Merge branch 'main' into copilot/add-self-hosted-ocr-support
This commit is contained in:
Christian Krakau-Louis
@@ -66,42 +66,59 @@ pytest --cov=app --cov-report=html
|
||||
## Lint / Format Commands
|
||||
|
||||
```bash
|
||||
# Format code with Black (line length 120)
|
||||
black app/ tests/
|
||||
|
||||
# Sort imports with isort (Black-compatible profile)
|
||||
isort app/ tests/
|
||||
|
||||
# Lint with flake8 (max line length 120, ignores E203/W503)
|
||||
flake8 app/ --max-line-length=120
|
||||
# Format and lint with Ruff (replaces Black, isort, Flake8, Bandit — all-in-one)
|
||||
ruff format app/ tests/
|
||||
ruff check app/ tests/ --fix
|
||||
|
||||
# Type checking with mypy
|
||||
mypy app/
|
||||
|
||||
# Security lint with bandit (excludes tests)
|
||||
bandit -r app/
|
||||
|
||||
# Check for dependency vulnerabilities
|
||||
safety check
|
||||
|
||||
# Run all pre-commit hooks at once
|
||||
# Run all pre-commit hooks at once (recommended — runs ruff, mypy, secret detection, etc.)
|
||||
pre-commit run --all-files
|
||||
```
|
||||
|
||||
## Agent Workflow (Follow for Every Task)
|
||||
|
||||
Follow these steps **in order** for every task — do not skip any:
|
||||
|
||||
1. **Understand** — read the issue/request in full before writing any code
|
||||
2. **Explore** — search the codebase for existing patterns and relevant implementations
|
||||
3. **Plan** — outline your changes as a checklist before starting
|
||||
4. **Implement** — make the smallest correct change that solves the problem
|
||||
5. **Test** — write or update tests; new code requires 100% test coverage
|
||||
6. **Document** — update all relevant docs in `docs/`; this is mandatory, not optional
|
||||
7. **Quality Gate** — run the single gate command below and fix every failure before committing:
|
||||
|
||||
```bash
|
||||
ruff format app/ tests/ && \
|
||||
ruff check app/ tests/ --fix && \
|
||||
safety check && \
|
||||
pytest --tb=short -q
|
||||
```
|
||||
|
||||
8. **Review** — re-read your own diff; confirm it is clean, secure, minimal, and well-documented
|
||||
|
||||
> All commands in the quality gate must exit with code 0. Never submit with failures.
|
||||
|
||||
## Core Principles
|
||||
|
||||
### Code Quality
|
||||
- Always use **Black** for formatting (line length: 120)
|
||||
- Use **isort** with Black profile for import sorting
|
||||
- Use **flake8** for linting (ignore E203, W503)
|
||||
- **Security first**: treat security as a non-negotiable requirement, not an afterthought — review [SECURITY_AUDIT.md](../SECURITY_AUDIT.md) for every change
|
||||
- Write **clean, modern, well-documented code** — prioritize readability, maintainability, and idiomatic Python
|
||||
- Use **Ruff** for all formatting, linting, import sorting, and security scanning — `ruff format` + `ruff check --fix` (replaces Black, isort, Flake8, Bandit)
|
||||
- Line length: 120 characters (configured in `pyproject.toml`)
|
||||
- Use **type hints** for all function parameters and return values
|
||||
- Write **docstrings** for all public functions, classes, and modules
|
||||
- Maintain **80% test coverage** for new code
|
||||
- Maintain **100% test coverage** for new code
|
||||
|
||||
### Python Conventions
|
||||
- Use descriptive variable names (e.g., `user_document_path`, not `udp`)
|
||||
- Follow PEP 8 naming: `snake_case` for functions/variables, `PascalCase` for classes
|
||||
- Use type hints from `typing` module (Dict, List, Optional, etc.)
|
||||
- Use modern Python 3.10+ type hints: `list[str]`, `dict[str, Any]`, `str | None` — avoid `List`, `Dict`, `Optional` from `typing`
|
||||
- Only import from `typing` for `Any`, `Callable`, `TypeVar`, `Protocol`, and other constructs unavailable natively
|
||||
- Prefer `pathlib.Path` over string paths for file operations
|
||||
- Use f-strings for string formatting, not `.format()` or `%`
|
||||
- Handle exceptions explicitly - avoid bare `except:` clauses
|
||||
@@ -112,7 +129,8 @@ pre-commit run --all-files
|
||||
- Validate and sanitize all user inputs
|
||||
- Use parameterized queries with SQLAlchemy (never raw SQL with user input)
|
||||
- Review [SECURITY_AUDIT.md](../SECURITY_AUDIT.md) before making security-related changes
|
||||
- Run `bandit` to check for security issues in Python code
|
||||
- Security linting is built into Ruff via `S` rules — runs automatically with `ruff check`; fix all `S`-prefixed findings
|
||||
- Run `safety check` to scan dependencies for known CVEs before submitting any PR
|
||||
|
||||
### FastAPI Patterns
|
||||
- Organize endpoints by feature in `app/api/` directory
|
||||
@@ -152,6 +170,8 @@ pre-commit run --all-files
|
||||
- Use `pytest.fixture` for test setup and teardown
|
||||
- Run tests with: `pytest -v`
|
||||
- Check coverage with: `pytest --cov=app --cov-report=term-missing`
|
||||
- **All tests must pass** before submitting changes — never leave failing tests
|
||||
- **All linters must pass** before submitting — run `pre-commit run --all-files`
|
||||
|
||||
### Configuration
|
||||
- All configuration is in `app/config.py` using Pydantic Settings
|
||||
@@ -161,7 +181,7 @@ pre-commit run --all-files
|
||||
|
||||
### Documentation
|
||||
- Keep documentation in `docs/` directory in Markdown format
|
||||
- Update relevant docs when adding features or changing behavior
|
||||
- **Always update** relevant docs when adding or changing any feature — documentation updates are mandatory, never optional
|
||||
- User-facing documentation should be clear and include examples
|
||||
- Reference existing docs: `docs/UserGuide.md`, `docs/API.md`, `docs/DeploymentGuide.md`
|
||||
- See [AGENTIC_CODING.md](../AGENTIC_CODING.md) for detailed development guide
|
||||
@@ -223,7 +243,8 @@ These files and directories are managed by automation or are critical infrastruc
|
||||
- Write clear, descriptive commit messages
|
||||
- **ALWAYS follow Conventional Commits format** (see below)
|
||||
- Keep commits focused and atomic
|
||||
- Run tests and linters before committing
|
||||
- **All tests must pass** before committing — `pytest` must succeed with no failures
|
||||
- **All linters must pass** before committing — `pre-commit run --all-files` must succeed
|
||||
- Pre-commit hooks are configured (`.pre-commit-config.yaml`)
|
||||
|
||||
## Conventional Commits (REQUIRED)
|
||||
@@ -313,7 +334,7 @@ These files are managed entirely by the semantic-release automation.
|
||||
- Configuration in `app/config.py`
|
||||
|
||||
### Common Patterns
|
||||
- Use `from typing import Optional, Dict, List, Any` for type hints
|
||||
- Use modern Python 3.10+ type hints: `list[str]`, `dict[str, Any]`, `str | None`; only import from `typing` for `Any`, `Callable`, `TypeVar`, `Protocol`
|
||||
- Import FastAPI dependencies: `from fastapi import Depends, HTTPException, status`
|
||||
- Get DB session: `db: Session = Depends(get_db)`
|
||||
- Current user: `current_user: User = Depends(get_current_user)`
|
||||
|
||||
@@ -29,6 +29,10 @@ network:
|
||||
- api.dropboxapi.com
|
||||
- content.dropboxapi.com
|
||||
|
||||
# Example/test domains - Used in test fixtures and SMTP configuration tests
|
||||
- example.com
|
||||
- smtp.example.com
|
||||
|
||||
# Package registries (if needed for dependency installation during tests)
|
||||
- pypi.org
|
||||
- files.pythonhosted.org
|
||||
|
||||
@@ -230,12 +230,15 @@ For API details, refer to the [API Documentation](./API.md).
|
||||
```
|
||||
|
||||
## Updating Documentation
|
||||
Documentation updates are **mandatory** — every PR that changes code must include matching documentation updates in the same PR. There are no exceptions.
