- Because we're using a FastAPI api framework with a Django ORM, we're running into some interesting conditions around connection pooling and clean-up. We're ending up with a large pile-up of open, stale connections to the DB recurringly when the server has been running for a while. To mitigate this problem, given starlette and django run in different python threads, add a middleware that will go and call the connection clean up method in each of the threads.
* Add support for chatting with Anthropic's suite of models
- Had to use a custom class because there was enough nuance with how the anthropic SDK works that it would be better to simply separate out the logic. The extract questions flow needed modification of the system prompt in order to work as intended with the haiku model
- Render crontime string in natural language in message & settings UI
- Show more fields in tasks web config UI
- Add link to the tasks settings page in task scheduled chat response
- Improve task variables names
Rename executing_query to query_to_run. scheduling_query to
scheduling_request
- Pass timezone string from ipapi to khoj via clients
- Pass this data from web, desktop and obsidian clients to server
- Use user tz to render next run time of scheduled task in user tz
- Detect when user intends to schedule a task, aka reminder
Add new output mode: reminder. Add example of selecting the reminder
output mode
- Extract schedule time (as cron timestring) and inferred query to run
from user message
- Use APScheduler to call chat with inferred query at scheduled time
- Handle reminder scheduling from both websocket and http chat requests
- Support constructing scheduled task using chat history as context
Pass chat history to scheduled query generator for improved context
for scheduled task generation
- Improve extract question prompts to explicitly request JSON list
- Use llama-3 chat format if HF repo_id mentions llama-3. The
llama-cpp-python logic for detecting when to use llama-3 chat format
isn't robust enough currently
Previously you couldn't configure the n_ctx of the loaded offline chat
model. This made it hard to use good offline chat model (which these
days also have larger context) on machines with lower VRAM
### Index more text file types
- Index all text, code files in Github repos. Not just md, org files
- Send more text file types from Desktop app and improve indexing them
- Identify file type by content & allow server to index all text files
### Deprecate Github Indexing Features
- Stop indexing commits, issues and issue comments in a Github repo
- Skip indexing Github repo on hitting Github API rate limit
### Fixes and Improvements
- **Fix indexing files in sub-folders from Desktop app**
- Standardize structure of text to entries to match other entry processors
* Don't trigger any re-indexing on server initailization
* Integrate Resend to send welcome emails when a new user signs up
- Only send if this is the first time they've signed in
- Configure welcome email with basic styling, as more complex designs don't work and style tag did not work
- Use Magika's AI for a tiny, portable and better file type
identification system
- Existing file type identification tools like `file' and `magic'
require system level packages, that may not be installed by default
on all operating systems (e.g `file' command on Windows)
- RapidOCR for indexing image PDFs doesn't currently support python 3.12.
It's an optional dependency anyway, so only install it if python < 3.12
- Run unit tests with python version 3.12 as well
Resolves#522
- Benefits of moving to llama-cpp-python from gpt4all:
- Support for all GGUF format chat models
- Support for AMD, Nvidia, Mac, Vulcan GPU machines (instead of just Vulcan, Mac)
- Supports models with more capabilities like tools, schema
enforcement, speculative ddecoding, image gen etc.
- Upgrade default chat model, prompt size, tokenizer for new supported
chat models
- Load offline chat model when present on disk without requiring internet
- Load model onto GPU if not disabled and device has GPU
- Load model onto CPU if loading model onto GPU fails
- Create helper function to check and load model from disk, when model
glob is present on disk.
`Llama.from_pretrained' needs internet to get repo info from
HuggingFace. This isn't required, if the model is already downloaded
Didn't find any existing HF or llama.cpp method that looked for model
glob on disk without internet
### Major
- Read web pages in parallel to improve chat response time
- Read web pages directly when Olostep proxy not setup
- Include search results & web page content in online context for chat response
### Minor
- Simplify, modularize and add type hints to online search functions
Latest sentence-transformer package uses GPU for cross-encoder. This
makes it fast enough to enable reranking on machines with GPU.
