### 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
- Show internet search, webpage read, image query, image generation steps
- Standardize, improve rendering of the intermediate steps on the web app
Benefits:
1. Improved transparency, allow users to see what Khoj is doing behind
the scenes and modify their query patterns to improve response quality
2. Reduced websocket connection keep alive timeouts for long running steps
- `file-type' doesn't handle mis-labelled files or files without
extensions well
- Only show supported file types in file selector dialog on Desktop app
Use Magika to get list of text file extensions. Combine with other
supported extensions to get complete list of supported file extensions.
Use it to limit selectable files in the File Open dialog.
Note: Folder selector will index text files with no extensions as well
* 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
### Enable copying chat messages. Improve copy button behavior and styling
- Add button to copy chat messages on Desktop, Web apps
- Improve copy button's icon, hover color & click animation in Desktop, Web apps
### Improve Navigation, Chat Session Panes on Desktop, Web apps
- Dynamically generate navigation menu based on user info from server
- Create API endpoint to get authenticated user information
- Collapse navigation tabs into icons on mobile. Add spacing to them
- Add Chat navigation tab back to top pane on Web app
- Use proper icons for Search, Chat and Agents tab on navigation pane
### Miscellaneous Improvements
- Make current chat expand to full width when session panel collapsed on Desktop App
- Add chat session loading spinner to Desktop App (same as Web app)
### Fixes
- Show title bar in Khoj desktop app on Windows to simplify close, minimize etc.
- Only render first run setup message once if error or server not running
- Fix showing Search navigation tab from Agent pages on web client
The username and location in system prompt should disambiguate user
context from user's actual message for the chat model.
It doesn't need to be told to not mention the context or acknowledge
the context instructions in it's response, as it understands that this
information is just context and not part of the user's actual message.
- Move new conversation button to right of "Conversation" title
- Reduce size of chat message loading ellipsis animation
- Add loading animation for chat session
The `has_documents' flag wasn't being passed. So the search tab
always showing up as empty instead of being dynamically enabled if
documents had been indexed.
- `fs.readdir' func in node version 18.18.2 has buggy `recursive' option
See nodejs/node#48640, effect-ts/effect#1801 for details
- We were recursing down a folder in two ways on the Desktop app.
Remove `recursive: True' option to the `fs.readdirSync' method call
to recurse down via app code only
Add process_single_plaintext_file func etc with similar signatures as
org_to_entries and markdown_to_entries processors
The standardization makes modifications, abstractions easier to create
Sleep until rate limit passed is too expensive, as it keeps a
app worker occupied.
Ideally we should schedule job to contine after rate limit wait time
has passed. But this can only be added once we support jobs scheduling.
Normal indexing quickly Github hits rate limits. Purpose of exposing
Github indexer is for indexing content like notes, code and other
knowledge base in a repo.
The current indexer doesn't scale to index metadata given Github's
rate limits, so remove it instead of giving a degraded experience of
partially indexed repos
- Allow syncing more file types from desktop app to index on server
- Use `file-type' package to identify valid text file types on Desktop app
- Split plaintext entries into smaller logical units than a whole file
Since the text splitting upgrades in #645, compiled chunks have more
logical splits like paragraph, sentence.
Show those (potentially) smaller snippets to the user as references
- Tangential Fix:
Initialize unbound currentTime variable for error log timestamp
- 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)
## Major
- Parse markdown, org parent entries as single entry if fit within max tokens
- Parse a file as single entry if it fits with max token limits
- Add parent heading ancestry to extracted markdown entries for context
- Chunk text in preference order of para, sentence, word, character
## Minor
- Create wrapper function to get entries from org, md, pdf & text files
- Remove unused Entry to Jsonl converter from text to entry class, tests
- Dedupe code by using single func to process an org file into entries
Resolves#620
### Why
- Python 3.12 is the default Python on Ubuntu 24.04 LTS, Windows and Mac via Homebrew
- Python 3.12 has a bunch of improvements that can be explored with Khoj (e.g per core GIL for performance)
## Changes
- The latest PyTorch now supports Python 3.12
- RapidOCR for indexing image PDFs doesn't currently support python 3.12.
But it's an optional dependency, so only install it if python < 3.12
### Testing
- Verified Khoj installs fine on Windows and Mac with Python 3.12
- Verified Khoj chat works fine on Mac, Windows with Python 3.12
Resolves#522
- 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
* Add support for using OAuth2.0 in the Notion integration
* Add notion to the admin page
* Remove unnecessary content_index and image search/setup references
* Trigger background job to start indexing Notion after user configures it
* Add a log line when a new Notion integration is setup
* Fix references to the configure_content methods
`re.MULTILINE' should be passed to the `flags' argument, not the
`max_splits' argument of the `re.split' func
This was messing up the indexing by only allowing a maximum of
re.MULTILINE splits. Fixing this improves the search quality to
previous state
More content indexed per entry would result in an overall scores
lowering effect. Increase default search distance threshold to counter that
- Details
- Fix expected results post indexing updates
- Fix search with max distance post indexing updates
- Minor
- Remove openai chat actor test for after: operator as it's not expected anymore
- Major
- Do not split org file, entry if it fits within the max token limits
- Recurse down org file entries, one heading level at a time until
reach leaf node or the current parent tree fits context window
- Update `process_single_org_file' func logic to do this recursion
- Convert extracted org nodes with children into entries
- Previously org node to entry code just had to handle leaf entries
- Now it recieve list of org node trees
- Only add ancestor path to root org-node of each tree
- Indent each entry trees headings by +1 level from base level (=2)
- Minor
- Stop timing org-node parsing vs org-node to entry conversion
Just time the wrapping function for org-mode entry extraction
This standardizes what is being timed across at md, org etc.
- Move try/catch to `extract_org_nodes' from `parse_single_org_file'
func to standardize this also across md, org
These changes improve context available to the search model.
Specifically this should improve entry context from short knowledge trees,
that is knowledge bases with sparse, short heading/entry trees
Previously we'd always split markdown files by headings, even if a
parent entry was small enough to fit entirely within the max token
limits of the search model. This used to reduce the context available
to the search model to select appropriate entries for a query,
especially from short entry trees
Revert back to using regex to parse through markdown file instead of
using MarkdownHeaderTextSplitter. It was easier to implement the
logical split using regexes rather than bend MarkdowHeaderTextSplitter
to implement it.
- DFS traverse the markdown knowledge tree, prefix ancestry to each entry
These changes improve entry context available to the search model
Specifically this should improve entry context from short knowledge trees,
that is knowledge bases with small files
Previously we split all markdown files by their headings,
even if the file was small enough to fit entirely within the max token
limits of the search model. This used to reduce the context available
to select the appropriate entries for a given query for the search model,
especially from short knowledge trees
- Previous simplistic chunking strategy of splitting text by space
didn't capture notes with newlines, no spaces. For e.g in #620
- New strategy will try chunk the text at more natural points like
paragraph, sentence, word first. If none of those work it'll split
at character to fit within max token limit
- Drop long words while preserving original delimiters
Resolves#620
This was earlier used when the index was plaintext jsonl file. Now
that documents are indexed in a DB this func is not required.
Simplify org,md,pdf,plaintext to entries tests by removing the entry
to jsonl conversion step
- Convert extract_org_entries function to actually extract org entries
Previously it was extracting intermediary org-node objects instead
Now it extracts the org-node objects from files and converts them
into entries
- Create separate, new function to extract_org_nodes from files
- Similarly create wrapper funcs for md, pdf, plaintext to entries
- Update org, md, pdf, plaintext to entries tests to use the new
simplified wrapper function to extract org entries
- Move green server connected dot to the bottom. Show status when
disconnected from server
- Move "New conversation" button to right of the "Conversation" title
- Center alignment of the new conversation and connection status buttons
- Overview
- Extract more structured date variants (e.g with dot(.) & slash(/) separators, 2-digit year)
- Extract some natural, partial dates as well from entries
- Capability
Add ability to extract the following additional date forms:
- Natural Dates: 21st April 2000, February 29 2024
- Partial Natural Dates: March 24, Mar 2024
- Structured Dates: 20/12/24, 20.12.2024, 2024/12/20
Note: Previously only YYYY-MM-DD ISO-8601 structured date form was extracted for date filters
- Performance
Using regexes is MUCH faster than using the `dateparser' python library
It's a little crude but gives acceptable performance for large datasets
## Benefits
- Support all GGUF format chat models
- Support more GPUs like AMD, Nvidia, Mac, Vulcan (previously just Vulcan, Mac)
- Support more capabilities like larger context window, schema enforcement, speculative decoding etc.
## Changes
### Major
- Use llama.cpp for offline chat models
- Support larger context window
- Automatically apply appropriate chat template. So offline chat models not using llama2 format are now supported
- Use better default offline chat model, NousResearch/Hermes-2-Pro-Mistral-7B
- Enable extract queries actor to improve notes search with offline chat
- Update documentation to use llama.cpp for offline chat in Khoj
### Minor
- Migrate to use NouseResearch's Hermes-2-Pro 7B as default offline chat model in khoj.yml
- Rename GPT4AllChatProcessor to OfflineChatProcessor Config, Model
- Only add location to image prompt generator when location known
- Much faster than using dateparser
- It took 2x-4x for improved regex to extracts 1-15% more dates
- Whereas It took 33x to 100x for dateparser to extract 65% - 400% more dates
- Improve date extractor tests to test deduping dates, natural,
structured date extraction from content
- Extract some natural, partial dates and more structured dates
Using regex is much faster than using dateparser. It's a little
crude but should pay off in performance.
Supports dates of form:
- (Day-of-Month) Month|AbbreviatedMonth Year|2DigitYear
- Month|AbbreviatedMonth (Day-of-Month) Year|2DigitYear
Previously we just extracted dates in YYYY-MM-DD format from content
for date filterings during search.
Use dateparser to extract dates across locales and natural language
This should improve notes returned as context when chat searches
knowledge base with date filters
Fallback to regex for date parsing from content if dateparser fails
- Limit natural date extractor capabilities to improve performance
- Assume language is english
Language detection otherwise takes a REALLY long time
- Do not extract unix timestamps, timezone
- This isn't required, as just using date and approximating dates as UTC
- When setting up the default agent, configure every conversation that doesn't have an agent to use the Khoj agent
- Fix reverse migration for the locale removal migration
Previously we were skipping the extract questions step for offline
chat as default offline chat model wasn't good enough to output proper
json given the time it took to extract questions.
The new default offline chat models gives json much more regularly and
with date filters, so the extract questions step becomes useful given
the impact on latency
- How to pip install khoj to run offline chat on GPU
After migration to llama-cpp-python more GPU types are supported but
require build step so mention how
- New default offline chat model
- Where to get supported chat models from on HuggingFace
- 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
* Initial pass at backend changes to support agents
- Add a db model for Agents, attaching them to conversations
- When an agent is added to a conversation, override the system prompt to tweak the instructions
- Agents can be configured with prompt modification, model specification, a profile picture, and other things
- Admin-configured models will not be editable by individual users
- Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications
* Customize default behaviors for conversations without agents or with default agents
* Add a new web client route for viewing all agents
* Use agent_id for getting correct agent
* Add web UI views for agents
- Add a page to view all agents
- Add slugs to manage agents
- Add a view to view single agent
- Display active agent when in chat window
- Fix post-login redirect issue
* Fix agent view
* Spruce up the 404 page and improve the overall layout for agents pages
* Create chat actor for directly reading webpages based on user message
- Add prompt for the read webpages chat actor to extract, infer
webpage links
- Make chat actor infer or extract webpage to read directly from user
message
- Rename previous read_webpage function to more narrow
read_webpage_at_url function
* Rename agents_page -> agent_page
* Fix unit test for adding the filename to the compiled markdown entry
* Fix layout of agent, agents pages
* Merge migrations
* Let the name, slug of the default agent be Khoj, khoj
* Fix chat-related unit tests
* Add webpage chat command for read web pages requested by user
Update auto chat command inference prompt to show example of when to
use webpage chat command (i.e when url is directly provided in link)
* Support webpage command in chat API
- Fallback to use webpage when SERPER not setup and online command was
attempted
- Do not stop responding if can't retrieve online results. Try to
respond without the online context
* Test select webpage as data source and extract web urls chat actors
* Tweak prompts to extract information from webpages, online results
- Show more of the truncated messages for debugging context
- Update Khoj personality prompt to encourage it to remember it's capabilities
* Rename extract_content online results field to webpages
* Parallelize simple webpage read and extractor
Similar to what is being done with search_online with olostep
* Pass multiple webpages with their urls in online results context
Previously even if MAX_WEBPAGES_TO_READ was > 1, only 1 extracted
content would ever be passed.
URL of the extracted webpage content wasn't passed to clients in
online results context. This limited them from being rendered
* Render webpage read in chat response references on Web, Desktop apps
* Time chat actor responses & chat api request start for perf analysis
* Increase the keep alive timeout in the main application for testing
* Do not pipe access/error logs to separate files. Flow to stdout/stderr
* [Temp] Reduce to 1 gunicorn worker
* Change prod docker image to use jammy, rather than nvidia base image
* Use Khoj icon when Khoj web is installed on iOS as a PWA
* Make slug required for agents
* Simplify calling logic and prevent agent access for unauthenticated users
* Standardize to use personality over tuning in agent nomenclature
* Make filtering logic more stringent for accessible agents and remove unused method:
* Format chat message query
---------
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
### Overview
Khoj can now read website directly without needing to go through the search step first
### Details
- Parallelize simple webpage read and extractor
- Rename extract_content online results field to web pages
- Tweak prompts to extract information from webpages, online results
- Test select webpage as data source and extract web urls chat actors
- Render webpage read in chat response references on Web, Desktop apps
- Pass multiple webpages with their urls in online results context
- Support webpage command in chat API
- Add webpage chat command for read web pages requested by user
- Create chat actor for directly reading webpages based on user message
Previously even if MAX_WEBPAGES_TO_READ was > 1, only 1 extracted
content would ever be passed.