|
||||
|
||||
When making code changes:
|
||||
1. Update relevant documentation in the same PR
|
||||
2. Check for outdated information
|
||||
1. **Update relevant documentation** in the same PR — never defer docs to a follow-up
|
||||
2. Check for outdated information in existing docs
|
||||
3. Add new sections for new features
|
||||
4. Update examples if behavior changes
|
||||
5. Review related documentation for consistency
|
||||
6. Update `docs/ConfigurationGuide.md` and `.env.demo` for any new or changed configuration options
|
||||
|
||||
## Screenshots and Diagrams
|
||||
- Use clear, high-quality images
|
||||
|
||||
@@ -7,16 +7,18 @@ applyTo: "app/**/*.py"
|
||||
These instructions apply to all Python code in the `app/` directory.
|
||||
|
||||
## Code Style
|
||||
- Use **Ruff** for linting and formatting with 120 character line length
|
||||
- Use **Ruff** for all formatting, linting, import sorting, and security scanning — `ruff format app/ tests/ && ruff check app/ tests/ --fix`
|
||||
- Line length: 120 characters (configured in `pyproject.toml` `[tool.ruff]`)
|
||||
- All functions must have type hints for parameters and return values
|
||||
- Use `from typing import Optional, Dict, List, Any, Union` as needed
|
||||
- Use modern Python 3.10+ type hints: `list[str]`, `dict[str, Any]`, `str | None`
|
||||
- Only import from `typing` for `Any`, `Callable`, `TypeVar`, `Protocol` (not `Dict`, `List`, `Optional`, `Union`)
|
||||
|
||||
## Import Order (Ruff enforces isort-compatible ordering)
|
||||
## Import Order (enforced by Ruff `I` rules)
|
||||
```python
|
||||
# Standard library imports
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Optional, Dict, List
|
||||
from typing import Any # Only for Any, Callable, TypeVar, Protocol
|
||||
|
||||
# Third-party imports
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
@@ -33,7 +35,7 @@ from app.models import Document, User
|
||||
def process_document(
|
||||
file_path: Path,
|
||||
user_id: int,
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
) -> DocumentMetadata:
|
||||
"""
|
||||
Process a document and extract metadata.
|
||||
@@ -127,7 +129,7 @@ import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@shared_task(bind=True, max_retries=3)
|
||||
def process_ocr(self, document_id: int) -> Dict[str, Any]:
|
||||
def process_ocr(self, document_id: int) -> dict[str, Any]:
|
||||
"""Process OCR for a document."""
|
||||
try:
|
||||
# Processing logic
|
||||
@@ -153,10 +155,11 @@ class Settings(BaseSettings):
|
||||
env_file = ".env"
|
||||
```
|
||||
|
||||
## Security
|
||||
- Never commit secrets
|
||||
- Validate all user inputs
|
||||
- Use parameterized queries
|
||||
- Sanitize file paths
|
||||
- Check file permissions
|
||||
- Review SECURITY_AUDIT.md for guidelines
|
||||
## Security (First and Foremost)
|
||||
- **Security first**: treat every change as a potential attack surface — review `SECURITY_AUDIT.md` before making any security-related change
|
||||
- Never commit secrets, tokens, or credentials
|
||||
- Validate and sanitize all user inputs
|
||||
- Use parameterized queries — never raw SQL with user data
|
||||
- Sanitize file paths; check file permissions before access
|
||||
- Security linting is built into Ruff via `S` rules — fix all `S`-prefixed findings before committing
|
||||
- Run `safety check` before submitting any PR to catch dependency CVEs
|
||||
|
||||
@@ -174,7 +174,8 @@ def test_create_document(db_session):
|
||||
```
|
||||
|
||||
## Test Coverage Goals
|
||||
- Aim for **80% code coverage** for all new code
|
||||
- Achieve **100% test coverage** for all new code — use `# pragma: no cover` only for genuinely unreachable or platform-specific branches, with an inline comment explaining why
|
||||
- Enforce the threshold: `pytest --cov=app --cov-fail-under=100`
|
||||
- Focus on critical paths and error handling
|
||||
- Test both success and failure scenarios
|
||||
- Don't test third-party library code
|
||||
|
||||
+1
-1
@@ -1 +1 @@
|
||||
2026-02-23T20:35:58Z
|
||||
2026-02-23T22:30:53Z
|
||||
|
||||
@@ -10,6 +10,80 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
<!-- version list -->
|
||||
|
||||
## v0.47.0 (2026-02-23)
|
||||
|
||||
### Documentation
|
||||
|
||||
- **changelog**: Update changelog [skip ci]
|
||||
([`d70fffd`](https://github.com/christianlouis/DocuElevate/commit/d70fffd4d21214900fdb018066f10a60019b5706))
|
||||
|
||||
- **copilot**: Checkpoint before comprehensive instructions overhaul
|
||||
([`dbea61d`](https://github.com/christianlouis/DocuElevate/commit/dbea61dc0b5808cb25db15553a950ca849a3228d))
|
||||
|
||||
- **copilot**: Overhaul instructions — Ruff toolchain, 100% coverage, agent workflow, modern Python
|
||||
([`156e0ba`](https://github.com/christianlouis/DocuElevate/commit/156e0ba524000a1063c0cb316063f1e28e5f0182))
|
||||
|
||||
### Features
|
||||
|
||||
- **ai**: Handle temperature incompatibility for gpt-5 and o-series models, add model picker UI
|
||||
([`a94b52e`](https://github.com/christianlouis/DocuElevate/commit/a94b52ee144e6e1f5a54ebc85d548bf5f860626b))
|
||||
|
||||
|
||||
## Unreleased
|
||||
|
||||
### Documentation
|
||||
|
||||
- **copilot**: Checkpoint before comprehensive instructions overhaul
|
||||
([`dbea61d`](https://github.com/christianlouis/DocuElevate/commit/dbea61dc0b5808cb25db15553a950ca849a3228d))
|
||||
|
||||
- **copilot**: Overhaul instructions — Ruff toolchain, 100% coverage, agent workflow, modern Python
|
||||
([`156e0ba`](https://github.com/christianlouis/DocuElevate/commit/156e0ba524000a1063c0cb316063f1e28e5f0182))
|
||||
|
||||
|
||||
## v0.46.0 (2026-02-23)
|
||||
|
||||
### Bug Fixes
|
||||
|
||||
- **api**: Swap parameter order in test_ai_extraction to fix 500 error
|
||||
([`ce4700a`](https://github.com/christianlouis/DocuElevate/commit/ce4700ae44abf55a87bc94b83c4d41e4cac2278e))
|
||||
|
||||
### Features
|
||||
|
||||
- **ui**: Add copy button to text modals in file detail view
|
||||
([`219e03e`](https://github.com/christianlouis/DocuElevate/commit/219e03ed3dc5cd80c1ed073167669db49dc01512))
|
||||
|
||||
|
||||
## v0.45.0 (2026-02-23)
|
||||
|
||||
### Chores
|
||||
|
||||
- Add example.com and smtp.example.com to copilot agent network allowlist
|
||||
([`d93cd96`](https://github.com/christianlouis/DocuElevate/commit/d93cd96b6205021c4739765090fa55641e23e2ee))
|
||||
|
||||
### Documentation
|
||||
|
||||
- **changelog**: Update changelog [skip ci]
|
||||
([`d5e4a74`](https://github.com/christianlouis/DocuElevate/commit/d5e4a74f693b6a458dcdc2978d834d41c5b4cb4d))
|
||||
|
||||
### Features
|
||||
|
||||
- **api**: Add POST /api/ai/test-extraction endpoint and Test Extraction UI button
|
||||
([`ce73f23`](https://github.com/christianlouis/DocuElevate/commit/ce73f23a8981ffae94594461362df3834481ced7))
|
||||
|
||||
### Testing
|
||||
|
||||
- **api**: Add comprehensive tests for AI extraction endpoint reaching 100% coverage
|
||||
([`3262d65`](https://github.com/christianlouis/DocuElevate/commit/3262d65bfe38d0669ba7eb6c0c51961ff6817976))
|
||||
|
||||
|
||||
## Unreleased
|
||||
|
||||
### Chores
|
||||
|
||||
- Add example.com and smtp.example.com to copilot agent network allowlist
|
||||
([`d93cd96`](https://github.com/christianlouis/DocuElevate/commit/d93cd96b6205021c4739765090fa55641e23e2ee))
|
||||
|
||||
|
||||
## v0.44.0 (2026-02-23)
|
||||
|
||||
### Code Style
|
||||
|
||||
+6
-6
@@ -1,10 +1,10 @@
|
||||
DocuElevate Build Information
|
||||
==============================
|
||||
Version: 0.44.0
|
||||
Build Date: 2026-02-23T20:35:58Z
|
||||
Git Commit: 5a64a3ec71d2e4cc788e2bc822023f879fef4370
|
||||
Git Short SHA: 5a64a3e
|
||||
Version: 0.47.0
|
||||
Build Date: 2026-02-23T22:30:53Z
|
||||
Git Commit: 7224e0be7de0eb258777f0817875aa772eaa459b
|
||||
Git Short SHA: 7224e0b
|
||||
Git Branch: main
|
||||
Commit Date: 2026-02-23T21:35:35+01:00
|
||||
Build Timestamp: 2026-02-23T20:35:58Z
|
||||
Commit Date: 2026-02-23T23:30:33+01:00
|
||||
Build Timestamp: 2026-02-23T22:30:53Z
|
||||
==============================
|
||||
|
||||
+133
-3
@@ -1,14 +1,20 @@
|
||||
"""
|
||||
AI provider and OpenAI API endpoints.
