",
"sender": "assistant",
"content": [
{"type": "thinking", "thinking": "Let me reason about this."},
{"type": "text", "text": "Here's the answer."},
],
}
],
}
p = self._get_provider()
result = p.normalize_conversation(raw)
assert result["message_count"] == len(result["messages"])
types = _block_types(result["messages"][0])
assert BLOCK_TYPE_THINKING in types
assert BLOCK_TYPE_TEXT in types
def test_roles_normalized(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
def test_tool_result_with_nested_text_blocks(self):
raw = {
"uuid": "tool-result-test",
"name": "T",
"chat_messages": [
{
"uuid": "m1",
"sender": "assistant",
"content": [
{
"type": "tool_result",
"tool_use_id": "tool-001",
"content": [
{"type": "text", "text": "search hit 1"},
{"type": "text", "text": "search hit 2"},
],
"is_error": False,
}
],
}
],
}
p = self._get_provider()
result = p.normalize_conversation(raw)
for msg in result["messages"]:
assert msg["role"] in ("user", "assistant", "system")
tool_result = _first_block(result["messages"][0], BLOCK_TYPE_TOOL_RESULT)
assert tool_result is not None
assert "search hit 1" in tool_result["output"]
assert "search hit 2" in tool_result["output"]
assert tool_result["is_error"] is False
def test_human_sender_maps_to_user(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
@@ -207,3 +455,10 @@ class TestClaudeNormalization:
roles = {m["role"] for m in result["messages"]}
assert "user" in roles
assert "human" not in roles
def test_loss_report_messages_recorded(self):
raw = json.loads((FIXTURES / "claude_conversation.json").read_text())
p = self._get_provider()
report = LossReport()
result = p.normalize_conversation(raw, report)
assert report.messages_rendered == len(result["messages"])