- Adds support for multiple users to be connected to the same Khoj instance using their Google login credentials
- Moves storage solution from in-memory json data to a Postgres db. This stores all relevant information, including accounts, embeddings, chat history, server side chat configuration
- Adds the concept of a Khoj server admin for configuring instance-wide settings regarding search model, and chat configuration
- Miscellaneous updates and fixes to the UX, including chat references, colors, and an updated config page
- Adds billing to allow users to subscribe to the cloud service easily
- Adds a separate GitHub action for building the dockerized production (tag `prod`) and dev (tag `dev`) images, separate from the image used for local building. The production image uses `gunicorn` with multiple workers to run the server.
- Updates all clients (Obsidian, Emacs, Desktop) to follow the client/server architecture. The server no longer reads from the file system at all; it only accepts data via the indexer API. In line with that, removes the functionality to configure org, markdown, plaintext, or other file-specific settings in the server. Only leaves GitHub and Notion for server-side configuration.
- Changes license to GNU AGPLv3
Resolves#467Resolves#488Resolves#303Resolves#345Resolves#195Resolves#280Resolves#461Closes#259Resolves#351Resolves#301Resolves#296
- Link to Django admin panel for user to create Chat Models on their
Khoj server
- This should only get hit when user is not using Khoj cloud, as Khoj
cloud would already have Chat models configured
- While sigmoid normalization isn't required for reranking.
Normalizing score to distance metrics for both encoder and cross
encoder scores is useful to reason about them
- Softmax wasn't required as don't need probabilities, sigmoid is good
enough to get distance metric
- Expose ability to modify search model via Django admin interface
- Previously the bi_encoder and cross_encoder models to use were set
in code
- Now it's user configurable but with a default config generated by
default
- During the migration, the confidence score stopped being used. It
was being passed down from API to some point and went unused
- Remove score thresholding for images as image search confidence
score different from text search model distance score
- Default score threshold of 0.15 is experimentally determined by
manually looking at search results vs distance for a few queries
- Use distance instead of confidence as metric for search result quality
Previously we'd moved text search to a distance metric from a
confidence score.
Now convert even cross encoder, image search scores to distance metric
for consistent results sorting
Remove the Results Count button from the web app. It's hanging weirdly
with not much context to its purpose.
Reintroduce it in the Search card when created under the Features section
Reduce user confusion by joining config update with index updation for
each content type.
So only a single click required to configure any content type instead
of two clicks on two separate pages
- Notes prompt doesn't need to be so tuned to question answering. User
could just want to talk about life. The notes need to be used to
response to those, not necessarily only retrieve answers from notes
- System and notes prompts were forcing asking follow-up questions a
little too much. Reduce strength of follow-up question asking
The Chat models sometime output reference notes directly in the chat
body in unformatted form, specifically as Notes:\n['. Prevent that.
Reference notes are shown in clean, formatted form anyway
- Make mutable syncing variable not a const
- Show next sync time to make users aware of data sync is automated
- Keep a single Save button to reduce confusion. It does what Save All
previously did. Intent to manual sync should Save All
- Default to using app.khoj.dev as default Khoj URL to ease setup
- Add fields to mark users as subscribed to a specific plan and
subscription renewal date in DB
- Add ability to unsubscribe a user using their email address
- Expose webhook for stripe to callback confirming payment
Previously hitting configure or disable wouldn't update the state of
the content cards. It needed page refresh to see if the content was
synced correctly.
Now cards automatically get set to new state on hitting disable button
on card or global configure buttons
Lock syncing to server if a sync is already in progress.
While the sync save button gets disabled while sync is in progress,
the background sync job can still trigger a sync in parallel. This
sync lock prevents that
Remove the table of all files indexed by Khoj. This seems overkill and
doesn't match the UI semantics of the other data sources like Github,
Notion.
Create instead a data source card for computer files with the same
update, disable semantics of the Github and Notion data source cards
Users can disable each data source from its card on the main config page.
They can see/delete individual files indexed from the computer data source
once they click into the computer files data source card on the config page
This will be useful for updating, deleting entries by their data
source. Data source can be one of Computer, Github or Notion for now
Store each file/entries source in database
Major
- Ensure search results logic consistent across migration to DB, multi-user
- Manually verified search results for sample queries look the same across migration
- Flatten indexing code for better indexing progress tracking and code readability
Minor
- a4f407f Test memory leak on MPS device when generating vector embeddings
- ef24485 Improve Khoj with DB setup instructions in the Django app readme (for now)
- f212cc7 Arrange remaining text search tests in arrange, act, assert order
- 022017d Fix text search tests to test updated indexing log messages
The Langchain HuggingFaceEmbeddings wrapper doesn't support disabling
progressbar, not especially for only query but not documents.
This makes the logs noisy with encoding progressbar for each
incremental queries
No features of the Langchain wrapper for SentenceTransformer was
currently being used anyway for now, and we can always switch back to
it if required
Flatten the nested loops to improve visibilty into indexing progress
Reduce spurious logs, report the logs at aggregated level and update
the logging description text to improve indexing progress reporting
- Given the separation of the client and server now, the web UI will no longer support configuration of local file paths of data to index
- Expose a way to show all the files that are currently set for indexing, along with an option to delete all or specific files
- Remove spurious whitespace in chat input box on page load being
added because text area element was ending on newline
- Do not insert newline in message when send message by hitting enter key
This would be more evident when send message with cursor in the
middle of the sentence, as a newline would be inserted at the cursor
point
- Remove chat message separator tokens from model output. Model
sometimes starts to output text in it's chat format
- Pass current khoj version from package.json to about page via
electron IPC between backend js and frontend page
- Update Khoj information in default About screen as well, in case
it's exposed anywhere else
- Update background color to a different shade of white
- Make primary and primary hover colors less intense and more aligned
with lantern flame shade
- Add water, leaf, flower color variables
Fix refactor bugs, CSRF token issues for use in production
* Add flags for samesite settings to enable django admin login
* Include tzdata to dependencies to work around python package issues in linux
* Use DJANGO_DEBUG flag correctly
* Fix naming of entry field when creating EntryDate objects
* Correctly retrieve openai config settings
* Fix datefilter with embeddings name for field
- Update background color to a different shade of white
- Make primary and primary hover colors less intense and more aligned
with lantern flame shade
- Add water, leaf, flower color variables
- Rather than having each individual user configure their conversation settings, allow the server admin to configure the OpenAI API key or offline model once, and let all the users re-use that code.
- To configure the settings, the admin should go to the `django/admin` page and configure the relevant chat settings. To create an admin, run `python3 src/manage.py createsuperuser` and enter in the details. For simplicity, the email and username should match.
- Remove deprecated/unnecessary endpoints and views for configuring per-user chat settings
Previously pico.css font-families were being selected for the config
page. This was different from the fonts used by index.html, chat.html
This improves spacing issue of heading further
- Create dropdown menu. Put settings page, logout action under it
- Make user's profile picture the dropdown menu heading
- Create khoj.js to store shared js across web client
It currently stores the dropdown menu open, close functionality
- Put shared styling for khoj dropdown menu under khoj.css
- Use a function to generate API Key table row HTML, to dedup logic
- Show delete, copy icon hints on hover
- Reduce length of copied message to not expand table width
- Truncating API key helps keep the API key table width within width
of smaller width displays
Emoji icons have already been added to the Search, Chat and Settings
top navigation menu in the desktop client. This change adds these to
the web client as well
Improves readability as name has closer match to underlying
constructs
- Entry is any atomic item indexed by Khoj. This can be an org-mode
entry, a markdown section, a PDF or Notion page etc.
- Embeddings are semantic vectors generated by the search ML model
that encodes for meaning contained in an entries text.
- An "Entry" contains "Embeddings" vectors but also other metadata
about the entry like filename etc.
- Add a productionized setup for the Khoj server using `gunicorn` with multiple workers for handling requests
- Add a new Dockerfile meant for production config at `ghcr.io/khoj-ai/khoj:prod`; the existing Docker config should remain the same
### ✨ New
- Use API keys to authenticate from Desktop, Obsidian, Emacs clients
- Create API, UI on web app config page to CRUD API Keys
- Create user API keys table and functions to CRUD them in Database
### 🧪 Improve
- Default to better search model, [gte-small](https://huggingface.co/thenlper/gte-small), to improve search quality
- Only load chat model to GPU if enough space, throw error on load failure
- Show encoding progress, truncate headings to max chars supported
- Add instruction to create db in Django DB setup Readme
### ⚙️ Fix
- Fix error handling when configure offline chat via Web UI
- Do not warn in anon mode about Google OAuth env vars not being set
- Fix path to load static files when server started from project root
- Add a data model which allows us to store Conversations with users. This does a minimal lift over the current setup, where the underlying data is stored in a JSON file. This maintains parity with that configuration.
- There does _seem_ to be some regression in chat quality, which is most likely attributable to search results.
This will help us with #275. It should become much easier to maintain multiple Conversations in a given table in the backend now. We will have to do some thinking on the UI.
- Make most routes conditional on authentication *if anonymous mode is not enabled*. If anonymous mode is enabled, it scaffolds a default user and uses that for all application interactions.
- Add a basic login page and add routes for redirecting the user if logged in
- 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
- Offline chat models outputing gibberish when loaded onto some GPU.
GPU support with Vulkan in GPT4All seems a bit buggy
- This change mitigates the upstream issue by allowing user to
manually disable using GPU for offline chat
Closes#516
GPT4all now supports gguf llama.cpp chat models. Latest
GPT4All (+mistral) performs much at least 3x faster.
On Macbook Pro at ~10s response start time vs 30s-120s earlier.
Mistral is also a better chat model, although it hallucinates more
than llama-2
Ignore .org, .pdf etc. suffixed directories under `input-filter' from
being evaluated as files.
Explicitly filter results by input-filter globs to only index files,
not directory for each text type
Add test to prevent regression
Closes#448
On Windows, the default locale isn't utf8. Khoj had regressed to
reading files in OS specified locale encoding, e.g cp1252, cp949 etc.
It now explicitly uses utf8 encoding to read text files for indexing
Resolves#495, resolves#472
* Changed globbing. Now doesn't clobber a users glob if they want to add it, but will (if just given a directory), add a recursive glob.
Note: python's glob engine doesn't support `{}` globing, a future option is to warn if that is included.
* Fix typo in globformat variable
* Use older glob pattern for plaintext files
---------
Co-authored-by: Saba <narmiabas@gmail.com>
### Overview
- Add ability to push data to index from the Emacs, Obsidian client
- Switch to standard mechanism of syncing files via HTTP multi-part/form. Previously we were streaming the data as JSON
- Benefits of new mechanism
- No manual parsing of files to send or receive on clients or server is required as most have in-built mechanisms to send multi-part/form requests
- The whole response is not required to be kept in memory to parse content as JSON. As individual files arrive they're automatically pushed to disk to conserve memory if required
- Binary files don't need to be encoded on client and decoded on server
### Code Details
### Major
- Use multi-part form to receive files to index on server
- Use multi-part form to send files to index on desktop client
- Send files to index on server from the khoj.el emacs client
- Send content for indexing on server at a regular interval from khoj.el
- Send files to index on server from the khoj obsidian client
- Update tests to test multi-part/form method of pushing files to index
#### Minor
- Put indexer API endpoint under /api path segment
- Explicitly make GET request to /config/data from khoj.el:khoj-server-configure method
- Improve emoji, message on content index updated via logger
- Don't call khoj server on khoj.el load, only once khoj invoked explicitly by user
- Improve indexing of binary files
- Let fs_syncer pass PDF files directly as binary before indexing
- Use encoding of each file set in indexer request to read file
- Add CORS policy to khoj server. Allow requests from khoj apps, obsidian & localhost
- Update indexer API endpoint URL to` index/update` from `indexer/batch`
Resolves#471#243
New URL query params, `force' and `t' match name of query parameter in
existing Khoj API endpoints
Update Desktop, Obsidian and Emacs client to call using these new API
query params. Set `client' query param from each client for telemetry
visibility
New URL follows action oriented endpoint naming convention used for
other Khoj API endpoints
Update desktop, obsidian and emacs client to call this new API
endpoint
Using fetch from Khoj Obsidian plugin was failing due to cross-origin
request and method: no-cors didn't allow passing x-api-key custom
header. And using Obsidian's request with multi-part/form-data wasn't
possible either.