|
||||
|
||||
Exposes two endpoints:
|
||||
- GET /api/ai/test – tests the currently configured AI provider (generic, provider-agnostic)
|
||||
- GET /api/openai/test – backward-compatible alias that tests the OpenAI API specifically
|
||||
Exposes three endpoints:
|
||||
- GET /api/ai/test – tests the currently configured AI provider (generic, provider-agnostic)
|
||||
- GET /api/openai/test – backward-compatible alias that tests the OpenAI API specifically
|
||||
- POST /api/ai/test-extraction – runs the metadata-extraction prompt against the configured AI provider
|
||||
with caller-supplied plaintext and returns the raw response, parsed JSON,
|
||||
and extracted tags so operators can evaluate model quality.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
|
||||
from fastapi import APIRouter, Request
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.auth import require_login
|
||||
from app.config import settings
|
||||
@@ -18,6 +24,10 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
# Maximum number of characters accepted for a test-extraction request.
|
||||
# Keeps individual requests reasonable without blocking any real-world document.
|
||||
_MAX_EXTRACTION_TEXT_LEN = 50_000
|
||||
|
||||
|
||||
def _get_exception_chain_detail(exc: Exception) -> str:
|
||||
"""
|
||||
@@ -202,3 +212,123 @@ async def test_ai_provider_connection(request: Request):
|
||||
"message": f"Connection failed: {detail}",
|
||||
"provider": provider_name,
|
||||
}
|
||||
|
||||
|
||||
class ExtractionTestRequest(BaseModel):
|
||||
"""Request body for the AI extraction test endpoint."""
|
||||
|
||||
text: str = Field(..., min_length=1, max_length=_MAX_EXTRACTION_TEXT_LEN, description="Plain-text document content")
|
||||
|
||||
|
||||
def _build_extraction_prompt(text: str) -> str:
|
||||
"""Return the metadata-extraction prompt used in the standard processing pipeline."""
|
||||
return (
|
||||
"You are a specialized document analyzer trained to extract structured metadata from documents.\n"
|
||||
"Your task is to analyze the given text and return a well-structured JSON object.\n\n"
|
||||
"Extract and return the following fields:\n"
|
||||
"1. **filename**: Machine-readable filename "
|
||||
"(YYYY-MM-DD_DescriptiveTitle, use only letters, numbers, periods, and underscores).\n"
|
||||
'2. **empfaenger**: The recipient, or "Unknown" if not found.\n'
|
||||
'3. **absender**: The sender, or "Unknown" if not found.\n'
|
||||
"4. **correspondent**: The entity or company that issued the document "
|
||||
'(shortest possible name, e.g., "Amazon" instead of "Amazon EU SARL, German branch").\n'
|
||||
"5. **kommunikationsart**: One of [Behoerdlicher_Brief, Rechnung, Kontoauszug, Vertrag, "
|
||||
"Quittung, Privater_Brief, Einladung, Gewerbliche_Korrespondenz, Newsletter, Werbung, Sonstiges].\n"
|
||||
"6. **kommunikationskategorie**: One of [Amtliche_Postbehoerdliche_Dokumente, "
|
||||
"Finanz_und_Vertragsdokumente, Geschaeftliche_Kommunikation, "
|
||||
"Private_Korrespondenz, Sonstige_Informationen].\n"
|
||||
"7. **document_type**: Precise classification (e.g., Invoice, Contract, Information, Unknown).\n"
|
||||
"8. **tags**: A list of up to 4 relevant thematic keywords.\n"
|
||||
'9. **language**: Detected document language (ISO 639-1 code, e.g., "de" or "en").\n'
|
||||
"10. **title**: A human-readable title summarizing the document content.\n"
|
||||
"11. **confidence_score**: A numeric value (0-100) indicating the confidence level "
|
||||
"of the extracted metadata.\n"
|
||||
"12. **reference_number**: Extracted invoice/order/reference number if available.\n"
|
||||
"13. **monetary_amounts**: A list of key monetary values detected in the document.\n\n"
|
||||
"### Important Rules:\n"
|
||||
"- **OCR Correction**: Assume the text has been corrected for OCR errors.\n"
|
||||
"- **Tagging**: Max 4 tags, avoiding generic or overly specific terms.\n"
|
||||
"- **Title**: Concise, no addresses, and contains key identifying features.\n"
|
||||
"- **Date Selection**: Use the most relevant date if multiple are found.\n"
|
||||
"- **Output Language**: Maintain the document's original language.\n\n"
|
||||
f"Extracted text:\n{text}\n\n"
|
||||
"Return only valid JSON with no additional commentary.\n"
|
||||
)
|
||||
|
||||
|
||||
def _extract_json_from_text(text: str):
|
||||
"""Try to extract a JSON object from the LLM response text."""
|
||||
pattern = r"```(?:json)?\s*(\{.*?\})\s*```"
|
||||
match = re.search(pattern, text, re.DOTALL)
|
||||
if match:
|
||||
return match.group(1)
|
||||
start = text.find("{")
|
||||
end = text.rfind("}")
|
||||
if start != -1 and end != -1 and end > start:
|
||||
return text[start : end + 1]
|
||||
return None
|
||||
|
||||
|
||||
@router.post("/ai/test-extraction")
|
||||
@require_login
|
||||
async def test_ai_extraction(request: Request, body: ExtractionTestRequest):
|
||||
"""
|
||||
Run the metadata-extraction prompt against the configured AI provider.
|
||||
|
||||
Accepts plain-text document content, sends it through the same prompt used
|
||||
by the background processing pipeline, and returns:
|
||||
- ``raw_response``: verbatim LLM output
|
||||
- ``parsed_json``: the extracted JSON object (null when parsing fails)
|
||||
- ``tags``: the ``tags`` list from the parsed JSON (empty list on failure)
|
||||
- ``provider`` / ``model``: which provider / model was used
|
||||
"""
|
||||
from app.utils.ai_provider import get_ai_provider
|
||||
|
||||
provider_name = settings.ai_provider
|
||||
model = settings.ai_model or settings.openai_model
|
||||
|
||||
logger.info(f"AI extraction test requested: provider={provider_name}, model={model}")
|
||||
|
||||
try:
|
||||
provider = get_ai_provider()
|
||||
prompt = _build_extraction_prompt(body.text)
|
||||
raw_response = provider.chat_completion(
|
||||
messages=[
|
||||
{"role": "system", "content": "You are an intelligent document classifier."},
|
||||
{"role": "user", "content": prompt},
|
||||
],
|
||||
model=model,
|
||||
temperature=0,
|
||||
)
|
||||
except ValueError as e:
|
||||
logger.warning(f"AI extraction test – configuration error: {e}")
|
||||
return {"status": "error", "message": str(e), "provider": provider_name}
|
||||
except Exception as e:
|
||||
detail = _get_exception_chain_detail(e)
|
||||
logger.error(f"AI extraction test failed for provider '{provider_name}': {detail}", exc_info=True)
|
||||
return {"status": "error", "message": f"AI call failed: {detail}", "provider": provider_name}
|
||||
|
||||
# Attempt to parse JSON from the response
|
||||
parsed_json = None
|
||||
tags: list = []
|
||||
parse_error = None
|
||||
json_text = _extract_json_from_text(raw_response)
|
||||
if json_text:
|
||||
try:
|
||||
parsed_json = json.loads(json_text)
|
||||
tags = parsed_json.get("tags", [])
|
||||
except json.JSONDecodeError as exc:
|
||||
parse_error = str(exc)
|
||||
logger.warning(f"AI extraction test: JSON parse error: {exc}")
|
||||
else:
|
||||
parse_error = "No JSON object found in response"
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"provider": provider_name,
|
||||
"model": model,
|
||||
"raw_response": raw_response,
|
||||
"parsed_json": parsed_json,
|
||||
"tags": tags,
|
||||
"parse_error": parse_error,
|
||||
}
|
||||
|
||||
+91
-49
@@ -11,6 +11,7 @@ See the Configuration Guide for full details on each provider's settings.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import re
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
@@ -19,6 +20,50 @@ from app.config import settings
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _resolve_temperature(model: str, requested: float) -> Optional[float]:
|
||||
"""Return a temperature value compatible with the given model, or ``None`` to omit it.
