This avoids installing pgserver on linux arm64 docker builds, which it
doesn't currently support and isn't required to support as Khoj docker
images can use standard postgres server made available via our
docker-compose.yml
Use pgserver python package as an embedded postgres db,
installed directly as a khoj python package dependency.
This significantly simplifies self-hosting with just a `pip install khoj'.
No need to also install postgres separately.
Still use standard postgres server for multi-user, production use-cases.
- Update default anthropic chat models to latest good models.
- Now that Google supports a good text to image model. Suggest adding
that if Google AI API is setup on first run.
Previously agent slug was not considered on create even when passed
explicitly in agent creation step.
This made the default agent slug different until next run when it was
updated after creation. And didn't allow chat to work on first run
The fix to use the agent slug when explicitly passed allows users to
chat on first run.
Previously messages got Anthropic specific formatting done before
being passed to Anthropic (chat) completion functions.
Move the code to format messages of type list[ChatMessage] into Anthropic
specific format down to the Anthropic (chat) completion functions.
This allows the rest of the functionality like prompt tracing to work
with normalized list[ChatMesssage] type of chat messages across AI API
providers
Previously we'd always request up to 3 webpage url via the prompt but
read only one of the requested webpage url.
This would degrade quality of research and default mode. As model may
request reading upto 3 webpage links but get only one of the requested
webpages read.
This change passes the number of webpages to read down to the AI model
dynamically via the updated prompt. So number of webpages requested to
be read should mostly be same as number of webpages actually read.
Note: For now, the max webpages to read is kept same as before at 1.
Previously the research mode planner ignored the current agent or
conversation specific chat model the user was chatting with. Only the
server chat settings, user default chat model, first created chat model
were considered to decide the planner chat model.
This change considers the agent chat model to be used for the planner
as well. The actual chat model picked is decided by the existing
prioritization of server > agent > user > first chat model.
This change enables the creator of a shared conversation to stop sharing the conversation publicly.
### Details
1. Create an API endpoint to enable the owner of the shared conversation to unshare it
2. Unshare a public conversations from the title pane of the public conversation on the web app
Only show the unshare button on public conversations created by the
currently logged in user. Otherwise hide the button
Set conversation.isOwner = true only if currently logged in user
shared the current conversation.
This isOwner information is passed by the get shared conversation API
endpoint
Previously messages passed to gemini (chat) completion functions
got a little of Gemini specific formatting mixed in.
These functions expect a message of type list[ChatMessage] to work
with prompt tracer etc.
Move the code to format messages of type list[ChatMessage] into gemini
specific format down to the gemini (chat) completion functions.
This allows the rest of the functionality like prompt tracing to work
with normalize list[ChatMesssage] type of chat messages across
providers
This is analogous to how we enable extended thinking for claude models
in research mode.
Default to medium effort irrespective of deepthought for openai
reasoning models as high effort is currently flaky with regular
timeouts and low effort isn't great.
Sets env vars to empty if condition not met so:
- Terrarium (not e2b) used as code sandbox on release triggered eval
- Internet turned off for math500 eval
- Anthropic expects a 0-1 range. Gemini & OpenAI expect a 0-2 range
- Anneal temperature to explore reasoning trajectories but respond factually
- Default send_message_to_model and extract_question temps to the same
Enable configuring a Khoj AI model API for Vertex AI using GCP credentials.
Specifically use the api key & api base url fields of the AI Model API
associated with the current chat model to extract gcp region, gcp
project id & credentials. This helps create a AnthropicVertex client.
The api key field should contain the GCP service account keyfile as a
base64 encoded string.
The api base url field should be of the form
`https://{MODEL_GCP_REGION}-aiplatform.googleapis.com/v1/projects/{YOUR_GCP_PROJECT_ID}`
Accepting GCP credentials via the AI model API makes it easy to use
across local and cloud environments. As it bypasses the need for a
separate service account key file on the Khoj server.
- The 3.4.1 release of sentence tranformer fixes offline load latency
of sentence transformer models (and Khoj) by avoiding call to HF
- The 4.50.0 release of transformers is resulting in
jax error (unexpected keyword argument 'flatten_with_keys') on load.
Previously google auth library was explicitly installed only for the
cloud variant of Khoj to minimize packages installed for non
production use-cases.
But it was being implicitly installed as a dependency of an explicit
package in the default installation anyway.
Making the dependency on google auth package explicit simplifies
the conditional import of google auth in code while not incurring any
additional cost in terms of space or complexity.
Reaching >94% in research mode on SimpleQA. When answers can be
researched online, it becomes too easy. And the FRAMES eval does a
more thorough job of evaluating that use-case anyway.
- Fix regression: Inline images were not getting passed to the AI
models since #992
- Format inline images passed to Gemini models correctly
- Format inline images passed to Anthropic models correctly
Verified vision working with inline and url images for OpenAI,
Anthropic and Gemini models.
Resolves#1112
Previously on slow connection you'd see the agent dropdown flicker
from undefined to Khoj default agent on phones and other thin screens.
This is unnecessary and jarring. Populate with default agent to remove
this issue
Previously the chat input area didn't allow inputting text while Khoj is
researching and generating response.
This change allows the user to add their next text while Khoj
responds. This should speed up interaction cycles as user can have
their next query ready to send when Khoj finishes its response.
- Trigger
Gemini 2.0 Flash doesn't always follow JSON schema in research prompt
- Details
- Use json schema to enforce generate online queries format
- Use json schema to enforce research mode tool pick format
- Support constraining Gemini model output to specified response schema
- Support constraining OpenAI model output to specified response schema
- Only enforce json output in supported AI model APIs
- Simplify OpenAI reasoning model specific arguments to OpenAI API
Previously OpenAI reasoning models didn't support stream_options and
response_format
Add reasoning_effort arg for calls to OpenAI reasoning models via API.
Right now it defaults to medium but can be changed to low or high
Previously was encoding E2B code execution text output content as b64.
This was breaking
- The AI model's ability to see the content of the file
- Downloading the output text file with appropriately encoded content
Issue created when adding E2B code sandbox in #1120
* Implement better bug issue template
* Fix IDs in new bug issue template
* Reduce, reorder and improve field descriptions in the bug issue template
---------
Co-authored-by: Debanjum <debanjum@gmail.com>
Claude 3.7 Sonnet is Anthropic's first reasoning model. It provides a
single model/api capable of standard and extended thinking. Utilize
the extended thinking in Khoj's research mode.
Increase default max output tokens to 8K for Anthropic models.
# Improve Code Tool, Sandbox
- Improve code gen chat actor to output code in inline md code blocks
- Stop code sandbox on request timeout to allow sandbox process restarts
- Use tenacity retry decorator to retry executing code in sandbox
- Add retry logic to code execution and add health check to sandbox container
- Add E2B as an optional code sandbox provider
# Improve Gemini Chat Models
- Default to non-zero temperature for all queries to Gemini models
- Default to Gemini 2.0 flash instead of 1.5 flash on setup
- Set default chat model to KHOJ_CHAT_MODEL env var if set
Simplify code gen chat actor to improve correct code gen success,
especially for smaller models & models with limited json mode support
Allow specify code blocks inline with reasoning to try improve
code quality
Infer input files based on user file paths referenced in code.
- Specify E2B api key and template to use via env variables
- Try load, use e2b library when E2B api key set
- Fallback to try use terrarium sandbox otherwise
- Enable more python packages in e2b sandbox like rdkit via custom e2b template
- Use Async E2B Sandbox
- Parallelize file IO with sandbox
- Add documentation on how to enable E2B as code sandbox instead of Terrarium
- Batch sync files by size to try not exceed API request payload size limits
- Fix force sync of large vaults from Obsidian
- Add API endpoint to delete all indexed files by file type
- Fix to also delete file objects when call DELETE content source API
Previously if you tried to force sync a vault with more than 1000
files it would only end up keeping the last batch because the PUT API
call would delete all previous entries.
This change calls DELETE for all previously indexed data first, followed by
a PATCH to index current vault on a force sync (regenerate) request.
This ensures that files from previous batches are not deleted.