URL of the extracted webpage content wasn't passed to clients in
online results context. This limited them from being rendered
- Fallback to use webpage when SERPER not setup and online command was
attempted
- Do not stop responding if can't retrieve online results. Try to
respond without the online context
* Initial pass at backend changes to support agents
- Add a db model for Agents, attaching them to conversations
- When an agent is added to a conversation, override the system prompt to tweak the instructions
- Agents can be configured with prompt modification, model specification, a profile picture, and other things
- Admin-configured models will not be editable by individual users
- Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications
* Customize default behaviors for conversations without agents or with default agents
* Use agent_id for getting correct agent
* Merge migrations
* Simplify some variable definitions, add additional security checks for agents
* Rename agent.tuning -> agent.personality
- Use the conversation id of the retrieved conversation rather than the
potentially unset conversation id passed via API
- await creating new chat when no chat id provided and no existing
conversations exist
- Move some common methods into separate functions to make the UI components more efficient
- The normal HTTP-based chat connection will still work and serves as a fallback if the websocket is unavailable
- Convert to a model of calling the search API directly with a function call (rather than using the API method)
- Gracefully handle websocket connection disconnects
- Ensure that the rest of the response is still saved, as it is currently, if the user disconects from the client
- Setup unchangeable context at the beginning of the session when the connection is established (like location, username, etc)
The recently added after: operator to online search actor was too
restrictive, gave worse results than when just use natural language
dates in search query
### Improve
- Improve delete, rename chat session UX in Desktop, Web app
- Get conversation by title when requested via chat API
### Fix
- Allow unset locale for Google authenticating user
- Handle truncation when single long non-system chat message
- Fix setting chat session title from Desktop app
- Only create new chat on get if a specific chat id, slug isn't requested
Previously was assuming the system prompt is being always passed as
the first message. So expected there to be at least 2 messages in logs.
This broke chat actors querying with single long non system message.
A more robust way to extract system prompt is via the message role
instead
- Ask for Confirmation before deleting chat session in Desktop, Web app
- Save chat session rename on hitting enter in title edit input box
- No need to flash previous conversation cleared status message
- Move chat session delete button after rename button in Desktop app
- Add prompt for the read webpages chat actor to extract, infer
webpage links
- Make chat actor infer or extract webpage to read directly from user
message
- Rename previous read_webpage function to more narrow
read_webpage_at_url function
### Major
- Enforce json mode response from OpenAI chat actors prev using string lists
- Use `gpt-4-turbo-preview' as default chat model, extract questions actor
- Make Khoj read khoj website to respond with accurate, up-to-date information about itself
- Dedupe query in notes prompt. Improve OAI chat actor, director tests
### Minor
- Test data source, output mode selector, web search query chat actors
- Improve notes search actor to always create a non-empty list of queries
- Construct available data sources, output modes as a bullet list in prompts
- Use consistent agent name across static and dynamic examples in prompts
- Add actor's name to extract questions prompt to improve context for guidance
Previously only the notes references would get rendered post response
streaming when when both online and notes references were used to
respond to the user's message
- Allow passing response format type to OpenAI API via chat actors
- Convert in-context examples to use json objects instead of str lists
- Update actors outputting str list to request output to be json_object
- OpenAI's json mode enforces the model to output valid json object
- Remove stale tests
- Improve tests to pass across gpt-3.5 and gpt-4-turbo
- The haiku creation director was failing because of duplicate query in
instantiated prompt
- Remove the option for Notes search query generation actor to return
no queries. Whether search should be performed is decided before,
this step doesn't need to decide that
- But do not throw warning if the response is a list with no elements
- Add examples where user queries requesting information about Khoj
results in the "online" data source being selected
- Add an example for "general" to select chat command prompt
Previously the examples constructed from chat history used "Khoj" as
the agent's name but all 3 prompts using the func used static examples
with "AI:" as the pertinent agent's name
- Add example to read khoj.dev website for up-to-date info to setup,
use khoj, discover khoj features etc.
- Online search should use site: and after: google search operators
- Show example of adding the after: date filter to google search
- Give local event lookup example using user's current location in
query
- Remove unused select search content type prompt
- Add a page to view all agents
- Add slugs to manage agents
- Add a view to view single agent
- Display active agent when in chat window
- Fix post-login redirect issue
### 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
Previously if a web page was read for a sub-query, only the extracted
web page content was provided as context for the given sub-query. But
the google results themselves have relevant snippets. So include them
- Simplify content arg to `extract_relevant_info' function. Validate,
clean the content arg inside the `extract_relevant_info' function
- Extract `search_with_google' function outside the parent function
- Call the parent function a more appropriate `search_online' instead
of `search_with_google'
- Simplify the `search_with_google' function using list comprehension.
Drop empty search result fields from chat model context for response
to reduce cost and response latency
- No need to show stacktrace when unable to read webpage, basic error
is enough
- Add type hints to online search functions to catch issues with mypy
- Time reading webpage, extract info from webpage steps for perf
analysis
- Deduplicate webpages to read gathered across separate google
searches
- Use aiohttp to make API requests non-blocking, pair with asyncio to
parallelize all the online search webpage read and extract calls
- Add a db model for Agents, attaching them to conversations
- When an agent is added to a conversation, override the system prompt to tweak the instructions
- Agents can be configured with prompt modification, model specification, a profile picture, and other things
- Admin-configured models will not be editable by individual users
- Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications
- Trigger
SentenceTransformer Cross Encoder models now run fast on GPU enabled machines, including Mac ARM devices since UKPLab/sentence-transformers#2463
- Details
- Use cross-encoder to rerank search results by default on GPU machines and when using an inference server
- Only call search API when pause in typing search query on web, desktop apps
Wait for 300ms since stop typing before calling search API.
This smooths out UI jitter when rendering search results, especially
now that we're reranking for every search query on GPU enabled devices
Emacs already has 300ms debounce time. More convoluted to add
debounce time to Obsidian search modal, so not updating that yet
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).
### Issue
Previously deleting a chat session from the side panel on desktop, web app would sometimes result in also creating a new chat session
### Fix
`get_conversation_by_user' shouldn't return new conversation if
conversation with requested id not found.
It should only return new conversation if no specific conversation
is requested and no conversations found for user at all
### Miscellaneous Improvements
- Chat history load should be logged as call to that chat_history api,
not the "chat" api
- Show status updates of clearing conversation history in chat input
- Simplify web, desktop client code by removing unnecessary new variables
### Repro
- Delete a new chat, this calls loadChat via window.onload which
calls server /chat/history API endpoint with conversationId set to
that of just deleted conversation sporadically
The call to GET chat/history API with conversationId set occurs
when window.onload triggers before the conversationId is deleted
by the delete button after the DELETE /chat/history API call (via race)
- In such a scenario, get_conversation_by_user called by
chat/history API with conversationId of deleted conversation
returns a new conversation
- Fix
`get_conversation_by_user' shouldn't return new conversation if
conversation with requested id not found.
It should only return new conversation if no specific conversation
is requested and no conversations found for user at all
- Repro
- Delete a new chat, this calls loadChat via window.onload which
calls server /chat/history API endpoint with conversationId set to
that of just deleted conversation sporadically
The call to GET chat/history API with conversationId set occurs
when window.onload triggers before the conversationId is deleted
by the delete button after the DELETE /chat/history API call (via race)
- In such a scenario, get_conversation_by_user called by
chat/history API with conversationId of deleted conversation
returns a new conversation
- Miscellaneous
- Chat history load should be logged as call to that chat_history api,
not the "chat" api
- Show status updates of clearing conversation history in chat input
- Simplify web, desktop client code by removing unnecessary new variables
* 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
* Add additional styling changes for showing UI changes when dragging file to the main screen
* Add a loading spinner when file upload is in progress, and don't index github/notion when indexing files
* Add an explicit icon for file uploading in the chat button menu
* Add appropriate dragover styling when picking a file from the file picker/browser
* Add a loading screen when retrieving chat history. Fix width of the chat window. Put attachment icon to the left of chat input
* Make major improvements to the image generation flow
- Include user context from online references and personal notes for generating images
- Dynamically select the modality that the LLM should respond with
- Retun the inferred context in the query response for the dekstop, web chat views to read
* Add unit tests for retrieving response modes via LLM
* Move output mode unit tests to the actor suite, rather than director
* Only show the references button if there is at least one available
* Rename aget_relevant_modes to aget_relevant_output_modes
* Use a shared method for generating reference sections, simplify some of the prompting logic
* Make out of space errors in the desktop client more obvious
- Open external links using the default link handler registered on OS
for the link type, e.g http:// -> firefox, mailto: thunderbird etc
- Confirm before opening non-http URL using an external app
- Improve render of inferred query in image chat messages in Web, Desktop apps
- Add inferred queries to image chat responses in Obsidian client
- Fix rendering images from Khoj response in Obsidian client
* Simplify and clarify prompt for selecting toolset dynamically
* Add error handling around call to OLOSTEP api
* Fix conversation admin page
* Skip adding none or empty entries in the chunking method
- Improve
- Only send files modified since their last sync for indexing on server from the Obsidian client
- Fix
- Invalidate static asset browser cache in Web client when Khoj version changes
Previously we'd send all files in vault and let the server
deduplicate.
This changes takes inspiration from the desktop app, and only pushes
files which were modified after their previous sync with the server.
This should reduce the processing load on the server
* Retrieve, create, and save conversations differently if they're coming from a client application
- Not all of our client apps will necessarily maintain state over the conversation IDs available to a user. For some (single-threaded conversations), it should just use a single conversation. Fix the code to do so
* Simplify conversation retrieval logic
* Keep 0 padding below chat response
* Add order_by sorting to retrieving the conversation without id
### Improvements to Chat UI on Web, Desktop apps
- Improve styling of chat session side panel
- Improve styling of chat message bubble in Desktop, Web app
- Add frosted, minimal chat UI to background of Login screen
- Improve PWA install experience of Khoj
### Fixes to Chat UI on Web, Desktop apps
- Fix creating new chat sessions from the Desktop app
- Only show 3 starter questions even when consecutive chat sessions created
### Other Improvements
- Update Khoj cloud trial period to a fortnight instead of a week
- Document using venv to handle dependency conflict on khoj pip install
Resolves#276
- Resolve PWA issues thrown by Chrome/Edge
- Add screenshot samples showcasing remember, browse and draw features
- This can provide a richer app store like experience when
installing Khoj PWA on Mobile or Desktop
- Add wide and narrow screenshots to show Mobile vs Desktop UX
- Add higher resolution favicon for PWA
- Use single web manifest instead of separate ones for Chat, Search
- Update manifest description with more details about Khoj features
Reset starter question suggestions before appending in web, desktop app
Otherwise previously it'd keep adding to existing starter question
suggestions on each new session creation if multiple consecutive new
chat sessions created.
This would result in more than the 3 expected starter questions being
displayed at a time
- Make collapse, expand toggle arrow point in the direction the action
will expand the side panel in
- Make the collapsed side panel reduce to a 1px sliver
- Improve rate limit error message wording
- Make the "too many requests" error message more robust. Should throw
that exception fix self.request >= self.subscribed_requests because
upgrading wouldn't fix this rate limiting
- Respect newline with pre-line but not for bullets to improve
formatting of responses by Khoj
- Respect bold font by loading tajawal font with other weights
- Reduce bottom margin in chat message bubble, its taking too much space
* Document original query when subqueries can't be generated
* Only add messages to the chat message log if it's non-empty
* When changing the search model, alert the user that all underlying data will be deleted
* Adding more clarification to the prompt input for username, location
* Check if has_more is in the notion results before getting next_cursor
* Update prompt template for user name/location, update confirmation message when changing search model
* Display given_name field only if it is not None
* Add default slugs in the migration script
* Ensure that updated_at is saved appropriately, make sure most recent chat is returned for default history
* Remove the bin button from the chat interface, given deletion is handled in the drop-down menus
* Refresh the side panel when a new chat is created
* Improveme tool retrieval prompt, don't let /online fail, and improve parsing of extract questions
* Fix ending chat response by offline chat on hitting a stop phrase
Previously the whole phrase wouldn't be in the same response chunk, so
chat response wouldn't stop on hitting a stop phrase
Now use a queue to keep track of last 3 chunks, and to stop responding
when hit a stop phrase
* Make chat on Obsidian backward compatible post chat session API updates
- Make chat on Obsidian get chat history from
`responseJson.response.chat' when available (i.e when using new api)
- Else fallback to loading chat history from
responseJson.response (i.e when using old api)
* Fix detecting success of indexing update in khoj.el
When khoj.el attempts to index on a Khoj server served behind an https
endpoint, the success reponse status contains plist with certs. This
doesn't mean the update failed.
Look for :errors key in status instead to determine if indexing API
call failed. This fixes detecting indexing API call success on the
Khoj Emacs client, even for Khoj servers running behind SSL/HTTPS
* Fix the mechanism for populating notes references in the conversation primer for both offline and online chat
* Return conversation.default when empty list for dynamic prompt selection, send all cmds in telemetry
* Fix making chat on Obsidian backward compatible post chat session API updates
New API always has conversation_id set, not `chat' which can be unset
when chat session is empty.