|
||||
|
||||
Certain model families have restrictions on the ``temperature`` parameter:
|
||||
|
||||
* **o-series reasoning models** (``o1``, ``o3``, ``o4``, …) – do not accept
|
||||
a ``temperature`` argument at all. Return ``None`` so callers can skip the
|
||||
parameter entirely.
|
||||
* **gpt-5 family** (``gpt-5``, ``gpt-5-nano``, ``gpt-5-codex``, …) – only
|
||||
``temperature=1`` is accepted; passing ``0`` raises a 400 error. Return
|
||||
``1`` and emit a debug log so the caller is aware of the coercion.
|
||||
* All other models – return the requested value unchanged.
|
||||
|
||||
The model string may include a provider prefix (e.g. ``openai/gpt-4o``);
|
||||
only the part after the last ``/`` is examined.
|
||||
|
||||
Args:
|
||||
model: Model identifier (may include a provider prefix).
|
||||
requested: The temperature the caller wants to use.
|
||||
|
||||
Returns:
|
||||
A compatible temperature float, or ``None`` if temperature should be
|
||||
omitted from the API call.
|
||||
"""
|
||||
bare = model.lower().split("/")[-1]
|
||||
|
||||
# o-series reasoning models (o1, o3, o4 …) do not support temperature
|
||||
if re.match(r"^o\d+(-|$)", bare):
|
||||
logger.debug("Dropping temperature parameter for reasoning model '%s' (not supported)", model)
|
||||
return None
|
||||
|
||||
# gpt-5 family only supports temperature=1
|
||||
if bare.startswith("gpt-5"):
|
||||
if requested != 1.0:
|
||||
logger.debug(
|
||||
"Coercing temperature from %s to 1 for model '%s' (only temperature=1 is supported)",
|
||||
requested,
|
||||
model,
|
||||
)
|
||||
return 1.0
|
||||
|
||||
return requested
|
||||
|
||||
|
||||
def _require_text_content(content: Optional[str]) -> str:
|
||||
"""Raise a clear error if the AI response contains no text content.
|
||||
|
||||
@@ -101,12 +146,12 @@ class OpenAIProvider(AIProvider):
|
||||
temperature: float = 0,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
completion = self._client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
completion = self._client.chat.completions.create(**call_kwargs)
|
||||
_content = completion.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -130,12 +175,12 @@ class AzureOpenAIProvider(AIProvider):
|
||||
temperature: float = 0,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
completion = self._client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
completion = self._client.chat.completions.create(**call_kwargs)
|
||||
_content = completion.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -161,13 +206,12 @@ class AnthropicProvider(AIProvider):
|
||||
import litellm
|
||||
|
||||
model_name = model if model.startswith("anthropic/") else f"anthropic/{model}"
|
||||
response = litellm.completion(
|
||||
model=model_name,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
api_key=self._api_key,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model_name, "messages": messages, "api_key": self._api_key}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
response = litellm.completion(**call_kwargs)
|
||||
_content = response.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -193,13 +237,12 @@ class GeminiProvider(AIProvider):
|
||||
import litellm
|
||||
|
||||
model_name = model if model.startswith("gemini/") else f"gemini/{model}"
|
||||
response = litellm.completion(
|
||||
model=model_name,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
api_key=self._api_key,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model_name, "messages": messages, "api_key": self._api_key}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
response = litellm.completion(**call_kwargs)
|
||||
_content = response.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -235,12 +278,12 @@ class OllamaProvider(AIProvider):
|
||||
temperature: float = 0,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
completion = self._client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
completion = self._client.chat.completions.create(**call_kwargs)
|
||||
_content = completion.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -269,12 +312,12 @@ class OpenRouterProvider(AIProvider):
|
||||
temperature: float = 0,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
completion = self._client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
completion = self._client.chat.completions.create(**call_kwargs)
|
||||
_content = completion.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -335,12 +378,12 @@ class PortkeyProvider(AIProvider):
|
||||
temperature: float = 0,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
completion = self._client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
**kwargs,
|
||||
)
|
||||
call_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
call_kwargs["temperature"] = safe_temp
|
||||
call_kwargs.update(kwargs)
|
||||
completion = self._client.chat.completions.create(**call_kwargs)
|
||||
_content = completion.choices[0].message.content
|
||||
return _require_text_content(_content)
|
||||
|
||||
@@ -371,11 +414,10 @@ class LiteLLMProvider(AIProvider):
|
||||
) -> str:
|
||||
import litellm
|
||||
|
||||
completion_kwargs: Dict[str, Any] = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"temperature": temperature,
|
||||
}
|
||||
completion_kwargs: Dict[str, Any] = {"model": model, "messages": messages}
|
||||
safe_temp = _resolve_temperature(model, temperature)
|
||||
if safe_temp is not None:
|
||||
completion_kwargs["temperature"] = safe_temp
|
||||
if self._api_key:
|
||||
completion_kwargs["api_key"] = self._api_key
|
||||
if self._api_base:
|
||||
|
||||
@@ -154,10 +154,33 @@ SETTING_METADATA = {
|
||||
"openai_model": {
|
||||
"category": "AI Services",
|
||||
"description": "Fallback model name used when AI_MODEL is not set (e.g. gpt-4o-mini)",
|
||||
"type": "string",
|
||||
"type": "model_picker",
|
||||
"sensitive": False,
|
||||
"required": False,
|
||||
"restart_required": False,
|
||||
"suggested_models": [
|
||||
"gpt-4o",
|
||||
"gpt-4o-mini",
|
||||
"gpt-4-turbo",
|
||||
"gpt-4",
|
||||
"gpt-3.5-turbo",
|
||||
"o1",
|
||||
"o1-mini",
|
||||
"o3",
|
||||
"o3-mini",
|
||||
"gpt-5",
|
||||
"gpt-5-nano",
|
||||
"claude-3-5-sonnet-20241022",
|
||||
"claude-3-5-haiku-20241022",
|
||||
"claude-3-opus-20240229",
|
||||
"gemini-1.5-pro",
|
||||
"gemini-1.5-flash",
|
||||
"gemini-2.0-flash-exp",
|
||||
"llama3.2",
|
||||
"qwen2.5:7b",
|
||||
"phi3",
|
||||
"mistral",
|
||||
],
|
||||
},
|
||||
"ai_provider": {
|
||||
"category": "AI Services",
|
||||
@@ -171,10 +194,33 @@ SETTING_METADATA = {
|
||||
"ai_model": {
|
||||
"category": "AI Services",
|
||||
"description": "Model name for the selected provider (overrides OPENAI_MODEL). E.g. gpt-4o, claude-3-5-sonnet-20241022, gemini-1.5-pro, llama3.2",
|
||||
"type": "string",
|
||||
"type": "model_picker",
|
||||
"sensitive": False,
|
||||
"required": False,
|
||||
"restart_required": False,
|
||||
"suggested_models": [
|
||||
"gpt-4o",
|
||||
"gpt-4o-mini",
|
||||
"gpt-4-turbo",
|
||||
"gpt-4",
|
||||
"gpt-3.5-turbo",
|
||||
"o1",
|
||||
"o1-mini",
|
||||
"o3",
|
||||
"o3-mini",
|
||||
"gpt-5",
|
||||
"gpt-5-nano",
|
||||
"claude-3-5-sonnet-20241022",
|
||||
"claude-3-5-haiku-20241022",
|
||||
"claude-3-opus-20240229",
|
||||
"gemini-1.5-pro",
|
||||
"gemini-1.5-flash",
|
||||
"gemini-2.0-flash-exp",
|
||||
"llama3.2",
|
||||
"qwen2.5:7b",
|
||||
"phi3",
|
||||
"mistral",
|
||||
],
|
||||
},
|
||||
"anthropic_api_key": {
|
||||