- Delete file objects on deleting content by source via API
Previously only entries were deleted, not the associated file objects
- Add new db adapter to delete multiple file objects (by name)
- Set KHOJ_ALLOWED_DOMAIN to the domain that Khoj is accessible on
from the host machine. This can be the internal i.p or domain of the
host machine.
It can be used by your load balancer/reverse_proxy to access Khoj.
For example, if the load balancer service is in the khoj docker
network, KHOJ_DOMAIN will be `server' (i.e service name).
- Set KHOJ_DOMAIN to your externally accessible DOMAIN or I.P to avoid
CSRF trusted origin or unset cookie issue when trying to access the
khoj admin panel.
Resolves#1114
- Also, when in not subscribed state, fallback to the default model when chatting with an agent
- With conversion, create a brand new agent from inside the chat view that can be managed separately
Make it easier to determine which model you're chatting with, and to effortlessly update said model from within a given chat.
In this change, we introduce a side bar that allows users to quickly change their chat model, tools, custom instructions, and file filters, directly within the chat view. This removes the need for setting up custom agents for simple instructions and mitigates the requirement to go to the settings page to verify the chat model in action.
The settings page will still configure a per-user *default*, but the sidebar will allow for greater customization based on the needs of a conversation.
We also extend the chat model to include more attributes that help users make decisions about model selection, including `strengths` and `description`. This can help people quickly understand which model might work best for their use case.
Currently, it's rather opaque and difficult to substantially browse through the uploaded knowledge base. Effectively, you can only do this through the small file modal in the settings page.
Update to include all indexed files in the search page for viewing & deletion. Function to delete all files is still in the settings page.
Add a migration that associates file objects with `entry`s using a foreign key. Add a migration command that deletes dangling fileobjects.
## Description
Added file path autocompletion to enhance the search experience in Khoj. Users can now easily search for specific files by typing "file:" followed by the file name, with real-time suggestions appearing as they type.
## Changes
- Added new API endpoint `/api/file-suggestions` to get file path suggestions
- Enhanced search UI with dropdown suggestions for file paths
- Implemented debounced search to optimize API calls
- Added keyboard (Enter) and mouse click support for selecting suggestions
## Features
- Type "file:" to trigger file path suggestions
- Real-time filtering of suggestions as you type
- Top 10 alphabetically sorted suggestions
- Case-insensitive matching
- Keyboard and mouse interaction support
- Clear visual feedback with a dropdown UI
A hidden agent basically allows each individual conversation to maintain custom settings, via an agent that's not exposed to the traditional functionalities allotted for manually created agents (e.g., browsing, maintenance in agents page).
This will be hooked up to the front-end such that any conversation that's initiated with the default agent can then be given custom settings, which in the background creates a hidden agent. This allows us to repurpose all of our existing agents infrastructure for chat-level customization.
Previously emails with url special characters would not get
successfully identified for login. Account creation was fine due to
email being in POST request body. But login with such emails did not
work due to query params not being escaped before being sent to server
This change escapes both the code and email in login URL sent to
server. So login with emails containing special characters like
`email+khoj@gmail.com' works. It fixes both the URL web app sent by
web app directly and the magic link sent to users to their email
This change also fixes accessibility issue of having a DialogTitle in
DialogContent for screen readers.
Resolves#1090
We've been having issues generating diagrams with Excalidraw that are any degree of complexity. By contrast, LLMs are able to handle Mermaid.js syntax a lot better, as it's much more forgiving and has a simpler declarative style. Refer to https://mermaid.js.org/.
Update so that new diagrams are generated with Mermaid.js, while old diagrams generated with Excalidraw can still be viewed.
Fix for #1082 pushed down adding the `data:image/webp;base64' prefix
of the base64 images to the server image gen API. But the code on the
Obsidian and Desktop client still add these prefixes.
This change stops the Desktop, Obsidian clients from adding the prefix
as it is being handled by the API now. It should resolve showing
images inline in those clients as well
Previously we'd use the general openai client, even if the image
generation model has a different api key and base url set.
This change uses the openai config of the image generation models when
set. Otherwise it fallbacks to use the first openai api provider set
This change adds the ability to use OpenAI, Azure OpenAI or any embedding model exposed behind an OpenAI compatible API (like Ollama, LiteLLM, vLLM etc.).
Khoj previously only supported HuggingFace embedding models running locally on device or via HuggingFaceW inference API endpoint. This allows using commercial embedding models to index your content with Khoj.
Previously if the call to this tool AI failed, the API call would
non-gracefully fail on server. This would leave the client hanging in
a wierd state (e.g with spinner running on web app with no indication
of issue).
Also do not show filters in query debug log lines when no filters in query
- Background
Access to the clipboard API is disabled by certain browsers in non
localhost http scenarios for security reasons.
So the copy API key button doesn't work when khoj is self-hosted
with authentication enabled at a non localhost domain
- Change
This change enables copying API keys by manual text highlight + copy
if copy button is disabled
Resolves#1070
This change makes Automations (and possibly other entrypoints) use the configured OpenAI-compatible server if that has been set. Without this change it tries to use the hardcoded OpenAI provider.
- One current issue in the Khoj application is that managing the files being referenced as the user's knowledge base is slightly opaque and difficult to access
- Add a migration for associating the fileobjects directly with the Entry objects, making it easier to get data via foreign key
- Add the new page that shows all indexed files in the search view, also allowing you to upload new docs directly from that page
- Support new APIs for getting / deleting files
- Just say using default config. This old khoj.yml settings mechamism
isn't standard, so not having a configured khoj.yml isn't a concern
- Deep link to desktop download instead of the whole download page as
android etc. are also on it, which don't help with syncing docs
OpenAI chat models deployed on Azure are (ironically) not OpenAI API compatible endpoints.
This change enables using OpenAI chat models deployed on Azure with Khoj.
The github integration has not been tested and may still be broken.
This change at least makes it easier to add repositories without
needing a PAT token if/when it does work.
- Add the mermaid package and apply front-end parsing for interpreting the diagrams. Retain processing of the excalidraw type for backwards compatibility
- Translated comments from French to English for better accessibility and understanding.
- Updated CSS comment for loading animation to reflect the change in language.
- Enhanced code readability by ensuring consistent language usage across multiple files.
- Improved user experience by clarifying the purpose of various functions and settings in the codebase.
Fix AttributeError: 'NoneType' object has no attribute 'model_extra'
* cost = chunk.usage.model_extra.get("estimated_cost", 0) if hasattr(chunk, "usage") and chunk.usage else 0 # Estimated costs returned by DeepInfra API
- Encode article urls in filename indexed in Khoj KB
Makes it easier for humans to compare, trace retrieval performance
by looking at logs than using content hash (which was previously
explored)
- Added visual loading indicators to the search modal for improved user experience during search operations.
- Implemented logic to check if search results correspond to files in the vault, with color-coded results for better clarity.
- Refactored the getSuggestions method to handle loading states and abort previous requests if necessary.
- Updated CSS styles to support new loading animations and result file status indicators.
- Improved the renderSuggestion method to display file status and provide feedback for files not in the vault.
- Added a new setting for users to configure the sync interval in minutes, allowing for more flexible automatic synchronization.
- Introduced methods to start and restart the synchronization timer based on the configured interval.
- Updated the synchronization logic to use the user-defined interval instead of a fixed 60 minutes.
- Improved code readability and organization by refactoring the sync timer logic.
- Added a new setting to manage sync folders, allowing users to specify which folders to sync or to sync the entire vault.
- Implemented a modal for folder suggestions to facilitate folder selection.
- Updated the folder list display to show currently selected folders with options to remove them.
- Improved CSS styles for chat interface and folder list for better user experience.
- Refactored code for consistency and readability across multiple files.
- Introduced a new command 'Sync new changes' to allow users to manually synchronize new modifications.
- The command updates the content index without regenerating it, ensuring only new changes are synced.
- User-triggered notifications are displayed upon successful sync.
- Format AI response to send in automation email
- Let Khoj infer chat query based on user automation query
- Decide if automation emails should be sent based on response
- Fix the `to_notify_or_not` decider AI
- Ask reason before decision to improve to_notify decider AI
- Show error message on web app when fail to create/update automation
Previously we sent the AI response directly. This change post
processes the AI response with the awareness that it is to be sent to
the user as an email to improve rendering and quality of the emails.