- Keep state of previously synced files to identify files to be deleted
- Last synced files stored in settings for persistence of this data
across Obsidian reboots
Use the multi-part/form-data request to sync Markdown, PDF files in
vault to index on khoj server
Run scheduled job to push updates to value for indexing every 1 hour
This prevents Khoj from polling the Khoj server until explicitly
invoked via `khoj' entrypoint function.
Previously it'd make a request to the khoj server every time Emacs or
khoj.el was loaded
Closes#243
Previously lookback turns was set to a static 2. But now that we
support more chat models, their prompt size vary considerably.
Make lookback_turns proportional to max_prompt_size. The truncate_messages
can remove messages if they exceed max_prompt_size later
This lets Khoj pass more of the chat history as context for models
with larger context window
- Dedupe offline_chat_model variable. Only reference offline chat
model stored under offline_chat. Delete the previous chat_model
field under GPT4AllProcessorConfig
- Set offline chat model to use via config/offline_chat API endpoint
This provides flexibility to use non 1st party supported chat models
- Create migration script to update khoj.yml config
- Put `enable_offline_chat' under new `offline-chat' section
Referring code needs to be updated to accomodate this change
- Move `offline_chat_model' to `chat-model' under new `offline-chat' section
- Put chat `tokenizer` under new `offline-chat' section
- Put `max_prompt' under existing `conversation' section
As `max_prompt' size effects both openai and offline chat models
Pass user configured chat model as argument to use by converse_offline
The proper fix for this would allow users to configure the max_prompt
and tokenizer to use (while supplying default ones, if none provided)
For now, this is a reasonable start.
- Format extract questions prompt format with newlines and whitespaces
- Make llama v2 extract questions prompt consistent
- Remove empty questions extracted by offline extract_questions actor
- Update implicit qs extraction unit test for offline search actor
* Strip the incoming query from the slash conversation command before passing it to the model or for search
* Return q when content index not loaded
* Remove -n 4 from pytest ini configuration to isolate test failures
- Make `bump_version.sh' script set version for the Khoj desktop app too
- Sync Khoj desktop app authors, license, description and version with
the other interfaces and server
- Update description in packages metadata to match project subtitle on Github
- Pass payloads as unibyte. This was causing the request to fail for
files with unicode characters
- Suppress messages with file content in on index updates
- Fix rendering response from server on index update API call
- Extract code to populate body of index update HTTP request with files
Previously global state of `url-request-method' would affect the
kind of request made to api/config/data API endpoint as it wasn't
being explicitly being set before calling the API endpoint
This was done with the assumption that the default value of GET for
url-request-method wouldn't change globally
But in some cases, experientially, it can get changed. This was
resulting in khoj.el load failing as POST request was being made
instead which would throw error
- Add elisp variable to set API key to engage with the Khoj server
- Use multi-part form to POST the files to index to the indexer API
endpoint on the khoj server
Previously only the the last filter's terms were getting effectively
applied as the `filter.defilter' operation was being done on
`user_query' but was updating the `defiltered_query'
- This uses existing HTTP affordance to process files
- Better handling of binary file formats as removes need to url encode/decode
- Less memory utilization than streaming json as files get
automatically written to disk once memory utilization exceeds preset limits
- No manual parsing of raw files streams required
Use mailbox closed with flag down once content index completed.
Use standard, existing logger messages in new indexer messages, when
files to index sent by clients
- Improves user experience by aligning idle time with search latency
to avoid display jitter (to render results) while user is typing
- Makes the idle time configurable
Closes#480
* Use separate functions for adding files and folders to configuration for indexing
* Add a loading bar while data is syncing
* Bump the minor version for the application
- GPT4All integration had ceased working with 0.1.7 specification. Update to use 1.0.12. At a later date, we should also use first party support for llama v2 via gpt4all
- Update the system prompt for the extract_questions flow to add start and end date to the yesterday date filter example.
- Update all setup data in conftest.py to use new client-server indexing pattern
* Remove GPT4All dependency in pyproject.toml and use multiplatform builds in the dockerization setup in GH actions
* Move configure_search method into indexer
* Add conditional installation for gpt4all
* Add hint to go to localhost:42110 in the docs. Addresses #477
* Remove PySide, gui option from code
* Remove pyside 6 dependency from code
* Remove workflows which build desktop applications
* Update unit tests and update line in documentation
* Remove additional references to pyinstaller, gui
* Add uninstall steps to normal uninstall instructions
* 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
* Initial version - setup a file-push architecture for generating embeddings with Khoj
* Update unit tests to fix with new application design
* Allow configure server to be called without regenerating the index; this no longer works because the API for indexing files is not up in time for the server to send a request
* Use state.host and state.port for configuring the URL for the indexer
* On application startup, load in embeddings from configurations files, rather than regenerating the corpus based on file system
- Make Khoj ask clarifying questions when answer not in provided context
- Add default conversation command to auto switch b/w general, notes modes
- Show filtered list of commands available with the currently input text
- Use general prompt when no references found and not in Notes mode
- Test general and notes slash commands in offline chat director tests
* 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
---------
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
* 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
* Update gpt4all tests to use md configuration
* Add a /help tooltip
* Add dynamic support for describing slash commands. Remove default and treat notes as the default type
---------
Co-authored-by: sabaimran <narmiabas@gmail.com>
* Allow indexing to continue even if there's an issue parsing a particular org file
* Use approximation in pytorch comparison in text_search UT, skip additional file parser errors for org files
* Change error of expected failure
* 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 starting a new line with shift-enter
* Remove useless comments. Set font-size: medium.
* Update src/khoj/interface/web/chat.html
Update the styling to have the padding, margin and line-height like before.
Co-authored-by: Debanjum <debanjum@gmail.com>
* Update src/khoj/interface/web/chat.html
Make the chat-body scroll to the bottom after resizing
Co-authored-by: Debanjum <debanjum@gmail.com>
---------
Co-authored-by: Debanjum <debanjum@gmail.com>
Previously the GUI mode (with khoj --gui or using the desktop app) would open the web interface in the users default web browser. Now the web interface is just rendered within the app itself using PyQT's Webview. This gives it a more proper app like feel
- Opens settings page on first run and landing page after in GUI mode
Previously was only opening the GUI on linux after first run as it
doesn't have a system tray
- Both the views are from the web interface but are rendered within
the app instead of the browser
* Add checksums to verify the correct model is downloaded as expected
- This should help debug issues related to corrupted model download
- If download fails, let the application continue
* If the model is not download as expected, add some indicators in the settings UI
* Add exc_info to error log if/when download fails for llamav2 model
* Simplify checksum checking logic, update key name in model state for web client
# Incoming
## Major
### Fix Prompt Size Exceeded Issue
- Fix issues related to prompt size, Closes#386. Use the correct tokenizer to calculate whether the input needs to be truncated or not.
### Improve Llama 2 Model Download
- Use the correct download link for LlamaV2 -- should have been using the small model, but was using the medium
- Add better downloading logic to retry download if it failed, Closes#379
### Fix Segmentation Fault due to Race
- Add a lock around generating chat responses from the offline model to avoid segmentation faults. Closes#367.
- Add a loading symbol to the web chat UI when the model is thinking. Closes#392
### Improve Chat Response Latency
- Improve performance of offline chat by increasing batch size (via `n_batch`) to automatically engage more cores/GPU, using smaller model and fixing prompt vs response token generation numbers. Closes#363
### Fix Fake Dialogue Continuation
- Fix formatting of user query with offline chat, this was contributing to #398
- Stop Llama 2 from Creating Fake Dialogue Continuations. Closes#398
## Minor
- Improve default message for Chat window on web when it's not configured. Include hint to use offline chat.
- Add null check in `perform_chat_checks` method
- Add offline chat director unit tests
## Performance Analysis (Time to First Token)
| | v0.10.0 | this branch |
|-|-|-|
| Query 1 | 52s | 28s |
| Query 2 | 33s| 42s |
| Query 3 | 67s| 38s|
It would previously some times start generating fake dialogue with
it's internal prompt patterns of <s>[INST] in responses.
This is a jarring experience. Stop generation response when hit <s>
Resolves#398
- Use same batch_size in extract question actor as the chat actor
- Log final location the chat model is to be stored in, instead of
it's temp filename while it is being downloaded
- Fix download url -- was mapping to q3_K_M, but fixed to use q4_K_S
- Use a proper Llama Tokenizer for counting tokens for truncation with Llama
- Add additional null checks when running
Previously the system message was getting dropped when the context
size with chat history would be more than the max prompt size
supported by the cat model
Now only the previous chat messages are dropped or the current
message is truncated but the system message is kept to provide
guidance to the chat model
* Add support for configuring/using offline chat from within Obsidian
* Fix type checking for search type
* If Github is not configured, /update call should fail
* Fix regenerate tests same as the update ones
* Update help text for offline chat in obsidian
* Update relevant description for Khoj settings in Obsidian
* Simplify configuration logic and use smarter defaults
- Configure using Offline Chat from Emacs:
- Enable, Disable Offline Chat from Emacs
- Use: Enable offline chat with `(setq khoj-chat-offline t)' during khoj setup
- Benefits: Offline chat models are better for privacy but not great at answering questions
* Let Offline chat override OpenAI API settings
* Download the offline model whenever offline chat is enabled
* Add progressbar for download for llamav2 model to track progress
* Change ordering of n due to switch of default processor
* Flip ordering of offline/openai checks when extracting questions from query
* Working example with LlamaV2 running locally on my machine
- Download from huggingface
- Plug in to GPT4All
- Update prompts to fit the llama format
* Add appropriate prompts for extracting questions based on a query based on llama format
* Rename Falcon to Llama and make some improvements to the extract_questions flow
* Do further tuning to extract question prompts and unit tests
* Disable extracting questions dynamically from Llama, as results are still unreliable
* 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
* Add docs for more organized, accessible information detailing Khoj setup
* Delete duplicated files
* Add a coverpage without enabling it. Add logo and theme
* Remove obsidian README.md
* Add plausible script to index.html via docsify
## Stabilize and Simplify Content Indexing
### Major Updates
- 9bcca43 Unify logic to update entries when indexing from scratch or incrementally
- 89c7819 Unify logic to update embeddings when indexing from scratch or incrementally
- 6a0297c Stable sort new entries when marking entries for update
- 58d86d7 Unify logic to configure server from API or on server start
- Create tests to ensure old entries, embeddings in index are unaffected on adding new entries
- Refer: 1482fd4, 7669b85, 88d1a29
- ad41ef3 Make normalization of embeddings configurable to test this in c73feeb
### Minor Updates
- 1673bb5 Add todo state to compiled form of each entry
- 6e70b91 Remove unused `dump_jsonl` helper method
- 7ad9603 Improve naming of lock
- b02323a Improve naming text search test methods
Resolves#190
Previous regenerate mechanism did not deduplicate entries with same key
So entries looked different between regenerate and update
Having single func, mark_entries_for_update, to handle both scenarios
will avoid this divergence
Update all text_to_jsonl methods to use the above method for
generating index from scratch
Reuse Search Models across Content Types to reduce Memory Consumption
- Memory consumption now only scales with search models used, not with content types.
Previously each content type had it's own copy of the search ML models.
That'd result in 300+ Mb per enabled text content type
- Split model state into 2 separate state objects, `search_models` and `content_index`.
This allows loading text_search and image_search models first
and then reusing them across all content_types in content_index
- The change should cut down memory utilization quite a bit for most users.
I see a >50% drop in memory utilization on my Khoj instance.
But this will vary for each user based on the amount of content indexed vs number of plugins enabled.