"category": "AI Services",
|
||||
|
||||
@@ -802,6 +802,50 @@
|
||||
}
|
||||
}
|
||||
|
||||
function showCopyFeedback(type, success) {
|
||||
const btn = document.getElementById(type + '-copy-btn');
|
||||
if (!btn) return;
|
||||
const original = btn.innerHTML;
|
||||
if (success) {
|
||||
btn.innerHTML = '<i class="fas fa-check"></i> Copied!';
|
||||
btn.style.backgroundColor = '#48bb78';
|
||||
} else {
|
||||
btn.innerHTML = '<i class="fas fa-exclamation-triangle"></i> Failed';
|
||||
btn.style.backgroundColor = '#f56565';
|
||||
}
|
||||
setTimeout(() => {
|
||||
btn.innerHTML = original;
|
||||
btn.style.backgroundColor = '#4299e1';
|
||||
}, 2000);
|
||||
}
|
||||
|
||||
function copyTextToClipboard(type) {
|
||||
const contentId = type + '-text-content';
|
||||
const content = document.getElementById(contentId);
|
||||
const text = content ? content.textContent : '';
|
||||
|
||||
if (!text) return;
|
||||
|
||||
navigator.clipboard.writeText(text).then(() => {
|
||||
showCopyFeedback(type, true);
|
||||
}).catch(() => {
|
||||
// Fallback for older browsers
|
||||
try {
|
||||
const textarea = document.createElement('textarea');
|
||||
textarea.value = text;
|
||||
textarea.style.position = 'fixed';
|
||||
textarea.style.opacity = '0';
|
||||
document.body.appendChild(textarea);
|
||||
textarea.select();
|
||||
const ok = document.execCommand('copy');
|
||||
document.body.removeChild(textarea);
|
||||
showCopyFeedback(type, ok);
|
||||
} catch (e) {
|
||||
showCopyFeedback(type, false);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// Close modal when clicking outside the content
|
||||
window.onclick = function(event) {
|
||||
const modals = document.querySelectorAll('.text-modal');
|
||||
@@ -1210,12 +1254,21 @@
|
||||
<div class="text-modal-content">
|
||||
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem; padding-bottom: 1rem; border-bottom: 2px solid #e2e8f0;">
|
||||
<h3 style="margin: 0; color: #2d3748;">Extracted Text (Original)</h3>
|
||||
<button
|
||||
onclick="toggleTextModal('original-text-modal')"
|
||||
style="background-color: #f56565; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-times"></i> Close
|
||||
</button>
|
||||
<div style="display: flex; gap: 0.5rem;">
|
||||
<button
|
||||
id="original-copy-btn"
|
||||
onclick="copyTextToClipboard('original')"
|
||||
style="background-color: #4299e1; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-copy"></i> Copy
|
||||
</button>
|
||||
<button
|
||||
onclick="toggleTextModal('original-text-modal')"
|
||||
style="background-color: #f56565; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-times"></i> Close
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
<div id="original-text-loading" style="text-align: center; padding: 3rem; color: #4299e1;">
|
||||
<i class="fas fa-spinner fa-spin" style="font-size: 2rem; margin-bottom: 1rem;"></i>
|
||||
@@ -1230,12 +1283,21 @@
|
||||
<div class="text-modal-content">
|
||||
<div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 1rem; padding-bottom: 1rem; border-bottom: 2px solid #e2e8f0;">
|
||||
<h3 style="margin: 0; color: #2d3748;">Extracted Text (Processed)</h3>
|
||||
<button
|
||||
onclick="toggleTextModal('processed-text-modal')"
|
||||
style="background-color: #f56565; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-times"></i> Close
|
||||
</button>
|
||||
<div style="display: flex; gap: 0.5rem;">
|
||||
<button
|
||||
id="processed-copy-btn"
|
||||
onclick="copyTextToClipboard('processed')"
|
||||
style="background-color: #4299e1; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-copy"></i> Copy
|
||||
</button>
|
||||
<button
|
||||
onclick="toggleTextModal('processed-text-modal')"
|
||||
style="background-color: #f56565; color: white; padding: 0.5rem 1rem; border: none; border-radius: 0.25rem; cursor: pointer; font-weight: 600;"
|
||||
>
|
||||
<i class="fas fa-times"></i> Close
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
<div id="processed-text-loading" style="text-align: center; padding: 3rem; color: #48bb78;">
|
||||
<i class="fas fa-spinner fa-spin" style="font-size: 2rem; margin-bottom: 1rem;"></i>
|
||||
|
||||
@@ -139,6 +139,7 @@
|
||||
Enable {{ setting.key.replace('_', ' ').title() }}
|
||||
</label>
|
||||
</div>
|
||||
<<<<<<< copilot/add-self-hosted-ocr-support
|
||||
{% elif setting.metadata.type == 'multiselect' and setting.metadata.options %}
|
||||
<!-- Multi-select checkboxes: stored as comma-separated string -->
|
||||
<div class="flex flex-wrap gap-2 mt-1">
|
||||
@@ -160,6 +161,31 @@
|
||||
{% endfor %}
|
||||
</div>
|
||||
<p class="mt-1 text-xs text-gray-400">Effective value: <code x-text="formData['{{ setting.key }}'] || '(none)'"></code></p>
|
||||
=======
|
||||
{% elif setting.metadata.type == 'model_picker' %}
|
||||
<!-- Model Picker: free-text input with datalist of common models -->
|
||||
<div class="relative">
|
||||
<input
|
||||
type="text"
|
||||
id="{{ setting.key }}"
|
||||
name="{{ setting.key }}"
|
||||
list="{{ setting.key }}_models"
|
||||
x-model="formData['{{ setting.key }}']"
|
||||
class="setting-input w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-blue-500 focus:border-blue-500"
|
||||
placeholder="Select a common model or type a custom name…"
|
||||
autocomplete="off"
|
||||
/>
|
||||
<datalist id="{{ setting.key }}_models">
|
||||
{% for m in setting.metadata.suggested_models %}
|
||||
<option value="{{ m }}">{{ m }}</option>
|
||||
{% endfor %}
|
||||
</datalist>
|
||||
<p class="text-xs text-gray-400 mt-1">
|
||||
<i class="fas fa-info-circle mr-1"></i>
|
||||
Pick from the list or type any model name supported by your provider.
|
||||
</p>
|
||||
</div>
|
||||
>>>>>>> main
|
||||
{% elif setting.metadata.options %}
|
||||
<!-- Dropdown Select for fields with a fixed list of values -->
|
||||
<select
|
||||
|
||||
@@ -181,6 +181,12 @@
|
||||
data-provider="ai_provider">
|
||||
Test Connection
|
||||
</button>
|
||||
<button
|
||||
id="testAiExtractionBtn"
|
||||
class="inline-flex items-center px-2.5 py-1.5 border border-transparent text-xs font-medium rounded text-indigo-700 bg-indigo-100 hover:bg-indigo-200 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500">
|
||||
<i class="fa-solid fa-flask mr-1"></i>
|
||||
Test Extraction
|
||||
</button>
|
||||
{% endif %}
|
||||
{% elif name == "Azure AI" %}
|
||||
{% if provider.configured %}
|
||||
@@ -273,6 +279,85 @@
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- AI Extraction Test Modal -->
|
||||
<div id="aiExtractionModal" class="fixed inset-0 bg-gray-600 bg-opacity-50 hidden overflow-y-auto h-full w-full z-50" aria-modal="true" role="dialog">
|
||||
<div class="relative top-10 mx-auto p-5 border w-11/12 md:w-3/4 lg:w-2/3 xl:w-1/2 shadow-lg rounded-md bg-white">
|
||||
<div class="absolute top-0 right-0 pt-4 pr-4">
|
||||
<button type="button" id="closeAiExtractionModal" class="text-gray-400 hover:text-gray-500">
|
||||
<span class="sr-only">Close</span>
|
||||
<i class="fa-solid fa-xmark h-6 w-6"></i>
|
||||
</button>
|
||||
</div>
|
||||
<div class="mt-3">
|
||||
<h3 class="text-lg leading-6 font-medium text-gray-900 mb-1">
|
||||
<i class="fa-solid fa-flask mr-2 text-indigo-600"></i>AI Extraction Test
|
||||
</h3>
|
||||
<p class="text-sm text-gray-500 mb-4">
|
||||
Paste the plain-text content of a document below and run it through the configured AI provider
|
||||
to inspect the raw response, extracted JSON, and tags.