Enabling search reranking by default allows (at least) users with GPUs
to side-step learning the UI affordance to rerank results
(i.e hitting Cmd/Ctrl-Enter or ENTER).
* Upload generated images to s3, if AWS credentials and bucket is available.
- In clients, render the images via the URL if it's returned with a text-to-image2 intent type
* Make the loading screen more intuitve, less jerky and update the programmatic copy button
* Update the loading icon when waiting for a chat response
This will reduce khoj dependencies to install for self-hosting users
- Move auth production dependencies to prod python packages group
- Only enable authentication API router if not in anonymous mode
- Improve error with requirements to enable authentication when not in
anonymous mode
* Fix license in pyproject.toml. Remove unused utils.state import
* Use single debug mode check function. Disable telemetry in debug mode
- Use single logic to check if khoj is running in debug mode.
Previously there were 3 different variants of the check
- Do not log telemetry if KHOJ_DEBUG is set to true. Previously didn't
log telemetry even if KHOJ_DEBUG set to false
* Respect line breaks in user, khoj chat messages to improve formatting
* Disable Whatsapp config section on web client if Twilio not configured
Simplify Whatsapp configuration status checking js by standardizing
external input to lower case
* Disable Phone API when Twilio not setup and rate limit calls to it
- Move phone api to separate router and only enable it if Twilio enabled
- Add rate-limiting to OTP and verification calls
* Add slugs for phone rate limiting
---------
Co-authored-by: sabaimran <narmiabas@gmail.com>
* Initailize changes to incporate web scraping logic after getting SERP results
- Do some minor refactors to pass a symptom prompt to the openai model when making a query
- integrate Olostep in order to perform the webscraping
* Fix truncation error with new line, fix typing in olostep code
* Use the authorization header for the token
* Add a small hint/indicator for how to use Khojs other modalities in the welcome prompt
* Add more detailed error message if Olostep query fails
* Add unit tests which invoke Olostep in chat director
* Add test for olostep tool
* Allow users to configure phone numbers with the Khoj server
* Integration of API endpoint for updating phone number
* Add phone number association and OTP via Twilio for users connecting to WhatsApp
- When verified, store the result as such in the KhojUser object
* Add a Whatsapp.svg for configuring phone number
* Change setup hint depending on whether the user has a number already connected or not
* Add an integrity check for the intl tel js dependency
* Customize the UI based on whether the user has verified their phone number
- Update API routes to make nomenclature for phone addition and verification more straightforward (just /config/phone, etc).
- If user has not verified, prompt them for another verification code (if verification is enabled) in the configuration page
* Use the verified filter only if the user is linked to an account with an email
* Add some basic documentation for using the WhatsApp client with Khoj
* Point help text to the docs, rather than landing page info
* Update messages on various callbacks and add link to docs page to learn more about the integration
* Add support for a first party client app
- Based on a client id and client secret, allow a first party app to call into the Khoj backend with a phone number identifier
- Add migration to add phone numbers to the KhojUser object
* Add plus in front of country code when registering a phone number.
- Decrease free tier limit to 5 (from 10)
- Return a response object when handling stripe webhooks
* Fix telemetry method which references authenticated user's client app
* Add better error handling for null phone numbers, simplify logic of authenticating user
* Pull the client_secret in the API call from the authorization header
* Add a migration merge to resolve phone number and other changes
### Major
- Short-circuit API rate limiter for unauthenticated user
Calls by unauthenticated users were failing at API rate limiter as it
failed to access user info object. This is a bug.
API rate limiter should short-circuit for unauthenicated users so a
proper Forbidden response can be returned by API
Add regression test to verify that unauthenticated users get 403
response when calling the /chat API endpoint
### Minor
- Remove trailing slash to normalize khoj url in obsidian plugin settings
- Move used /api/config API controllers into separate module
- Delete unused /api/beta API endpoint
- Fix error message rendering in khoj.el, khoj obsidian chat
- Handle deprecation warnings for subscribe renew date, langchain, pydantic & logger.warn
