mirror of
https://github.com/khoj-ai/khoj.git
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76562f4250
* Initial version - setup a file-push architecture for generating embeddings with Khoj * Use state.host and state.port for configuring the URL for the indexer * Fix parsing of PDF files * Read markdown files from streamed data and update unit tests * On application startup, load in embeddings from configurations files, rather than regenerating the corpus based on file system * Init: refactor indexer/batch endpoint to support a generic file ingestion format * Add features to better support indexing from files sent by the desktop client * Initial commit with Electron application - Adds electron app * Add import for pymupdf, remove import for pypdf * Allow user to configure khoj host URL * Remove search type configuration from index.html * Use v1 path for current indexer routes
377 lines
15 KiB
Python
377 lines
15 KiB
Python
# System Packages
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import logging
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from pathlib import Path
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import os
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# External Packages
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import pytest
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from khoj.utils.config import SearchModels
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# Internal Packages
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from khoj.utils.state import content_index, search_models
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from khoj.search_type import text_search
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from khoj.utils.rawconfig import ContentConfig, SearchConfig, TextContentConfig
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from khoj.processor.org_mode.org_to_jsonl import OrgToJsonl
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from khoj.processor.github.github_to_jsonl import GithubToJsonl
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from khoj.utils.fs_syncer import get_org_files
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# Test
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# ----------------------------------------------------------------------------------------------------
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def test_text_search_setup_with_missing_file_raises_error(
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org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig
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):
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# Arrange
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# Ensure file mentioned in org.input-files is missing
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single_new_file = Path(org_config_with_only_new_file.input_files[0])
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single_new_file.unlink()
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# Act
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# Generate notes embeddings during asymmetric setup
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with pytest.raises(FileNotFoundError):
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data = get_org_files(org_config_with_only_new_file)
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# ----------------------------------------------------------------------------------------------------
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def test_text_search_setup_with_empty_file_raises_error(
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org_config_with_only_new_file: TextContentConfig, search_config: SearchConfig
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):
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# Arrange
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data = get_org_files(org_config_with_only_new_file)
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# Act
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# Generate notes embeddings during asymmetric setup
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with pytest.raises(ValueError, match=r"^No valid entries found*"):
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text_search.setup(OrgToJsonl, data, org_config_with_only_new_file, search_config.asymmetric, regenerate=True)
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# ----------------------------------------------------------------------------------------------------
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def test_text_search_setup(content_config: ContentConfig, search_models: SearchModels):
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# Arrange
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data = get_org_files(content_config.org)
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# Act
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# Regenerate notes embeddings during asymmetric setup
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notes_model = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True
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)
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# Assert
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assert len(notes_model.entries) == 10
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assert len(notes_model.corpus_embeddings) == 10
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# ----------------------------------------------------------------------------------------------------
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def test_text_index_same_if_content_unchanged(content_config: ContentConfig, search_models: SearchModels, caplog):
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# Arrange
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caplog.set_level(logging.INFO, logger="khoj")
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data = get_org_files(content_config.org)
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# Act
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# Generate initial notes embeddings during asymmetric setup
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text_search.setup(OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True)
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initial_logs = caplog.text
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caplog.clear() # Clear logs
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# Run asymmetric setup again with no changes to data source. Ensure index is not updated
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text_search.setup(OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=False)
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final_logs = caplog.text
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# Assert
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assert "Creating index from scratch." in initial_logs
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assert "Creating index from scratch." not in final_logs
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.anyio
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async def test_text_search(content_config: ContentConfig, search_config: SearchConfig):
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# Arrange
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data = get_org_files(content_config.org)
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search_models.text_search = text_search.initialize_model(search_config.asymmetric)
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content_index.org = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True
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)
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query = "How to git install application?"