|
||||
</p>
|
||||
|
||||
<!-- Input area -->
|
||||
<div id="aiExtractionInput">
|
||||
<label for="aiExtractionText" class="block text-sm font-medium text-gray-700 mb-1">Document Text</label>
|
||||
<textarea
|
||||
id="aiExtractionText"
|
||||
rows="10"
|
||||
class="w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm text-sm font-mono focus:outline-none focus:ring-indigo-500 focus:border-indigo-500"
|
||||
placeholder="Paste the plain-text content of your document here…"></textarea>
|
||||
<div class="mt-3 flex justify-end space-x-2">
|
||||
<button type="button" id="cancelAiExtractionBtn"
|
||||
class="px-4 py-2 border border-gray-300 text-sm font-medium rounded-md text-gray-700 bg-white hover:bg-gray-50 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500">
|
||||
Cancel
|
||||
</button>
|
||||
<button type="button" id="runAiExtractionBtn"
|
||||
class="inline-flex items-center px-4 py-2 border border-transparent text-sm font-medium rounded-md shadow-sm text-white bg-indigo-600 hover:bg-indigo-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500">
|
||||
<i class="fa-solid fa-play mr-2"></i>Run Extraction
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Results area (hidden until extraction completes) -->
|
||||
<div id="aiExtractionResults" class="hidden mt-4 space-y-4">
|
||||
<!-- Provider / model badge -->
|
||||
<div id="aiExtractionMeta" class="text-xs text-gray-500"></div>
|
||||
|
||||
<!-- Parse warning -->
|
||||
<div id="aiParseWarning" class="hidden bg-yellow-50 border-l-4 border-yellow-400 p-3 text-sm text-yellow-800"></div>
|
||||
|
||||
<!-- Raw response -->
|
||||
<div>
|
||||
<h4 class="text-sm font-medium text-gray-900 mb-1">Raw LLM Response</h4>
|
||||
<pre id="aiRawResponse" class="bg-gray-50 border border-gray-200 rounded p-3 text-xs overflow-auto max-h-48 whitespace-pre-wrap break-words"></pre>
|
||||
</div>
|
||||
|
||||
<!-- Parsed JSON -->
|
||||
<div id="aiParsedJsonSection">
|
||||
<h4 class="text-sm font-medium text-gray-900 mb-1">Parsed JSON</h4>
|
||||
<pre id="aiParsedJson" class="bg-gray-50 border border-gray-200 rounded p-3 text-xs overflow-auto max-h-64 whitespace-pre-wrap break-words"></pre>
|
||||
</div>
|
||||
|
||||
<!-- Tags -->
|
||||
<div id="aiTagsSection">
|
||||
<h4 class="text-sm font-medium text-gray-900 mb-1">Extracted Tags</h4>
|
||||
<div id="aiTags" class="flex flex-wrap gap-2"></div>
|
||||
</div>
|
||||
|
||||
<div class="flex justify-end space-x-2 pt-2">
|
||||
<button type="button" id="aiExtractionBackBtn"
|
||||
class="px-4 py-2 border border-gray-300 text-sm font-medium rounded-md text-gray-700 bg-white hover:bg-gray-50 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-500">
|
||||
<i class="fa-solid fa-arrow-left mr-1"></i>Back
|
||||
</button>
|
||||
<button type="button" id="aiExtractionCloseBtn"
|
||||
class="px-4 py-2 bg-indigo-600 text-white text-sm font-medium rounded-md shadow-sm hover:bg-indigo-700 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-indigo-600">
|
||||
Close
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
{% endblock %}
|
||||
|
||||
@@ -573,6 +658,152 @@ document.addEventListener('DOMContentLoaded', function() {
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
// ── AI Extraction Test Modal ──────────────────────────────────────────────
|
||||
|
||||
const aiExtractionModal = document.getElementById('aiExtractionModal');
|
||||
const aiExtractionText = document.getElementById('aiExtractionText');
|
||||
const aiExtractionInput = document.getElementById('aiExtractionInput');
|
||||
const aiExtractionResults = document.getElementById('aiExtractionResults');
|
||||
const aiExtractionMeta = document.getElementById('aiExtractionMeta');
|
||||
const aiRawResponse = document.getElementById('aiRawResponse');
|
||||
const aiParsedJson = document.getElementById('aiParsedJson');
|
||||
const aiParsedJsonSection = document.getElementById('aiParsedJsonSection');
|
||||
const aiTags = document.getElementById('aiTags');
|
||||
const aiTagsSection = document.getElementById('aiTagsSection');
|
||||
const aiParseWarning = document.getElementById('aiParseWarning');
|
||||
const testAiExtractionBtn = document.getElementById('testAiExtractionBtn');
|
||||
const runAiExtractionBtn = document.getElementById('runAiExtractionBtn');
|
||||
const cancelAiExtractionBtn= document.getElementById('cancelAiExtractionBtn');
|
||||
const aiExtractionBackBtn = document.getElementById('aiExtractionBackBtn');
|
||||
const aiExtractionCloseBtn = document.getElementById('aiExtractionCloseBtn');
|
||||
const closeAiExtractionModal = document.getElementById('closeAiExtractionModal');
|
||||
|
||||
function openAiExtractionModal() {
|
||||
// Reset to input view
|
||||
aiExtractionText.value = '';
|
||||
aiExtractionInput.classList.remove('hidden');
|
||||
aiExtractionResults.classList.add('hidden');
|
||||
aiExtractionModal.classList.remove('hidden');
|
||||
}
|
||||
|
||||
function closeAiExtractionModalFn() {
|
||||
aiExtractionModal.classList.add('hidden');
|
||||
}
|
||||
|
||||
function showAiExtractionResults(data) {
|
||||
// Provider / model meta
|
||||
aiExtractionMeta.textContent = `Provider: ${data.provider || '—'} | Model: ${data.model || '—'}`;
|
||||
|
||||
// Raw response
|
||||
aiRawResponse.textContent = data.raw_response || '(empty)';
|
||||
|
||||
// Parse warning
|
||||
if (data.parse_error) {
|
||||
aiParseWarning.textContent = `JSON parse issue: ${data.parse_error}`;
|
||||
aiParseWarning.classList.remove('hidden');
|
||||
} else {
|
||||
aiParseWarning.classList.add('hidden');
|
||||
}
|
||||
|
||||
// Parsed JSON
|
||||
if (data.parsed_json) {
|
||||
aiParsedJson.textContent = JSON.stringify(data.parsed_json, null, 2);
|
||||
aiParsedJsonSection.classList.remove('hidden');
|
||||
} else {
|
||||
aiParsedJsonSection.classList.add('hidden');
|
||||
}
|
||||
|
||||
// Tags
|
||||
aiTags.innerHTML = '';
|
||||
const tags = Array.isArray(data.tags) ? data.tags : [];
|
||||
if (tags.length > 0) {
|
||||
tags.forEach(tag => {
|
||||
const span = document.createElement('span');
|
||||
span.className = 'inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-indigo-100 text-indigo-800';
|
||||
span.textContent = tag;
|
||||
aiTags.appendChild(span);
|
||||
});
|
||||
aiTagsSection.classList.remove('hidden');
|
||||
} else {
|
||||
aiTagsSection.classList.add('hidden');
|
||||
}
|
||||
|
||||
// Switch view
|
||||
aiExtractionInput.classList.add('hidden');
|
||||
aiExtractionResults.classList.remove('hidden');
|
||||
}
|
||||
|
||||
if (testAiExtractionBtn) {
|
||||
testAiExtractionBtn.addEventListener('click', openAiExtractionModal);
|
||||
}
|
||||
|
||||
if (closeAiExtractionModal) {
|
||||
closeAiExtractionModal.addEventListener('click', closeAiExtractionModalFn);
|
||||
}
|
||||
|
||||
if (cancelAiExtractionBtn) {
|
||||
cancelAiExtractionBtn.addEventListener('click', closeAiExtractionModalFn);
|
||||
}
|
||||
|
||||
if (aiExtractionCloseBtn) {
|
||||
aiExtractionCloseBtn.addEventListener('click', closeAiExtractionModalFn);
|
||||
}
|
||||
|
||||
if (aiExtractionBackBtn) {
|
||||
aiExtractionBackBtn.addEventListener('click', function() {
|
||||
aiExtractionResults.classList.add('hidden');
|
||||
aiExtractionInput.classList.remove('hidden');
|
||||
});
|
||||
}
|
||||