So use conversation_id to decide whether to get chat logs from
`responseJson.response.chat' or `responseJson.response' instead
---------
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
- Remove unused git dependency from Docker images
- Move python packages used for test into dev dependency group
- Only enable API token, Whatsapp cards on Web UI when Stripe, Twilio setup
- Move production dependencies to prod python packages group
- Fix docs links in Khoj welcome chat message
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
* Add location metadata to chat history
* Add support for custom configuration of the user name
* Add region, country, city in the desktop app's URL for context in chat
* Update prompts to specify user location, rather than just location.
* Add location data to Obsidian chat query
* Use first word for first name, last word for last name when setting profile name
* Have Khoj dynamically select which conversation command(s) are to be used in the chat flow
- Intercept the commands if in default mode, and have Khoj dynamically guess which tools would be the most relevant for answering the user's query
* Remove conditional for default to enter online search mode
* Add multiple-tool examples in the prompt, make prompt for tools more specific to info collection
* Add chat sessions to the desktop application
* Increase width of the main chat body to 90vw
* Update the version of electron
* Render the default message if chat history fails to load
* Merge conversation migrations and fix slug setting
* Update the welcome message, use the hostURL, and update background color for chat actions
* Only update the window's web contents if the page is config
* Enable support for multiple chat sessions within the web client
- Allow users to create multiple chat sessions and manage them
- Give chat session slugs based on the most recent message
- Update web UI to have a collapsible menu with active chats
- Move chat routes into a separate file
* Make the collapsible side panel more graceful, improve some styling elements of the new layout
* Support modification of the conversation title
- Add a new field to the conversation object
- Update UI to add a threedotmenu to each conversation
* Get the default conversation if a matching one is not found by id
- Removed node-fetch dependency to work on mobile.
- Fix CORS issue for Khoj (streaming) chat on Obsidian mobile
- Verified Khoj plugin, search, chat work on Obsidian mobile.
## Details
### Major
- Allow calls to Khoj server from Obsidian mobile app to fix CORS issue
- Chat stream using default `fetch' not `node-fetch' in obsidian plugin
### Minor
- Load chat history after other elements in chat modal on Obsidian are rendered
- Scroll to bottom of chat modal on Obsidian across mobile & desktop
- Provide more context and instructions to offline chat on Khoj
- Upgrade offilne chat quality tests to support more use-cases
### Details
- Improve offline chat system prompt to think step by step
- Make offline chat model current date aware. Improve system prompts
- Fix actor, director tests using freeze time by ignoring transformers package
- Obsidian mobile uses capacitor js. Requests from it have origin as
http://localhost on Android and capacitor://localhost on iOS
- Allow those Obsidian mobile origins in CORS middleware of server
- Can now expect date awareness chat quality test to pass
- Prevent offline chat model from printing verbatim user Notes and
special tokens
- Make it ask follow-up questions if it needs more context
This allows using open or commerical, local or hosted LLM models that
are not supported in Khoj by default.
It also allows users to use other local LLM API servers that support
their GPU
Closes#407
- Put Whatsapp card back in Client section.
- Fixes side spacing on cards
- Improve Whatsapp card row gaps
- Hide notification banner on web app load. Previously it showed up as
a yellow dot on smaller displays
* Fix subscription state detection for users based on phone numbers, emails
* Fix unit tests for api_user4
* Use a single method for determining subscription from user
* Pass user object, rather than user.email for getting subscription state
* 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>
* Add a page about privacy and organize some of the documentation
* Add notice about telemetry
* Improve copy for privacy section, link to telemetry section
* Store rate limiter-related metadata in the database for more resilience
- This helps maintain state even between server restarts
- Allows you to scale up workers on your service without having to implement sticky routing
* Make the usage exceeded message less abrasive
* Fix rate limiter for specific conversation commands and improve the copy
* Add retries in case the embeddings API fails
* Improve error handling in the inference endpoint API request handler
- retry only if HTTP exception
- use logger to output information about errors
* 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
* Improve subqueries for online search and prompt generation for image
- Include conversation history so that subqueries or intermediate prompts are generated with the appropriate context
* 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
## Major
### Move to single click audio chat UX on Obsidian, Desktop, Web clients
New default UX has 1 long-press on mobile, 2-click on desktop to send transcribed audio message
- New Audio Chat Flow
1. Record audio while microphone button pressed
2. Show auto-send 3s countdown timer UI for audio chat message
Provide a visual cue around send button for how long before audio
message is automatically sent to Khoj for response
3. Auto-send msg in 3s unless stop send message button clicked
- Why
- Removes the previous default of 3 clicks required to send audio message
The record > stop > send process to send audio messages was unclear and effortful
- Still allows stopping message from being sent, to make correction to transcribed audio
- Removes inadvertent long audio transcriptions if forget to press stop while recording
### Improve chat input pane actions & icons on Obsidian. Desktop, Web clients
- Use SVG icons in chat footer on web, desktop app
- Move delete icon to left of chat input. This makes it harder to inadvertently click it
- Add send button to chat input pane
- Color chat message send button to make it primary CTA
- Make chat footer shorter. Use no or round border on action buttons
## Minor
- Stop rendering empty starter questions element when no questions present
- Add round border, hover color to starter questions in web, desktop apps
- Fix auto resizing chat input box when transcribed text added
- Convert chat input into a text area in the Obsidian client
- Capabillity
New default UX has 1 long-press to send transcribed audio message
- Removes the previous default of 3 clicks required to send audio message
- The record > stop > send process to send audio messages was unclear
- Still allows stopping message from being sent, if users want to make
correction to transcribed audio
- Removes inadvertent long audio transcriptions if user forgets to
press stop when recording
- Changes
- Record audio while microphone button pressed
- Show auto-send 3s countdown timer UI for audio chat message
Provide a visual cue around send button for how long before audio
message is automatically sent to Khoj for response
- Auto-send msg in 3s unless stop send message button clicked
- Capabillity
New default UX has 1 long-press to send transcribed audio message
- Removes the previous default of 3 clicks required to send audio message
- The record > stop > send process to send audio messages was unclear
- Still allows stopping message from being sent, if users want to make
correction to transcribed audio
- Removes inadvertent long audio transcriptions if user forgets to
press stop when recording
- Changes
- Record audio while microphone button pressed
- Show auto-send 3s countdown timer UI for audio chat message
Provide a visual cue around send button for how long before audio
message is automatically sent to Khoj for response
- Auto-send msg in 3s unless stop send message button clicked
- Capabillity
New default UX has 1 long-press to send transcribed audio message
- Removes the previous default of 3 clicks required to send audio message
- The record > stop > send process to send audio messages was unclear
- Still allows stopping message from being sent, if users want to make
correction to transcribed audio
- Removes inadvertent long audio transcriptions if user forgets to
press stop when recording
- Changes
- Record audio while microphone button pressed
- Show auto-send 3s countdown timer UI for audio chat message
Provide a visual cue around send button for how long before audio
message is automatically sent to Khoj for response
- Auto-send msg in 3s unless stop send message button clicked
- Move delete icon to left of chat input. This makes it harder to
inadvertently click
- Add send button to chat footer. Enter being the only way to send
messages is not intuitive, outside standard modern UI patterns
- Color chat message send button to make it primary CTA on web client
- Make chat footer shorter. Use no or round border on action buttons
- Use SVG icons in chat footer on web
- Move delete icon to left of chat input. This makes it harder to
inadvertently click
- Add send button to chat footer. Enter being the only way to send
messages is not intuitive, outside standard modern UI patterns
- Color chat message send button to make it primary CTA on web client
- Make chat footer shorter. Use no or round border on action buttons
- Use SVG icons in chat footer on web
- Move delete icon to left of chat input. This makes it harder to
inadvertently click
- Add send button to chat footer. Enter being the only way to send
messages is not intuitive, outside standard modern UI patterns
- Color chat message send button to make it primary CTA on web client
- Make chat footer shorter. Use no or round border on action buttons
* 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
The actual issue was that `get_or_create_user_by_email' tried to
create a subscription even if it already existed.
With updated logic:
- New subscription is only created when it doesn't already exist in
`get_or_create_user_by_email'
- `set_user_subscription' just updates the subscription state as
user subscription object creation is already managed by
`get_or_create_user_by_email'. So the other conditionals are
unnecessary
- Issue
Users with Dataview plugin would have error as its markdown
post-processor expects the sourcePath to be a string
This prevents Khoj from responding to chat messages in the Obsidian
chat modal. Search via Obsidian still works but it throws the same
dataview plugin error
- Fix
Pass a string as sourcePath to markdownRenderer to fix failing chat response
and stop throwing dataview errors on search
Resolves#614, Resolves#606
- Update health API to pass authenticated users their info
- Improve Khoj server status check in Khoj Obsidian client
- Show Khoj Obsidian commands even if no connection to server
- Show Khoj chat by default in Obsidian side pane instead of search
Server connection check can be a little flaky in Obsidian. Don't gate
the commands behind it to improve usability of Khoj.
Previously the commands would get disabled when server connection
check failed, even though server was actually accessible
- Update server connection status on every edit of khoj url, api key in
settings instead of only on plugin load
The error message was stale if connection fixed after changes in
Khoj plugin settings to URL or API key, like on plugin install
- Show better welcome message on first plugin install.
Include API key setup instruction
- Show logged in user email on Khoj settings page
- Issue: Users with Dataview plugin would have error as its markdown
post-processor expects the sourcePath to be a string
This prevents Khoj from responding to chat messages in the Obsidian
chat modal. Search via Obsidian still works but it throws the same
dataview error
- Fix: Pass a string as sourcePath to markdownRenderer to fix
failing chat response
Resolves#614, Resolves#606
* Add support for custom inference endpoints for the cross encoder model
- Since there's not a good out of the box solution, I've deployed a custom model/handler via huggingface to support this use case.
* Use langchain.community for pdf, openai chat modules
* Add an explicit stipulation that the api endpoint for crossencoder inference should be for huggingface for now
This allows Khoj clients to get email address associated with
user's API token for display in client UX
In anonymous mode, default user information is passed
### 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
- Use /api/health for server up check instead of api/config/default
- Remove unused `khoj--post-new-config' method
- Remove the now unused /config/data GET, POST API endpoints
### Major
- Push 1000 files at a time from the Desktop client for indexing
- Push 1000 files at a time from the Obsidian client for indexing
- Test 1000 file upload limit to index/update API endpoint
### Minor
- Show relevant error message in desktop app, e.g when can't connect to server
- Pass indexed filenames in API response for client validation
- Collect files to index in single dict to simplify index/update controller
Resolves#573
- Only run /online command offline chat director test when `SERPER DEV_API_KEY' present
- Decode URL encoded query string in chat API endpoint before processing
- Make references and online_results optional params to converse_offline
- Pass max context length to fix using updated `GPT4All.list_gpu' method
* Support using hosted Huggingface inference endpoint for embeddings generation
* Since the huggingface inference endpoint is model-specific, make the URL an optional property of the search model config
* Handle ECONNREFUSED error in desktop app
* Drive API key via the search model config model and use more generic names
- Ensure langchain less than 0.2.0 is used, to prevent breaking
ChatOpenAI, PyMuPDF usage due to their deprecation after 0.2.0
- Set subscription renewal date to a timezone aware datetime
- Use logger.warning instead of logger.warn as latter is deprecated
- Use `model_dump' not deprecated dict to get all configured content_types
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
FastAPI API endpoints only support uploading 1000 files at a time.
So split all files to index into groups of 1000 for upload to
index/update API endpoint
- 26f96e00 Use Khoj Client, Data sources diagrams in feature docs
- c82d34b6 Add Docs footer, nav pane links. Fix tagline, Remove announcement topbar
- d920e4d0 Make the docs overview page as the main docs landing page
- 80d1ad5b Fix image urls on docs overview page. Remove logo header in client docs
- Make the docs overview page available at docs.khoj.dev root instead of
under docs.khoj.dev/docs path
- Remove the new landing page, it is unnecessary.
- Remove /docs path prefix from links to internal doc pages
- Remove .md path suffix in internal doc pages for consistency
FastAPI API endpoints only support uploading 1000 files at a time.
So split all files to index into groups of 1000 for upload to
index/update API endpoint
Make usage of the first offline/openai chat model as the default LLM
to use for background tasks more explicit
The idea is to use the default/first chat model for all background
activities, like user message to extract search queries to perform.
This is controlled by the server admin.