- This change does not solve the RAM utilization scaling with size of the index,
as the whole content index is still kept in RAM while Khoj is running
Should help with #195, #301 and #303
Wrap acquire/release locks in try/catch/finally when updating content
index and search models to prevent lock not being released on error
and causing a deadlock
* Add additional telemetry in order to understand which data sources are the most useful
* Make actions side by side in the configuration page
* Restore main run command
* Update links to point to wiki pages for Github, Notion integrations
* Stanardize nomenclature of the api_type to use _config suffix
Remove header fields that aren't actually helpful for understanding config usage
- Memory consumption now only scales with search models used, not with
content types as well. Previously each content type had it's own
copy of the search ML models. That'd result in 300+ Mb per enabled
content type
- Split model state into 2 separate state objects, `search_models' and
`content_index'.
This allows loading text_search and image_search models first and then
reusing them across all content_types in content_index
- This should cut down memory utilization quite a bit for most users.
I see a ~50% drop in memory utilization.
This will, of course, vary for each user based on the amount of
content indexed vs number of plugins enabled
- This does not solve the RAM utilization scaling with size of the index.
As the whole content index is still kept in RAM while Khoj is running
Should help with #195, #301 and #303
My account doesn't have gpt-4 enabled and it wouldn't work as the default value was always used from extract_questions, where the caller could use the configured model.
- Provide more details on what clicking configure, initialize buttons
or changing the results count slider does
- This shows up on user hovering over those buttons
* For the demo instance, re-instate the scheduler, but infrequently for api updates
- In constants, determine the cadence based on whether it's a demo instance or not
- This allow us to collect telemetry again. This will also allow us to save the chat session
* Conditionally skip updating the index altogether if it's a demo isntance
* Add backend support for Notion data parsing
- Add a NotionToJsonl class which parses the text of Notion documents made accessible to the API token
- Make corresponding updates to the default config, raw config to support the new notion addition
* Add corresponding views to support configuring Notion from the web-based settings page
- Support backend APIs for deleting/configuring notion setup as well
- Streamline some of the index updating code
* Use defaults for search and chat queries results count
* Update pagination of retrieving pages from Notion
* Update state conversation processor when update is hit
* frequency_penalty should be passed to gpt through kwargs
* Add check for notion in render_multiple method
* Add headings to Notion render
* Revert results count slider and split Notion files by blocks
* Clean/fix misc things in the function to update index
- Use the successText and errorText variables appropriately
- Name parameters in function calls
- Add emojis, woohoo
* Clean up and further modularize code for processing data in Notion
* Add langchain static files and pytorch metadata to Khoj native app
* Add pillow static files, metadata & hidden imports to Khoj native app
* Fix path to web interface static files on Khoj native app
* Add tiktoken hidden imports to make chat work from Khoj native app
* Fix Khoj native app to run with GUI mode enabled
This got broken when we moved from using the --no-gui flag to using
--gui in https://github.com/khoj-ai/khoj/pull/263
* Update the /chat endpoint to conditionally support streaming
- If streams are enabled, return the threadgenerator as it does currently
- If stream is disabled, return a JSON response with the response/compiled references separated out
- Correspondingly, update the chat.html UI to use the streamed API, as well as Obsidian
- Rename chat/init/ to chat/history
* Update khoj.el to use the /history endpoint
- Update corresponding unit tests to use stream=true
* Remove & from call to /chat for obsidian
* Abstract functions out into a helpers.py file and clean up some of the error-catching
Deprecate usage of the older gpt3 models in-place of the newer chat
based models
- text-davinci-003 is only 50% cheaper than gpt4 and less reliable for
question extraction
- Using gpt-3.50turbo for summarization should reduce cost of chat
- Keep conversation.chat_session as a list instead of a string
- Update completion_with_backoff func to use ChatML format
- Fix testing gpt converse method after it started streaming responses
- Pass stop in model_kwargs dictionary and api key in openai_api_key
parameter to chat completion methods. This should resolve the arg
warning thrown by OpenAI module
The previous json parsing was failing to handle questions with date
filters
Fix the chat actor tests to run without throwing error with freezegun
complaining about importing transformers.local_llama model
Remove quote escapes from date filter examples provided to
extract_questions actor
- Before
Only the search interface had the results count configuration option
- After
- The results count is set on the settings page instead of the
search page
- Both search and chat can use the configured results count instead
of just search
* For the demo instance, re-instate the scheduler, but infrequently for api updates
- In constants, determine the cadence based on whether it's a demo instance or not
- This allow us to collect telemetry again. This will also allow us to save the chat session
* Conditionally skip updating the index altogether if it's a demo isntance
- What
- Stream chat responses from OpenAI API to Web, Obsidian clients
- Implement using a callback function which manages a queue where new tokens can be placed as they come on. As the thread is read from, tokens are removed.
- When the final token has been processed, add the `compiled_references` to the queue to be rendered by the `chat` client
- When the thread has been closed, save the accumulated conversation log in the user's history using a `partial func`
- Incrementally decode tokens on the front end and add them as they appear from the streamed response
- Why
This significantly reduces perceived latency and OpenAI API request timeouts for Chat
Closes https://github.com/khoj-ai/khoj/issues/257
- I needed to installed node-fetch to accomplish this, as the built-in request object from Obsidian doesn't seem to support streaming and the built-in fetch object is very sensitive to any and all cross origin requests
Removing unused content types will reduce khoj code to manage
- 0f993b3 Drop support for Ledger as a separate content type
Khoj will soon get a generic text indexing content type in Index plain text files #237.
This along with a file filter should suffice for searching through Ledger transactions
- c9db532 Remove unused org-music as an indexable content type from Khoj
Org-music was just a custom content type that worked with org-music.
It was mostly only useful for me.
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.
Org-music was just a custom content type that worked with org-music.
It was mostly only useful for me.
Cleaning up that code will reduce number of content types for khoj to
manage.
- Add one-click disablement
- Remove fields that probably don't need to be edited (our implementation details)
- Add a green tick if a given field is configured
- In theory, this will be suitable for any Khoj instance that's meant for external-facing purposes (as in, outside of the user's network)
- Prevent re-indexing for Github data if this is a demo instance
- Fix up some issues with the CSS which made settings page small in mobile
- In the frontend views for Khoj, add a button to get on the waitlist and links to the landing page
- Break out of rendering list if at end of org block in org.js
- This would previous hang rendering results in web interface
Should try fix this upstream in org.js as well
- Previously Khoj could only support Python upto 3.10 due to pytorch.
But lots of folks had python 3.11 installed by default on their machines.
This required installing python 3.10 and dealing with virtual envs.
With Torch >= 2.0.1 now able to support python 3.11, at least one
class of installation troubles for Khoj should drop. See
https://github.com/pytorch/pytorch/issues/86566 for reference
- Preliminary testing indicates using the new torch 2.x may reduce
search time by 25% (from 80ms to 60ms on Mac M1)
- Update Docs to not require mentioning python <=3.10 required
- Update Github test workflow to run khoj tests with python 3.11 too
- Use a request session to reduce the overhead of setting up a new connection with the Github URL each request
- Use the streaming feature for the REST api to reduce some of the memory footprint
- Set image_search.query to async to use it with multi-threading
This is same as text_search.query being set to an async method
- Exit search early if no search_model is defined in state.model
- So when searching across content types (with content-type = "all")
org-mode results get rendered differently than markdown, PDF etc. results
- Set div class for each result separately instead of a single uber div
for styling. This allows styling div of each result based on the
content-type of that result
- No need to create placeholder "all" content type on web interface as
server is passing an all content type by itself
- Add cards to configure each of the Github repositories
- Fix a bug in the API which caused all other settings to be wiped when updating one of the content types
- Provide an error message to the user if they have a misconfiguration in their chat settings
- Add support for indexing org files as well as markdown files from the Github repository and update corresponding search view
- Support indexing a list of repositories
- Show success/failure status message much closer to the save button
Previously status message was shown on top of the page, which wasn't
always in view and wasn't easily seen
- Improve the status message to more clearly show next steps on success
If no content-type selected in transient menu option, khoj.el queries
khoj server without content-type parameter (t) set.
This results in search across all enabled asymmetric search text
content types
- Add new filter abstract method to remove filter terms from query
- Use the filter method to remove filter terms, encode this defiltered
query and pass it to the query methods of each search types
TODO: Encoding query is still taking 100-200 ms unlike before. Need to
investigate why
- Update API to return content from all enabled content types when type
is not set to specific type in HTTP request param
- To do this efficiently run the search queries in parallel threads
- Default is 30. So number of paginated requests required to get all
items (commits, files) will reduce by 67%
- No need to increase page size for the get tree Github API request from
`get_markdown_files'
Get tree Github API doesn't support pagination and return 100K items
in response. This should be way more than enough for our current
use-cases
- Previously wasn't prefixing "token" to PAT token in Auth header
This resulted in the request being considered unauthenticated
- Unauthenticated requests to Github API are limited to 60 requests/hour
Authenticated requests to Github API are allowed 5000 requests/hour
- Add a central configuration management page to make management of config details easier
- Add relevant api endpoints both for client and server to update/request data as necessary
- Attempt to update the favicon
The Llama_Hub Github plugin is fairly limited.
The Github Rest API is well supported and can easily be extended to
index commit messages, issues, discussions, PRs etc.
- Make API endpoints on Khoj server accept `client` as request parameter
- Khoj API endpoints: /chat, /search, /update
- Make Khoj clients set `client` request param when calling the API endpoints on the Khoj server
- Khoj clients: Emacs, Obsidian and Web
- Also log khoj server_version running to telemetry server
- This improves latency of @general chat by avoiding unnecessary
compute
- It also avoids passing references in API response when they haven't
been used to generate the chat response. So interfaces don't have to
add logic to not render them unnecessarily
- Make plugin update khoj server config to index PDF files in vault too
- Make Obsidian plugin update index for PDF files in vault too
- Show PDF results in Khoj Search modal as well
- Ensure combined results are sorted by score across both types
- Jump to PDF file when select it PDF search result from modal
- Match argument names passed to khoj openai completion funcs with
arguments passed to langchain calls to OpenAI
- This simplifies the logic in the khoj openai completion funcs
- Fix bug where both LangChain and Khoj retry requests 6 times each.
So a total of 12 requests at >1minute intervals for each chat
response in case of OpenAI API being down
- Retrying too many times when the API is failing doesn't help
- The earlier 60 second request timeout was spacing out the interval
between retries way too much. This slowed down chat response times
quite a bit when API was being flaky
- With these updates you'll know if call to chat API failed in under a
minute
- Use ChatModel and ChatOpenAI to call OpenAI chat model instead of
using OpenAI package directly
- This is being done as part of migration to rely on LangChain for
creating agents and managing their state
- Khoj chat will now respond to general queries if:
1. no relevant reference notes available or
2. when explicitly induced by prefixing the chat message with "@general"
- Previously Khoj Chat would a lot of times refuse to respond to
general queries not answerable from reference notes or chat history
- Make chat quality tests more robust
- Add more equivalent chat response options refusing to answer
- Force haiku writing to not give any preable, just the haiku
- Simplifies switching between different OpenAI chat models. E.g GPT4
- It was previously hard-coded to use gpt-3.5-turbo. Now it just
defaults to using gpt-3.5-turbo, unless chat-model field under
conversation processor updated in khoj.yml
Otherwise if heading > max_tokens than the search models will just see
a heading (with repeated filename) for each compiled entry and not
actual content.
100 characters should be sufficient to include filename (not path) and
entry heading. If longer rather truncate to pass entry unique text to
model for search context
Previously filename was appended to the end of the compiled entry.
This didn't provide appropriate structured context
Test filename getting prepended as heading to compiled entry
All compiled snippets split by max tokens (apart from first) do not
get the heading as context.