This tries to decouple the automation query from the chat query. So
the chat model doesn't have to know it is running in an automation
context or figure how to notify user or send automation response. It
just has to respond to the AI generated `query_to_run' corresponding
to the `scheduling_request` automation by the user.
For example, a `scheduling_request' of `notify me when X happens'
results in the automation calling the chat api with a `query_to_run`
like `tell me about X` and deciding if to notify based on information
gathered about X from the scheduled run. If these two are not
decoupled, the chat model may respond with how it can notify about X
instead of just asking about it.
Swap query_to_run with scheduling_request on the automation web page
- Rather than chunky generic cards, make the suggested actions more action oriented, around the problem a user might want to solve. Give them follow-up options. Design still in progress.
- De facto, was being assumed everywhere if authenticatedData is null, that it's not logged in. This isn't true because the data can still be loading. Update the hook to send additional states.
- Bonus: Delete model picker code and a slew of unused imports.
- Add support for seeing all steps of the agent modification flow via tabs at the top of the modal
- Separate knowledge base & tool selection into two separate parts
Use the [shadcn sidebar](https://ui.shadcn.com/docs/components/sidebar#sidebarmenusub) across Khoj and standardize how the side panel experience works across the app. This helps us generalize the code better, while re-using the same components.
Deprecates current usage of the chat history side panel, replacing it with the new `appSidebar.tsx` component.
We'll eventually move out the `Manage Files` section and move it into a separate panel dedicated to chat-level controls.
- Rather than chunky generic cards, make the suggested actions more action oriented, around the problem a user might want to solve. Give them follow-up options. Design still in progress.
- De facto, was being assumed everywhere if authenticatedData is null, that it's not logged in. This isn't true because the data can still be loading. Update the hook to send additional states.
- Bonus: Delete model picker code and a slew of unused imports.
New conversation have a slug, but older conversation may not. This
change allows those older conversations to still be shareable by using
a random uuid for constructing their url instead
- Some of the instructions were duplicated (e.g illegal, harmful)
- Return format requested was inconsistent
- Safety prompt felt overtly prudish which lowered their utility.
Make it laxer for now, add checks later if required
- Print hash in CI to ease verifying ci built python package matches
khoj package published on pypi
- Newer pypi publish github action should speed up workflow by ~30s
Major
---
Previously we couldn't enable research mode or use other slash
commands in automated tasks.
This change separates determining if a chat query is triggered via
automated task from the (other) conversation commands to run the query
with.
This unlocks the ability to enable research mode in automations
apart from other variations like /image or /diagram etc.
Minor
---
- Clean the code to get data sources and output formats
- Have some examples shown on automations page run in research mode now
This allows online search to work out of the box again
for self-hosting users, as no auth/api key setup required.
Docker users do not need to change anything in their setup flow.
Direct installers can setup Searxng locally or use public instances if
they do not want to use any of the other providers (like Jina, Serper)
Resolves#749. Resolves#990
This allows online search to work out of the box again for
self-hosting users, as no auth/api key setup required.
Docker users do not need to change anything in their setup flow.
Direct installers can setup searxng locally or use public instances if
they do not want to use any of the other providers (like Jina, Serper)
Resolves#749. Resolves#990
The welcome, feedback and automation emails were still using the Khoj
domain, which wouldnt work for self-hosted users with their RESEND key
Resolves#1004
- Previous was incorrectly plural but was defining only a single model
- Rename chat model table field to name
- Update documentation
- Update references functions and variables to match new name
- Move khoj message border to left like in web ui
- Remove user, sender emojis and name
- Ensure conversation title always at top of chat sessions view,
even if no chat sessions loaded yet, instead of causing layout shift
on chat sessions load
Improve handling of multiple output modes
- Use the generated descriptions / inferred queries to supply context to the model about what it's created and give a richer response
- Stop sending the generated image in user message. This seemed to be confusing the model more than helping.
- Collect generated assets in a structured objects to provide model context. This seems to help it follow instructions and separate responsibility better
- Also, rename the open ai converse method to converse_openai to follow patterns with other providers
- Use the generated descriptions / inferred queries to supply context to the model about what it's created and give a richer response
- Stop sending the generated image in user message. This seemed to be confusing the model more than helping.
- Also, rename the open ai converse method to converse_openai to follow patterns with other providers
Previously id were used (by default) for model display strings.
This made it hard to select chat model options, server chat settings
etc. in the admin panel dropdowns.
This change uses more recognizable names for the DB objects to ease
selection in dropdowns and display in general on the admin panel.
* Rename OpenAIProcessorConversationConfig to more apt AiModelAPI
The DB model name had drifted from what it is being used for,
a general chat api provider that supports other chat api providers like
anthropic and google chat models apart from openai based chat models.
This change renames the DB model and updates the docs to remove this
confusion.
Using Ai Model Api we catch most use-cases including chat, stt, image generation etc.
Simplify the desktop app
- Make the desktop app mainly a file-syncing client for users who have lots of documents that they need to share with Khoj. This is because the web app provides a fairly robust chat client which can be used by anyone on their computer.
- The chat client in the desktop app had significantly drifted from our current brand / them, and didn't provide enough value add to update. Later, we will make it easier to install the existing web app as a desktop PWA.
Currently, Khoj has terminal states with respect to what assets it outputs. We limit it to image, text, and excalidraw diagram. This limitation is unnecessary and provides undue constraints for creating more dynamic, diverse experiences. For instance, we may want the chat view to morph for document editing or generation, in which case having limited output modes would be a detriment.
This change allows us to emit generated assets and then continue on to more text generation in final response. It forces text response for all messages. It adds a new stream event, GENERATED_ASSETS, which holds content that the AI is emitting in response to the query.
Overview
- The default django admin panel UI looks pretty dated and didn't
have any Khoj specific branding
- Used the Unfold Django admin panel theme for a modern look
- Used the Khoj logo and name in Admin panel title, headings, favicons
Details:
All models shown on Admin panel need to inherit from unfold's
ModelAdmin to get styling applied. So
- Make all models on Admin panel inherit from unfold's ModelAdmin
- Subclassed UserAdmin to inherit from unfold's ModelAdmin
- Deregistered the unused Auth Group model from the Admin panel
We can add it back when its actually used. Avoid confusion for now
- Explicitly register DjangoJobExecution on admin panel and again make
it inherit from the unfold.admin.ModelAdmin
- Make the desktop app mainly a file-syncing client for users who have lots of documents that they need to share with Khoj. This is because the web app provides a fairly robust chat client which can be used by anyone on their computer.
- The chat client in the desktop app had significantly drifted from our current brand / them, and didn't provide enough value add to update. Later, we will make it easier to install the existing web app as a desktop PWA.
- Use bubblewrap generated splash screen, notification icons from
1200x1200 high res khoj icon in assets.khoj.dev.
- Discard bubblewrap generated launcher icons.
- Fix orientation to respect phone orientation settings. "any" was not.
- Add 512, 192 Khoj maskable icons to web app manifest for android rendering
- Add id, categories etc suggested by pwabuilder
- Use higher quality icon images for splash screen than what
bubblewrap creates by default
Encode api key in header, POST request body and GET query param for
search as utf-8 to avoid the multibyte char in request issue when
making API calls from khoj.el to khoj server.
Resolves#935
### Issue
- Path with / are converted to \\ on Windows using the `Path' operator.
- The `markdown_to_entries' module was trying to normalize file paths with`Path' for some reason.
This would store the file paths in DB Entry differently than the file to entries map if Khoj ran on Windows.
That'd result in a KeyError when trying to look up the entry file path from `file_to_text_map' in the `text_to_entries:update_embeddings()' function.
### Fix
- Removing the unnecessary OS dependent Path normalization in `markdown_to_entries' should keep the file path storage consistent across `file_to_text_map' var, `FileObjectAdaptor', `Entry' DB tables on Windows for Markdown files as well.
This issue will affect users hosting Khoj server on Windows and attempting to index markdown files.
Resolves#984
- Add type hints to improve maintainability of stabilzed indexing code
- It shouldn't be necessary to wrap string variables in an f-string
This change aims to improve code quality. It should not affect
functionality.