The chat model set by the user is used for user-facing functions like
generating chat responses
Previously you could only index org-mode files and directories from
khoj.el
Mark the `khoj-org-directories', `khoj-org-files' variables for
deprecation, since `khoj-index-directories', `khoj-index-files'
replace them as more appropriate names for the more general case
Resolves#597
- Add more accurate steps for building Khoj locally
- Remove outdated instructions
- Add specific steps to create a Github Issue
- Add instructions for Obsidian plugin development
- Fix streaming chat response in Obsidian client
- Fix first-run, chat error message in obsidian, desktop and web clients
- Set Khoj app version to latest version in Docker images
- Tag Khoj Docker image built on release with the `latest` tag
This align docker image release cadence with client, server releases
- Tag docker image with `tag_name' on release (i.e tag push)
- Else tag with 'pre' on push to master
- Else tag with 'dev' on push to PR branch
- Only tag the latest release with release tag
Previously the latest commit on master was being tagged with the
latest tag. This doesn't sync with the release cadence of the rest
of Khoj
This will allow troubleshooting by getting the actual khoj version
being used. Previously it was always set to a static 0.0.0 version
Command to build Khoj docker image with dynamically set current app version:
`docker-compose build server --build-arg VERSION=$(pipx run hatch version)'
- Honor this setting across the relevant places where embeddings are used
- Convert the VectorField object to have None for dimensions in order to make the search model easily configurable
- Improve the prompt before sending it for image generation
- Update the help message to include online, image functionality
- Improve styling for the voice, trash buttons
The source URL returned by OpenAI would expire soon. This would make
the chat sessions contain non-accessible images/messages if using
OpenaI image URL
Get base64 encoded image from OpenAI and store directly in
conversation logs. This resolves the image link expiring issue
- All search models are loaded into memory, and stored in a dictionary indexed by name
- Still need to add database migrations and create a UI for user to select their choice. Presently, it uses the default option
- Move it out to conversation.utils from generate_chat_response function
- Log new optional intent_type argument to capture type of response
expected. This can be type responses by Khoj e.g speech, image. It
can be used to render responses by Khoj appropriately on clients
- Make user_message_time argument optional, set the time to now by
default if not passed by calling function
- Wasn't able to login to the admin panel when KHOJ_DEBUG was not True. Fix this error so self-hosted users can get unblocked from accessing the admin settings
- Don't force users to set their KHOJ_DJANGO_SECRET_KEY
- Show temporary status message when copied to clipboard
- Render chat responses as markdown in Desktop client
- Render chat responses as markdown in chat modal of Obsidian client
- Render references of new responses in chat modal on Obsidian client. Use new style for references
- Properly stop `mediaRecorder` stream to clear microphone in-use state
- Render newlines when references expanded in Web, Desktop and Obsidian clients
- Use new style references for Khoj chat modal in Obsidian
- Khoj Chat responses in Obsidian had regressed to not show references
for new questions after modal has been opened. Now even those are
rendered, and use new references style
- Render chat response as markdown while it's being streamed
- Create speech to text API endpoint
- Use OpenAI Whisper for ASR offline (by downloading Whisper model) or online (via OpenAI API)
- Add speech to text model configuration to Database
- Speak to Khoj from the Web, Desktop or Obsidian client
- Add transcription button with mic icon
- Collect audio recording on pressing mic
- Process and send audio recording to server for transcription
- Extract the functionality to flash status in chat input for reuse
- Allow server admin to configure offline speech to text model during
initialization
- Use offline speech to text model to transcribe audio from clients
- Set offline whisper as default speech to text model as no setup api key reqd
- Extract flashing status message in chat input placeholder into
reusable function
- Use emoji prefixes for status messages
- Improve alt text of transcribe button to indicate what the button does
- Conflicts:
- src/interface/desktop/chat.html
Combine and use common class names for speak component
- src/khoj/database/adapters/__init__.py
Combine imports
- src/khoj/interface/web/chat.html
Combine and use common class names for speak component
- src/khoj/routers/api.py
Combine imports
- Add a dependency on the indexer API endpoint that rounds up the amount of data indexed and uses that to determine whether the next set of data should be processed
- Delete any files that are being removed for adminstering the calculation
- Show current amount of data indexed in the config page
- Ignore errors in deleting network requests to khoj server
- Also delete open network connection to khoj server on auto reindex
Otherwise when server is unreachable a bunch of failed network
connections accrue in the processes list
- Make auto-update of content index user configurable from khoj.el
- Handle server unavailable error on auto-index schedule job in khoj.el
Resolves#567
- Append chat message to chat logs as TextNodes in web, desktop clients
- Simplify Code to Identify Files from Github, Notion on Web, Desktop Client
- Use file source to find entries from github, notion on web, desktop client
- Pass file source to clients via text search API response
- Make Django Logs Follow Khoj Log Format, Verbosity
- Handle image search setup related warning
- Format Django initializing outputs using Khoj logger format
- Use `KHOJ_HOST` env var to set allowed/trusted domains to host Khoj
Ideally should rename model_directory to config_directory or some such
but the current image search code will need to be migrated soon. So
changing the variable name and creating a migration script for old
khoj.yml files using model-directory variable isn't worth it
Remove the explicity set of number of threads to use by pytorch. Use
the default used by it.
- Collect STDOUT from the `migrate', `collectstatic' commands and
output using the Khoj logger format and verbosity settings
- Only show Django `collectstatic' command output in verbose mode
- Fix showing the Initializing Khoj log line by moving it after logger
level set
This bug was causing the search results on the Obsidian client to be shown in the reverse order of their actual relevance.
It reversed since entry scores returned by Khoj server are a distance metric since the move to Postgres. So lesser distance is better. Previously higher score was better.
Previously it was only searching for PDF and Markdown files. This was
meant to show only content from current vault as results.
But it has not scaled well as other clients also allow syncing PDF and
markdown files now. So remove this content type filter for now.
A proper solution would limit by using file/dir filters on server or
client side.
- PyPi doesn't like to have files that start with numbers, however all of the generated django migration files start with numbers. To accommodate, skip this check.
- Refer to https://pypi.org/project/check-wheel-contents/ for documentation and recommendation
- Our pypi package currently does not work because the django app and associated database is not included. To remedy this issue, move the app into the src/khoj folder. This has the added benefit of improved organization of the codebase, as all server related code is now in a single folder
- Update associated file paths and system references
- c07401cf Fix, Improve chat config via CLI on first run by using defaults
- d61b0dd5 Add Khoj Django app package to sys path to load Django module via pip install
- 4e98acbc Update minimum pydantic version to one with model_validate function
### Overview
The parent hierarchy of org-mode entries can store important context.
This change updates OrgNode to track parent headings for each org entry and adds the parent outline for each entry to the index
### Details
- Test search uses ancestor headings as context for improved results
- Add ancestor headings of each org-mode entry to their compiled form
- Track ancestor headings for each org-mode entry in org-node parser
Resolves#85
- Update docs to show how to use Khoj Cloud
- Move self-hosting Khoj to separate section
- Add page to setup Desktop app
- Set default URL to Khoj Cloud URL in Obsidian, Emacs clients
- No Khoj server setup required to start using Khoj from Obsidian, Emacs
- Use tabs for install, upgrade in Emacs with different package
managers
- Use default subtitles in Khoj Docs
- Deduplicate query filters, remove backend setup instructions in
plugin pages
- Remove stale Setup demo on Khoj Obsidian plugin docs
- Upgrade FastAPI to >= latest version. Required upgrade of FastAPI.
Earlier version didn't support wrapping common query params in class
- Use per fixture app instead of a global FastAPI app in conftest
- Upgrade minimum required Django version
- Fix no notes chat director test with updated no notes message
No notes message was updated in commit 118f1143
- Use the knowledgeGraph, answerBox, peopleAlsoAsk and organic responses of serper.dev to provide online context for queries made with the /online command
- Add it as an additional tool for doing Google searches
- Render the results appropriately in the chat web window
- Pass appropriate reference data down to the LLM
- By default assume the audience of this website is people looking to understand the featuer offering of Khoj, and then people who are looking to self-host
- Most important updates include the depedency requirement to setup Postgres when running/setting Khoj up locally
- Add instructiosn for Docker
- Shift to recommend desktop client and update instructions for how to configure Khoj for user
- 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
- Update test data to add deeper outline hierarchy for testing
hierarchy as context
- Update collateral tests that need count of entries updated, deleted
asserts to be updated
- Make search model configurable on server
- Update migration script to get search model from `khoj.yml` to Postgres
- Update first run message on Khoj Desktop and Web app landing page
- Other miscellaneous bug fixes
- 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
### Overview
Prepare Khoj with multi-user, db support for Khoj Cloud release
### Details
- Add first run experience to configure Khoj via khoj CLI
- Improve Web app settings page: Move files data into content section card. Move content index update button(s) to content section
- Improve OpenAI chat prompts
- Push more general information for OpenAI models into system prompt
- Make it more aware of it's current capabilities
- Weaken asking follow-up questions
- Rate-limit calls to the chat API
- Add back search results quality threshold
- Normalize quality score definitions across cross_encoder, encoder to distance metric
- Remove reference to deprecated button
- Await result of the search query
- Fixed Langchain issue by allowing the Docker image to rebuild with a later package version
- 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
- 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 Cloud sync setup
- Add detailed chat intro message, mention download desktop app for docs sync
- Only show search in web app nav pane if user has documents indexed
- Hide download desktop app message in web app if synced files exist
- Mark generated profile pic with subscription circle in web app
- 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
### Major
- Expose Billing via Stripe on Khoj Web app for Khoj Cloud subscription
- Expose card on web app config page to manage subscription to Khoj cloud
- Create API webhook, endpoints for subscription payments using Stripe
- Put Computer files to index into Card under Content section
- Show file type icons for each indexed file in config card of web app
- Enable deleting all indexed desktop files from Khoj via Desktop app
- Create config page on web app to manage computer files indexed by Khoj
- Track data source (computer, github, notion) of each entry
- Update content by source via API. Make web client use this API for config
- Store the data source of each entry in database
### Cleanup
- Set content enabled status on update via config buttons on web app
- Delete deprecated content config pages for local files from web client
- Rename Sync button, Force Sync toggle to Save, Save All buttons
### Fixes
- Prevent Desktop app triggering multiple simultaneous syncs to server
- Upgrade langchain version since adding support for OCR-ing PDFs
- Bubble up content indexing errors to notify user on client apps
- 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
- Update theme for Desktop, Web and Obsidian client apps to use lighter colors
- Show splash screen on starting Desktop app
- Make chat the landing page on Desktop and Web clients
- Simplify style of login page on Web app
- Add About page for Desktop app accessible from system tray menu
- 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
### ✨ New
- Create profile pic drop-down menu in navigation pane
Put settings page, logout action under drop-down menu
### ⚙️ Fix
- Add Key icon for API keys table on Web Client's settings page
### 🧪 Improve
- Rename `TextEmbeddings` to `TextEntries` for improved readability
- Rename `Db.Models` `Embeddings`, `EmbeddingsAdapter` to `Entry`, `EntryAdapter`
- Show truncated API key for identification & restrict table width for config page responsiveness
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
Instead of using the previous method to push data as json payload of POST request
pass it as files to upload via the multi-part/form to the batch indexer API endpoint
- 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
Git tag tests/data files with the linguist-vendored attribute to
prevent github from including them in stats.
Otherwise Khoj is getting marked as an HTML project due to the
tardigrades html page in tests data, when it's primarily a python
project currently
- Overview
- Allow applying word, file or date filters on your knowledge base from the chat interface
- This will limit the portion of the knowledge base Khoj chat can use to respond to your query
- 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>
The test workflow fails regularly with an OperationCancelled error.
This is an intermittent failure that gets resolved on running the
failed workflows a few times.
* 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
Build the Debian package using Ubuntu 20.04 instead of 22.04 as Ubuntu 20.04 comes pre-installed with glibc_2.31 unlike Ubuntu 22.04 which uses glibc_2.35
This should reduce chances of installation errors due to regex package
being built from source for python3.11
Previously, the regex dependency of dateparser = 1.1.1 didn't have a
wheel for python 3.11. This would trigger building the regex package
from scratch which would fail for a lot of folks
* 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
# Use the following line to use the latest version of khoj. Otherwise, it will build from source.
image:ghcr.io/khoj-ai/khoj:latest
# Uncomment the following line to build from source. This will take a few minutes. Comment the next two lines out if you want to use the offiicial image.
# build:
# context: .
ports:
# If changing the local port (left hand side), no other changes required.
# If changing the remote port (right hand side),
@@ -10,19 +32,23 @@ services:
- "42110:42110"
working_dir:/app
volumes:
- .:/app
# These mounted volumes hold the raw data that should be indexed for search.
# The path in your local directory (left hand side)
# points to the files you want to index.
# The path of the mounted directory (right hand side),
# must match the path prefix in your config file.
- ./tests/data/org/:/data/org/
- ./tests/data/images/:/data/images/
- ./tests/data/markdown/:/data/markdown/
- ./tests/data/pdf/:/data/pdf/
# Embeddings and models are populated after the first run
# You can set these volumes to point to empty directories on host
<b>An AI personal assistant for your digital brain</b>
</div>
<divalign="center">
[📜 Explore Code](https://github.com/khoj-ai/khoj)
<span> • </span>
[🌍 Try Khoj Cloud](https://khoj.dev)
<span> • </span>
[💬 Get Involved](https://discord.gg/BDgyabRM6e)
</div>
## Introduction
Welcome to the Khoj Docs! This is the best place to [get started](./setup.md) with Khoj.