This limits search context required to retrieve these continuation
entries
- cl-push expects a generatlized variable. Else throws (setf quote)
undefined warning
- This results in the config call failing on calling khoj entrypoint
- Remove waiting for server message as it hides the messages from the
server
- Fix the nil message that were being rendered, by checking before
showing messages from server
- Consistently prefix messages from khoj with khoj.el
Previously khoj.el was calling the server configure API even when
config was same as before.
This had broken the khoj search as you type experience from emacs
Also show more details to user about what in khoj is being configured
Resolves#185, #199
- Issue
IndexName created from Obsidian Absolute Vault path wasn't replacing
windows path, drive separators with underscore. It was only
replacing unix path separators
- Fix
Also replace windows drive and path separators with _ while creating
IndexName in Khoj Obsidian plugin
Makes it easier to tell pip associated with which python is being
used. Easier to debug when users have different versions of python
installed (e.g 3.10 and 3.11)
- Explicity split entry string by space during split by max_tokens
- Prevent formatting of compiled entry from being lost
- The formatting itself contains useful information
No point in dropping the formatting unnecessarily,
even if (say) the currrent search models don't account for it (yet)
Append originating filename to compiled string of each entry for
better search quality by providing more context to model
Update markdown_to_jsonl tests to ensure filename being added
Resolves#142
This follows expected behavior for obsidain search modals
E.g Ominsearch and default Obsidian search.
The note creation code is borrowed from Omnisearch.
Resolves#133
- Give space in the input field. Too narrow previously
- References should be indexed from 1 instead of 0
- Use Obsidian font size variables to scale fonts in chat appropriately
- Add message sender, date metadata as message footer
- Use css directly from Khoj Chat Web Interface.
- Modify it to work under a Obsidian modal
- So replace html, body styling from web interface to instead
styling new "khoj-chat" class attached to contentEl of modal
Converts paths to glob style regexes that will index all org files
recursively under the specified list of path
Should help setup for org-roam users from khoj.el
- khoj-auto-setup controls whether to automatically check for and
setup khoj server from within Emacs
- extract install, start, configure sequence into public, interactive
method. Allows calling khoj-setup during package load via init.el
- Fix: Do not attempt to configure or wait for server ready if
user has said no to auto-setup request
- Fix logic to mark server started vs ready
- Previously the started/running vs ready variables defs were getting
intertwined
- Server started indicates server bootup has been triggered
- Server ready indicates server API ready to accept requests
- If khoj server started outside emacs, khoj--server-ready should be set
to true by khoj--server-running method (instead of waiting for proc msg)
- If khoj server is unconfigured the /config/types endpoint wouldn't
return anything. Using config/data/default allows checking khoj server
running status without requiring it to be configured as well
If the config hasn't changed there'll be no update. If config has
changed indexing will get triggered asynchronously. But user cannot
make query till indexing done
As easier to know when server ready to configure
- Use process filter, sentinel to mark when khoj server is ready or not
- Display server messages for visibility into server boot-up process
- Wait until server ready to open khoj transient menu in Emacs
Until then khoj features wouldn't work anyway, so avoids confusion
- Move completion and chat_completion into helper methods under utils.py
- Add retry with exponential backoff on OpenAI exceptions using
tenacity package. This is officially suggested and used by other
popular GPT based libraries
- Use tiktoken to count tokens for chat models
- Make conversation turns to add to prompt configurable via method
argument to generate_chatml_messages_with_context method
- Remove the need to split by magic string in emacs and chat interfaces
- Move compiling references into string as context for GPT to GPT layer
- Update setup in tests to use new style of setting references
- Name first argument to converse as more appropriate "references"
- Render references as superscript
- Show reference definitions on hover over reference links to ease access
- Truncate reference def shown on hover to 70 char
- Add continuation suffix, ..., when reference definition truncated
- Style Message as Org Entries instead of List
- Put khoj response as child of user query entry
- Improves color coding for readability
- Allows folding each back-n-forth
- Put timestamp of message received into property drawer
- Use standardized time format for new and old chat messages
- Generalize the render-chat-response method to handle rendering
history or chat response from chat API reponse
- Trigger rendering of khoj chat history if Khoj chat buffer not
created for this session yet
- Use org-insert-link method to improve link rendering robustness
Previous simple mechanism to crete org-links would result in links
escaping out of formating. Use a user-facing org-mode method to
remove/reduce probability of this
- Replace newlines with space to render reference notes as links
- Query khoj chat API to get Khoj Chat response to user message
- Render chat messages as a org-mode list in format:
- [sender-name]: *[message]*
- /[receive-date]/
- Add references as org links with context visible on hover,
but no jump to note
- Require dash library for khoj.el to simplify list manipulation.
Use `-map-indexed' method from dash
- Reasons:
- GPT can extract date aware search queries with date filters
better than ChatGPT given the same prompt.
- Need quality more than cost savings for now.
- Need to figure ways to improve prompt for ChatGPT before using it
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
- Keep inferred questions in logs
- Improve prompt to GPT to try use past questions as context
- Pass past user message and inferred questions as context to help GPT
extract complete questions
- This should improve search results quality
- Example Expected Inferred Questions from User Message using History:
1. "What is the name of Arun's daughter?"
=> "What is the name of Arun's daughter"
2. "Where does she study?" =>
=> "Where does Arun's daughter study?" OR
=> "Where does Arun's daughter, Reena study?"
The Search Actor allows for
1. Looking up multiple pieces of information from the notes
E.g "Is Bob older than Tom?" searches for age of Bob and Tom in 2 searches
2. Allow date aware user queries in Khoj chat
Answer time range based questions
Limit search to specified timeframe in question using date filter
E.g "What national parks did I visit last year?" adds
dt>="2022-01-01" dt<"2023-01-01" to Khoj search
Note: Temperature set to 0. Message to search queries should be deterministic
Create Rubric to Test Chat Quality and Capabilities
### Issues
- Previously the improvements in quality of Khoj Chat on changes was uncertain
- Manual testing on my evolving set of notes was slow and didn't assess all expected, desired capabilities
### Fix
1. Create an Evaluation Dataset to assess Chat Capabilities
- Create custom notes for a fictitious person (I'll publish a book with these soon 😅😋)
- Add a few of Paul Graham's more personal essays. *[Easy to get as markdown](https://github.com/ofou/graham-essays)*
2. Write Unit Tests to Measure Chat Capabilities
- Measure quality at 2 separate layers
- **Chat Actor**: These are the narrow agents made of LLM + Prompt. E.g `summarize`, `converse` in `gpt.py`
- **Chat Director**: This is the chat orchestration agent. It calls on required chat actors, search through user provided knowledge base (i.e notes, ledger, image) etc to respond appropriately to the users message. This is what the `/api/chat` API exposes.
- Mark desired but not currently available capabilities as expected to fail <br />
This still allows measuring the chat capability score/percentage while only failing capability tests which were passing before on any changes to chat
- Set conversation_log arg default to dict
- Increase default temperature to 0.2 for a little creativity in
answering
- Make GPT be more reliable in looking at past conversations for
forming response
# Improve Khoj Chat
## Main Changes
- Use the new [API](https://openai.com/blog/introducing-chatgpt-and-whisper-apis) for [ChatGPT](https://openai.com/blog/chatgpt) to improve conversation quality and cost
- Improve Prompt to answer query using indexed notes
- Previously was asking GPT to summarize the notes
- Both the chat and answer API use this new prompt
- Support Multi-Turn conversations
- Pass previous messages and associated reference notes to ChatGPT for context
- Show note snippets referenced to generate response
- Allows fact-checking, getting details
- Simplify chat interface by using only single unified chat type for now
## Miscellaneous
- Replace summarize with answer API. Summarize via API not useful for now
- Only pass Khoj search results above a threshold confidence to GPT for context
- Allows Khoj to say don't know if it can't find answer to query from notes
- Allows relying on (only) conversation history to generate response in multi-turn conversation
- Move Chat API out of beta. Update Readme
GPT still mostly says I don't know when answer not in notes or chats
But with this its more inclined to answer general questions not in
chats or notes while informing user that the information is not from
existing chats or notes
- Chat uses compiled form of search results, not the raw entries to
provide context for chat. The compiled snipped search results
themselves are unique and using multiple of them for context from
the same raw note is fine if they cross the score and rank thresholds
This should improve the context provided for chat
- Also apply score_threshold, no deduplication to the answers API
- Issue
The file path separator by khoj server and the Obsidian vault were
different on Windows
- Fix
Normalize file path to use forward slash(/) to find the matching
note file in the Obsidian vault for jump to it
Resolves#177
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
- Only use decent quality search results, if any, as context
- Pass source results used by previous chat messages as context
- Loosen prompt to allow looking at previous chats and notes to answer
- Pass current date for context
- Make GPT provide reason when it can't answer the question. Gives
user context to tune their questions
- Set context by either including last 2 chat messages from active
session or past 2 conversation summaries from conversation logs
- Set personality in system message
- Place personality system message before last completed back & forth
This may stop ChatGPT forgetting its personality as conversation progresses given:
- The conditioning based on system role messages is light
- If system message is too far back in conversation history, the
model may forget its personality conditioning
- If system message at end of conversation, the model can think its
the start of a new conversation
- Inserting the system message before last completed back & forth should
prevent ChatGPT from assuming its the start of a new conversation
while not losing personality conditioning from the system message
- Simplfy the Khoj Chat API to for now just answer from users notes
instead of trying to infer other potential interaction types.
- This is the default expected behavior from the feature anyway
- Use the compiled text of the top 2 search results for context
- Benefits of using ChatGPT
- Better model
- 1/10th the price
- No hand rolled prompt required to make GPT provide more chatty,
assistant type responses
- Improve GPT prompt
- Make GPT answer users query based on provided notes instead
of summarizing the provided notes
- Make GPT be truthful using prompt and reduced temperature
- Use Official OpenAI Q&A prompt from cookbook as starting reference
- Replace summarize API with the improved answer API endpoint
- Default to answer type in chat web interface. The chat type is not
fit for default consumption yet
Previous behavior was resulting in a null reference error. As key for
the core content/search type was not present in current config
Fallback to using default config for unconfigured core content type
instead
See #165 for details
- Use emojis to make info logs easier to read
- Inform when khoj is ready to use
- Provide information on what khoj is doing while starting up
- Inform when content/search types and processors are setup
- Inform when models are being loaded from the web as this step can
take time
- Convert all other info logs to be only shown in verbose mode
- 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
Repro:
1. Open khoj server with `khoj` on first run
2. Install/enable Khoj Obsidian plugin (to configure khoj server)
3. Restart khoj server with `khoj`
Bug:
- Unconfigured processor and search_types are instantiated as None in
self.current_config
- While creating the desktop GUI, these null configs are attempted to
be accessed as valid dictionaries for creating their GUI panels
- This results in the null ref errors
Fix:
Use default config to create their GUI elements for unconfigured
search and processor types
Resolves#167
- 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
- Remove need for interfaces to downcase content types returned by API
before using the type in search and other API endpoint
- Fix to check for search_type.name in plugin keys instead of value
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.
- Remove unneeded type ignore for mps with the latest mypy
- Stop excluding PyQT desktop GUI code from MyPy checks
- Do not warn about unused ignores. Some issue with mypy giving
different errors in different environments (venv, system and pre-commit)
- Use Rich to render uvicorn, fastAPI logs as well
The previous CustomFormatter only worked on khoj logs
- Improve rendering stacktrace on errors using Rich
- 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'
- By default the obsidian plugin automatically configures the khoj
backend to index the current vault
- For more complex scenarios, users can manage their ~/.khoj/khoj.yml
manually by toggling the auto-configure setting off in the khoj
plugin settings
Resolves#156
- Background
1. Obsidian stores markdown notes as utf8[1]
2. By default, the python `open' command uses the OS locale encoding[2]
This was causing the `UnicodeDecodeError: <locale_encoding> codec can't decode byte' error
- Fix
- Read markdown files as utf8
The Obsidian plugin is the main use-case for markdown files in
khoj currently and that stores md files as utf8.
Do not assume utf8 for other content types like org-mode, beancount for now.