Issue
- Path with / are converted to \\ on Windows using the Path operator.
- The markdown to entries method for some reason was doing this.
This would store the file paths in DB entry differently than the file
to entries map. Resulting in a KeyError when trying to look up the
entry file path from file_to_text_map in the
text_to_entries:update_embeddings() function.
Fix
- Removing the unnecessary OS dependendent Path normalization in
markdown_to_entries should keep the file path storage consistent
across file_to_text_map var, FileObjectAdaptor, Entry DB tables on
Windows for Markdown files as well
This issue would only affect users hosting Khoj server on Windows and
attempting to index markdown files.
Resolves#984
- Added it to the Django migrations so that it auto-triggers when someone updates their server and starts it up again for the first time. This will require that they update their clients as well in order to view/consume image content.
- Remove server-side references in the code that allow to parse the text-to-image intent as it will no longer be necessary, given the chat logs will be migrated
This avoid unnecessarily throwing an internal server error when the
user tries to sign-up using multiple mechanisms (e.g first by email, then
by google oauth)
- Improve escaping to load complex json objects
- Fallback to a more forgiving [json5](https://json5.org/) loader if `json.loads` cannot parse complex json str
This should reduce failures to pick research tool and run code by agent
JSON5 spec is more flexible, try to load using a fast json5 parser if
the stricter json.loads from the standard library can't load the
raw complex json string into a python dictionary/list
Gemini doesn't work well when trying to output json objects. Using it
to output raw json strings with complex, multi-line structures
requires more intense clean-up of raw json string for parsing
- Use pre-built wheels for torch and llama-cpp-python
- Install and link musl as it's used by llama-cpp-python pre-built
wheel instead of glibc
- Join Install git and Install Dependencies steps in pytest workflow
To remove unnecessary steps
- Building arm64 image on an ubuntu arm64 runner reduces `yarn build'
step time by 75% from 12mins to 3mins.
- This is because no QEMU emulation for arm64 on x86 is required now
- Parallelizing x64 and arm64 platform builds halves build time on top
- Revert to use standard ubuntu-latest runner as large x64 runner
doesn't give much more speed improvements
This results an effective additional 50%-66% reduction in build time
on top of #987.
So a full dockerize workflow run now takes *10 mins* vs previous 35+mins.
This is a total of *72% improvement* in max dockerize run time.
Get additional speed improvements when docker layer cache hit.
## Objective
Improve build speed and size of khoj docker images
## Changes
### Improve docker image build speeds
- Decouple web app and server build steps
- Build the web app and server in parallel
- Cache docker layers for reuse across dockerize github workflow runs
- Split Docker build layers for improved cacheability (e.g separate `yarn install` and `yarn build` steps)
### Reduce size of khoj docker images
- Use an up-to-date `.dockerignore` to exclude unnecessary directories
- Do not installing cuda python packages for cpu builds
### Improve web app builds
- Use consistent mechanism to get fonts for web app
- Make tailwind extensions production instead of dev dependencies
- Make next.js create production builds for the web app (via `NODE_ENV=production` env var)
The current fix should improve Khoj responses when charts in response
context. It truncates code context before sharing with response chat actors.
Previously Khoj would respond with it not being able to create chart
but than have a generated chart in it's response in default mode.
The truncate code context was added to research chat actor for
decision making but it wasn't added to conversation response
generation chat actors.
When khoj generated charts with code for its response, the images in
the context would exceed context window limits.
So the truncation logic to drop all past context, including chat
history, context gathered for current response.
This would result in chat response generator 'forgetting' all for the
current response when code generated images, charts in response context.
It needs to be used across routers and processors. It being in
run_code tool makes it hard to be used in other chat provider contexts
due to circular dependency issues created by
send_message_to_model_wrapper func
Previous changes to depend on just the PROMPTRACE_DIR env var instead
of KHOJ_DEBUG or verbosity flag was partial/incomplete.
This fix adds all the changes required to only depend on the
PROMPTRACE_DIR env var to enable/disable prompt tracing in Khoj.
Pass your domain cert files via the --sslcert, --sslkey cli args.
For example, to start khoj at https://example.com, you'd run command:
KHOJ_DOMAIN=example.com khoj --sslcert example.com.crt --sslkey
example.com.key --host example.com
This sets up ssl certs directly with khoj without requiring a
reverse proxy like nginx to serve khoj behind https endpoint for
simple setups. More complex setups should, of course, still use a
reverse proxy for efficient request processing
- Track, return cost and usage metrics in chat api response
Track input, output token usage and cost of interactions with
openai, anthropic and google chat models for each call to the khoj chat api
- Collect, display and store costs & accuracy of eval run currently in progress
This provides more insight into eval runs during execution
instead of having to wait until the eval run completes.
Collect, display and store running costs & accuracy of eval run.
This provides more insight into eval runs during execution instead of
having to wait until the eval run completes.
- Previously when settings list became long the dropdown height would
overflow screen height. Now it's max height is clamped and y-scroll
- Previously the dropdown content would take width of content. This
would mean the menu could sometimes be less wide than the button. It
felt strange. Now dropdown content is at least width of parent button
- Track input, output token usage and cost for interactions
via chat api with openai, anthropic and google chat models
- Get usage metadata from OpenAI using stream_options
- Handle openai proxies that do not support passing usage in response
- Add new usage, end response events returned by chat api.
- This can be optionally consumed by clients at a later point
- Update streaming clients to mark message as completed after new
end response event, not after end llm response event
- Ensure usage data from final response generation step is included
- Pass usage data after llm response complete. This allows gathering
token usage and cost for the final response generation step across
streaming and non-streaming modes
- Previously online chat actors, director tests only worked with openai.
This change allows running them for any supported onlnie provider
including Google, Anthropic and Openai.
- Enable online/offline chat actor, director in two ways:
1. Explicitly setting KHOJ_TEST_CHAT_PROVIDER environment variable to
google, anthropic, openai, offline
2. Implicitly by the first API key found from openai, google or anthropic.
- Default offline chat provider to use Llama 3.1 3B for faster, lower
compute test runs
- Set output mode to single string. Specify output schema in prompt
- Both thesee should encourage model to select only 1 output mode
instead of encouraging it in prompt too many times
- Output schema should also improve schema following in general
- Standardize variable, func name of io selector for readability
- Fix chat actors to test the io selector chat actor
- Make chat actor return sources, output separately for better
disambiguation, at least during tests, for now
- Evaluate khoj on random 200 questions from each of google frames and openai simpleqa benchmarks across *general*, *default* and *research* modes
- Run eval with Gemini 1.5 Flash as test giver and Gemini 1.5 Pro as test evaluator models
- Trigger eval workflow on release or manually
- Make dataset, khoj mode and sample size configurable when triggered via manual workflow
- Enable Web search, webpage read tools during evaluation
- JSON extract from LLMs is pretty decent now, so get the input tools and output modes all in one go. It'll help the model think through the full cycle of what it wants to do to handle the request holistically.
- Make slight improvements to tool selection indicators
Previously, we'd replace the generated message with an error message
when message generation stopped via stop button on chat page of web app.
So the partially generated message (which could be useful) gets lost.
This change just stops generation, while keeping the generated
response so any useful information from the partially generated
message can be retrieved.
- Allows managing chat models in the OpenAI proxy service like Ollama.
- Removes need to manually add, remove chat models from Khoj Admin Panel
for these OpenAI compatible API services when enabled.
- Khoj still mantains the chat models configs within Khoj, so they can
be configured via the Khoj admin panel as usual.
Previously Jina search didn't API key. Now that it does need API key,
we should re-use the API key set in the Jina web scraper config,
otherwise fallback to using JINA_API_KEY from environment variable, if
either is present.
Resolves#978
- Integrate with Ollama or other openai compatible APIs by simply
setting `OPENAI_API_BASE' environment variable in docker-compose etc.