|
||||
// Close when clicking the backdrop
|
||||
if (aiExtractionModal) {
|
||||
aiExtractionModal.addEventListener('click', function(e) {
|
||||
if (e.target === aiExtractionModal) {
|
||||
closeAiExtractionModalFn();
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
if (runAiExtractionBtn) {
|
||||
runAiExtractionBtn.addEventListener('click', function() {
|
||||
const text = aiExtractionText.value.trim();
|
||||
if (!text) {
|
||||
aiExtractionText.focus();
|
||||
return;
|
||||
}
|
||||
|
||||
const originalHTML = runAiExtractionBtn.innerHTML;
|
||||
runAiExtractionBtn.innerHTML = '<i class="fa-solid fa-spinner fa-spin mr-2"></i>Running…';
|
||||
runAiExtractionBtn.disabled = true;
|
||||
cancelAiExtractionBtn.disabled = true;
|
||||
|
||||
fetch('/api/ai/test-extraction', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ text: text }),
|
||||
})
|
||||
.then(response => response.json())
|
||||
.then(data => {
|
||||
if (data.status === 'success') {
|
||||
showAiExtractionResults(data);
|
||||
} else {
|
||||
closeAiExtractionModalFn();
|
||||
showModal('error', 'AI Extraction Failed', data.message || 'Unknown error');
|
||||
}
|
||||
})
|
||||
.catch(error => {
|
||||
closeAiExtractionModalFn();
|
||||
showModal('error', 'Connection Error', 'Error running extraction: ' + error.message);
|
||||
})
|
||||
.finally(() => {
|
||||
runAiExtractionBtn.innerHTML = originalHTML;
|
||||
runAiExtractionBtn.disabled = false;
|
||||
cancelAiExtractionBtn.disabled = false;
|
||||
});
|
||||
});
|
||||
}
|
||||
});
|
||||
</script>
|
||||
{% endblock %}
|
||||
|
||||
@@ -16,6 +16,7 @@ from app.utils.ai_provider import (
|
||||
OpenRouterProvider,
|
||||
PortkeyProvider,
|
||||
_require_text_content,
|
||||
_resolve_temperature,
|
||||
get_ai_provider,
|
||||
)
|
||||
|
||||
@@ -74,6 +75,77 @@ class TestRequireTextContent:
|
||||
_require_text_content(None)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _resolve_temperature helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestResolveTemperature:
|
||||
"""Tests for the _resolve_temperature compatibility helper."""
|
||||
|
||||
def test_regular_model_returns_requested_temperature(self):
|
||||
"""Standard models return the temperature unchanged."""
|
||||
assert _resolve_temperature("gpt-4o", 0) == 0
|
||||
assert _resolve_temperature("gpt-4o-mini", 0.7) == 0.7
|
||||
|
||||
def test_gpt4_model_returns_requested_temperature(self):
|
||||
"""gpt-4 models are not gpt-5, so temperature is returned as-is."""
|
||||
assert _resolve_temperature("gpt-4-turbo", 0) == 0
|
||||
|
||||
def test_gpt5_model_forces_temperature_1(self):
|
||||
"""gpt-5 models only accept temperature=1; any other value is coerced."""
|
||||
assert _resolve_temperature("gpt-5", 0) == 1.0
|
||||
|
||||
def test_gpt5_nano_forces_temperature_1(self):
|
||||
"""gpt-5-nano (gpt-5 variant) gets temperature coerced to 1."""
|
||||
assert _resolve_temperature("gpt-5-nano", 0) == 1.0
|
||||
|
||||
def test_gpt5_codex_forces_temperature_1(self):
|
||||
"""gpt-5-codex (gpt-5 variant) gets temperature coerced to 1."""
|
||||
assert _resolve_temperature("gpt-5-codex", 0) == 1.0
|
||||
|
||||
def test_gpt5_already_at_1_unchanged(self):
|
||||
"""gpt-5 with temperature=1 returns 1 (no unnecessary log noise)."""
|
||||
assert _resolve_temperature("gpt-5", 1.0) == 1.0
|
||||
|
||||
def test_o1_returns_none(self):
|
||||
"""o1 reasoning model does not support temperature; None is returned."""
|
||||
assert _resolve_temperature("o1", 0) is None
|
||||
|
||||
def test_o1_mini_returns_none(self):
|
||||
"""o1-mini returns None (temperature not supported)."""
|
||||
assert _resolve_temperature("o1-mini", 0) is None
|
||||
|
||||
def test_o1_preview_returns_none(self):
|
||||
"""o1-preview returns None (temperature not supported)."""
|
||||
assert _resolve_temperature("o1-preview", 0) is None
|
||||
|
||||
def test_o3_returns_none(self):
|
||||
"""o3 reasoning model does not support temperature."""
|
||||
assert _resolve_temperature("o3", 0) is None
|
||||
|
||||
def test_o3_mini_returns_none(self):
|
||||
"""o3-mini returns None (temperature not supported)."""
|
||||
assert _resolve_temperature("o3-mini", 0) is None
|
||||
|
||||
def test_o4_mini_returns_none(self):
|
||||
"""o4-mini returns None (temperature not supported)."""
|
||||
assert _resolve_temperature("o4-mini", 0) is None
|
||||
|
||||
def test_provider_prefix_is_stripped(self):
|
||||
"""Provider prefix (e.g. 'openai/') is ignored when matching."""
|
||||
assert _resolve_temperature("openai/gpt-5-nano", 0) == 1.0
|
||||
assert _resolve_temperature("openai/o1-mini", 0) is None
|
||||
assert _resolve_temperature("openai/gpt-4o", 0) == 0
|
||||
|
||||
def test_model_names_are_case_insensitive(self):
|
||||
"""Model matching is case-insensitive."""
|
||||
assert _resolve_temperature("GPT-5", 0) == 1.0
|
||||
assert _resolve_temperature("O1", 0) is None
|
||||
assert _resolve_temperature("O3-Mini", 0) is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Abstract base class
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@@ -309,3 +309,363 @@ class TestOpenAIConnectionErrors:
|
||||
|
||||
assert data["status"] == "error"
|
||||
assert data.get("is_auth_error") is False
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestAiExtractionEndpoint:
|
||||
"""Tests for POST /api/ai/test-extraction endpoint."""
|
||||
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_missing_text_returns_422(self, mock_settings, client):
|
||||
"""Test that missing text body returns 422 validation error."""
|
||||
response = client.post("/api/ai/test-extraction", json={})
|
||||
assert response.status_code == 422
|
||||
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_empty_text_returns_422(self, mock_settings, client):
|
||||
"""Test that empty string text returns 422 validation error."""
|
||||
response = client.post("/api/ai/test-extraction", json={"text": ""})
|
||||
assert response.status_code == 422
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_returns_raw_response_and_parsed_json(self, mock_settings, mock_get_provider, client):
|
||||
"""Test successful extraction returns raw response, parsed JSON, and tags."""
|
||||
import json as json_module
|
||||
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
expected_json = {
|
||||
"filename": "2024-01-01_Invoice",
|
||||
"tags": ["invoice", "payment"],
|
||||
"title": "January Invoice",
|
||||
"document_type": "Invoice",
|
||||
}
|
||||
raw = "```json\n" + json_module.dumps(expected_json) + "\n```"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = raw
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Invoice content here"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["raw_response"] == raw
|
||||
assert data["parsed_json"]["filename"] == "2024-01-01_Invoice"
|
||||
assert data["tags"] == ["invoice", "payment"]
|
||||
assert data["parse_error"] is None
|
||||
assert data["provider"] == "openai"
|
||||
assert data["model"] == "gpt-4o-mini"
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_handles_invalid_json_in_response(self, mock_settings, mock_get_provider, client):
|
||||
"""Test that invalid JSON in LLM response is reported via parse_error."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = "Sorry, I cannot help with that."