- Fail if error in reading file as utf8, instead of ignoring errors.
Would rather have user realize that their files are not going to
get indexed correctly.
[1]: https://forum.obsidian.md/t/better-handle-md-files-not-stored-in-utf8-format/13524/3
[2]: https://docs.python.org/3/library/functions.html#open
Khoj plugin page from within Obsidian isn't recognized. Seems like it
needs an uppercase readme file only. So it doesn't show the Khoj
readme from within Obsidian itself.
- Update khoj.el test to reflect updated rendering logic
- Move ledger render function before image rendered to group functions
with similar logic closer
Split find file, jump to file code to make onChooseSuggestion more readable
- Use find, instead of using return in forEach to get first match
- Move the jump to file+heading code out from forEach
Do not reference global app object from child objects and funcs
directly.
It is only available for debugging purposes and access to it maybe
dropped in the future.
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
- Support querying with text surrounding point in any text buffer
Previously could only find items similar to org entry at point
- Find similar items of specified content type indexed on khoj
Previously only looked for similar org entries indexed on khoj
Now uses the content-type configured in khoj transient menu to find
items of the specified content type
- Details
- Generalize the get-current-org-entry-text func to get text for any
outline section
- Replace leading whitespaces from query text as well
- Create method to get current paragraph text from non-outline mode
buffers
- Update transient, find-similar funcs to pass, use content-type
configured in khoj transient menu
- Generalize query title creation logic to remove markdown headings
prefix (#) apart from org heading prefix (*) as well
- Update last used khoj content-type and results from the
find-similar and update funcs for later reuse
- Jump to top of results buffer after results rendered
Enable searching for notes similar to the current note being viewed
## Main Changes
- 39a18e2 Extend search modal to search for similar notes
- Hide input field on init, Trigger search on opening modal when in similar notes mode
- Set input to contents of current markdown file and get notes similar to it
- Re-rank, by default, when searching for similar notes
- Filter out current note from similar note search results
- 0bed410 Only show `Find Similar Note' command in Editor
- Hide input field on init, Trigger search on opening modal in similar notes mode
- Set input to current markdown file and get similar notes to it
- Enable rerank when searching for similar notes
- Filter out current note from similar note search results
- Screenshot querying "Setup Editor" on test vault with Khoj Readmes
- New features showcase:
- information keybindings, rerank keybinding at bottom of modal
- fixed top level headings in search results
- search results snipped if greater than N words
- 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
Provides a more consistent rendering of results in modal.
Makes it easier to see more results in modal.
To see complete entry, user can always just jump to entry from modal
### Overview
- Provide a chat interface to engage with and inquire your notes
- Simplify interacting with the beta `chat` and `summarize` APIs
### Use
- Open `<khoj-url>/chat`, by default at http://localhost:8000/chat?type=summarize
- Type your queries, see summarized response by Khoj from your notes
**Note**:
- **You will need to add an API key from OpenAI to your khoj.yml**
- **Your query and top note from search result will be sent to OpenAI for processing**
## Details
- 177756b Show chat history on loading chat page on web interface
- d8ee0f0 Save chat history to disk for persistence, seeing chat logs
- 5294693 Style chat messages as speech bubbles
- d170747 Add khoj web interface and chat styling to new chat page on khoj web
- de6c146 Implement functional, unstyled chat page for khoj web interface
- The previous mechanism to trigger saving on shutdown event did not work
- Use scheduler to persist chat sessions to disk at a 5 minute interval
- This improve time granularity, fixed interval of saving chat logs
- It may lose ~5 minutes of chat history until mechanism to also
write on shutdown found/resolved
- Create conversation directory if it doesn't exist before attempting write
- Reset chat_session after writing it to disk
- Wrap messages into speech bubbles
- Color messages by khoj blue, sender grey
- Add those standard protrusions to the speech bubbles for fun
- Align bubbles left or right based on sender
- messages by khoj are left aligned, message by self are right aligned
- Put message metadata like sender and time under speech bubble
- use data-* attribute and ::after css pseudo-selector for this
- Update renderMessage func to accept time param, remove unused type_ param
Not all notes are in the past. Notes can be about stuff in the future.
Casting them to past tense gives the impression that they've already
happened / been done.
- Changes
- Use blue color for khoj heading font
- This fixes the title color issue
- Update background to lighter shade
- This fixes the body text color issue
- Update colors for todo, done, miscellaneous todo state, tag color
- This does not fix the color contrast issue but seems like an acceptable solution
- Using white text rather than black text on blue background
better even though the black text on blue background passes the
WCAG acceptable contrast score
- For details see blog post:
https://uxmovement.com/buttons/the-myths-of-color-contrast-accessibility/
- Add border to tags to give them tag pills look and differntiate
from todo states
- Buttons and inputs
- Change background color of input fields like type dropdown,
update button and results count counter, to match background
color of page
- Add shadow on hover over button, dropdowns
Resolves#111
- Ensure message input box sticks to bottom of screen
- Ensure chat logs div is scrollable when logs become longer than screen
Do not make the whole page scroll, just the chat logs body div
Uses longest file path match to find markdown file in vault
corresponding to file of search result returned by Khoj
Allow jumping to search result from khoj plugin modal on Android too
Previous mechanism of manually triggering getSuggestions,
renderSuggestions flow was corrupting traversing and opening
reranked search results in KhojModal
Emulate event that would anyway trigger the get & render of results in
modal. This lets obsidian core handle the flow without digging too
deep into obsidian cores handling of the flow. Lowers the chance of
breakage
We need the index file paths to make sense on the khoj backend server
Having path of index on backend relative to current vault directory
on frontend ignores the fact that the frontend maybe on a different
machine than the khoj backend server
Using unique index name per vault allows switching vaults without
overwriting indices of other vaults created on khoj backend when khoj
obsidian plugin is loaded on opening a different vault
- Overview
Limits using Khoj with a single vault at a time. This is
automatically configured to the most recently opened vault.
Once directory filters are supported on backend, the plugin will be
updated to index multiple vault but search only current vault from
current vaults khoj obsidian plugin
- Code Details
- Remove setting to configure Vault directory from Khoj Obsidian plugin
- Automatically configure Khoj to index only current Vault.
- Overwrites any previous vaults that were intended to be indexed by
Khoj backend
- Force update of index after configuring vault
- Why
It's not helpful for now and can lead to more problems, confusion.
Once directory filters
- Previously the backend was just throwing backend error.
The frontend calling the /update API wasn't getting notified
- Now the frontend can react appropriately and make the issue
visible to the user
- Only show notification on plugin load and failure.
- In settings page, set current backend status at top of pane instead
of showing notification
Notices bubbles cluttered the UI while typing updates to settings
- Show notification once index updated via settings pane button click
There was no notification on index updated, which usually takes time
on the backend
- Display warning at top of khoj obsidian plugin settings
- Make search command available only if connected to backend
- Show warning notice on clicking khoj search ribbon button
- Call saveData after configureKhojBackend to ensure
connnectedToBackend setting saved after being (potentially) updated
in configureKhojBackend function
- Previously the plugin would not load if cannot connect to Khoj backend
- Silently failing to load with no reason provided is not helpful
- Load plugin to allow user to fix the Khoj URL in their plugin setting
- Show reason for khoj plugin not working. More helpful than failing silently
Use the timer context manager in all places where code was being timed
- Benefits
- Deduplicate timing code scattered across codebase.
- Provides single place to manage perf timing code
- Use consistent timing log patterns
The query method had become too big.
Extract out filter, score, sort and deduplicate logic used by
text_search.query into separate methods.
This should improve readabilty of code.
- Changes
- Fix method signatures of BaseFilter subclasses.
Else typing information isn't translating to them
- Explicitly pass `entries: list[Entry]' as arg to `load' method
- Fix type of `raw_entries' arg to `apply' method
to list[Entry] from list[str]
- Rename `raw_entries' arg to `apply' method to `entries'
- Fix `raw_query' arg used in `apply' method of subclasses to `query'
- Set type of entries, corpus_embeddings in TextSearchModel
- Verification
Ran `mypy --config-file .mypy.ini src' to verify typing
- `torch.Tensor' is apparently a legacy tensor constructor
- Using that to create tensor on MPS devices throws error:
RuntimeError: legacy constructor expects device type: cpu but device type: mps was passed
- `torch.tensor' can handle creating tensors on Mac GPU (MPS) fine
This is unlike the more general chat API that combines summarization
of top search result and conversing with the OpenAI model
This should give faster summary results. As no intent categorization
API call required
- 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
- Default to using `text-davinci-003' if conversation model not
explicitly configured by user. Stop using the older `davinci' and
`davinci-instruct' models
- Use `model' instead of `engine' as parameter.
Usage of `engine' parameter in OpenAI API is deprecated
- Init processor before search to instantiate `openai_api_key'
from `khoj.yml'. The key is used to configure search with openai models
- To use OpenAI models for search in Khoj
- Set `encoder' to name of an OpenAI model. E.g text-embedding-ada-002
- Set `encoder-type' in `khoj.yml' to `src.utils.models.OpenAI'
- Set `model-directory' to `null', as online model cannot be stored on disk
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
- Previously `model_type' was set in the setup of each `search_type'
- All encoders were of type `SentenceTransformer'
- All cross_encoders were of type `CrossEncoder'
- Now `encoder-type' can be configured via the new `encoder_type' field
in `TextSearchConfig' under `search-type` in `khoj.yml`.
- All the specified `encoder-type' class needs is an `encode' method
that takes entries and returns embedding vectors
- Ensure all tensors are on MPS device before doing operations across them
- Background
- GPU is used by default for Khoj on MacOS now
- Needed PyTorch > 1.13.0 on Macs to use GPU, which we do now
- MPS should speed up search and indexing on MacOS
Fix usage warning for unescaped single quote in `khoj.el' docstring.
Converts usage of '<text>' into `<text>' to use the correct quote forms in generated docs
⛔ Warning (comp): khoj.el:119:2: Warning: docstring has wrong usage of unescaped single quotes (use \= or different quoting)
⛔ Warning (comp): khoj.el:120:2: Warning: docstring has wrong usage of unescaped single quotes (use \= or different quoting)
⛔ Warning (comp): khoj.el:121:2: Warning: docstring has wrong usage of unescaped single quotes (use \= or different quoting)
⛔ Warning (comp): khoj.el:168:2: Warning: docstring has wrong usage of unescaped single quotes (use \= or different quoting)
- Features
- Search using Khoj from within the Obsidian app
Allow Natural language search on your (markdown) notes in Obsidian Vault
- Show search results as rendered (instead of raw) Markdown
Improve legibility of the results
- Jump to selected note from search result in Khoj search modal
Simplify seeing result within its original note context
- Automatically configure khoj to index markdown files in current vault
Reduce khoj setup steps for plugin users by using reasonable defaults
- Code updates the markdown config in khoj.yml and triggers index update
- It can be configured by user in khoj plugin settings, if required
- Add Demo and detailed Readme for the Obsidian plugin
Ease setup and usage. Give context about capabilities
- Miscellaneous
- Trying keep a mono repo until the Khoj project is mature enough
to reduce maintainance burden
This can ease configuring khoj from the different interfaces
- Don't need to know all the (default) config used by khoj.
- Just get default config by calling the above API endpoint.
- Then modify desired portions and call POST /api/config/data to
configure khoj.
- Start khoj server (in non-GUI mode) without needing config file
already instantiated.
- But throw warning to configure khoj to use it
- This allows plugins to configure the app via the /config/data APIs
- To be used by the Khoj obsidian plugin to configure markdown content
in khoj
- Poll scheduler every minute using threading.Timer
- Use 60 seconds polling interval to avoid fork bombing
- Schedule next via the same poll scheduler
- Allow clean program interrupt by running scheduler in daemon mode
- There are 3 paths to updating/setting the index (stored in state.model)
- App start
- API
- Scheduler
- Put all updates to the index behind a lock. As multiple updates path
that could (potentially) run at the same time (via API or Scheduler)
- 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
- Required because entries are now split by the max_word count supported
by the ML models
- This would now result in potentially duplicate hits, entries being
returned to user
- Do deduplication after ranking to get the top ranked deduplicated
results
- The instructions suggest installing khoj-assistant via pip install.