- Update docs on integrating with Ollama, openai proxies on first run
- Auto populate all chat models supported by openai compatible APIs
- Auto set vision enabled for all commercial models
- Minor
- Add huggingface cache to khoj_models volume. This is where chat
models and (now) sentence transformer models are stored by default
- Reduce verbosity of yarn install of web app. Otherwise hit docker
log size limit & stops showing remaining logs after web app install
- Suggest `ollama pull <model_name>` to start it in background
- Update to latest initialize with new claude 3.5 sonnet and haiku models
- Update to set vision enabled for google and anthropic models by
default. Previously we didn't support but we've supported this for a
month or two now
- Just load the raw csv from OpenAI bucket. Normalize it into FRAMES format
- Improve docstring for frames datasets as well
- Log the load dataset perf timer at info level
- Explictly adding a slash command is a higher priority intent than
research mode being enabled in the background. Respect that for a
more intuitive UX flow.
- Explicit slash commands do not currently work in research mode.
You've to turn research mode off to use other slash commands. This
is strange, unnecessary given intent priority is clear.
- JSON extract from LLMs is pretty decent now, so get the input tools and output modes all in one go. It'll help the model think through the full cycle of what it wants to do to handle the request holistically.
- Make slight improvements to tool selection indicators
Previously errors would get eaten up but the model wouldn't see
anything. And the model wouldn't be allowed re-run the same query-tool
combination in the next iteration.
This update should give it insight into why it didn't get a result. So
it can make an informed (hopefully better) decision on what to do next.
And re-run the previous query if appropriate.
Previously when call to online search API etc. failed, it'd error out
of response to query in research mode. Khoj should skip tool use that
iteration but continue to try respond.
Previously chatml messages were just strings, now they can be list of
strings or list of dicts as well.
- Use json seriallization to manage their variations and truncate them
before printing for context.
- Put logic in single function for use across chat models
- Default to evaluation decision of None when either agent or
evaluator llm fails. This fixes accuracy calculations on errors
- Fix showing color for decision True
- Enable arg flags to specify output results file paths
Previously chatml messages were just strings.
Since gemini, anthropic models always have messages as list of
strings, truncate those strings instead of the list of message content
Removing binary data and truncating large data in output files
generated by code runs should improve speed and cost of research mode
runs with large or binary output files.
Previously binary data in code results was passed around in iteration
context during research mode. This made the context inefficient
because models have limited efficiency and reasoning capabilities over
b64 encoded image (and other binary) data and would hit context limits
leading to unnecessary truncation of other useful context
Also remove image data when logging output of code execution
- Allow passing user files as input into code sandbox for analysis
- Update prompt to give more example of complex, multi-line code
- Simplify logic for model. Run one program at a time,
instead of allowing model to run multiple programs in parallel
- Show Code generated charts and docs in Reference pane of web app and make them downloaded
- Add a border below heading
- Show code snippet in pre block
- Overflow-x when reference side panel open to allow seeing whole text
via x-scroll
- Align header, body position of reference cards with each other
- Only show filename in doc reference cards at message bottom.
Show full file path in hover and reference side panel
- Improve rendering code reference with better icons, smaller text and
different line clamps for better visibility
- Show code output files as sub card of code card in reference section
- Allow downloading files generated by code instead of rendering it in
chat message directly
- Show executed code before online references in reference panel
- Fix to render code generated chart with images, excalidraw diagrams
- Fix to save code context to chat history in image, diagram output modes
- Fix bug in image markdown being wrapped twice in markdown syntax
- Render newline in code references shown on chat page of web app
Previously newlines weren't getting rendered. This made the code
executed by Khoj hard to read in references. This changes fixes that.
`dangerouslySetInnerHTML' usage is justified as rendered code
snippet is being sanitized by DOMPurify before rendering.
- Run one program at a time, instead of allowing model to pass
multiple programs to run in parallel to simplify logic for model
- Update prompt to give more example of complex, multi-line code
- Allow passing user files as input into code sandbox for analysis
- Log code execution timer at info level to evaluate execution latencies
in production
- Type the generated code for easier processing by caller functions
Support including file attachments in the chat message
Now that models have much larger context windows, we can reasonably include full texts of certain files in the messages. Do this when an explicit file filter is set in a conversation. Do so in a separate user message in order to mitigate any confusion in the operation.
Pipe the relevant attached_files context through all methods calling into models.
This breaks certain prior behaviors. We will no longer automatically be processing/generating embeddings on the backend and adding documents to the "brain". You'll have to go to settings and go through the upload documents flow there in order to add docs to the brain (i.e., have search include them during question / response).
This will ensure only unique online references are shown in all
clients.
The duplication issue was exacerbated in research mode as even with
different online search queries, you can get previously seen results.
This change does a global deduplication across all online results seen
across research iterations before returning them in client reponse.
- Deduplicate online, doc search queries across research iterations.
This avoids running previously run online, doc searches again and
dedupes online, doc context seen by model to generate response.
- Deduplicate online search queries generated by chat model for each
user query.
- Do not pass online, docs, code context separately when generate
response in research mode. These are already collected in the meta
research passed with the user query
- Improve formatting of context passed to generate research response
- Use xml tags to delimit context. Pass per iteration queries in each
iteration result
- Put user query before meta research results in user message passed
for generating response
This deduplications will improve speed, cost & quality of research mode
Previously the whole research mode response would fail if the pick
next tool call to chat model failed. Now instead of it completely
failing, the researcher actor is told to try again in next iteration.
This allows for a more graceful degradation in answering a research
question even if a (few?) calls to the chat model fail.
Jina search API returns content of all webpages in search results.
Previously code wouldn't remove content beyond max_webpages_to_read
limit set. Now, webpage content in organic results aree explicitly
removed beyond the requested max_webpage_to_read limit.
This should align behavior of online results from Jina with other
online search providers. And restrict llm context to a reasonable size
when using Jina for online search.
This fixes chat with old chat sessions. Fixes issue with old Whatsapp
users can't chat with Khoj because chat history doc context was
stored as a list earlier
Command rate limit wouldn't be shown to user as server wouldn't be
able to handle HTTP exception in the middle of streaming.
Catch exception and render it as LLM response message instead for
visibility into command rate limiting to user on client
Log rate limmit messages for all rate limit events on server as info
messages
Convert exception messages into first person responses by Khoj to
prevent breaking the third wall and provide more details on wht
happened and possible ways to resolve them.
Previously the batch start index wasn't being passed so all batches
started in parallel were showing the same processing example index
This change doesn't impact the evaluation itself, just the index shown
of the example currently being evaluated
- Document is first converted in the chatinputarea, then sent to the chat component. From there, it's sent in the chat API body and then processed by the backend
- We couldn't directly use a UploadFile type in the backend API because we'd have to convert the api type to a multipart form. This would require other client side migrations without uniform benefit, which is why we do it in this two-phase process. This also gives us capacity to repurpose the moe generic interface down the road.
- Why
We need better, automated evals to measure performance shifts of Khoj
across prompt, model and capability changes.
Google's FRAMES benchmark evaluates multi-step retrieval and reasoning
capabilities of AI agents. It's a good starter benchmark to evaluate Khoj.
- Details
This PR adds an eval script to evaluate Khoj responses on the the FRAMES
benchmark prompts against the ground truth provided by it.
Script allows configuring sample size, batch size, sampling queries from the
eval dataset.
Gemini is used as an LLM Judge to auto grade Khoj responses vs ground truth
data from the benchmark.
This was previously required, but now it's only usefuly for more
advanced settings, not typical for self-hosting users.
With recent updates, the user's selected chat model is used for both
Khoj's train of thought and response. This makes it easy to
switch your preferred chat model directly from the user settings
page and not have to update this in the admin panel as well.
Reflect these code changse in the docs, by removing the unnecessary
step for self-hosted users to create a server chat setting when using
an OpenAI proxy service like Ollama, LiteLLM etc.
Now that models have much larger context windows, we can reasonably include full texts of certain files in the messages. Do this when an explicit file filter is set in a conversation. Do so in a separate user message in order to mitigate any confusion in the operation.
Pipe the relevant attached_files context through all methods calling into models.
We'll want to limit the file sizes for which this is used and provide more helpful UI indicators that this sort of behavior is taking place.
- The server has moved to a model of standardization for the embeddings generation workflow. Remove references to the support for differentiated models.
- The migration script fo ra new model needs to be updated to accommodate full regeneration.
Google's FRAMES benchmark evaluates multi-step retrieval and reasoning
capabilities of an agent.
