Update Search Actor prompt with answers, more precise primer and
two more examples for context
Mark the 3 chat quality tests using answer as context to generate
queries as expected to pass. Verify that the 3 tests pass now, unlike
before when the Search Actor did not have the answers for context
- Remove stale message_to_prompt test
It is too broad, reduces maintainability.
Remove as it doesn't really need its own test right now
- Setting skipif at module level for chat actor, director tests
reduces code duplication as earlier was using decorator on each chat
test
Combine hand-written custom notes and PG essays with personal
content to bulk up notes count
Delete old documentation markdown as not a representative dataset for
application (which is more tuned for personal notes)
- Chat directors are broad agents.
- Chat directors orchestrate narrow actor agents to synthesize
final response for the user
- Agents are Prompts + ML Model
- Test Chat Director Capabilities
1. [X] Answer from retrieved notes
2. [X] Answer from chat history
3. [X] Answer general questions
4. [X] Carry out multi-turn conversation
5. [X] Say don't know when answer not in provided context
6. [X] Answers that require current date awareness
This test is expected to fail as the chat is not capable of doing
this without the Search actor. But the test allows assessing chat quality
7. [X] Date-aware aggregation across multiple different notes
This test is expected to fail as the chat is not capable of doing
this without the Search actor. But the test allows assessing chat quality
8. [X] Ask clarification questions if no unambiguous answer in provided context
9. [X] Retrieve answer from chat history beyond lookback window
This test is expected to fail as the chat director is not capable
of searching chat history yet. But the test allows assessing chat quality
10. [X] Retrieve context for answer using multiple independent
searches on knowledge base
This test is expected to fail as the chat is not capable of doing
this without the Search actor. But the test allows assessing chat quality
- Index markdown test data as knowledge base. As easier to get good
markdown content (vs org)
- Setup markdown_content_config, processor_config and chat_client to
test chat API
- Mark chat quality tests, register custom mark for chat quality
- Filter unhelpful deprecation warnings from within dateparser library
- Error if tests use unregistered marks
- Chat actors are narrow agents (prompt + ML model)
Chat actors are different from the Chat director. who orchestrates
the narrow actor agents to synthesize final response to the user
- Test Chat Actor Capabilities
1. Answer from retrieved notes
2. Answer from chat history
3. Answer general questions
4. Carry out multi-turn conversation
5. Say don't know when answer not in provided context
6. Answers that require current date awareness
7. Date-aware aggregation across multiple different notes
8. Ask clarification questions if no unambiguous answer in provided context
This test is expected to fail as the chat is not capable of doing
this consistently yet. But having the test allows assessing chat quality
- Use Openai API Key from OPENAI_API_KEY environment variable
- Gitignore .env file, python virtualenv directory
Put OpenAI API Key in .env file to run chatbot tests via vscode
The .env file is default location for importing env vars
Answer does not rely on past conversations, just the knowledge base.
It is meant for one off interactions, like search rather than a
continuing conversation like chat
For now it is only exposed via API. Later it will be expose in the
interfaces as well
Remove ability to select different chat types from the chat web
interface as there is only a single chat type
Stop appending answers to the conversation logs
- Text before headings was not being indexed due to buggy orgnode
parsing logic
- Resolved indexing intro text from files with and without headings in
them
- Ensure intro text node has heading set to all title lines collected
from the file
Resolves#165
- Test /config/types API when no plugin configured, only plugin configured
and no content configured scenarios
- Do not throw null reference exception while configuring search types
when no plugin configured
- Do not throw null reference exception on calling /config/types API
when no plugin configured
Resolves bug introduced by #173
- Previously was return all core content types even if they had not been
setup
- Add test to validate only configured content types are returned by
the api/config/types API endpoint
Configure app routes after configuring server.
Import API routers after search type is dynamically populated.
Allow API to recognize the dynamically populated plugin search types
as valid type query param.
Enable searching for plugin type content.