This installs the latest tagged/release version of khoj
- To match that version user should install khoj.el from MELPA stable
instead of MELPA
- 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
Update readme to ask user to install khoj.el from MELPA when a
pre-release version of the main khoj app is installed. Else install
khoj.el from MELPA Stable
- 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
- Both Text, Image Search were already giving list of entry, score
- This change just concretizes this change and exposes this in the API
documentation (i.e OpenAPI, Swagger, Redocs)
- 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
In my installation, it appears that `url-request-method` is sometimes set
globally to POST. Need to explicitly set it to ensure that GET is always
used as intended.
- Pass force=true to /update API to force regenerating index from
scratch
- Otherwise calls to the /update API endpoint will result in an
incremental update to index
- 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
- Simplify tracking khoj query history, saving/sharing links
- Do not execute search, when query only contains whitespaces
- Prevents error when try process results of empty query
- As `/reload` updates index incrementally, it's relatively quick
- This makes exposing `/reload` endpoint a better default to expose
via the web interface than `the /regenerate' endpoint
- 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
- 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
- Provides more control to invalidate cache on update to entries, embeddings
- Allows logging when results are being returned from cache etc
- FastAPI, Swagger API docs look better as the `search' controller not
wrapped in generically named function when using functools LRU decorator
- Issue
- Indent regex was previously catching escape sequences like newlines
- This was resulting in entries with only escape sequences in body to
be prepended to property drawers etc during rendering
- Fix
- Update indent regex to only look for spaces in each line
- Only render body when body contains non-escape characters
- Create test to prevent this regression from silently resurfacing
- Previously heading entries were not indexed to maintain search quality
- But given that there are use-cases for indexing entries with no body
- Add a configurable `index_heading_entries' field to index heading entries
- This `TextContentConfig' field is currently only used for OrgMode content
- Let the specific text_to_jsonl method decide which of the
TextContentConfig fields it needs to convert <text> type to jsonl
- This simplifies extending TextContentConfig for a specific type without
modifying all text_to_jsonl methods
- It keeps the number of args being passed to the `text_to_jsonl'
methods in check
- It's more of a hassle to not let word filter go stale on entry
updates
- Generating index on 120K lines of notes takes 1s. Loading from file
takes 0.2s. For less content load time difference will be even smaller
- Let go of startup time improvement for simplicity for now
- Comparing compiled entries is the appropriately narrow target to
identify entries that need to encode their embedding vectors. Given we
pass the compiled form of the entry to the model for encoding
- Hashing the whole entry along with it's raw form was resulting in a
bunch of entries being marked for updated as LINE: <entry_line_no>
is a string added to each entries raw format.
- This results in an update to a single entry resulting in all entries
below it in the file being marked for update (as all their line
numbers have changed)
- Log performance metrics for steps to convert org entries to jsonl
- Having Tags as sets was returning them in a different order
everytime
- This resulted in spuriously identifying existing entries as new
because their tags ordering changed
- Converting tags to list fixes the issue and identifies updated new
entries for incremental update correctly
- What
- Hash the entries and compare to find new/updated entries
- Reuse embeddings encoded for existing entries
- Only encode embeddings for updated or new entries
- Merge the existing and new entries and embeddings to get the updated
entries, embeddings
- Why
- Given most note text entries are expected to be unchanged
across time. Reusing their earlier encoded embeddings should
significantly speed up embeddings updates
- Previously we were regenerating embeddings for all entries,
even if they had existed in previous runs
- Parsed `level` argument passed to OrgNode during init is expected to
be a string, not an integer
- This was resulting in app failure only when parsing org files with
no headings, like in issue #83, as level is set to string of `*`s
the moment a heading is found in the current file
- Previously we were failing if no valid entries while computing
embeddings. This was obscuring the actual issue of no valid entries
found in the specified content files
- Throwing an exception early with clear message when no entries found
should make clarify the issue to be fixed
- See issue #83 for details
- Default config has `input_files' set to None
- This was being passed to `FileBrowser' on Initialization
- But `FileBrowser' expects `content_files' of list type, not None
- This resulted in an unexpected NoneType failure
- The logging to file code expects the config directory to already be setup
- But parent directory of config file was being set up later in code
- This resulted in app start failing with ~/.khoj dir does not exist error
- Pass file associated with entries in markdown, beancount to json converters
- Add File, Word, Date Filters to Ledger, Markdown Types
- Word, Date Filters were accidently removed from the above types yesterday
- File Filter is the only filter that newly got added
- Filter entries, embeddings by ids satisfying all filters in query
func, after each filter has returned entry ids satisfying their
individual acceptance criteria
- Previously each filter would return a filtered list of entries.
Each filter would be applied on entries filtered by previous filters.
This made the filtering order dependent
- Benefits
- Filters can be applied independent of their order of execution
- Precomputed indexes for each filter is not in danger of running
into index out of bound errors, as filters run on original entries
instead of on entries filtered by filters that have run before it
- Extract entries satisfying filter only once instead of doing
this for each filter
- Costs
- Each filter has to process all entries even if previous filters
may have already marked them as non-satisfactory
- This will help filter query to org content type using file filter
- Do not explicitly specify items being extracted from json of each
entry in text_search as all text search content types do not have
file being set in jsonl converters
- Specify just file name to get all notes associated with file at path
- E.g `query` with `file:"file1.org"` will return `entry1`
if `entry1` is in `file1.org` at `~/notes/file.org`
- Test
- Test converting simple file name filter to regex for path match
- Test file filter with space in file name
- Code Changes
- Use list comprehension and `torch.index_select' methods
- to speed selection of entries, embedding tensors satisfying filter
- avoid deep copy of entries, embeddings
- avoid updating existing lists (of entries, embeddings)
- Use word to entry map and set operations to mark entries satisfying
inclusion, exclusion filters
- Results
- Speed up explicit filtering by two orders of magnitude
- Improve consistency of speed up across inclusion and exclusion filtering
- Only the filter knows when entries, embeddings are to be manipulated.
So move the responsibility to deep copy before manipulating entries,
embeddings to the filters
- Create deep copy in filters. Avoids creating deep copy of entries,
embeddings when filter results are being loaded from cache etc
- Do not run the more expensive explicit filter until the word to be
filtered is completed by user. This requires an end sequence marker
to identify end of explicit word filter to trigger filtering
- Space isn't a good enough delimiter as the explicit filter could be
at the end of the query in which case no space
- Stop passing verbose flag around app methods
- Minor remap of verbosity levels to match python logging framework levels
- verbose = 0 maps to logging.WARN
- verbose = 1 maps to logging.INFO
- verbose >=2 maps to logging.DEBUG
- Minor clean-up of app: unused modules, conversation file opening
- This also pushes the updated URL state to history
- Allows jumping back to the web interface after clicking on an image
and having the type set to image search
- Previously type would get reset to the default search type on
jumping back
- CLIP doesn't need full size images for generating embeddings with
decent search results. The sentence transformers docs use images
scaled to 640px width
- Benefits
- Normalize image sizes
- Increase image embeddings generation speed
- Decrease memory usage while generating embeddings from images
- 5e6625a Fix file browser to not add empty line when no file/dir selected
- 8098b8c Bring main window to Top when open from System Tray
- 1c122a8 Place window near top so buttons are not hidden by OS bottom bar
- dfe2546 Set Khoj Icon on Main Desktop Window
- 1b1f8f9 Move Splash screen text below icon. Set the text color to black
- 450f644 Fix path to remove shared libraries when packaging the Windows app
- When no file selected in file browser an empty line/entry gets added
to input entries list
- Bug got introduced due to insufficient update on change to add
instead of insert
- Update is_none_or_empty helper method to also check for empty string
- It is a non-user configurable, app state that is set on app start
- Reduce passing unneeded arguments around. Just set device where
required by looking for ML compute device in global state
- Note: Support for MPS in Pytorch is currently in v1.13.0 nightly builds
- Users will have to wait for PyTorch MPS support to land in stable builds
- Until then the code can be tweaked and tested to make use of the GPU
acceleration on newer Macs
- Pass device to load models onto from app state.
- SentenceTransformer models accept device to load models onto during initialization
- Pass device to load corpus embeddings onto from app state
- CLIP Image score and XMP metadata score are not combining well.
When combined they give non sensical results. Enable only once
figure how best to combine the two.
- Show scores with higher precision for image search
- Image search scores seem to be mostly be between 0.2 - 0.3 for some reason
- Higher precision scores make it easier to understand the quality
of returned results perceived by the model itself
- Allows adding multiple image directories via GUI
- Allow adding multiple files in different directories via GUI
- Previously users couldn't add multiple directories via GUI
They'd have to manually append to input field if multiple files, directories
- To clear/overwrite is much easier.
The user can just select text to delete in input area
- Issue
Fix configuring image search from Desktop GUI. It was broken before.
The Desktop GUI was updating input-files field under content-type > image.
This field is not used for image search. So image search couldn't be
configured from the Desktop GUI
- Fix
- Set input-directories when field of search type image is set from GUI
- Otherwise set input-files field in config
- Show a helpful error message in the GUI to the user, instead of the
crashing if loading config fails, for e.g if file wasn't found
- Collate GUI errors into an ErrorType enum class
- Remove previous error messages before showing the new one
Previously if the embeddings were already there only the khoj.yml
config file would get updated. The embeddings would remain old.
1. This results in a stale app state where the config doesn't
match the embeddings
2. Currently the user cannot update their config from the config
screen. They'd have to use a combination of config screen and web
interface>regenerate button to trigger it or delete their ~/.khoj dir
This commit should resolve the above issues
- Prevent immediate overwrite of re-ranked results by
incremental-search without rerank triggered via post-command-hook.
- This triggers right after the reranking results are rendered, so
user never ends up seeing them
- Add docstrings, mention args in them. Make docstring crisper
- prefix funcs, variables with khoj--
- Require emacs >27.1 for json-parse-buffer
- Use lexical binding
- Add quickstart docs to elisp file itself
- Bump version of khoj.el
- What
- Convert the config screen into the main application window
with configuration as just one of the functionality it provides
- Rename config screen to main window to match new designation
- Why
- System Tray isn't available everywhere (e.g Linux)
- This requires moving functionality into a normal window for cross-compat
- What
- On Linux
- Show Configure Screen, even if not first run experience
- Do no show system tray on Linux
- Quit app on closing Configure Screen
- On Windows, Mac
- Show Configure screen only if first run experience
- Show system tray always
- Do not quit app on closing Configure Screen
- Why
- Configure screen is the only GUI element on Linux. So closing it
should close the application
- On Windows, Mac the system tray exists, so app should not be closed
on closing configure screen
- Start evolving configure screen away from just being a configure screen
- Update Window Title to just say Khoj
- Allow Opening Web Interface to Search from Khoj configure screen
- Rename "Start" Button to more accurate "Configure"
- Disable Search button on first run and while configuring app
- Issue
- In the previous form, updates to self.current_config would update
default_config as python does a shallow copy
- So self.current_config is just referencing the values of default_config
- Hence updates to current_config updates the default_config values too
- This is not what we want
- Fix
- Deep copy the default_config values. Now updates to
self.current_config wouldn't affect the default_config
- Generating embeddings takes time
- If user enables a content type and clicks start.
The app starts to generate embeddings when loading the new settings
- Run this function in a separate thread to keep config screen responsive
- But disable start button to prevent re-entrant threads
- Also show a minimal visual indication that the app is saving state
- Convert Input Fields into PlainTextEdit
- Display Each Selected File on a Separate Line in Field
- Set Height of FileBrowser Input Field based on Number of Lines/Files
- This fixes the field expanding when configure screen is expanded
- Allows for reusability of the labelled text field
- Simplifies the logic to save settings for conversation processor
- Avoid having to pass the khoj_sample.yml data file into pip, native apps
- Packaging data files into python packages is annoying.
