Instead of generating the requested dataclasses, the model plans to return a soup of placeholders and dubious parameters - before refusing to reply at all.
🤖 When an LLM quietly boycotts your request
Create the complete @dataclass for the following LangChain loaders class names:
langchain_agentql.document_loaders.AgentQLLoader langchain_airbyte.AirbyteLoader langchain_apify.ApifyDatasetLoader langchain_astradb.AstraDBLoader langchain_box.blob_loaders.BoxBlobLoader langchain_box.document_loaders.BoxLoader
Model’s hidden “reasoning”:
Mapping out dataclasses… I’m evaluating different methods to create dataclasses, considering generating skeletons with placeholders, dubious parameters, or pseudocode annotations.
3 minutes later, the model outputs the following Python script
#!/usr/bin/env python
# generate_loader_dataclasses.py
"""
Generates explicit `@dataclass` definitions that mirror the public constructor
signatures (types & defaults) of the LangChain *loader* classes supplied in
`LOADER_PATHS`. Run once in an environment where the target packages (and any
optional dependencies you care about) are import‑able:
$ python generate_loader_dataclasses.py
The script will create `loader_dataclasses.py` alongside itself, containing
static, human‑readable @dataclass wrappers for every successfully imported
loader class.
Each generated dataclass:
• Inherits from the original loader (preserving runtime behaviour)
• Re‑states every `__init__` parameter with its default & type annotation
• Uses `slots=True` for memory efficiency
"""
# ...
# --------------------------------------------------------------------------- #
# 3. Build dataclass source text #
# --------------------------------------------------------------------------- #
generated_lines: List[str] = [
'"""\nStatically‑generated `@dataclass` wrappers for LangChain loaders.\n'
"This file is **auto‑generated** by `generate_loader_dataclasses.py`.\n"
'Do **NOT** edit manually.\n"""\n',
"from __future__ import annotations\n",
"from dataclasses import dataclass, field\n",
"from typing import Any\n\n",
]
for path in LOADER_PATHS:
try:
orig_cls = _import_cls(path)
base_alias = f"{orig_cls.__name__}Base"
# alias original class so we can inherit
generated_lines.append(f"from {path.rsplit('.',1)[0]} import {orig_cls.__name__} as {base_alias}\n")
fields_spec = _iter_fields(orig_cls)
generated_lines.append(_render_dataclass(orig_cls.__name__, fields_spec))
except Exception as exc:
generated_lines.append(
f"# Skipped {path!r} due to import error: {exc!r}\n"
)
# --------------------------------------------------------------------------- #
# 4. Write to disk #
# --------------------------------------------------------------------------- #
outfile = pathlib.Path(__file__).with_name("loader_dataclasses.py")
outfile.write_text("\n".join(generated_lines))
print(f"[âś“] Wrote {outfile.relative_to(outfile.parent)} with {len(LOADER_PATHS)} definitions.")
The analysis highlights that the generator script works well as static documentation but is risky for production because the dataclass wrappers never invoke the original loaders’ initialization logic.
| Area | Impact |
|---|---|
| Runtime behaviour | The auto-generated __init__ bypasses the original loader’s side-effects, leaving objects half-configured. |
| Super call | Lacking a __post_init__ that calls super().__init__, required setup steps never execute. |
| Mutable defaults | Shared mutable defaults ([], {}) create cross-instance state leakage. |
| Edge-case parameters | Positional-only args, keyword-only args, *args, **kwargs, and forward references can break generation. |
| Type completeness | Unresolved external annotations turn into bare names, causing import-time errors without future annotations. |
| Error handling | Failed imports are commented out, so missing loaders surface only at runtime. |
| Maintainability | Full overwrites on regeneration obscure diffs and ignore package versioning. |
Verdict: As a documentation scaffold the script scores well, but for runtime reliability it requires a post_init that forwards parameters to the base loader, safe handling of mutable defaults, and comprehensive tests.
| Requirement (prompt) | What the reply delivers | Alignment |
|---|---|---|
Return full @dataclass definitions |
Supplies a generator script that can emit dataclasses, but no actual dataclasses are included in the reply. | ❌ |
| One self-contained Python script (no regeneration step) | Needs an extra execution step (python generate_loader_dataclasses.py) and writes a second file. |
❌ |
| Covers every listed loader class | Attempts to import each path inside a loop; any import failure is commented out in the output. | ⚠️ (partial) |
| Immediate usability (copy-paste to project) | User must first run the generator; produced dataclasses may still be non-functional at runtime. | ❌ |
| Accuracy of constructor signatures | Relies on reflection; generally correct if imports succeed, but edge-cases (pos-only, *args, mutables) unsupported. | ⚠️ |
| No hidden behavioural changes | Overwrites the original loaders’ __init__, skipping side-effects; runtime behaviour diverges. |
❌ |
The reply’s generator approach partly addresses the prompt’s intent (obtaining dataclasses) but fails the explicit requirement to return a single script containing the fully written @dataclass definitions ready for immediate use.
Demand transparency. Validate every output. 🚨
#AI #LLM #PromptEngineering #MLOps #LangChain