- Khoj is a desktop application to [search](./search.md) and [chat](./chat.md) with your notes, documents and images
- It is an offline-first, open source AI personal assistant accessible from your [Emacs](./emacs.md), [Obsidian](./obsidian.md) or [Web browser](./web.md)
- It works with jpeg, markdown, [notion](./notion_integration.md) org-mode, pdf files and [github repositories](./github_integration.md)
To search for notes in multiple, different languages, you can use a [multi-lingual model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models).<br/>
For example, the [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) supports [50+ languages](https://www.sbert.net/docs/pretrained_models.html#:~:text=we%20used%20the%20following%2050%2B%20languages), has good search quality and speed. To use it:
1. Manually update `search-type > asymmetric > encoder` to `paraphrase-multilingual-MiniLM-L12-v2` in your `~/.khoj/khoj.yml` file for now. See diff of `khoj.yml` below for illustration:
2. Regenerate your content index. For example, by opening [\<khoj-url\>/api/update?t=force](http://localhost:42110/api/update?t=force)
### Access Khoj on Mobile
1. [Setup Khoj](/#/setup) on your personal server. This can be any always-on machine, i.e an old computer, RaspberryPi(?) etc
2. [Install](https://tailscale.com/kb/installation/) [Tailscale](tailscale.com/) on your personal server and phone
3. Open the Khoj web interface of the server from your phone browser.<br /> It should be `http://tailscale-ip-of-server:42110` or `http://name-of-server:42110` if you've setup [MagicDNS](https://tailscale.com/kb/1081/magicdns/)
4. Click the [Add to Homescreen](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps/Add_to_home_screen) button
5. Enjoy exploring your notes, documents and images from your phone!

### Use OpenAI Models for Search
#### Setup
1. Set `encoder-type`, `encoder` and `model-directory` under `asymmetric` and/or `symmetric` `search-type` in your `khoj.yml` (at `~/.khoj/khoj.yml`):
2. [Setup your OpenAI API key in Khoj](/#/chat?id=setup)
3. Restart Khoj server to generate embeddings. It will take longer than with the offline search models.
#### Warnings
This configuration *uses an online model*
- It will **send all notes to OpenAI** to generate embeddings
- **All queries will be sent to OpenAI** when you search with Khoj
- You will be **charged by OpenAI** based on the total tokens processed
- It *requires an active internet connection* to search and index
### Bootstrap Khoj Search for Offline Usage later
You can bootstrap Khoj pre-emptively to run on machines that do not have internet access. An example use-case would be to run Khoj on an air-gapped machine.
Note: *Only search can currently run in fully offline mode, not chat.*
- With Internet
1. Manually download the [asymmetric text](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), [symmetric text](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and [image search](https://huggingface.co/sentence-transformers/clip-ViT-B-32) models from HuggingFace
1. Copy each of the search models into their respective folders, `asymmetric`, `symmetric` and `image` under the `~/.khoj/search/` directory on the air-gapped machine
2. Copy the khoj virtual environment directory onto the air-gapped machine, activate the environment and start and khoj as normal. E.g `source .venv/bin/activate && khoj`
Online chat requires internet to use ChatGPT but is faster, higher quality and less compute intensive.
!> **Warning**: This will enable Khoj to send your chat queries and notes to OpenAI for processing
1. Get your [OpenAI API Key](https://platform.openai.com/account/api-keys)
2. Open your [Khoj Online Chat settings](http://localhost:42110/config/processor/conversation), add your OpenAI API key, and click *Save*. Then go to your [Khoj settings](http://localhost:42110/config) and click `Configure`. This will refresh Khoj with your OpenAI API key.
We have beta desktop images available for download with new releases. This is recommended if you don't want to bother with the command line. Download the latest release from [here](https://github.com/khoj-ai/khoj/releases). You can find the latest release under the `Assets` section.
## MacOS
1. Download the latest release from [here](https://github.com/khoj-ai/khoj/releases).
- If your Mac uses one of the Silicon chips, then download the `Khoj_<version>_arm64.dmg` file. Otherwise, download the `Khoj_<version>_amd64.dmg` file.
2. Open the downloaded file and drag the Khoj app to your Applications folder.
## Windows
Make sure you meet the prerequisites for Windows installation. You can find them [here](windows_install.md#prerequisites).
1. Download the latest release from [here](https://github.com/khoj-ai/khoj/releases). You'll want the `khoj_<version>_amd64.exe` file.
2. Open the downloaded file and double click to install.
## Linux
1. Download the latest release from [here](https://github.com/khoj-ai/khoj/releases). You'll want the `khoj_<version>_amd64.deb` file.
2. In your downloads folder, run `sudo dpkg -i khoj_<version>_amd64.deb` to install Khoj.
# Uninstall
If you decide you want to uninstall the application, you can uninstall it like any other application on your system. For example, on MacOS, you can drag the application to the trash. On Windows, you can uninstall it from the `Add or Remove Programs` menu. On Linux, you can uninstall it with `sudo apt remove khoj`.
In addition to that, you might want to `rm -rf` the following directories:
- **Via the Settings UI**: Add files, directories to index the [Khoj settings](http://localhost:42110/config) UI once Khoj has started up. Once you've saved all your settings, click `Configure`.
- **Manually**:
- Copy the `config/khoj_sample.yml` to `~/.khoj/khoj.yml`
- Set `input-files` or `input-filter` in each relevant `content-type` section of `~/.khoj/khoj.yml`
- Set `input-directories` field in `image` `content-type` section
- Delete `content-type` and `processor` sub-section(s) irrelevant for your use-case
- Restart khoj
Note: Wait after configuration for khoj to Load ML model, generate embeddings and expose API to query notes, images, documents etc specified in config YAML
### Using Docker
#### 1. Clone
```shell
git clone https://github.com/khoj-ai/khoj && cd khoj
```
#### 2. Configure
- **Required**: Update [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml) to mount your images, (org-mode or markdown) notes, PDFs and Github repositories
- **Optional**: Edit application configuration in [khoj_docker.yml](https://github.com/khoj-ai/khoj/blob/master/config/khoj_docker.yml)
#### 3. Run
```shell
docker-compose up -d
```
*Note: The first run will take time. Let it run, it\'s mostly not hung, just generating embeddings*
#### 4. Upgrade
```shell
docker-compose build --pull
```
## Validate
### Before Making Changes
1. Install Git Hooks for Validation
```shell
pre-commit install -t pre-push -t pre-commit
```
- This ensures standard code formatting fixes and other checks run automatically on every commit and push
- Note 1: If [pre-commit](https://pre-commit.com/#intro) didn't already get installed, [install it](https://pre-commit.com/#install) via `pip install pre-commit`
- Note 2: To run the pre-commit changes manually, use `pre-commit run --hook-stage manual --all` before creating PR
### Before Creating PR
1. Run Tests. If you get an error complaining about a missing `fast_tokenizer_file`, follow the solution [in this Github issue](https://github.com/UKPLab/sentence-transformers/issues/1659).
```shell
pytest
```
2. Run MyPy to check types
```shell
mypy --config-file pyproject.toml
```
### After Creating PR
- Automated [validation workflows](.github/workflows) run for every PR.
Ensure any issues seen by them our fixed
- Test the python packge created for a PR
1. Download and extract the zipped `.whl` artifact generated from the pypi workflow run for the PR.
2. Install (in your virtualenv) with `pip install /path/to/download*.whl>`
3. Start and use the application to see if it works fine
## Create Khoj Release
Follow the steps below to [release](https://github.com/debanjum/khoj/releases/) Khoj. This will create a stable release of Khoj on [Pypi](https://pypi.org/project/khoj-assistant/), [Melpa](https://stable.melpa.org/#%252Fkhoj) and [Obsidian](https://obsidian.md/plugins?id%253Dkhoj). It will also create desktop apps of Khoj and attach them to the latest release.
1. Create and tag release commit by running the bump_version script. The release commit sets version number in required metadata files.
```shell
./scripts/bump_version.sh -c "<release_version>"
```
2. Push commit and then the tag to trigger the release workflow to create Release with auto generated release notes.
- *Make sure [python](https://realpython.com/installing-python/) and [pip](https://pip.pypa.io/en/stable/installation/) are installed on your machine*
- *Ensure you follow the ordering of the setup steps. Install the plugin after starting the khoj backend. This allows the plugin to configure the khoj backend*
### 1. Setup Backend
Open terminal/cmd and run below command to install and start the khoj backend
- On Linux/MacOS
```shell
python -m pip install khoj-assistant && khoj
```
- On Windows
```shell
py -m pip install khoj-assistant && khoj
```
### 2. Setup Plugin
1. Open [Khoj](https://obsidian.md/plugins?id=khoj) from the *Community plugins* tab in Obsidian settings panel
2. Click *Install*, then *Enable* on the Khoj plugin page in Obsidian
3. [Optional] To enable Khoj Chat, set your [OpenAI API key](https://platform.openai.com/account/api-keys) in the Khoj plugin settings
See [official Obsidian plugin docs](https://help.obsidian.md/Extending+Obsidian/Community+plugins) for details
## Use
### Chat
Run *Khoj: Chat* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette) and ask questions in a natural, conversational style.<br />
E.g "When did I file my taxes last year?"
Notes:
- *Using Khoj Chat will result in query relevant notes being shared with OpenAI for ChatGPT to respond.*
- *To use Khoj Chat, ensure you've set your [OpenAI API key](https://platform.openai.com/account/api-keys) in the Khoj plugin settings.*
See [Khoj Chat](/chat) for more details
### Search
Click the *Khoj search* icon 🔎 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or run *Khoj: Search* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
*Note: Ensure the khoj server is running in the background before searching. Execute `khoj` in your terminal if it is not already running*
Use structured query syntax to filter the natural language search results
- **Word Filter**: Get entries that include/exclude a specified term
- Entries that contain term_to_include: `+"term_to_include"`
- Entries that contain term_to_exclude: `-"term_to_exclude"`
- **Date Filter**: Get entries containing dates in YYYY-MM-DD format from specified date (range)
- Entries from April 1st 1984: `dt:"1984-04-01"`
- Entries after March 31st 1984: `dt>="1984-04-01"`
- Entries before April 2nd 1984 : `dt<="1984-04-01"`
- **File Filter**: Get entries from a specified file
- Entries from incoming.org file: `file:"incoming.org"`
- Combined Example
- `what is the meaning of life? file:"1984.org" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"`
- Adds all filters to the natural language query. It should return entries
- from the file *1984.org*
- containing dates from the year *1984*
- excluding words *"big"* and *"brother"*
- that best match the natural language query *"what is the meaning of life?"*
### Find Similar Notes
To see other notes similar to the current one, run *Khoj: Find Similar Notes* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
## Upgrade
### 1. Upgrade Backend
```shell
pip install --upgrade khoj-assistant
```
### 2. Upgrade Plugin
1. Open *Community plugins* tab in Obsidian settings
2. Click the *Check for updates* button
3. Click the *Update* button next to Khoj, if available
- Open <http://localhost:42110/> in your web browser
- **From Obsidian**
- Click the *Khoj search* icon 🔎 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or Search for *Khoj: Search* in the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
These are the general setup instructions for Khoj.
- Make sure [python](https://realpython.com/installing-python/) and [pip](https://pip.pypa.io/en/stable/installation/) are installed on your machine
- Check the [Khoj Emacs docs](/emacs?id=setup) to setup Khoj with Emacs<br/>
Its simpler as it can skip the server *install*, *run* and *configure* step below.
- Check the [Khoj Obsidian docs](/obsidian?id=_2-setup-plugin) to setup Khoj with Obsidian<br/>
Its simpler as it can skip the *configure* step below.
### 1. Install
Run the following command in your terminal to install the Khoj backend.
- On Linux/MacOS
```shell
python -m pip install khoj-assistant
```
- On Windows
```shell
py -m pip install khoj-assistant
```
For more detailed Windows installation and troubleshooting, see [Windows Install](./windows_install.md).
### 2. Start
Run the following command from your terminal to start the Khoj backend and open Khoj in your browser.
```shell
khoj --gui
```
Note: To start Khoj automatically in the background use [Task scheduler](https://www.windowscentral.com/how-create-automated-task-using-task-scheduler-windows-10) on Windows or [Cron](https://en.wikipedia.org/wiki/Cron) on Mac, Linux (e.g with `@reboot khoj`)
### 3. Configure
1. Set `File`, `Folder` and hit `Save` in each Plugins you want to enable for Search on the Khoj config page
2. Add your OpenAI API key to Chat Feature settings if you want to use Chat
3. Click `Configure` and wait. The app will download ML models and index the content for search and (optionally) chat
Khoj exposes a web interface to search, chat and configure by default.<br />
The optional steps below allow using Khoj from within an existing application like Obsidian or Emacs.
- **Khoj Obsidian**:<br />
[Install](/obsidian?id=_2-setup-plugin) the Khoj Obsidian plugin
- **Khoj Emacs**:<br />
[Install](/emacs?id=setup) khoj.el
## Upgrade
### Upgrade Khoj Server
```shell
pip install --upgrade khoj-assistant
```
*Note: To upgrade to the latest pre-release version of the khoj server run below command*
```shell
# Maps to the latest commit on the master branch
pip install --upgrade --pre khoj-assistant
```
### Upgrade Khoj on Emacs
- Use your Emacs Package Manager to Upgrade
- See [khoj.el package setup](/emacs?id=setup) for details
### Upgrade Khoj on Obsidian
- Upgrade via the Community plugins tab on the settings pane in the Obsidian app
- See the [khoj plugin setup](/obsidian.md?id=_2-setup-plugin) for details
## Uninstall
1. (Optional) Hit `Ctrl-C` in the terminal running the khoj server to stop it
2. Delete the khoj directory in your home folder (i.e `~/.khoj` on Linux, Mac or `C:\Users\<your-username>\.khoj` on Windows)
3. Uninstall the khoj server with `pip uninstall khoj-assistant`
4. (Optional) Uninstall khoj.el or the khoj obsidian plugin in the standard way on Emacs, Obsidian
## Troubleshoot
#### Install fails while building Tokenizer dependency
- **Details**: `pip install khoj-assistant` fails while building the `tokenizers` dependency. Complains about Rust.