The script uses Gemini as an LLM Judge to evaluate Khoj responses to
the FRAMES benchmark prompts against the ground truth provided by it.
- Improve chat actors and their prompts for research mode.
- Add documentation to enable the code tool when self-hosting Khoj
- Edit Chat Messages
- Store Turn Id in each chat message.
- Expose API to delete chat message.
- Expose delete chat message button to turn delete chat message from web app
- Set LLM Generation Seed for Reproducible Debugging and Testing
- Setting seed for LLM generation is supported by Llama.cpp and OpenAI models.
This can (somewhat) restrain LLM output
- Getting fixed responses for fixed inputs helps test, debug longer reasoning chains like used in advanced reasoning
## Overview
Khoj can now go into research mode and use a python code interpreter. These are experimental features that are being released early for feedback and testing.
- Research mode allows Khoj to dynamically select the tools it needs to best answer the question. It is also allowed more iterations to get to a satisfactory answer. Its more dynamic train of thought is shown to improve visibility into its thinking.
- Adding ability for Khoj to use a python code interpreter is an adjacent capability. It can help Khoj do some data analysis and generate charts for you. A sandboxed python to run code is provided using [cohere-terrarium](https://github.com/cohere-ai/cohere-terrarium?tab=readme-ov-file), [pyodide](https://pyodide.org/).
## Analysis
Research mode (significantly?) improves Khoj's information retrieval for more complex queries requiring multi-step lookups but takes longer to run. It can research for longer, requiring less back-n-forth with the user to find an answer.
Research mode gives most gains when used with more advanced chat models (like o1, 4o, new claude sonnet and gemini-pro-002). Smaller models improve their response quality but tend to get into repetitive loops more often.
## Next Steps
- Get community feedback on research mode. What works, what fails, what is confusing, what'd be cool to have.
- Tune Khoj's capabilities for longer autonomous runs and to generalize across a larger range of model sizes
## Miscellaneous Improvements
- Khoj's train of thought is saved and shown for all messages, not just the latest one
- Render charts generated by Khoj and code running using the code tool on the web app
- Align chat input color to currently selected agent color
- Dedent code for readability
- Use better name for in research mode check
- Continue to remove inferred summarize command when multiple files in
file filter even when not in research mode
- Continue to show select information source train of thought.
It was removed by mistake earlier
## Overview
Use git to capture prompt traces of khoj's train of thought. View, analyze and debug them using your favorite git client (e.g vscode, magit).
- Each commit captures an interaction with an LLM
The commit writes the query, response and system message each to a separate file in the repo.
The commit message captures the chat model, Khoj version and other metadata
- Each conversation turn can have multiple interactions with an LLM (e.g Khoj's train of thought)
- Each new conversation turn forks from and merges back into its conversation branch
- Each new conversation branches from the user branch
- Each new user branches from root commit on the main branch
## Usage
1. Set `KHOJ_DEBUG=true` or start khoj in very verbose mode with `khoj -vv` to turn on prompt tracing
2. Chat with Khoj as usual
3. Open the promptrace git repo to view the generated prompt traces using your favorite git porcelain.
The Khoj prompt trace git repo is created at `/tmp/khoj_promptrace` by default. You can configure the prompt trace directory by setting the `PROMPTRACE_DIR`environment variable.
## Implementation
- Add utility functions to capture prompt traces using git (via `gitpython`)
- Make each model provider in Khoj commit their LLM interactions with promptrace
- Weave chat metadata from chat API through all chat actors and commit it to the prompt trace
- Match the online query generator prompt to match the formatting of
extract questions
- Separate iteration results by newline
- Improve webpage and online tool descriptions
- Allow server to start if loading embedding model fails with an error.
This allows fixing the embedding model config via admin panel.
Previously server failed to start if embedding model was configured
incorrectly. This prevented fixing the model config via admin panel.
- Convert boolean string in config json to actual booleans when passed
via admin panel as json before passing to model, query configs
- Only create default model if no search model configured by admin.
Return first created search model if its been configured by admin.
Models were getting a bit confused about who is search for who's
information. Using third person to explicitly call out on who's behalf
these searches are running seems to perform better across
models (gemini's, gpt etc.), even if the role of the message is user.
Use placeholder for newline in json object values until json parsed
and values extracted. This is useful when research mode models outputs
multi-line codeblocks in queries etc.
Anthropic API doesn't have ability to enforce response with valid json
object, unlike all the other model types.
While the model will usually adhere to json output instructions.
This step is meant to more strongly encourage it to just output json
object when response_type of json_object is requested.
Separate conversation history with user from the conversation history
between the tool AIs and the researcher AI.
Tools AIs don't need top level conversation history, that context is
meant for the researcher AI.
The invoked tool AIs need previous attempts at using the tool in this
research runs iteration history to better tune their next run.
Or at least that is the hypothesis to break the models looping.
Models weren't generating a diverse enough set of questions. They'd do
minor variations on the original query. What is required is asking
queries from a bunch of different lenses to retrieve the requisite
information.
This prompt updates shows the AIs the breadth of questions to by
example and instruction. Seem like performance improved based on vibes
- Improve mobile friendliness with new research mode toggle, since chat input area is now taking up more space
- Remove clunky title from the suggestion card
- Fix fk lookup error for agent.creator
Overview
---
- Put context into separate user message before sending to chat model.
This should improve model response quality and truncation logic in code
- Pass online context from chat history to chat model for response.
This should improve response speed when previous online context can be reused
- Improve format of notes, online context passed to chat models in prompt.
This should improve model response quality
Details
---
The document, online search context are now passed as separate user
messages to chat model, instead of being added to the final user message.
This will improve
- Models ability to differentiate data from user query.
That should improve response quality and reduce prompt injection
probability
- Make truncation logic simpler and more robust
When context window hit, can simply pop messages to auto truncate
context in order of context, user, assistant message for each
conversation turn in history until reach current user query
The complex, brittle logic to extract user query from context in
last user message isn't required.
Align context passed to offline chat model with other chat models
- Pass context in separate message for better separation between user
query and the shared context
- Pass filename in context
- Add online results for webpage conversation command
Context role was added to allow change message truncation order based
on context role as well.
Revert it for now since currently this is not currently being done.
Previously model would rarely read webpages after webpage search. Need
the model to webpages more regularly for deeper research and to stop
getting stuck in repetitive online search loops
Previous passing of online results as json dump in prompts was less
readable for humans, and I'm guessing less readable for
models (trained on human data) as well?
- Start from this branches src/khoj/routers/api_chat.py
Add tracer to all old and new chat actors that don't have it set
when they are called.
- Update the new chat actors like apick next tool etc to use tracer too
- Conflicts:
Combine both sides of the conflict in all 3 files below
- src/khoj/processor/conversation/utils.py
- src/khoj/routers/helpers.py
- src/khoj/utils/helpers.py
- Message train of thought forks and merges from its conversation branch
- Conversation branches from user branch
- User branches from root commit on the main branch
- Weave chat tracer metadata from api endpoint through all chat actors
and commit it to the prompt trace
### Major
- Give Vision to Anthropic models in Khoj
### Minor
- Reuse logic to format messages for chat with anthropic models
- Make the get image from url function more versatile and reusable
- Encourage output mode chat actor to output only json and nothing else
- Use a single standard search model across the server. There's diminishing benefits for having multiple user-customizable search models.
- We may want to add server-level customization for specific tasks
- Store the search model used to generate a given entry on the `Entry` object
- Remove user-facing APIs and view
- Add a management command for migrating the default search model on the server
In a future PR (after running the migration), we'll also remove the `UserSearchModelConfig`
Latest claude model wanted to say more than just give the json output.
The updated prompt encourages the model to ouput just json. This is
similar to what is already being done for other prompts
It was previously added under the google utils. Now it can be used by
other conversation processors as well.
The updated function
- can get both base64 encoded and PIL formatted images from url
- will return the media type of the image as well in response
* Create explicit flow to enable the free trial
The current design is confusing. It obfuscates the fact that the user is on a free trial. This design will make the opt-in explicit and more intuitive.
* Use the Subscription Type enum instead of hardcoded strings everywhere
* Use length of free trial in the frontend code as well
Had temporarily updated the default selected agent to last used.