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Some document text"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["parsed_json"] is None
|
||||
assert data["tags"] == []
|
||||
assert data["parse_error"] is not None
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_provider_config_error(self, mock_settings, mock_get_provider, client):
|
||||
"""Test that configuration errors (missing keys) are returned as error status."""
|
||||
mock_settings.ai_provider = "anthropic"
|
||||
mock_settings.ai_model = "claude-3"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_get_provider.side_effect = ValueError("ANTHROPIC_API_KEY must be set")
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Some document text"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "error"
|
||||
assert "ANTHROPIC_API_KEY" in data["message"]
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_provider_runtime_error(self, mock_settings, mock_get_provider, client):
|
||||
"""Test that runtime errors during AI call are returned as error status."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.side_effect = Exception("Connection refused")
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Some document text"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "error"
|
||||
assert "Connection refused" in data["message"]
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_plain_json_without_code_fences(self, mock_settings, mock_get_provider, client):
|
||||
"""Test that JSON returned without code fences is still parsed correctly."""
|
||||
import json as json_module
|
||||
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
expected_json = {"tags": ["contract"], "title": "Service Agreement"}
|
||||
raw = json_module.dumps(expected_json)
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = raw
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Contract content"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["tags"] == ["contract"]
|
||||
assert data["parse_error"] is None
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_json_found_but_invalid_reports_parse_error(self, mock_settings, mock_get_provider, client):
|
||||
"""Test JSONDecodeError branch: response contains '{...}' but is not valid JSON."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
# Looks like JSON (has { and }) but is NOT parseable
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = "{this is not: valid json!!}"
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Some document text"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["parsed_json"] is None
|
||||
assert data["tags"] == []
|
||||
assert data["parse_error"] is not None
|
||||
assert "raw_response" in data
|
||||
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_text_too_long_returns_422(self, mock_settings, client):
|
||||
"""Test that text exceeding max length returns 422 validation error."""
|
||||
from app.api.openai import _MAX_EXTRACTION_TEXT_LEN
|
||||
|
||||
oversized_text = "x" * (_MAX_EXTRACTION_TEXT_LEN + 1)
|
||||
response = client.post("/api/ai/test-extraction", json={"text": oversized_text})
|
||||
assert response.status_code == 422
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_extraction_response_with_no_tags_key(self, mock_settings, mock_get_provider, client):
|
||||
"""Test extraction where parsed JSON has no 'tags' key returns empty tags list."""
|
||||
import json as json_module
|
||||
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
# Valid JSON but no 'tags' key
|
||||
payload = {"title": "Report", "document_type": "Report"}
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = json_module.dumps(payload)
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.post("/api/ai/test-extraction", json={"text": "Report content"})
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["tags"] == []
|
||||
assert data["parsed_json"]["title"] == "Report"
|
||||
assert data["parse_error"] is None
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestAiProviderTestEndpoint:
|
||||
"""Tests for GET /api/ai/test endpoint."""
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_ai_test_success(self, mock_settings, mock_get_provider, client):
|
||||
"""Test successful AI provider connection."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = "ok"
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.get("/api/ai/test")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert "reachable" in data["message"].lower()
|
||||
assert data["provider"] == "openai"
|
||||
assert data["model"] == "gpt-4o-mini"
|
||||
assert "response_preview" in data
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_ai_test_value_error(self, mock_settings, mock_get_provider, client):
|
||||
"""Test GET /api/ai/test returns error on provider configuration ValueError."""
|
||||
mock_settings.ai_provider = "anthropic"
|
||||
mock_settings.ai_model = None
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_get_provider.side_effect = ValueError("ANTHROPIC_API_KEY must be set")
|
||||
|
||||
response = client.get("/api/ai/test")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "error"
|
||||
assert "ANTHROPIC_API_KEY" in data["message"]
|
||||
assert data["provider"] == "anthropic"
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_ai_test_connection_exception(self, mock_settings, mock_get_provider, client):
|
||||
"""Test GET /api/ai/test returns error on provider runtime exception."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = "gpt-4o-mini"
|
||||
mock_settings.openai_model = "gpt-4o-mini"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.side_effect = Exception("Connection refused")
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.get("/api/ai/test")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "error"
|
||||
assert "Connection refused" in data["message"]
|
||||
assert data["provider"] == "openai"
|
||||
|
||||
@patch("app.utils.ai_provider.get_ai_provider")
|
||||
@patch("app.api.openai.settings")
|
||||
def test_ai_test_uses_openai_model_fallback(self, mock_settings, mock_get_provider, client):
|
||||
"""Test that ai_model=None falls back to openai_model."""
|
||||
mock_settings.ai_provider = "openai"
|
||||
mock_settings.ai_model = None
|
||||
mock_settings.openai_model = "gpt-3.5-turbo"
|
||||
|
||||
mock_provider = MagicMock()
|
||||
mock_provider.chat_completion.return_value = "ok"
|
||||
mock_get_provider.return_value = mock_provider
|
||||
|
||||
response = client.get("/api/ai/test")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
|
||||
assert data["status"] == "success"
|
||||
assert data["model"] == "gpt-3.5-turbo"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestExceptionChainDetail:
|
||||
"""Unit tests for the _get_exception_chain_detail helper."""
|
||||
|
||||
def test_single_exception_no_chain(self):
|
||||
"""Test with a simple exception that has no cause."""
|
||||
from app.api.openai import _get_exception_chain_detail
|
||||
|
||||
exc = ValueError("root cause")
|
||||
result = _get_exception_chain_detail(exc)
|
||||
assert result == "root cause"
|
||||
|
||||
def test_exception_with_cause(self):
|
||||
"""Test that chained exceptions are surfaced in the message."""
|
||||
from app.api.openai import _get_exception_chain_detail
|
||||
|
||||
inner = OSError("DNS resolution failed")
|
||||
outer = ConnectionError("Connection failed")
|
||||
outer.__cause__ = inner
|
||||
|
||||
result = _get_exception_chain_detail(outer)
|
||||
assert "Connection failed" in result
|
||||
assert "DNS resolution failed" in result
|
||||
assert "caused by" in result
|
||||
|
||||
def test_empty_cause_string_skipped(self):
|
||||
"""Test that a cause with empty string representation is not appended."""
|
||||
from app.api.openai import _get_exception_chain_detail
|
||||
|
||||
inner = Exception("") # str() returns ""
|
||||
outer = RuntimeError("outer error")
|
||||
outer.__cause__ = inner
|
||||
|
||||
result = _get_exception_chain_detail(outer)
|
||||
# The empty-string cause should be skipped (branch 47->49)
|
||||
assert result == "outer error"
|
||||
|
||||
def test_duplicate_cause_string_skipped(self):
|
||||
"""Test that a cause whose str() is already in parts is not duplicated."""
|
||||
from app.api.openai import _get_exception_chain_detail
|
||||
|
||||
outer = RuntimeError("same message")
|
||||
inner = RuntimeError("same message") # same text as outer
|
||||
outer.__cause__ = inner
|
||||
|
||||
result = _get_exception_chain_detail(outer)
|
||||
# "same message" should appear only once (branch: cause_str already in parts)
|
||||
assert result.count("same message") == 1
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestExtractJsonFromText:
|
||||
"""Unit tests for the _extract_json_from_text helper."""
|
||||
|
||||
def test_json_in_code_fence(self):
|
||||
"""Test extraction from markdown code fence."""
|
||||
from app.api.openai import _extract_json_from_text
|
||||
|
||||
text = '```json\n{"key": "value"}\n```'
|
||||
result = _extract_json_from_text(text)
|
||||
assert result == '{"key": "value"}'
|
||||
|
||||
def test_json_in_plain_code_fence(self):
|
||||
"""Test extraction from plain (non-json) code fence."""
|
||||
from app.api.openai import _extract_json_from_text
|
||||
|
||||
text = '```\n{"key": "value"}\n```'
|
||||
result = _extract_json_from_text(text)
|
||||
assert result == '{"key": "value"}'
|
||||
|
||||
def test_bare_json_object(self):
|
||||
"""Test extraction of a bare JSON object without code fence."""
|
||||
from app.api.openai import _extract_json_from_text
|
||||
|
||||
text = 'Here is the result: {"title": "Invoice"} done.'
|
||||
result = _extract_json_from_text(text)
|
||||
assert result == '{"title": "Invoice"}'
|
||||
|
||||
def test_no_json_returns_none(self):
|
||||
"""Test that text with no JSON object returns None."""
|
||||
from app.api.openai import _extract_json_from_text
|
||||
|
||||
result = _extract_json_from_text("No JSON here at all.")
|
||||
assert result is None
|
||||
|
||||
Reference in New Issue
Block a user