- There's `MANIFEST.in`, `data_files` and `package_data` in setup.py
- Bdist, wheel, generated source tarball use different set of these fields
and put the data files in different locations
- Rather just code the default config into a constant. Avoid
pointless file reads as well this way
- Assume path is absolute in yaml util module while saving, loading file
- This follows same convention as jsonl. Which just operates on
passed file path, assuming it is of appropriate form.
Responsibility to put it in appropriate form is on the caller, for now
- Include khoj_sample.yml in pip package to load default config from
- Create khoj config directory if it doesn't exist
- Load config from khoj_sample.yml if khoj.yml config doesn't exist
Conflicts:
- src/main.py
- router functions have moved to router
- move logic to handle null query perf timer variables into
router.py
- set main.py to current branch, not master
- If a user manually edits the input file lines, clicking start should
use that. Currently it just looks at the files selected last via file
browser
- We want to allow users to manually enter file paths in field. Which
is why the field hasn't been set to read-only
- Track current (saved/loaded) config separate from the new config (to
be written) when user clicks Start
- Fallback to using default config when no config for the specific
content type or processor is specified in khoj.yml
- Earlier were only loading default config on first run, not after
- Create Child CheckBox, LineEdit classes for Processor Widgets
- Create ProcessorType, similar to SearchType
- Track ProcessorType the widgets are associated with
- Simplify update, save, load of config based on type
- Update configuration to use by the backend, while app is running
- Trigger after user hits start button with their config.
The config gets written to khoj.yml file first, then the updated
config is loaded onto memory
- Decouple configuring backend from starting server.
Backend search and processors can be configured after the backend
server has started
- Set global state in main instead of in configure_server method.
This allows the app to start even if configure_server exits early in
the first run scenario, where no config available to configure server
- Now start server, even if no config, before GUI started in main
- This refactor of app startup flow will allow users to configure
backend using the configure screen after server start
- Only pass processor config arg required by configure_processor. Not
the unused full config object
- Type arguments passed to methods configure processors
- Import json for use by conversation processor to load logs
- Results get priority screen real estate
- Allows quick speed key based traversal of results as cursor
on switching to buffer is at top level heading
- E.g C-x o n n o 2 jumps to entry in actual file of second result
- Unlike before when it is at the #+STARTUP org buffer customization
settings
- Only allow adding files with appropriate file extension for each search type
- e.g .org for org-mode search, directory for image search
- Extract file paths added to config and enablement state of each search type
- This extracted state will be used to populate the khoj.yml config file
- Simplifies the configure screen layout and allows it to be of constant width
- It was buggy, the configure screen would dynamically expand but not
restore back to original size on disabling search type after enable
- Follow convention, two hyphens indicate variable private to library
- Defcustom are user configurable variables. So they should have single -
- Use khoj-results-count variable directly in code
- Make config_file an optional arg. It defaults to default khoj config dir
- Return args.config as None if no config_file explicitly passed by user
- Parent can use args.config = None as signal to trigger first run experience
- Fix regression since moving to use `which-key-show-full-keymap~
- The above function reads user keypress, so eats up 1 keypress
before starting to enter query
- No way to pass no-paging config via the external function to the
internally used which-key--show-keymap function that does allow
setting no-paging to not read user keypress
- So use the internal function instead and set no-paging arg to t
- The keybindings to select search types was previously confusing as
it only highlighted the final symbol to press (the C-x was shown but
it wasn't made apparent that it had to be pressed before)
- Previously some keybindings unrelated to khoj were also being shown
in the which-key popup. Now only the khoj keybindings are visible
- Search is being reconfigured multiple times in /regenerate and
n/reload. More appropriate name is configure_ rather than initialize_
for it
- Standardize name of methods under configure.py
- Main.py was becoming too big to manage. It had both
controllers/routers and component configurations (search, processors)
in it
- Now that the native app GUI code is also getting added to the main
path, good time to split/modularize/clean main.py
- Put global state into a separate file to share across modules
- Run FastAPI server in a separate thread.
- This allows starting both the server and gui in parallel
- Create System Tray for Khoj
- Contains menu items that open search or config pages in browser
- Rearrange code to have only the code required to start Backend and
GUI in the run() method
- Move the backend setup code into a separate method
- More generally, this allows configuring the khoj search anytime
while in khoj minibuffer window
- Earlier could only configure search type at the start of the search
- What
- Default to last used search type, when no search type specified
- Allow user to change search type before they enter query (and
after they've called khoj), if they want
- Why
- Reduce time from intent to results by using reasonable defaults
- Make interactions smoother, more intuitive
- Test invalid config file path throws. Remove redundant cli test
- Simplify cli parser code
- Do not need to explicitly check if args.config_file set.
argparser checks for positional arguments automatically
- Use standard semantics for cli args
- All positional args are required. Non positional args are optional
- Improve command line --help description
- Add custom validator to throw if neither input_filter or
input_<files|directories> are specified
- Set field expecting paths to type Path
- Now that default_config isn't used in code. We can update
fields in rawconfig to specify whether they're required or not.
This lets pydantic validate config file and throw appropriate error
- Reason
- Simplifies code. No merge_dict required
- 1 place for user to see all configurables, defaults and required values
- Details
- Remove default_config from code. Set defaults in khoj_sample.yml itself
- Keep fields required to be set by user as empty in khoj_sample to YAML
- Set defaults for fields not requiring configuration by user
- Do not want browsers to use the small, grainy favicons
- Firefox for Android does use the bigger icon, when it's the only one available
- Update svg to match the 144x144 ratio just for consistency
Currently only get into this state when debug breakpoints on backend
are keeping the connection open and user exits khoj search from Emacs
Results in a number of open connections that slow khoj down.
- Most concretely right now,
it eliminates the re-rank latency hit
on re-rank triggered on user hitting enter
after re-rank is already done on user idle
in the emacs interface
- Improves search latency of (incremental) search
- Makes it easier to fold/unfold, traverse and read results
- This 2 level nesting is already being used on the web interface
- Previously we were using the original nesting depth of the entry.
This was aimed at providing more of the orginal context of the
results. But currently this additional information does not provide
as much, for the decreased legibility of the results
- Improve code layout by ensuring all web interface specific code
under the src/interface/web directory
- Rename config API to more specifi /config instead of /ui
- Rename config data GET, POST api to /config/data instead of /config
- Previously we were statically populating types dropdown field in the web interface with all available search types
- This change populates the type dropdown field with only search types that are enabled/configured
- It queries the `/config` backend API to see which of the available search types are configured
- Populate via `.then` after enabled search types in dropdown are
populated
- Call to `/config` API is async and will usually complete after the value of type field is set from url
- So value of type field would earlier be overridden when search types
dropdown is populated after the call to `/config` API completes
- Get /config API and check config for which available search types is
populated. This gives us the list of enabled search types
- Dynamically populate search type field with enabled search types only
- Setting query value to default option when query param wasn't
passed via URL was overriding placeholder text in query field
- We wanted placeholder text in field, not the query field to actually
be populated by placeholder text
- This clears field when user starts typing query into the query field,
instead of them having to manually delete the default text populated
- Setting up default compressed-jsonl, embeddings-file was only required
for org search_type, while org-files and org-filter were allowed to be
passed as command line argument
- This avoided having to set compressed-jsonl and embeddings-file via
command line argument as well for org search type
- Now that all search types are only configurable via config file, We
can default all search types to None. The default config for the
rest of the search types wasn't being used anyway
- Previously org-files were configurable via cmdline args.
Where as none of the other search types are
- This is an artifact of how the application grew
- It can be removed for better consistency and
equal preference given all search types
Having org-mode result headings change size based on their depth in
the source document makes is a confusing UI experience.
Improve font-size, line-spacing and margins of results to make
delineation between entries, and differntiating between entry heading
and it's body easier to visually infer.
Do not white-space: pre-line. Improves rendering of Markdown results
## Support Incremental Search on Khoj Web Interface
- Use default, fast path to query /search API while user is typing
- Upgrade to cross-encoder re-ranked results once user hits enter on search box
## Improve Render of Org Results on Web Interface
- We were previously just wrapping results from /search API into a pre formatted div field. This was not easy to read
- Use [org.js](https://mooz.github.io/org-js/) to render results from Khoj `/search` API as proper HTML
- Improve org.js to render all task states, stylize task tags and make org-mode results look more like original content
Closes#42#41
In current state:
- Rerank results:
- If user idles while entering query OR
- exits normally
- Do not rerank results:
- If user exits abnormally, e.g via C-g from query
- Rename functions to more standard, descriptive names
- Keep known, required code for incremental search
- E.g Do not set buffer local flag in hooks on minibuffer setup
- Only query when user in khoj minibuffer
- Use active-minibuffer-window and track khoj minibuffer
- (minibuffer-prompt) is not useful for our use-case here
- (For now) Run re-rank only if user idle while querying
- Do not run rerank on teardown/completion
- The reranking lag (~2s) is annoying; hit enter,
wait to see results
- Also triggered when user exits abnormally,
so C-g also results in rerank which is even more annoying
- Emacs will still hang if re-ranking gets triggered on idle but
that's better than always getting triggered. And better than not
having mechanism to get results re-ranked via cross-encoder at all
- Update khoj-simple to work cross-encoder re-ranked results like before
- Increment major version as incremental search considered a breaking
change and a major update to search capability
- Improve search speed by ~10x
Tested on corpus of 125K lines, 12.5K entries
- Allow cross-encoder to re-rank results by settings &?r=true when querying /search API
- It's an optional param that default to False
- Earlier all results were re-ranked by cross-encoder
- Making this configurable allows for much faster results, if desired
but for lower accuracy
- Formalize filters into class with can_filter() and filter() methods
- Use can_filter() method to decide whether to apply filter and
create deep copies of entries and embeddings for it
- Improve search speed for queries with no filters
as deep copying entries, embeddings takes the most time
after cross-encodes scoring when calling the /search API
Earlier we would create deep copies of entries, embeddings
even if the query did not contain any filter keywords
- Reason:
Allow natural search on markdown based notes, documentation,
websites etc
- Details:
- Create markdown processor to extract Markdown entries (identified by
Heading) into standard jsonl format required by text_search
- Update API, Configs to support interfacing with new markdown type
- Update Emacs, Web clients to support interfacing with new markdown
type via API
- Update Readme to mentiond markdown is also supported
Closes#35
- The code for both the text search types were mostly the same
It was earlier done this way for expedience while experimenting
- The minor differences were reconciled and merged into a single
text_search type
- This simplifies the app and making it easier to process other
text types
Now that the logic to compile entries is in the processor layer, the
extract_entries method is standard across (text) search_types
Extract the load_jsonl method as a utility helper method.
Use it in (a)symmetric search types
- The logic for compiling a beancount entry (for later encoding) now
completely resides in the org-to-jsonl processor layer
- This allows symmetric search to be generic and not be aware of
beancount specific properties that were extracted by the
beancount-to-jsonl processor layer
- Now symmetric search just expects the jsonl to (at least) have the
'compiled' and 'raw' keys for each entry. What original text the
entry was compiled from is irrelevant to it. The original text
could be location, transaction, chat etc, it doesn't have to care
- The logic for compiling an org-mode entry (for later encoding) now
completely resides in the org-to-jsonl processor layer
- This allows asymmetric search to be generic and not be aware of
org-mode specific properties that were extracted by the org-to-jsonl
processor layer
- Now asymmetric search just expects the jsonl to (at least) have the
'compiled' and 'raw' keys for each entry. What original text the
entry was compiled from is irrelevant to it. The original text
could be mail, chat, markdown, org-mode etc, it doesn't have to care
- Pass Scheduled, Closed Dates of Entries to Include in Embeddings
- The (new?) model seems to understand dates. So can give more
relevant entries if date in natural language mentioned in query
- E.g "Went Surfing with Friends" vs "Went Surfing with Friends in 1984"
will give different results, with the second prioritizing entries
mentioning any entries with closed, scheduled dates from 1984
- While it's true those strings are going to be used to generated
embeddings, the more generic term allows them to be used elsewhere as
well
- Their main property is that they are processed, compiled for
usage by semantic search
- Unlike the 'raw' string which contains the external representation
of the data, as is
- Had already made some progress on this earlier by updating the image
search responses. But needed to update the text search responses to
use lowercase entry and score
- Update khoj.el to consume the updated json response keys for text
search
- Image order returned by glob is OS dependent
- This prevented sharing image embeddings across machines running different OS
- A stable sort order for processed images allows sharing embeddings
across machines.