- **Fix**: Install Rust to build the tokenizers package. For example on Mac run:
```shell
brew install rustup
rustup-init
source ~/.cargo/env
```
- **Refer**: [Issue with Fix](https://github.com/khoj-ai/khoj/issues/82#issuecomment-1241890946) for more details
#### Search starts giving wonky results
- **Fix**: Open [/api/update?force=true](http://localhost:42110/api/update?force=true) in browser to regenerate index from scratch
- **Note**: *This is a fix for when you perceive the search results have degraded. Not if you think they've always given wonky results*
#### Khoj in Docker errors out with \"Killed\" in error message
- **Fix**: Increase RAM available to Docker Containers in Docker Settings
- **Refer**: [StackOverflow Solution](https://stackoverflow.com/a/50770267), [Configure Resources on Docker for Mac](https://docs.docker.com/desktop/mac/#resources)
#### Khoj errors out complaining about Tensors mismatch or null
- **Mitigation**: Disable `image` search using the desktop GUI
These steps can be used to setup Khoj on a clean, new Windows 11 machine. It has been tested on a Windows VM
## Prerequisites
1. Ensure you have Visual Studio C++ Build tools installed. You can download it [from Microsoft here](https://visualstudio.microsoft.com/visual-cpp-build-tools/). At the minimum, you should have the following configuration:
<imgwidth="1152"alt="Screenshot 2023-07-12 at 3 56 25 PM"src="https://github.com/khoj-ai/khoj/assets/65192171/b506a858-2f5e-4c85-946b-5422d83f112a">
2. Ensure you have Python installed. You can check by running `python --version`. If you don't, install the latest version [from here](https://www.python.org/downloads/).
- Ensure you have pip installed: `py -m ensurepip --upgrade`.
## Quick start
1. Open a PowerShell terminal.
2. Run `pip install khoj-assistant`
3. Start Khoj with `khoj`
## Installation in a Virtual Environment
Use this if you want to install with a virtual environment. This will make it much easier to manage your dependencies. You can read more about [virtual environments](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/) here.
1. Open a PowerShell terminal with the `Run as Administrator` privileges.
2. Create a virtual environment: `mkdir khoj && cd khoj && py -m venv .venv`
3. Activate the virtual environment: `.\.venv\Scripts\activate`. If you get a permissions error, then run `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned`.
1. Open [Khoj](https://obsidian.md/plugins?id=khoj) from the *Community plugins* tab in Obsidian settings panel
2. Click *Install*, then *Enable* on the Khoj plugin page in Obsidian
3. Generate an API key on the [Khoj Web App](https://app.khoj.dev/config#clients)
4. Set your Khoj API Key in the Khoj plugin settings in Obsidian
See the official [Obsidian Plugin Docs](https://help.obsidian.md/Extending+Obsidian/Community+plugins) for more details on installing Obsidian plugins.
## Use
### Chat
Click the *Khoj chat* icon 💬 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or run *Khoj: Chat* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette) and ask questions in a natural, conversational style.<br/>
E.g *"When did I file my taxes last year?"*
See [Khoj Chat](/features/chat) for more details
### Find Similar Notes
To see other notes similar to the current one, run *Khoj: Find Similar Notes* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
### Search
Run *Khoj: Search* from the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
See [Khoj Search](/features/search) for more details. Use [query filters](/miscellaneous/advanced#query-filters) to limit entries to search
Without any desktop clients, you can start chatting with Khoj on the web. Bear in mind you do need one of the desktop clients in order to share and sync your data with Khoj.
## Features
- **Chat**
- **Faster answers**: Find answers quickly, from your private notes or the public internet
- **Assisted creativity**: Smoothly weave across retrieving answers and generating content
- **Iterative discovery**: Iteratively explore and re-discover your notes
- **Search**
- **Natural**: Advanced natural language understanding using Transformer based ML Models
- **Incremental**: Incremental search for a fast, search-as-you-type experience
## Setup
No setup required. The Khoj web app is the default Khoj client. You can access it from any web browser. Try it on [Khoj Cloud](https://app.khoj.dev)
## Upload Documents
You can upload documents to Khoj from the web interface, one at a time. This is useful for uploading documents from your phone or tablet. To upload a document:
1. You can drag and drop the document into the chat window.
2. Or click the paperclip icon in the chat window and select the document from your file system.

### Install on Phone
You can optionally install Khoj as a [Progressive Web App (PWA)](https://web.dev/learn/pwa/installation). This makes it quick and easy to access Khoj on your phone.
1. Login to [Khoj Cloud](https://app.khoj.dev) or your self-hosted Khoj server from the web browser (prefer Chrome/Edge) on your phone
2. Open the 3 dot menu on the browser and click the "Add to Home screen" option
3. Click "Install" on the next screen to add the Khoj icon to your phone Home screen
Text [+1 (848) 800 4242](https://wa.me/18488004242) or scan [this QR code](https://khoj.dev/whatsapp) on your phone to chat with Khoj on WhatsApp.
Without any desktop clients, you can start chatting with Khoj on WhatsApp. Bear in mind you do need one of the desktop clients in order to share and sync your data with Khoj. The WhatsApp AI bot will work right away for answering generic queries and using Khoj in default mode.
In order to use Khoj on WhatsApp with your own data, you need to setup a Khoj Cloud account and connect your WhatsApp account to it. This is a one time setup and you can do it from the [Khoj Cloud config page](https://app.khoj.dev/config).
If you hit usage limits for the WhatsApp bot, upgrade to [a paid plan](https://khoj.dev/pricing) on Khoj Cloud.
## Features
- **Slash Commands**: Use slash commands to quickly access Khoj features
-`/online`: Get responses from Khoj powered by online search.
-`/dream`: Generate an image in response to your prompt.
-`/notes`: Explicitly force Khoj to retrieve context from your notes. Note: You'll need to connect your WhatsApp account to a Khoj Cloud account for this to work.
We have more commands under development, including `/share` to uploading documents directly to your Khoj account from WhatsApp, and `/speak` in order to get a speech response from Khoj. Feel free to [raise an issue](https://github.com/khoj-ai/flint/issues) if you have any suggestions for new commands.
## Nerdy Details
You can find all of the code for the WhatsApp bot in the the [flint repository](https://github.com/khoj-ai/flint). As all of our code, it is open source and you can contribute to it.
Welcome to the development docs of Khoj! Thanks for you interesting in being a contributor ❤️. Open source contributors are a corner-store of the Khoj community. We welcome all contributions, big or small.
To get started with contributing, check out the official GitHub docs on [contributing to an open-source project](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project).
Join the [Discord](https://discord.gg/WaxF3SkFPU) server and click the ✅ for the question "Are you interested in becoming a contributor?" in the `#welcome-and-rules` channel. This will give you access to the `#contributors` channel where you can ask questions and get help from other contributors.
If you're looking for a place to get started, check out the list of [Github Issues](https://github.com/khoj-ai/khoj/issues) with the tag `good first issue` to find issues that are good for first-time contributors.
## Local Server Installation
### Using Pip
#### 1. Install
```mdx-code-block
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
```
```mdx-code-block
<Tabs>
<TabItem value="macos" label="MacOS">
```shell
# Get Khoj Code
git clone https://github.com/khoj-ai/khoj && cd khoj
- **Via the Desktop application**: Add files, directories to index using the settings page of your desktop application. Click "Save" to immediately trigger indexing.
Note: Wait after configuration for khoj to Load ML model, generate embeddings and expose API to query notes, images, documents etc specified in config YAML
### Using Docker
Make sure you install the latest version of [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/).
#### 1. Clone
```shell
git clone https://github.com/khoj-ai/khoj && cd khoj
```
#### 2. Configure
1. Update [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml) to use relevant environment variables.
2. Comment out the `image` line and uncomment the `build` line in the `server` service
#### 3. Run
This will start the Khoj server, and the database.
```shell
docker-compose up -d
```
#### 4. Upgrade
If you've made changes to the codebase, you'll need to rebuild the Docker image before running the container again.
```shell
docker-compose build --no-cache
```
## Update clients
In whichever clients you're using for testing, you'll need to update the server URL to point to your local server. By default, the local server URL should be `http://127.0.0.1:42110`.
## Validate
### Before Making Changes
1. Install Git Hooks for Validation
```shell
pre-commit install -t pre-push -t pre-commit
```
- This ensures standard code formatting fixes and other checks run automatically on every commit and push
- Note 1: If [pre-commit](https://pre-commit.com/#intro) didn't already get installed, [install it](https://pre-commit.com/#install) via `pip install pre-commit`
- Note 2: To run the pre-commit changes manually, use `pre-commit run --hook-stage manual --all` before creating PR
### Before Creating PR
:::tip[Note]
You should be in an active virtual environment for Khoj in order to run the unit tests and linter.
:::
1. Ensure that you have a [Github Issue](https://github.com/khoj-ai/khoj/issues) that can be linked to the PR. If not, create one. Make sure you've tagged one of the maintainers to the issue. This will ensure that the maintainers are notified of the PR and can review it. It's best discuss the code design on an existing issue or Discord thread before creating a PR. This helps get your PR merged faster.
1. Run unit tests.
```shell
pytest
```
2. Run the linter.
```shell
mypy
```
4. Think about how to add unit tests to verify the functionality you're adding in the PR. If you're not sure how to do this, ask for help in the Github issue or on Discord's `#contributors` channel.
### After Creating PR
1. Automated [validation workflows](https://github.com/khoj-ai/khoj/tree/master/.github/workflows) should run for every PR. Tag one of the maintainers in the PR to trigger it.
## Obsidian Plugin Development
### Plugin development setup
The core code for the Obsidian plugin is under `src/interface/obsidian`. The file `main.ts` is a good place to start.
1. In your CLI, go to the directory `src/interface/obsidian` in the Khoj repository.
2. Run `yarn install` to install the dependencies.
3. Run `yarn dev` to start the development server. This will continually rebuild the plugin as you make changes to the code.
- Your code changes will be outputted to a file called `main.js` in the `obsidian` directory.
### Loading your development plugin in Obsidian
1. Make sure you have the Khoj plugin installed in Obsidian. [See the plugin page](https://publish.obsidian.md/hub/02+-+Community+Expansions/02.05+All+Community+Expansions/Plugins/khoj).
1. Open Obsidian and go to your settings (gear icon in the bottom left corner)
2. Click on 'Community Plugins' in the left panel
3. Next to the 'Installed Plugins' heading, click on the folder icon to open the folder with the plugin's source code.
4. Open the `khoj` folder in the file explorer that opens. You'll see a file called `main.js` in this folder. To test your changes, replace this file with the `main.js` file that was generated by the development server in the previous section.
## Create Khoj Release (Only for Maintainers)
Follow the steps below to [release](https://github.com/debanjum/khoj/releases/) Khoj. This will create a stable release of Khoj on [Pypi](https://pypi.org/project/khoj-assistant/), [Melpa](https://stable.melpa.org/#%252Fkhoj) and [Obsidian](https://obsidian.md/plugins?id%253Dkhoj). It will also create desktop apps of Khoj and attach them to the latest release.
1. Create and tag release commit by running the bump_version script. The release commit sets version number in required metadata files.
```shell
./scripts/bump_version.sh -c "<release_version>"
```
2. Push commit and then the tag to trigger the release workflow to create Release with auto generated release notes.
You can use agents to setup custom system prompts with Khoj. The server host can setup their own agents, which are accessible to all users. You can see ours at https://app.khoj.dev/agents.

## Creating an Agent (Self-Hosted)
Go to `server/admin/database/agent` on your server and click `Add Agent` to create a new one. You have to set it to `public` in order for it to be accessible to all the users on your server. To limit access to a specific user, do not set the `public` flag and add the user in the `Creator` field.
Set your custom prompt in the `personality` field.
Khoj supports a variety of features, including search and chat with a wide range of data sources and interfaces.
#### [Search](/features/search)
- **Local**: Your personal data stays local. All search and indexing is done on your machine when you [self-host](/get-started/setup)
- **Incremental**: Incremental search for a fast, search-as-you-type experience
#### [Chat](/features/chat)
- **Faster answers**: Find answers faster, smoother than search. No need to manually scan through your notes to find answers.
- **Iterative discovery**: Iteratively explore and (re-)discover your notes
- **Assisted creativity**: Smoothly weave across answers retrieval and content generation
- **Works online or offline**: Chat using online or offline AI chat models
#### General
- **Cloud or Self-Host**: Use [cloud](https://app.khoj.dev/login) to use Khoj anytime from anywhere or [self-host](/get-started/setup) for privacy
- **Natural**: Advanced natural language understanding using Transformer based ML Models
- **Pluggable**: Modular architecture makes it easy to plug in new data sources, frontends and ML models
- **Multiple Sources**: Index your Org-mode, Markdown, PDF, plaintext files, Github repos and Notion pages
- **Multiple Interfaces**: Interact from your Web Browser, Emacs, Obsidian, Desktop app or even Whatsapp
### Supported Interfaces
Khoj is available as a [Desktop app](/clients/desktop), [Emacs package](/clients/emacs), [Obsidian plugin](/clients/obsidian), [Web app](/clients/web) and a [Whatsapp AI](https://khoj.dev/whatsapp).

### Supported Data Sources
Khoj can understand your org-mode, markdown, PDF, plaintext files, [Github projects](/online-data-sources/github_integration) and [Notion pages](/online-data-sources/notion_integration).
You can configure Khoj to chat with you about anything. When relevant, it'll use any notes or documents you shared with it to respond.
