- Added support for uploading .jpeg, .jpg, and .png files to Khoj from Web, Desktop app
- Updating indexer to generate raw text and entries using RapidOCR
- Details
* added support for indexing images via ocr
* fixed pyproject.toml
* Update src/khoj/processor/content/images/image_to_entries.py
Co-authored-by: Debanjum <debanjum@gmail.com>
* Update src/khoj/processor/content/images/image_to_entries.py
Co-authored-by: Debanjum <debanjum@gmail.com>
* removed redudant try except blocks
* updated desktop js file to support image formats
* added tests for jpg and png
* Fix processing for image to entries files
* Update unit tests with working image indexer
* Change png test from version verificaition to open-cv verification
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Co-authored-by: Debanjum <debanjum@gmail.com>
Co-authored-by: sabaimran <narmiabas@gmail.com>
- Update test data to add deeper outline hierarchy for testing
hierarchy as context
- Update collateral tests that need count of entries updated, deleted
asserts to be updated
- Partition configuration for indexing local data based on user accounts
- Store indexed data in an underlying postgres db using the `pgvector` extension
- Add migrations for all relevant user data and embeddings generation. Very little performance optimization has been done for the lookup time
- Apply filters using SQL queries
- Start removing many server-level configuration settings
- Configure GitHub test actions to run during any PR. Update the test action to run in a containerized environment with a DB.
- Update the Docker image and docker-compose.yml to work with the new application design
* Store conversation command options in an Enum
* Move to slash commands instead of using @ to specify general commands
* Calculate conversation command once & pass it as arg to child funcs
* Add /notes command to respond using only knowledge base as context
This prevents the chat model to try respond using it's general world
knowledge only without any references pulled from the indexed
knowledge base
* Test general and notes slash commands in openai chat director tests
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Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
* Add support for indexing plaintext files
- Adds backend support for parsing plaintext files generically (.html, .txt, .xml, .csv, .md)
- Add equivalent frontend views for setting up plaintext file indexing
- Update config, rawconfig, default config, search API, setup endpoints
* Add a nifty plaintext file icon to configure plaintext files in the Web UI
* Use generic glob path for plaintext files. Skip indexing files that aren't in whitelist
* Add support for gpt4all's falcon model as an additional conversation processor
- Update the UI pages to allow the user to point to the new endpoints for GPT
- Update the internal schemas to support both GPT4 models and OpenAI
- Add unit tests benchmarking some of the Falcon performance
* Add exc_info to include stack trace in error logs for text processors
* Pull shared functions into utils.py to be used across gpt4 and gpt
* Add migration for new processor conversation schema
* Skip GPT4All actor tests due to typing issues
* Fix Obsidian processor configuration in auto-configure flow
* Rename enable_local_llm to enable_offline_chat
Khoj will soon get a generic text indexing content type. This along
with a file filter should suffice for searching through Ledger
transactions, if required.
Having a specific content type for niche use-case like ledger isn't
useful. Removing unused content types will reduce khoj code to manage.
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)
- 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'
- 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
- 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
- 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
- Update existings code, tests to process input-filters as list
instead of str
- Test `text_to_jsonl' get files methods to work with combination of
`input-files' and `input-filters'
Resolves#84
- That is, sample_config.yml is renamed to khoj_sample.yml
- This makes the application config filename less generic,
more easily identifiable with the application
- Update docs, app accordingly
- The multi-qa-MiniLM-L6-cos-v1 is more extensively benchmarked[1]
- It has the right mix of model query speed, size and performance on benchmarks
- On hugging face it has way more downloads and likes than the msmarco model[2]
- On very preliminary evaluation of the model
- It doubles the encoding speed of all entries (down from ~8min to 4mins)
- It gave more entries that stay relevant to the query (3/5 vs 1/5 earlier)
[1]: https://www.sbert.net/docs/pretrained_models.html
[2]: https://huggingface.co/sentence-transformers
- Put test data for each content type into separate directories
- Makes config.yml for docker and local host consistent
- Prepending tests to /data in sample_config.yml makes application
run on local host using test data
- Allows mounting separate volume for each content type in docker-compose
- Ignore gitignore to only add tests content, not generated models or embeddings