Revert for now as
1. The previous logic was buggy. It didn't select the default agent
even when the last used agent was the default agent. Which would
require more work.
2. It maybe too early anyway to set the default agent to last used.
Adding div elements to message to render degraded text copied to
clipboard for messages with user uploaded images.
This change fixes that by separating message to render from message
for clipboard. It ensures differently formatted forms of the user
images are added to the two to allow proper rendering while still
having decently formatted text copied to clipboard
Add newline instead of sending message when hit Enter key on mobile
displays. As on phones shift key doesn't exist and send button is easily
clickable.
Limit hitting Enter key to send message to computers = larger display
= expected to have full fledged keyboards.
## Overview
Allow quickly selecting, switching agents from agents pane on home page of web app
## Details
- Show all agents in carousel on home screen agent pane of web app
- Smart Sort
1. Pin default agent as first for ease of access
2. Show used agents by MRU for ease of access
3. Shuffle unused agents for discoverability
- Select most recently used agent to chat with by default
- Push smart sort logic down to API
- Common logic can be reused across clients
- Agent sort was previously done in web app
- Focus on chat input on agent select
- Double click agent on home page to open edit agent card on agents page
## Overview
- Add vision support for Gemini models in Khoj
- Allow sharing multiple images as part of user query from the web app
- Handle multiple images shared in query to chat API
- Remove border from agent detail hover card on home page
- Do not wrap long agent names in agent pills on home page
- Handle scenario where chatInputRef is null
Add support for generating dynamic diagrams in flow with Excalidraw (https://github.com/excalidraw/excalidraw). This happens in three steps:
1. Default information collection & intent determination step.
2. Improving the overall guidance of the prompt for generating a JSON, Excalidraw-compatible declaration.
3. Generation of the diagram to output to the final UI.
Add support in the web UI.
Previously only notes context from chat history was included.
This change includes online context from chat history for model to use
for response generation.
This can reduce need for online lookups by reusing previous online
context for faster responses. But will increase overall response time
when not reusing past online context, as faster context buildup per
conversation.
Unsure if inclusion of context is preferrable. If not, both notes and
online context should be removed.
The document, online search context are now passed as separate user
messages to chat model, instead of being added to the final user message.
This will improve
- Models ability to differentiate data from user query.
That should improve response quality and reduce prompt injection
probability
- Make truncation logic simpler and more robust
When context window hit, can simply pop messages to auto truncate
context in order of context, user, assistant message for each
conversation turn in history until reach current user query
The complex, brittle logic to extract user query from context in
last user message isn't required.
Marking the context message with assistant role doesn't translate well
across chat models. E.g
- Gemini can't handle consecutive messages by role = model well
- Claude will merge consecutive messages by same role. In current
message ordering the context message will result get merged into the
previous assistant response. And if move context message after user
query. The truncation logic will have to hop and skip while doing
deletions
- GPT seems to handle consecutive roles of any type fine
Using context role = user generalizes better across chat models for
now and aligns with previous behavior.
Improve separation of note snippets and show its origin file in notes
prompt to have more readable, contextualized text shared with model.
Previously the references dict was being directly passed as a string.
The documents don't look well formatted and are less intelligible.
- Passing file path along with notes snippets will help contextualize
the notes better.
- Better formatting should help with making notes more readable by the
chat model.
- Double click on agent to open edit agent card
- Focus on chat input pane when agent selected/clicked
for quick, smooth agent switch and message flow
- Hover on agent to see agent detail card on non-mobile displays
- Use debounce to only show when hover on card for a bit
- Default to None for the input_tools and output_modes so that they can be managed in the admin panel
- Hold off on showing off all Public Agents until we have a better experience for user profiles etc.
Have get agents API return agents ordered intelligently
- Put the default agent first
- Sort used agents by most recently chatted with agent for ease of access
- Randomly shuffle the remaining unused agents for discoverability
This change wraps the agent pane in a scroll area with all agents shown.
It allows selecting an agent to chat with directly from the home
screen without breaking flow and having to jump to the agents page.
The previous flow was not convenient to quickly and consistently start
chat with one of your standard agents.
This was because a random subet of agents were shown on the home page.
To start chat with an agent not shown on home screen load you had to
open the agents page and initiate the conversation from there.
Exposes a transient switch with available agents as selectable options
in the Khoj chat sub-menu.
Currently shows agent slugs instead of agent names as options. This
isn't the cleanest but gets the job done for now.
Only new conversations with a different agent can be started. Existing
conversations will continue with the original agent it was created with.
The ability to switch the conversation's agent doesn't exist on the
server yet.
One limitation of this methodology is that localStorage has a limit in how much data it can take. Should add more graceful error handling here as well.
Currently experiencing difficulty instruction following when an image is shared. It's more likely to try and output an image. Update to make a clearer distinction.
- Put the attached images display div inside the same parent div as
the text area
- Keep the attachment, microphone/send message buttons aligned with
the text area. So the attached images just show up at the top of the
text area but everything else stays at the same horizontal height as
before.
- This improves the UX by
- Ensuring that the attached images do not obscure the agents pane
above the chat input area
- The attached images visually look like they are inside the actual
input area, rather than floating above it. So the visual aligns
with the semantics
Previously the web app only expected a single image to be shared by
the user as part of their query.
This change allows sharing multiple images from the web app.
Closes#921
Previously Khoj could respond to a single shared image at a time.
This changes updates the chat API to accept multiple images shared by
the user and send it to the appropriate chat actors including the
openai response generation chat actor for getting an image aware
response
Recent changes made Khoj try respond even when document lookup fails.
This change missed handling downstream effects of a failed document
lookup, as the defiltered_query was null and so the text response
didn't have the user query to respond to.
This code initializes defiltered_query to original user query to
handle that.
Also response_type wasn't being passed via
send_message_to_model_wrapper_sync unlike in the async scenario
## Overview
### New
- Support using Firecrawl(https://firecrawl.dev) to read web pages
- Add, switch and re-prioritize web page reader(s) to use via the admin panel
### Speed
- Improve response speed by aggregating web page read, extract queries to run only once for each web page
### Response Resilience
- Fallback through enabled web page readers until web page read
- Enable reading web pages on the internal network for self-hosted Khoj running in anonymous mode
- Try respond even if web search, web page read fails during chat
- Try respond even if document search via inference endpoint fails
### Fix
- Return data sources to use if exception in data source chat actor
## Details
### Configure web page readers to use
- Only the web scraper set in Server Chat Settings via the Django admin panel, if set
- Otherwise use the web scrapers added via the Django admin panel (in order of priority), if set
- Otherwise, use all the web scrapers enabled by settings API keys via environment variables (e.g `FIRECRAWL_API_KEY', `JINA_API_KEY' env vars set), if set
- Otherwise, use Jina to web scrape if no scrapers explicitly defined
For self-hosted setups running in anonymous-mode, the ability to directly read webpages is also enabled by default. This is especially useful for reading webpages in your internal network that the other web page readers will not be able to access.
### Aggregate webpage extract queries to run once for each distinct web page
Previously, we'd run separate webpage read and extract relevant
content pipes for each distinct (query, url) pair.
Now we aggregate all queries for each url to extract information from
and run the webpage read and extract relevant content pipes once for
each distinct URL.
Even though the webpage content extraction pipes were previously being
run in parallel. They increased the response time by
1. adding more ~duplicate context for the response generation step to read
2. being more susceptible to variability in web page read latency of the parallel jobs
The aggregated retrieval of context for all queries for a given
webpage could result in some hit to context quality. But it should
improve and reduce variability in response time, quality and costs.
This should especially help with speed and quality of online search
for offline or low context chat models.
- Simplifies changing order in which web scrapers are invoked to read
web page by just changing their priority number on the admin panel.
Previously you'd have to delete/, re-add the scrapers to change
their priority.
- Add help text for each scraper field to ease admin setup experience
- Friendlier env var to use Firecrawl's LLM to extract content
- Remove use of separate friendly name for scraper types.
Reuse actual name and just make actual name better
The other webpage scrapers will not work for internal webpages. Try
access those urls directly if they are visible to the Khoj server over
the network.
Only enable this by default for self-hosted, single user setups.