### Overview
- Creates a personal assistant for you to inquire and engage with your notes
- You can choose to use Online or Offline Chat depending on your requirements
- Supports multi-turn conversations with the relevant notes for context
- Shows reference notes used to generate a response
### Setup (Self-Hosting)
#### Offline Chat
Offline chat stays completely private and can work without internet using open-source models.
> **System Requirements**:
> - Minimum 8 GB RAM. Recommend **16Gb VRAM**
> - Minimum **5 GB of Disk** available
> - A CPU supporting [AVX or AVX2 instructions](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) is required
> - An Nvidia, AMD GPU or a Mac M1+ machine would significantly speed up chat response times
1. Open your [Khoj offline settings](http://localhost:42110/server/admin/database/offlinechatprocessorconversationconfig/) and click *Enable* on the Offline Chat configuration.
2. Open your [Chat model options settings](http://localhost:42110/server/admin/database/chatmodeloptions/) and add any [GGUF chat model](https://huggingface.co/models?library=gguf) to use for offline chat. Make sure to use `Offline` as its type. For a balanced chat model that runs well on standard consumer hardware we recommend using [Hermes-2-Pro-Mistral-7B by NousResearch](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF) by default.
:::tip[Note]
Offline chat is not supported for a multi-user scenario. The host machine will encounter segmentation faults if multiple users try to use offline chat at the same time.
:::
#### Online Chat
Online chat requires internet to use ChatGPT but is faster, higher quality and less compute intensive.
:::danger[Warning]
This will enable Khoj to send your chat queries and query relevant notes to OpenAI for processing.
:::
1. Get your [OpenAI API Key](https://platform.openai.com/account/api-keys)
2. Open your [Khoj Online Chat settings](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/). Add a new setting with your OpenAI API key, and click *Save*. Only one configuration will be used, so make sure that's the only one you have.
3. Open your [Chat model options](http://localhost:42110/server/admin/database/chatmodeloptions/) and add a new option for the OpenAI chat model you want to use. Make sure to use `OpenAI` as its type.
### Use
1. Open Khoj Chat
- **On Web**: Open [/chat](https://app.khoj.dev/chat) in your web browser
- **On Obsidian**: Search for *Khoj: Chat* in the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
- **On Emacs**: Run `M-x khoj <user-query>`
2. Enter your queries to chat with Khoj. Use [slash commands](#commands) and [query filters](/miscellaneous/advanced#query-filters) to change what Khoj uses to respond

#### Details
1. Your query is used to retrieve the most relevant notes, if any, using Khoj search
2. These notes, the last few messages and associated metadata is passed to the enabled chat model along with your query to generate a response
#### Commands
Slash commands allows you to change what Khoj uses to respond to your query
- **/notes**: Limit chat to only respond using your notes, not just Khoj's general world knowledge as reference
- **/general**: Limit chat to only respond using Khoj's general world knowledge, not using your notes as reference
- **/default**: Allow chat to respond using your notes or it's general knowledge as reference. It's the default behavior when no slash command is used
- **/online**: Use online information and incorporate it in the prompt to the LLM to send you a response.
- **/image**: Generate an image in response to your query.
- **/help**: Use /help to get all available commands and general information about Khoj
You can use Khoj to generate images from text prompts. You can get deeper into the details of our image generation flow in this blog post: https://blog.khoj.dev/posts/how-khoj-generates-images/.
To generate images, you just need to provide a prompt to Khoj in which the image generation is in the instructions. Khoj will automatically detect the image generation intent, augment your generation prompt, and then create the image. Here are some examples:
| Prompt | Image |
| --- | --- |
| Paint a picture of the plants I got last month, pixar-animation |  |
| Create a picture of my dream house, based on my interests |  |
## Setup (Self-Hosting)
Right now, we only support integration with OpenAI's DALL-E. You need to have an OpenAI API key to use this feature. Here's how you can set it up:
1. Setup your OpenAI API key. See instructions [here](/get-started/setup#2-configure)
2. Create a text to image config at http://localhost:42110/server/admin/database/texttoimagemodelconfig/. We recommend the value `dall-e-3`.
By default, Khoj will try to infer which information-sourcing tools are required to answer your question. Sometimes, you'll have a need for outside questions that the LLM's knowledge doesn't cover. In that case, it will use the `online` search feature.
For example, these queries would trigger an online search:
- What's the latest news about the Israel-Palestine war?
- Where can I find the best pizza in New York City?
- Deadline for filing taxes 2024.
- Give me a summary of this article: https://en.wikipedia.org/wiki/Haitian_Revolution
Try it out yourself! https://app.khoj.dev
## Self-Hosting
The general online search function currently requires an API key from Serper.dev. You can grab one here: https://serper.dev/, and then add it as an environment variable with the name `SERPER_DEV_API_KEY`.
Without any API keys, Khoj will use the `requests` library to directly read any webpages you give it a link to. This means that you can use Khoj to read any webpage that you have access in your local network.
Take advantage of super fast search to find relevant notes and documents from your Second Brain.
### Use
1. Open Khoj Search
- **On Web**: Open https://app.khoj.dev/ in your web browser
- **On Obsidian**: Click the *Khoj search* icon 🔎 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or Search for *Khoj: Search* in the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
- **On Emacs**: Run `M-x khoj <user-query>`
2. Query using natural language to find relevant entries from your knowledge base. Use [query filters](/miscellaneous/advanced#query-filters) to limit entries to search
You can talk to Khoj using your voice. Khoj will respond to your queries using the same models as the chat feature. You can use voice chat on the web, Desktop, and Obsidian apps. Click on the little mic icon to send your voice message to Khoj. It will send back what it heard via text. You'll have some time to edit it before sending it, if required. Try it at https://app.khoj.dev/.
:::info[Voice Response]
Khoj doesn't yet respond with voice, but it will send back a text response. Let us know if you're interested in voice responses at team at khoj.dev.
:::
## Setup (Self-Hosting)
Voice chat will automatically be configured when you initialize the application. The default configuration will run locally. If you want to use the OpenAI whisper API for voice chat, you can set it up by following these steps:
1. Setup your OpenAI API key. See instructions [here](/get-started/setup#2-configure).
2. Create a new configuration at http://localhost:42110/server/admin/database/speechtotextmodeloptions/. We recommend the value `whisper-1` and model type `Openai`.
If you're using Khoj to index you personal data, it's almost certain you'll have sensitive and private information you'd like to index.
Khoj is designed to be a personal AI, so one of our cornerstone principles is to make it as privacy-friendly as possible. That's why, you can *always* choose to run Khoj on your own hardware, and never share your data outside of your device. You can generate your embeddings directly on your machine, and then use an offline chat client so that your data never leaves your machine. You'll find the instructions to [self-hosting](./setup.mdx) here.
Here's what to consider if you're using Khoj, whether self-hosted or on our cloud:
1. Some of your relevant indexed data may be included as context when you chat with Khoj. This means that it may be sent to OpenAI, if you use one of the OpenAI models.
1. We collect completely anonymized usage telemetry and send it to [PostHog](https://posthog.com/). This includes data like unique chat requests, unique search requests, unique requests to index data. Usage data is collected to help us understand how people are using Khoj, and to help us prioritize features.
- We do not log your IP address, nor upload any of your personal data to PostHog.
- You can see our telemetry aggregation code [here](https://github.com/khoj-ai/khoj/blob/master/src/khoj/routers/helpers.py#L71) and see our telemetry server [here](https://github.com/khoj-ai/khoj/blob/master/src/telemetry/telemetry.py).
- If you're self-hosting, you can opt out of telemetry by following [these instructions](./miscellaneous/telemetry).
Self-hosting isn't for everyone, so we've still taken steps to make Khoj privacy-friendly, even if you choose to use our [cloud offering](https://app.khoj.dev/login). Here's what to consider when using Khoj Cloud:
1. Your embeddings are generated by an open source model within our own dedicated endpoint [hosted on AWS with Huggingface](https://huggingface.co/inference-endpoints/dedicated). There's zero persistent memory to the Huggingface Inference endpoints (it's stateless).
1. Your embeddings and the associated raw text are stored in a secure Postgres DB in our private AWS cloud. Your data is sharded on a unique user ID. We store the raw text in your files to improve file syncing and provide context when you chat with Khoj.
1. When you use the single-sign-on option with Google, we only receive your name, a link to your profile photo, and your email address.
:::tip[Info]
Your data is yours. We do not sell your data or use it for training models. Khoj is a sustainable, open-source alternative to closed-source, commercial personal AI. We have no interest in selling your data to make a quick buck.
:::
We have lots of ideas of how to make Khoj really robust as a personal AI and cloud offering, but also trust-less and privacy-centric. Please [reach out](mailto:team@khoj.dev) if this is important to you, and you'd like to help us build it.
Learn about how to self-host Khoj on your own machine.
Benefits to self-hosting:
1. **Privacy**: Your data will never have to leave your private network. You can even use Khoj without an internet connection if deployed on your personal computer.
2. **Customization**: You can customize Khoj to your liking, from models, to host URL, to feature enablement.
```mdx-code-block
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
```
## Setup
These are the general setup instructions for self-hosted Khoj.
- Make sure [python](https://realpython.com/installing-python/) and [pip](https://pip.pypa.io/en/stable/installation/) are installed on your machine
- Check the [Khoj Emacs docs](/clients/emacs#setup) to setup Khoj with Emacs<br />
It's simpler as it can skip the server *install*, *run* and *configure* step below.
- Check the [Khoj Obsidian docs](/clients/obsidian#setup) to setup Khoj with Obsidian<br />
Its simpler as it can skip the *configure* step below.
For Installation, you can either use Docker or install the Khoj server locally.
:::info[Offline Model + GPU]
If you want to use the offline chat model and you have a GPU, you should use Installation Option 2 - local setup via the Python package directly. Our Docker image doesn't currently support running the offline chat model on GPU, making inference times really slow.
:::
### Installation Option 1 (Docker)
#### Prerequisites
1. Install Docker Engine. See [official instructions](https://docs.docker.com/engine/install/).
2. Ensure you have Docker Compose. See [official instructions](https://docs.docker.com/compose/install/).
#### Setup
Use the sample docker-compose [in Github](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml) to run Khoj in Docker. Start by configuring all the environment variables to your choosing. Your admin account will automatically be created based on the admin credentials in that file, so pay attention to those. To start the container, run the following command in the same directory as the docker-compose.yml file. This will automatically setup the database and run the Khoj server.
```shell
docker-compose up
```
Khoj should now be running at http://localhost:42110. You can see the web UI in your browser.
### Installation Option 2 (Local)
#### Prerequisites
##### Install Postgres (with PgVector)
Khoj uses the `pgvector` package to store embeddings of your index in a Postgres database. In order to use this, you need to have Postgres installed.
```mdx-code-block
<Tabs groupId="operating-systems">
<TabItem value="macos" label="MacOS">
Install [Postgres.app](https://postgresapp.com/). This comes pre-installed with `pgvector` and relevant dependencies.
</TabItem>
<TabItem value="win" label="Windows">
1. Use the [recommended installer](https://www.postgresql.org/download/windows/).
2. Follow instructions to [Install PgVector](https://github.com/pgvector/pgvector#windows) in case you need to manually install it. Windows support is experimental for pgvector currently, so we recommend using Docker.
</TabItem>
<TabItem value="unix" label="Linux">
From [official instructions](https://wiki.postgresql.org/wiki/Apt)
</TabItem>
<TabItem value="source" label="From Source">
1. Follow instructions to [Install Postgres](https://www.postgresql.org/download/)
2. Follow instructions to [Install PgVector](https://github.com/pgvector/pgvector#installation) in case you need to manually install it.
</TabItem>
</Tabs>
```
##### Create the Khoj database
Make sure to update your environment variables to match your Postgres configuration if you're using a different name. The default values should work for most people. When prompted for a password, you can use the default password `postgres`, or configure it to your preference. Make sure to set the environment variable `POSTGRES_PASSWORD` to the same value as the password you set here.
```mdx-code-block
<Tabs groupId="operating-systems">
<TabItem value="macos" label="MacOS">
```shell
createdb khoj -U postgres --password
```
</TabItem>
<TabItem value="win" label="Windows">
```shell
createdb -U postgres khoj --password
```
</TabItem>
<TabItem value="unix" label="Linux">
```shell
sudo -u postgres createdb khoj --password
```
</TabItem>
</Tabs>
```
#### Install package
##### Local Server Setup
- *Make sure [python](https://realpython.com/installing-python/) and [pip](https://pip.pypa.io/en/stable/installation/) are installed on your machine*
- Check [llama-cpp-python setup](https://python.langchain.com/docs/integrations/llms/llamacpp#installation) if you hit any llama-cpp issues with the installation
Run the following command in your terminal to install the Khoj backend.
Before getting started, configure the following environment variables in your terminal for the first run
```mdx-code-block
<Tabs groupId="operating-systems">
<TabItem value="macos" label="MacOS">
```shell
export KHOJ_ADMIN_EMAIL=<your-email>
export KHOJ_ADMIN_PASSWORD=<your-password>
```
</TabItem>
<TabItem value="win" label="Windows">
If you're using PowerShell:
```shell
$env:KHOJ_ADMIN_EMAIL="<your-email>"
$env:KHOJ_ADMIN_PASSWORD="<your-password>"
```
</TabItem>
<TabItem value="unix" label="Linux">
```shell
export KHOJ_ADMIN_EMAIL=<your-email>
export KHOJ_ADMIN_PASSWORD=<your-password>
```
</TabItem>
</Tabs>
```
Run the following command from your terminal to start the Khoj backend and open Khoj in your browser.