- Use case:
A more powerful, always on machine actually computes the image embeddings regularly
The client machine just load these periodically to provide semantic search functionality
- Handle case where current image batch smaller than batch_size
- Handle case where no XMP metadata for current image
- return empty strings in such a scenario instead of ". "
Issue:
- Had different schema of extracted entries for symmetric_ledger vs asymmetric
- Entry extraction for asymmetric was dirty, relying on cryptic
indices to store raw entry vs cleaned entry meant to be passed to embeddings
- This was pushing the load of figuring out what property to extract
from each entry to downstream processes like the filters
- This limited the filters to only work for asymmetric search, not for
symmetric_ledger
- Fix
- Use consistent format for extracted entries
{
'embed': entry_string_meant_to_be_passed_to_model_and_get_embeddings,
'raw' : raw_entry_string_meant_to_be_passed_to_use
}
- Result
- Now filters can be applied across search types, and the specific
field they should be applied on can be configured by each search
type
- The all-MiniLM-L6-v2 is more accurate
- The exact previous model isn't benchmarked but based on the
performance of the closest model to it. Seems like the new model
maybe similar in speed and size
- On very preliminary evaluation of the model, the new model seems
faster, with pretty decent results
- 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
- Avoids having to click the query input box
- Just open page, type whatever and hit enter to do image search
- For other search types select appropriate type from dropdown
- Use shr to render image response from html in result buffer
Earlier was using org-mode. But rendering HTML with shr seems cleaner
- Use Headings to Add highlights
- Use Random to Force fetch of Image. Similar to what was done for Web interface
- Remove trailing elisp brackets from response
- Show query match scores by image model for each image in results
- Metadata match score were consistently giving higher scores by a
factor of ~3x wrt to image match score. This was resulting in all
results being from the metadata match with query and none from the
image match with query.
- Scaling the metadata match scores down by scaling factor seems to
give more consistently give a blend of results from both image and
metadata matches
Adding a random, unused url param at the end of the img.src string
fixes the issue. As the browser thinks it's a new image and doesn't
use the image data that's already cached because of which it wasn't
even making the fetch call for the image
- Allow viewing image results returned by Semantic Search.
Until now there wasn't any interface within the app to view image
search results. For text results, we at least had the emacs interface
- This should help with debugging issues with image search too
For text the Swagger interface was good enough
- Copy images to accessible directory
- Return URL paths to them to ease access
- This is to be used in the web interface to render image results
directly in browser
- Return image, metadata scores for each image in response as well
This should help get a better sense of image scores along both
XMP metadata and whole image axis
- With \t Last Word in Headings was suffixed by \t and so couldn't be
filtered by
- User interacts with raw entries, so run explicit filters on raw entry
- For semantic search using the filtered entry is cleaner, still
- Fix date_filter date_in_entry within query range check
- Extracted_date_range is in [included_date, excluded_date) format
- But check was checking for date_in_entry <= excluded_date
- Fixed it to do date_in_entry < excluded_date
- Fix removal of date filter from query
- Add tests for date_filter
- Default to looking at dates from past, as most notes are from past
- Look for dates in future for cases where it's obvious query is for
dates in the future but dateparser's parse doesn't parse it at all.
E.g parse('5 months from now') returns nothing
- Setting PREFER_DATES_FROM_FUTURE in this case and passing just
parse('5 months') to dateparser.parse works as expected
Details
--
- The filters to apply are configured for each type in the search controller
- Muliple filters can be applied on the query, entries etc before search
- The asymmetric query method now just applies the passed filters to the
query, entries and embeddings before semantic search is performed
Reason
--
This abstraction will simplify adding other pre-search filters. E.g datetime filter
Details
--
- Move explicit_filters function into separate module under search_filter
- Update signature of explicit filter to take and return query, entries, embeddings
- Use this explicit_filter func from search_filters module in query
Reason
--
Abstraction will simplify adding other pre-search filters. E.g datetime filter
- Issue
- Explicit filtering was earlier being done after search by bi-encoder
but before re-ranking by cross-encoder
- This was limiting the quality of results being returned. As the
bi-encoder returned results which were going to be excluded. So the
burden of improving those limited results post filtering was on the
cross-encoder by re-ranking the remaining results based on query
- Fix
- Given the embeddings corresponding to an entry are at the same index
in their respective lists. We can run the filter for blocked,
required words before the search by the bi-encoder model. And limit
entries, embeddings being considered for the current query
- Result
- Semantic search by the bi-encoder gets to return most relevant
results for the query, knowing that the results aren't going to be
filtered out after. So the cross-encoder shoulders less of the
burden of improving results
- Corollary
- This pre-filtering technique allows us to apply other explicit
filters on entries relevant for the current query
- E.g limit search for entries within date/time specified in query
- Use local variable to pass device to asymmetric.setup method via /reload, /regenerate API
- Set default argument to torch.device('cpu') instead of 'cpu' to be more formal
- The reload API adds the ability to separate out the loading of
embeddings from file without having to restart app or (re-)generate embeddings
- Before this the only way to load model from file was by restarting app
- The other way to reload the model embeddings by regenerating them
was to expensive for larger datasets
- This unlocks at least 1 use-case, where
- we regenerate model via an app instance running on a separate server and
- just reload the generated embeddings on the client device
- This allows us to offload the expensive embedding generation
compute to a background server while letting
- This avoids having to (re-)restart application on client device or
be forced to generate embeddings on the client device itself
- But it requires the model relevant files to be synced to the client device
This can be done with any file syncing application like Syncthing
- We can then call /regenerate on server and /reload client on a
regular schedule to keep our data up to date on semantic search
- This is still clunky but it should be commitable
- General enough that it'll work even when a users notes are not in the home directory
- While solving for the special case where:
- Notes are being processed on a different machine and used on a different machine
- But the notes directory is in the same location relative to home on both the machines
- Use Set for Tags instead of dictionary with empty keys
- No Need to store First Tag separately
- Remove properties methods associated with storing first tag separately
- Simplify extraction of tags string in org_to_jsonl
- Split notes_string creation into multiple f-string in separate line
for code readability
- Now that excluding the times line from the raw body of node,
show it in repr so user can see it for reference
- But the model doesn't need to see it for it's embeddings to be
confused by
- Add links to property drawer
- This ensures results returned by semantic search contain these links
- This allows the user to jump to entry within original file for context
- The ID, file+heading based links are more robust to find relevant
entry in original file than the line no based link,
as edits being done by user to original files between embedding regenerations
Sentence Transformer MSMarco Model isn't date aware
So no use of adding scheduled, deadline dates to model embeddings for consideration
This reverts commit a2a08d1354.
- Introduce prompt for GPT to automatically extract user's search intent
- Expose new search api endpoint to use that to set SearchType being
passed to search API
- Currently meant as an experimental API to gauge usefulness,
extendability. Evaluating for phone or voice use-case
To prompt improve readability:
- Remove newline escape sequence and use actual newline directly
- This avoids one long line of text as prompt and
- Remove escaping of double quotes
- Add search query to top of buffer as Beancount comment
- Remove trailing ) from response
- Separate entries by empty line
- Load beancount-mode in semantic search on ledger buffer
- Fix loading entries from jsonl in extract_entries method
- Only extract Title from jsonl of each entry
This is the only thing written to the jsonl for symmetric ledger
- This fixes the trailing escape seq in loaded entries
- Remove the need for semantic-search.el response reader to do pointless complicated cleanup
- Make symmetric_ledger:extract_entries use beancount_to_jsonl:load_jsonl
Both methods were doing similar work
- Make load_jsonl handle loading entries from both gzip and uncompressed jsonl
Conversation logs structure now has session info too instead of just chat info
Session info will allow loading past conversation summaries as context for AI in new conversations
{
"session": [
{
"summary": <chat_session_summary>,
"session-start": <session_start_index_in_chat_log>,
"session-end": <session_end_index_in_chat_log>
}],
"chat": [
{
"intent": <intent-object>
"trigger-emotion": <emotion-triggered-by-message>
"by": <AI|Human>
"message": <chat_message>
"created": <message_created_date>
}]
}
- Allow conversing with user using GPT's contextually aware, generative capability
- Extract metadata, user intent from user's messages using GPT's general understanding
Details
- Rename method query_* to query in search_types for standardization
- Wrapping Config code in classes simplified mocking test config
- Reduce args beings passed to a function by passing it as single
argument wrapped in a class
- Minimize setup in main.py:__main__. Put most of it into functions
These functions can be mocked if required in tests later too
Setup Flow:
CLI_Args|Config_YAML -> (Text|Image)SearchConfig -> (Text|Image)SearchModel
- Wrap Image, Music, Ledger search into the type of SearchModel they use
Similar to what was done for notes model by wrapping it's config
into an AsymmetricSearchModel.
- Use the uber wrapper class to expose all type specific search models
- Wrap asymmetric search model parameters into AsymmetricSearchModel class
- Create wrapper for all search type models. Put notes search model into it
- Test notes search end-to-end from client API layer to results.
Use model build on test data
- Cleaner, more idiomatic usage of a global variable
- Simplifies mocking when testing client in pytest as setting wrapped
in object rather than a simple type. So passed around by reference
- Use a SearchType to limit types that can be passed by user
- FastAPI automatically validates type passed in query param
- Available type options show up in Swagger UI, FastAPI docs
- controller code looks neater instead of doing string comparisons for type
- Test invalid, valid search types via pytest
- Break the compute embeddings method into separate methods:
compute_image_embeddings and compute_metadata_embeddings
- If image_metadata_embeddings isn't defined, do not use it to enhance
search results. Given image_metadata_embeddings wouldn't be defined
if use_xmp_metadata is False, we can avoid unnecessary addition of
args to query method
- Issue:
Process would get killed while encoding images
for consuming too much memory
- Fix:
- Encode images in batches and append to image_embeddings
- No need to use copy or deep_copy anymore with batch processing.
It would earlier throw too many files open error
Other Changes:
- Use tqdm to see progress even when using batch
- See progress bar of encoding independent of verbosity (for now)
- Details
- The CLIP model can represent images, text in the same vector space
- Enhance CLIP's image understanding by augmenting the plain image
with it's text based metadata.
Specifically with any subject, description XMP tags on the image
- Improve results by combining plain image similarity score with
metadata similarity scores for the highest ranked images
- Minor Fixes
- Convert verbose to integer from bool in image_search.
It's already passed as integer from the main program entrypoint
- Process images with ".jpeg" extensions too
- Previously:
The text the model was trained on was being used to
re-create a semblance of the original org-mode entry.
- Now:
- Store raw entry as another key:value in each entry json too
Only return actual raw org entries in results
But create embeddings like before
- Also add link to entry in file:<filename>::<line_number> form
in property drawer of returned results
This can be used to jump to actual entry in it's original file
- YAML Config
- Can specify all params[1] earlier being passed via cmd args in config YAML
- Can now also configure sentence-transformer models to use etc for search
- [1] Config params
- org files
- compressed entries file config path
- embeddings file config path
- Include sample_config.yaml
- Include sample .org file from this repos readmes
- CLI
- Configuration Priority: Config via cmd > Config via YAML > Default Config
- Test CLI, include test config.yml for the tests
- Set default type to None unless set via query param to API
Run notes search if search_enabled, also if type is None (default)
Prepares for running queries on all search types unless type
specified in API query param
- Update Readme