- What
- The Emacs and Obsidian interfaces stay in their original
directories under src/
- src/khoj now only contains code meant for pypi packaging
- Benefits
- This avoids having to update khoj MELPA, Obsidian plugin config as
the Emacs, Obsidian code is under their original directories
- It separates the code in src/khoj meant for python packaging from
code for external interfaces like Emacs and Obsidian
- Why
The khoj pypi packages should be installed in `khoj' directory.
Previously it was being installed into `src' directory, which is a
generic top level directory name that is discouraged from being used
- Changes
- move src/* to src/khoj/*
- update `setup.py' to `find_packages' in `src' instead of project root
- rename imports to form `from khoj.*' in complete project
- update `constants.web_directory' path to use `khoj' directory
- rename root logger to `khoj' in `main.py'
- fix image_search tests to use the newly rename `khoj' logger
- update config, docs, workflows to reference new path `src/khoj'
Previously no query syntax helpers, like the "file:" prefix, were used
before checking if query contains file path.
This made query to image search brittle to misinterpretation and
pointless checking
Add test to verify search by image at file works as expected
- Previously top level headings would have get stripped of the
space between heading text and the prefix # symbols. That is,
`# Top Level Heading' would get converted to `#Top Level Heading'
- This would mess up their rendering as a heading in search results
- Add unit tests to text_to_jsonl processors to prevent regression
- Use latest davinci model for tests
- Wrap prompt in triple quotes to improve legibilty
- `understand' method returns dictionary instead of string. Fix its test
- Fix prompt for new model to pass `chat_with_history' test
Long words (>500 characters) provide less useful context to models.
Dropping very long words allow models to create better embeddings by
passing more of the useful context from the entry to the model
- Remove property drawer from test entry for max_words splitting test
- Property drawer is not required for the test
- Keep minimal test case to reduce chance for confusion
- Issue
ML Models truncate entries exceeding some max token limit.
This lowers the quality of search results
- Fix
Split entries by max tokens before indexing.
This should improve searching for content in longer entries.
- Miscellaneous
- Test method to split entries by max tokens
- Reason
- All clients that currently consume the API are part of Khoj
- Any breaking API changes will be fixed in clients immediately
- So decoupling client from API is not required
- This removes the burden of maintaining muliple versions of the API
- Context
- The app maintains all text content in a standard, intermediate format
- The intermediate format was loaded, passed around as a dictionary
for easier, faster updates to the intermediate format schema initially
- The intermediate format is reasonably stable now, given it's usage
by all 3 text content types currently implemented
- Changes
- Concretize text entries into `Entries' class instead of using dictionaries
- Code is updated to load, pass around entries as `Entries' objects
instead of as dictionaries
- `text_search' and `text_to_jsonl' methods are annotated with
type hints for the new `Entries' type
- Code and Tests referencing entries are updated to use class style
access patterns instead of the previous dictionary access patterns
- Move `mark_entries_for_update' method into `TextToJsonl' base class
- This is a more natural location for the method as it is only
(to be) used by `text_to_jsonl' classes
- Avoid circular reference issues on importing `Entries' class
- Split router.py into v1.0, beta and frontend (no-prefix) api modules
under new router package. Version tag in main.py via prefix
- Update frontends to use the versioned api endpoints
- Update tests to work with versioned api endpoints
- Update docs to mentioned, reference only versioned api endpoints
- Start standardizing implementation of the `text_to_jsonl' processors
- `text_to_jsonl; scripts already had a shared structure
- This change starts to codify that implicit structure
- Benefits
- Ease adding more `text_to_jsonl; processors
- Allow merging shared functionality
- Help with type hinting
- Drawbacks
- Lower agility to change. But this was already an implicit issue as
the text_to_jsonl processors got more deeply wired into the app
- Pillow already supports reading XMP metadata from Images
- Removes need to maintain my fork of unmaintained PyExiftool
- This also removes dependency on system Exiftool package for
XMP metadata extraction
- Add test to verify XMP metadata extracted from test images
- Remove references to Exiftool from Documentation
- For queries with only filters in them short-circuit and return
filtered results. No need to run semantic search, re-ranking.
- Add client test for filter only query and quote query in client tests
- Image search already uses a sorted list of images to process
- Prevents index of entries to desync when entries, embeddings
generated by a separate server/app instance
