khoj/tests/conftest.py

188 lines
6.3 KiB
Python

# External Packages
import os
from copy import deepcopy
from fastapi.testclient import TestClient
from pathlib import Path
import pytest
# Internal Packages
from khoj.main import app
from khoj.configure import configure_processor, configure_routes, configure_search_types
from khoj.processor.markdown.markdown_to_jsonl import MarkdownToJsonl
from khoj.search_type import image_search, text_search
from khoj.utils.helpers import resolve_absolute_path
from khoj.utils.rawconfig import (
ContentConfig,
ConversationProcessorConfig,
ProcessorConfig,
TextContentConfig,
ImageContentConfig,
SearchConfig,
TextSearchConfig,
ImageSearchConfig,
)
from khoj.utils import state
from khoj.processor.jsonl.jsonl_to_jsonl import JsonlToJsonl
from khoj.processor.org_mode.org_to_jsonl import OrgToJsonl
from khoj.search_filter.date_filter import DateFilter
from khoj.search_filter.word_filter import WordFilter
from khoj.search_filter.file_filter import FileFilter
@pytest.fixture(scope="session")
def search_config() -> SearchConfig:
model_dir = resolve_absolute_path("~/.khoj/search")
model_dir.mkdir(parents=True, exist_ok=True)
search_config = SearchConfig()
search_config.symmetric = TextSearchConfig(
encoder="sentence-transformers/all-MiniLM-L6-v2",
cross_encoder="cross-encoder/ms-marco-MiniLM-L-6-v2",
model_directory=model_dir / "symmetric/",
)
search_config.asymmetric = TextSearchConfig(
encoder="sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
cross_encoder="cross-encoder/ms-marco-MiniLM-L-6-v2",
model_directory=model_dir / "asymmetric/",
)
search_config.image = ImageSearchConfig(
encoder="sentence-transformers/clip-ViT-B-32", model_directory=model_dir / "image/"
)
return search_config
@pytest.fixture(scope="session")
def content_config(tmp_path_factory, search_config: SearchConfig):
content_dir = tmp_path_factory.mktemp("content")
# Generate Image Embeddings from Test Images
content_config = ContentConfig()
content_config.image = ImageContentConfig(
input_directories=["tests/data/images"],
embeddings_file=content_dir.joinpath("image_embeddings.pt"),
batch_size=1,
use_xmp_metadata=False,
)
image_search.setup(content_config.image, search_config.image, regenerate=False)
# Generate Notes Embeddings from Test Notes
content_config.org = TextContentConfig(
input_files=None,
input_filter=["tests/data/org/*.org"],
compressed_jsonl=content_dir.joinpath("notes.jsonl"),
embeddings_file=content_dir.joinpath("note_embeddings.pt"),
)
filters = [DateFilter(), WordFilter(), FileFilter()]
text_search.setup(OrgToJsonl, content_config.org, search_config.asymmetric, regenerate=False, filters=filters)
content_config.plugins = {
"plugin1": TextContentConfig(
input_files=[content_dir.joinpath("notes.jsonl")],
input_filter=None,
compressed_jsonl=content_dir.joinpath("plugin.jsonl.gz"),
embeddings_file=content_dir.joinpath("plugin_embeddings.pt"),
)
}
filters = [DateFilter(), WordFilter(), FileFilter()]
text_search.setup(
JsonlToJsonl, content_config.plugins["plugin1"], search_config.asymmetric, regenerate=False, filters=filters
)
return content_config
@pytest.fixture(scope="session")
def md_content_config(tmp_path_factory):
content_dir = tmp_path_factory.mktemp("content")
# Generate Embeddings for Markdown Content
content_config = ContentConfig()
content_config.markdown = TextContentConfig(
input_files=None,
input_filter=["tests/data/markdown/*.md"],
compressed_jsonl=content_dir.joinpath("markdown.jsonl"),
embeddings_file=content_dir.joinpath("markdown_embeddings.pt"),
)
return content_config
@pytest.fixture(scope="session")
def processor_config(tmp_path_factory):
openai_api_key = os.getenv("OPENAI_API_KEY")
processor_dir = tmp_path_factory.mktemp("processor")
# The conversation processor is the only configured processor
# It needs an OpenAI API key to work.
if not openai_api_key:
return
# Setup conversation processor, if OpenAI API key is set
processor_config = ProcessorConfig()
processor_config.conversation = ConversationProcessorConfig(
openai_api_key=openai_api_key,
conversation_logfile=processor_dir.joinpath("conversation_logs.json"),
)
return processor_config
@pytest.fixture(scope="session")
def chat_client(md_content_config: ContentConfig, search_config: SearchConfig, processor_config: ProcessorConfig):
# Initialize app state
state.config.content_type = md_content_config
state.config.search_type = search_config
state.SearchType = configure_search_types(state.config)
# Index Markdown Content for Search
filters = [DateFilter(), WordFilter(), FileFilter()]
state.model.markdown_search = text_search.setup(
MarkdownToJsonl, md_content_config.markdown, search_config.asymmetric, regenerate=False, filters=filters
)
# Initialize Processor from Config
state.processor_config = configure_processor(processor_config)
configure_routes(app)
return TestClient(app)
@pytest.fixture(scope="function")
def client(content_config: ContentConfig, search_config: SearchConfig, processor_config: ProcessorConfig):
state.config.content_type = content_config
state.config.search_type = search_config
state.SearchType = configure_search_types(state.config)
# These lines help us Mock the Search models for these search types
state.model.org_search = {}
state.model.image_search = {}
configure_routes(app)
return TestClient(app)
@pytest.fixture(scope="function")
def new_org_file(content_config: ContentConfig):
# Setup
new_org_file = Path(content_config.org.input_filter[0]).parent / "new_file.org"
new_org_file.touch()
yield new_org_file
# Cleanup
if new_org_file.exists():
new_org_file.unlink()
@pytest.fixture(scope="function")
def org_config_with_only_new_file(content_config: ContentConfig, new_org_file: Path):
new_org_config = deepcopy(content_config.org)
new_org_config.input_files = [f"{new_org_file}"]
new_org_config.input_filter = None
return new_org_config