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# Act
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hits, entries = await text_search.query(
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query, search_model=search_models.text_search, content=content_index.org, rank_results=True
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)
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results = text_search.collate_results(hits, entries, count=1)
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# Assert
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# search results should contain "git clone" entry
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search_result = results[0].entry
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assert "git clone" in search_result
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# ----------------------------------------------------------------------------------------------------
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def test_entry_chunking_by_max_tokens(org_config_with_only_new_file: TextContentConfig, search_models: SearchModels):
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# Arrange
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# Insert org-mode entry with size exceeding max token limit to new org file
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max_tokens = 256
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new_file_to_index = Path(org_config_with_only_new_file.input_files[0])
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with open(new_file_to_index, "w") as f:
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f.write(f"* Entry more than {max_tokens} words\n")
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for index in range(max_tokens + 1):
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f.write(f"{index} ")
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data = get_org_files(org_config_with_only_new_file)
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# Act
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# reload embeddings, entries, notes model after adding new org-mode file
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initial_notes_model = text_search.setup(
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OrgToJsonl, data, org_config_with_only_new_file, search_models.text_search.bi_encoder, regenerate=False
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)
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# Assert
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# verify newly added org-mode entry is split by max tokens
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assert len(initial_notes_model.entries) == 2
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assert len(initial_notes_model.corpus_embeddings) == 2
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# ----------------------------------------------------------------------------------------------------
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# @pytest.mark.skip(reason="Flaky due to compressed_jsonl file being rewritten by other tests")
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def test_entry_chunking_by_max_tokens_not_full_corpus(
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org_config_with_only_new_file: TextContentConfig, search_models: SearchModels
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):
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# Arrange
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# Insert org-mode entry with size exceeding max token limit to new org file
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data = {
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"readme.org": """
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* Khoj
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/Allow natural language search on user content like notes, images using transformer based models/
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All data is processed locally. User can interface with khoj app via [[./interface/emacs/khoj.el][Emacs]], API or Commandline
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** Dependencies
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- Python3
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- [[https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links][Miniconda]]
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** Install
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#+begin_src shell
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git clone https://github.com/khoj-ai/khoj && cd khoj
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conda env create -f environment.yml
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conda activate khoj
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#+end_src"""
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}
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text_search.setup(
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OrgToJsonl,
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data,
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org_config_with_only_new_file,
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search_models.text_search.bi_encoder,
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regenerate=False,
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)
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max_tokens = 256
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new_file_to_index = Path(org_config_with_only_new_file.input_files[0])
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with open(new_file_to_index, "w") as f:
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f.write(f"* Entry more than {max_tokens} words\n")
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for index in range(max_tokens + 1):
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f.write(f"{index} ")
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data = get_org_files(org_config_with_only_new_file)
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# Act
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# reload embeddings, entries, notes model after adding new org-mode file
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initial_notes_model = text_search.setup(
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OrgToJsonl,
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data,
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org_config_with_only_new_file,
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search_models.text_search.bi_encoder,
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regenerate=False,
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full_corpus=False,
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)
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# Assert
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# verify newly added org-mode entry is split by max tokens
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assert len(initial_notes_model.entries) == 5
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assert len(initial_notes_model.corpus_embeddings) == 5
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# ----------------------------------------------------------------------------------------------------
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def test_regenerate_index_with_new_entry(
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content_config: ContentConfig, search_models: SearchModels, new_org_file: Path
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):
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# Arrange
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data = get_org_files(content_config.org)
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initial_notes_model = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True
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)
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assert len(initial_notes_model.entries) == 10
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assert len(initial_notes_model.corpus_embeddings) == 10
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# append org-mode entry to first org input file in config
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content_config.org.input_files = [f"{new_org_file}"]
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with open(new_org_file, "w") as f:
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f.write("\n* A Chihuahua doing Tango\n- Saw a super cute video of a chihuahua doing the Tango on Youtube\n")
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data = get_org_files(content_config.org)
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# Act
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# regenerate notes jsonl, model embeddings and model to include entry from new file
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regenerated_notes_model = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True
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)
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# Assert
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assert len(regenerated_notes_model.entries) == 11
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assert len(regenerated_notes_model.corpus_embeddings) == 11
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# verify new entry appended to index, without disrupting order or content of existing entries
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error_details = compare_index(initial_notes_model, regenerated_notes_model)
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if error_details:
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pytest.fail(error_details, False)
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# Cleanup
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# reset input_files in config to empty list
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content_config.org.input_files = []
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# ----------------------------------------------------------------------------------------------------
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def test_update_index_with_duplicate_entries_in_stable_order(
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org_config_with_only_new_file: TextContentConfig, search_models: SearchModels
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):
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# Arrange
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new_file_to_index = Path(org_config_with_only_new_file.input_files[0])
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# Insert org-mode entries with same compiled form into new org file
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new_entry = "* TODO A Chihuahua doing Tango\n- Saw a super cute video of a chihuahua doing the Tango on Youtube\n"