```shell
khoj --anonymous-mode
```
`--anonymous-mode` allows you to run the server without setting up Google credentials for login. This allows you to use any of the clients without a login wall. If you want to use Google login, you can skip this flag, but you will have to add your Google developer credentials.
On the first run, you will be prompted to input credentials for your admin account and do some basic configuration for your chat model settings. Once created, you can go to http://localhost:42110/server/admin and login with the credentials you just created.
Khoj should now be running at http://localhost:42110. You can see the web UI in your browser.
Note: To start Khoj automatically in the background use [Task scheduler](https://www.windowscentral.com/how-create-automated-task-using-task-scheduler-windows-10) on Windows or [Cron](https://en.wikipedia.org/wiki/Cron) on Mac, Linux (e.g with `@reboot khoj`)
### Setup Notes
Optionally, you can use Khoj with a custom domain as well. To do so, you need to set the `KHOJ_DOMAIN` environment variable to your domain (e.g., `export KHOJ_DOMAIN=my-khoj-domain.com` or add it to your `docker-compose.yml`). By default, the Khoj server you set up will not be accessible outside of `localhost` or `127.0.0.1`.
:::warning[Must use an SSL certificate]
If you're using a custom domain, you must use an SSL certificate. You can use [Let's Encrypt](https://letsencrypt.org/) to get a free SSL certificate for your domain.
:::
### 2. Configure
1. Go to http://localhost:42110/server/admin and login with your admin credentials.
1. Go to [OpenAI settings](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/) in the server admin settings to add an OpenAI processor conversation config. This is where you set your API key. Alternatively, you can go to the [offline chat settings](http://localhost:42110/server/admin/database/offlinechatprocessorconversationconfig/) and simply create a new setting with `Enabled` set to `True`.
2. Go to the ChatModelOptions if you want to add additional models for chat.
- Set the `chat-model` field to a supported chat model[^1] of your choice. For example, you can specify `gpt-4-turbo-preview` if you're using OpenAI or `NousResearch/Hermes-2-Pro-Mistral-7B-GGUF` if you're using offline chat.
- Make sure to set the `model-type` field to `OpenAI` or `Offline` respectively.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only when using a non-standard model (i.e not mistral, gpt or llama2 model).
1. Select files and folders to index [using the desktop client](/get-started/setup#2-download-the-desktop-client). When you click 'Save', the files will be sent to your server for indexing.
- Select Notion workspaces and Github repositories to index using the web interface.
[^1]: Khoj, by default, can use [OpenAI GPT3.5+ chat models](https://platform.openai.com/docs/models/overview) or [GGUF chat models](https://huggingface.co/models?library=gguf). See [this section](/miscellaneous/advanced#use-openai-compatible-llm-api-server-self-hosting) to use non-standard chat models
:::tip[Note]
Using Safari on Mac? You might not be able to login to the admin panel. Try using Chrome or Firefox instead.
:::
### 3. Download the desktop client (Optional)
You can use our desktop executables to select file paths and folders to index. You can simply select the folders or files, and they'll be automatically uploaded to the server. Once you specify a file or file path, you don't need to update the configuration again; it will grab any data diffs dynamically over time.
**To download the latest desktop client, go to https://download.khoj.dev** and the correct executable for your OS will automatically start downloading. You can also go to https://khoj.dev/downloads to explicitly download your image of choice. Once downloaded, you can configure your folders for indexing using the settings tab. To set your chat configuration, you'll have to use the web interface for the Khoj server you setup in the previous step.
To use the desktop client, you need to go to your Khoj server's settings page (http://localhost:42110/config) and copy the API key. Then, paste it into the desktop client's settings page. Once you've done that, you can select files and folders to index. Set the desktop client settings to use `http://127.0.0.1:42110` as the host URL.
### 4. Install Client Plugins (Optional)
Khoj exposes a web interface to search, chat and configure by default.<br />
The optional steps below allow using Khoj from within an existing application like Obsidian or Emacs.
- **Khoj Obsidian**:<br />
[Install](/clients/obsidian#setup) the Khoj Obsidian plugin
- **Khoj Emacs**:<br />
[Install](/clients/emacs#setup) khoj.el
#### Setup host URL
To configure your host URL on your clients when self-hosting, use `http://127.0.0.1:42110`. This is the default port for the Khoj server. Note that `localhost` will not work.
### 5. Use Khoj 🚀
You can head to http://localhost:42110 to use the web interface. You can also use the desktop client to search and chat.
## Upgrade
### Upgrade Khoj Server
```mdx-code-block
<Tabs groupId="environment">
<TabItem value="localsetup" label="Local Setup">
```shell
pip install --upgrade khoj-assistant
```
*Note: To upgrade to the latest pre-release version of the khoj server run below command*
</TabItem>
<TabItem value="docker" label="Docker">
From the same directory where you have your `docker-compose` file, this will fetch the latest build and upgrade your server.
```shell
docker-compose up --build
```
</TabItem>
<TabItem value="emacs" label="Emacs">
- Use your Emacs Package Manager to Upgrade
- See [khoj.el package setup](/clients/emacs#setup) for details
</TabItem>
<TabItem value="obsidian" label="Obsidian">
- Upgrade via the Community plugins tab on the settings pane in the Obsidian app
- See the [khoj plugin setup](/clients/obsidian#setup) for details
</TabItem>
</Tabs>
```
## Uninstall
### Uninstall Khoj Server
```mdx-code-block
<Tabs groupId="environment">
<TabItem value="localsetup" label="Local Setup">
```shell
# uninstall khoj server
pip uninstall khoj-assistant
# delete khoj postgres db
dropdb khoj -U postgres
```
</TabItem>
<TabItem value="docker" label="Docker">
From the same directory where you have your `docker-compose` file, run the command below to remove the server to delete its containers, networks, images and volumes.
```shell
docker-compose down --volumes
```
</TabItem>
<TabItem value="emacs" label="Emacs">
Uninstall the khoj Emacs, or desktop client in the standard way from Emacs or your OS respectively
You can also `rm -rf ~/.khoj` to remove the Khoj data directory if did a local install.
</TabItem>
<TabItem value="obsidian" label="Obsidian">
Uninstall the khoj Obisidan, or desktop client in the standard way from Obsidian or your OS respectively
You can also `rm -rf ~/.khoj` to remove the Khoj data directory if did a local install.
</TabItem>
</Tabs>
```
## Troubleshoot
#### Dependency conflict when trying to install Khoj python package with pip
- **Reason**: When conflicting dependency versions are required by Khoj vs other python packages installed on your system
- **Fix**: Install Khoj in a python virtual environment using [venv](https://docs.python.org/3/library/venv.html) or [pipx](https://pypa.github.io/pipx) to avoid this dependency conflicts
2. Use `pipx` to install Khoj to avoid dependency conflicts with other python packages.
```shell
pipx install khoj-assistant
```
3. Now start `khoj` using the standard steps described earlier
#### Install fails while building Tokenizer dependency
- **Details**: `pip install khoj-assistant` fails while building the `tokenizers` dependency. Complains about Rust.
- **Fix**: Install Rust to build the tokenizers package. For example on Mac run:
```shell
brew install rustup
rustup-init
source ~/.cargo/env
```
- **Refer**: [Issue with Fix](https://github.com/khoj-ai/khoj/issues/82#issuecomment-1241890946) for more details
#### Khoj in Docker errors out with \"Killed\" in error message
- **Fix**: Increase RAM available to Docker Containers in Docker Settings
- **Refer**: [StackOverflow Solution](https://stackoverflow.com/a/50770267), [Configure Resources on Docker for Mac](https://docs.docker.com/desktop/mac/#resources)
## Search across Different Languages (Self-Hosting)
To search for notes in multiple, different languages, you can use a [multi-lingual model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models).<br/>
For example, the [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) supports [50+ languages](https://www.sbert.net/docs/pretrained_models.html#:~:text=we%20used%20the%20following%2050%2B%20languages), has good search quality and speed. To use it:
1. Manually update the search config in server's admin settings page. Go to [the search config](http://localhost:42110/server/admin/database/searchmodelconfig/). Either create a new one, if none exists, or update the existing one. Set the bi_encoder to `sentence-transformers/multi-qa-MiniLM-L6-cos-v1` and the cross_encoder to `cross-encoder/ms-marco-MiniLM-L-6-v2`.
2. Regenerate your content index from all the relevant clients. This step is very important, as you'll need to re-encode all your content with the new model.
## Query Filters
Use structured query syntax to filter entries from your knowledge based used by search results or chat responses.
- **Word Filter**: Get entries that include/exclude a specified term
- Entries that contain term_to_include: `+"term_to_include"`
- Entries that contain term_to_exclude: `-"term_to_exclude"`
- **Date Filter**: Get entries containing dates in YYYY-MM-DD format from specified date (range)
- Entries from April 1st 1984: `dt:"1984-04-01"`
- Entries after March 31st 1984: `dt>="1984-04-01"`
- Entries before April 2nd 1984 : `dt<="1984-04-01"`
- **File Filter**: Get entries from a specified file
- Entries from incoming.org file: `file:"incoming.org"`
- Combined Example
-`what is the meaning of life? file:"1984.org" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"`
- Adds all filters to the natural language query. It should return entries
- from the file *1984.org*
- containing dates from the year *1984*
- excluding words *"big"* and *"brother"*
- that best match the natural language query *"what is the meaning of life?"*
## Use OpenAI compatible LLM API Server (Self Hosting)
Use this if you want to use non-standard, open or commercial, local or hosted LLM models for Khoj chat
1. Setup your desired chat LLM by installing an OpenAI compatible LLM API Server like [LiteLLM](https://docs.litellm.ai/docs/proxy/quick_start), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#openai-compatible-web-server)
2. Set environment variable `OPENAI_API_BASE="<url-of-your-llm-server>"` before starting Khoj
3. Add ChatModelOptions with `model-type``OpenAI`, and `chat-model` to anything (e.g `gpt-3.5-turbo`) during [Config](/get-started/setup#3-configure)
- *(Optional)* Set the `tokenizer` and `max-prompt-size` relevant to the actual chat model you're using
#### Sample Setup using LiteLLM and Mistral API
```shell
# Install LiteLLM
pip install litellm[proxy]
# Start LiteLLM and use Mistral tiny via Mistral API
Many Open Source projects are used to power Khoj. Here's a few of them:
- [Multi-QA MiniLM Model](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), [All MiniLM Model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for Text Search. See [SBert Documentation](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)
- [OpenAI CLIP Model](https://github.com/openai/CLIP) for Image Search. See [SBert Documentation](https://www.sbert.net/examples/applications/image-search/README.html)
- Charles Cave for [OrgNode Parser](http://members.optusnet.com.au/~charles57/GTD/orgnode.html)
- [Org.js](https://mooz.github.io/org-js/) to render Org-mode results on the Web interface
- [Markdown-it](https://github.com/markdown-it/markdown-it) to render Markdown results on the Web interface
- [GPT4All](https://github.com/nomic-ai/gpt4all) to chat with local LLM
- [Llama.cpp](https://github.com/ggerganov/llama.cpp) to chat with local LLM
The Github integration allows you to index as many repositories as you want. It's currently default configured to index Issues, Commits, and all Markdown/Org files in each repository. For large repositories, this takes a fairly long time, but it works well for smaller projects.
# Configure your settings
1. Go to [http://localhost:42110/config](http://localhost:42110/config) and enter in settings for the data sources you want to index. You'll have to specify the file paths.
1. Go to [https://app.khoj.dev/config](https://app.khoj.dev/config) and enter in settings for the data sources you want to index. You'll have to specify the file paths.
## Use the Github plugin
1. Generate a [classic PAT (personal access token)](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens) from [Github](https://github.com/settings/tokens) with `repo` and `admin:org` scopes at least.
2. Navigate to [http://localhost:42110/config/content_type/github](http://localhost:42110/config/content_type/github) to configure your Github settings. Enter in your PAT, along with details for each repository you want to index.
2. Navigate to [https://app.khoj.dev/config/content-source/github](https://app.khoj.dev/config/content-source/github) to configure your Github settings. Enter in your PAT, along with details for each repository you want to index.
3. Click `Save`. Go back to the settings page and click `Configure`.
4. Go to [http://localhost:42110/](http://localhost:42110/) and start searching!
4. Go to [https://app.khoj.dev/](https://app.khoj.dev/) and start searching!
Khoj now supports search/chat with pages in your Notion workspaces. [Notion](notion.so/) is a platform people use for taking notes, especially for collaboration.
The Notion integration allows you to search/chat with your Notion workspaces. [Notion](https://notion.so/) is a platform people use for taking notes, especially for collaboration.
We haven't setup a fancy integration with OAuth yet, so this integration still requires some effort on your end to generate an API key.
Go to https://app.khoj.dev/config to connect your Notion workspace(s) to Khoj.
4. In the first step, you generated an API key. Use the newly generated API Key in your Khoj settings, by default at http://localhost:42110/config/content_type/notion. Click `Save`.
4. In the first step, you generated an API key. Use the newly generated API Key in your Khoj settings, by default at http://localhost:42110/config/content-source/notion. Click `Save`.
5. Click `Configure` in http://localhost:42110/config to index your Notion workspace(s).
That's it! You should be ready to start searching and chatting. Make sure you've configured your OpenAI API Key for chat.
That's it! You should be ready to start searching and chatting. Make sure you've configured your [chat settings](/get-started/setup#2-configure).
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