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with open(new_file_to_index, "w") as f:
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f.write(f"{new_entry}{new_entry}")
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data = get_org_files(org_config_with_only_new_file)
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# Act
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# load embeddings, entries, notes model after adding new org-mode file
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initial_index = text_search.setup(
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OrgToJsonl, data, org_config_with_only_new_file, search_models.text_search.bi_encoder, regenerate=True
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)
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data = get_org_files(org_config_with_only_new_file)
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# update embeddings, entries, notes model after adding new org-mode file
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updated_index = text_search.setup(
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OrgToJsonl, data, org_config_with_only_new_file, search_models.text_search.bi_encoder, regenerate=False
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)
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# Assert
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# verify only 1 entry added even if there are multiple duplicate entries
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assert len(initial_index.entries) == len(updated_index.entries) == 1
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assert len(initial_index.corpus_embeddings) == len(updated_index.corpus_embeddings) == 1
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# verify the same entry is added even when there are multiple duplicate entries
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error_details = compare_index(initial_index, updated_index)
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if error_details:
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pytest.fail(error_details)
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# ----------------------------------------------------------------------------------------------------
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def test_update_index_with_deleted_entry(org_config_with_only_new_file: TextContentConfig, search_models: SearchModels):
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# Arrange
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new_file_to_index = Path(org_config_with_only_new_file.input_files[0])
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# Insert org-mode entries with same compiled form into new org file
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new_entry = "* TODO A Chihuahua doing Tango\n- Saw a super cute video of a chihuahua doing the Tango on Youtube\n"
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with open(new_file_to_index, "w") as f:
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f.write(f"{new_entry}{new_entry} -- Tatooine")
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data = get_org_files(org_config_with_only_new_file)
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# load embeddings, entries, notes model after adding new org file with 2 entries
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initial_index = text_search.setup(
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OrgToJsonl, data, org_config_with_only_new_file, search_models.text_search.bi_encoder, regenerate=True
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)
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# update embeddings, entries, notes model after removing an entry from the org file
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with open(new_file_to_index, "w") as f:
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f.write(f"{new_entry}")
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data = get_org_files(org_config_with_only_new_file)
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# Act
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updated_index = text_search.setup(
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OrgToJsonl, data, org_config_with_only_new_file, search_models.text_search.bi_encoder, regenerate=False
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)
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# Assert
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# verify only 1 entry added even if there are multiple duplicate entries
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assert len(initial_index.entries) == len(updated_index.entries) + 1
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assert len(initial_index.corpus_embeddings) == len(updated_index.corpus_embeddings) + 1
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# verify the same entry is added even when there are multiple duplicate entries
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error_details = compare_index(updated_index, initial_index)
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if error_details:
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pytest.fail(error_details)
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# ----------------------------------------------------------------------------------------------------
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def test_update_index_with_new_entry(content_config: ContentConfig, search_models: SearchModels, new_org_file: Path):
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# Arrange
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data = get_org_files(content_config.org)
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initial_notes_model = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=True, normalize=False
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)
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# append org-mode entry to first org input file in config
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with open(new_org_file, "w") as f:
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new_entry = "\n* A Chihuahua doing Tango\n- Saw a super cute video of a chihuahua doing the Tango on Youtube\n"
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f.write(new_entry)
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data = get_org_files(content_config.org)
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# Act
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# update embeddings, entries with the newly added note
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content_config.org.input_files = [f"{new_org_file}"]
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final_notes_model = text_search.setup(
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OrgToJsonl, data, content_config.org, search_models.text_search.bi_encoder, regenerate=False, normalize=False
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)
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# Assert
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assert len(final_notes_model.entries) == len(initial_notes_model.entries) + 1
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assert len(final_notes_model.corpus_embeddings) == len(initial_notes_model.corpus_embeddings) + 1
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# verify new entry appended to index, without disrupting order or content of existing entries
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error_details = compare_index(initial_notes_model, final_notes_model)
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if error_details:
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pytest.fail(error_details, False)
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# Cleanup
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# reset input_files in config to empty list
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content_config.org.input_files = []
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# ----------------------------------------------------------------------------------------------------
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@pytest.mark.skipif(os.getenv("GITHUB_PAT_TOKEN") is None, reason="GITHUB_PAT_TOKEN not set")
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def test_text_search_setup_github(content_config: ContentConfig, search_models: SearchModels):
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# Act
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# Regenerate github embeddings to test asymmetric setup without caching
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github_model = text_search.setup(
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GithubToJsonl, content_config.github, search_models.text_search.bi_encoder, regenerate=True
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)
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# Assert
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assert len(github_model.entries) > 1
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def compare_index(initial_notes_model, final_notes_model):
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mismatched_entries, mismatched_embeddings = [], []
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for index in range(len(initial_notes_model.entries)):
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if initial_notes_model.entries[index].to_json() != final_notes_model.entries[index].to_json():
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mismatched_entries.append(index)
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# verify new entry embedding appended to embeddings tensor, without disrupting order or content of existing embeddings
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for index in range(len(initial_notes_model.corpus_embeddings)):
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if not initial_notes_model.corpus_embeddings[index].allclose(final_notes_model.corpus_embeddings[index]):
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mismatched_embeddings.append(index)
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error_details = ""
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if mismatched_entries:
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mismatched_entries_str = ",".join(map(str, mismatched_entries))
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error_details += f"Entries at {mismatched_entries_str} not equal\n"
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if mismatched_embeddings:
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mismatched_embeddings_str = ", ".join(map(str, mismatched_embeddings))
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error_details += f"Embeddings at {mismatched_embeddings_str} not equal\n"
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return error_details
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