Otherwise ability to scan internal network would be a liability!
For use-cases where it makes sense, the Khoj server admin can
explicitly add the direct webpage scraper via the admin panel
- Set up scrapers via API keys, explicitly adding them via admin panel
or enabling only a single scraper to use via server chat settings.
- Use validation to ensure only valid scrapers added via admin panel
Example API key is present for scrapers that require it etc.
- Modularize the read webpage functions to take api key, url as args
Removes dependence on constants loaded in online_search. Functions
are now mostly self contained
- Improve ability to read webpages by using the speed, success rate of
different scrapers. Optimal configuration needs to be discovered
This should reduce webpage read and response generation time.
Previously, we'd run separate webpage read and extract relevant
content pipes for each distinct (query, url) pair.
Now we aggregate all queries for each url to extract information from
and run the webpage read and extract relevant content pipes once for
each distinct url.
Even though the webpage content extraction pipes were previously being
in parallel. They increased response time by
1. adding more context for the response generation chat actor to
respond from
2. and by being more susceptible to page read and extract latencies of
the parallel jobs
The aggregated retrieval of context for all queries for a given
webpage could result in some hit to context quality. But it should
improve and reduce variability in response time, quality and costs.
Set the FIRECRAWL_TO_EXTRACT environment variable to true to have
Firecrawl scrape and extract content from webpage using their LLM
This could be faster, not sure about quality as LLM used is obfuscated
Firecrawl is open-source, self-hostable with a default hosted service
provided, similar to Jina.ai. So it can be
1. Self-hosted as part of a private Khoj cloud deployment
2. Used directly by getting an API key from the Firecrawl.dev service
This is as an alternative to Olostep and Jina.ai for reading webpages.
Khoj shouldn't refuse to respond to user if web lookups fail.
It should transparently mention that online search etc. failed.
But try respond as best as it can without those references
This change ensures a response to the users query is attempted even
when web info retrieval fails.
The huggingface endpoint can be flaky. Khoj shouldn't refuse to
respond to user if document search fails.
It should transparently mention that document lookup failed.
But try respond as best as it can without the document references
This changes provides graceful failover when inference endpoint
requests fail either when encoding query or reranking retrieved docs
- Remove unused subscribed variable from the chat API
- Unexpectedly dropped client app logging when migrated API chat to do
advanced streaming in july
- Only set addedFiles to selectedFiles when selectedFiles is an array
- Only set seleectedFiles, addedFiles to API response json when
response succeeded. Previously we set it to response json
on errors as well. This made the variables into json objects instead
of arrays on API call failure
- Check if selectedFiles, addedFiles are arrays before running
operations on them. Previously the addedFiles.includes was where the
code would fail
finish_reason (google.ai.generativelanguage_v1beta.types.Candidate.FinishReason):
Optional. Output only. The reason why the
model stopped generating tokens.
If empty, the model has not stopped generating
the tokens.
- Advanced chat model should also fallback to user chat model if set
- Get conversation config should falback to user chat model if set
These assume no server chat model settings is configured
Khoj shouldn't refuse to respond to user if web lookups fail.
It should transparently mention that online search etc. failed.
But try respond as best as it can without those references
This change ensures a response to the users query is attempted even
when web info retrieval fails.
The huggingface endpoint can be flaky. Khoj shouldn't refuse to
respond to user if document search fails.
It should transparently mention that document lookup failed.
But try respond as best as it can without the document references
This changes provides graceful failover when inference endpoint
requests fail either when encoding query or reranking retrieved docs
- Advanced chat model should also fallback to user chat model if set
- Get conversation config should falback to user chat model if set
These assume no server chat model settings is configured
- Advanced chat model should also fallback to user chat model if set
- Get conversation config should falback to user chat model if set
These assume no server chat model settings is configured
# Overview
- Default to use user chat models for train of thought when no server chat settings created by admins
- Default to not create server chat settings on first run
# Details
This change simplifies switching chat models for self-hosted setups
by just changing the chat model on the user settings page.
It falls back to use the user chat model for train of thought
if server chat settings have not been created on the admin panel.
Server chat settings, when set, controls the chat model used
for Khoj's train of thought and the default user chat model.
Previously a self-hosted user had to update
1. the server chat settings in the admin panel and
2. their own user chat model in the user settings panel
to completely switch to a different chat model
for both train of thought & response generation respectively
You can still set server chat settings via the admin panel
to use a different chat model for train of thought vs response generation.
But this is only useful for advanced, multi-user setups.
Update regex to also include any links to code generated images that
aren't explicitly meant to be displayed inline. This allows folks to
download the image (unlike the fake link that doesn't work created by
model)
Previously Khoj would start answering the previous query. This maybe
because the prompt uses User for prompt in chat history but was using
Q for current user prompt.
Make webpages to read automatically on search_online configurable via
a argument.
Set it to default to 1, so other callers of the function
are unaffected.
But iterative chat director can still decide which, if
any, webpages to read based on the online search it performs
This change allows the iterative director to dive deeper into its
research as the data extracted contains relevant links from the webpage
Previous summarization prompt didn't extract relevant links from the
webpage which limited further explorations from webpages
Move construct_chat_history and ChatEvent enum into conversation.utils
and move send_message_to_model_wrapper to conversation.helper to
modularize code. And start thinning out the bloated routers.helper
- conversation.util components are shared functions that conversation
child packages can use.
- conversation.helper components can't be imported by conversation
packages but it can use these child packages
This division allows better modularity while avoiding circular
import dependencies
Create python code executing chat actor
- The chat actor generate python code within sandbox constraints
- Run the generated python code in the cohere terrarium, pyodide
based sandbox accessible at sandbox url
- Create a more dynamic reasoning agent that can evaluate information and understand what it doesn't know, making moves to get that information
- Lots of hacks and code that needs to be reversed later on before submission
Update chat actors to use user's chat model for train of thought. This
requires passing the user info as argument to all the chat actors.
Whether the user is subscribed or not can be inferred from the user
info being passed, so it doesn't need to be passed as a separate
argument to chat actor functions
Let send_message_to_model function infer chat model instead of passing
it as an argument from some chat actors. Better if this logic can be
done in a single place.
Server chat settings can be set for advanced self-hosted or multi-user
cloud setups. They are not necessary anymore as we fallback to use the
users chat model for train of thought now
Fallback to use user chat model for train of thought if server chat
settings not defined.
This simplifies switching chat models for single-user, self-hosted
setups by just changing the chat model on the user settings page.
Server chat settings, when set, controls the default user chat model
and the chat model that is used for Khoj's train of thought.
Previously a self-hosted user had to update both the server chat
settings in the admin panel and their own user chat model in the user
settings panel to explicitly switch to a different chat model (i.e to
switch to a new model for both train of thought & response generation)
You can still set server chat settings to use a different chat
model for train of thought and response generation. But this is only
necessary for advanced self-hosted or cloud hosted setups of Khoj.
Previously you had to refresh the page to see the updated data on
reopening the agents edit card after a save operation.
Now you see the latest saved agent data on reopening the agents edit
card. This should avoid confusion on whether the data was saved
correctly
If a public or protected agent is made private. Other users who were
having conversation with that agent will have to carry on their
conversation using default agent instead
Loading the embeddings model, even locally seems to be taking much
longer. Use timer to track visibility into embedding, cross-encoder
model load times
We should start disambiguating the the max input from output size. Max
prompt size should only be used for the max input context to an LLM.
If required max_output_tokens should be set as a separate new field
Currently, the personality of the agent is only included in the final response that it returns to the user. Historically, this was because models were quite bad at navigating the additional context of personality, and there was a bias towards having more control over certain operations (e.g., tool selection, question extraction).
Going forward, it should be more approachable to have prompts included in the sub tasks that Khoj runs in order to response to a given query. Make this possible in this PR. This also sets us up for agent creation becoming available soon.
Create custom agents in #928
Agents are useful insofar as you can personalize them to fulfill specific subtasks you need to accomplish. In this PR, we add support for using custom agents that can be configured with a custom system prompt (aka persona) and knowledge base (from your own indexed documents). Once created, private agents can be accessible only to the creator, and protected agents can be accessible via a direct link.
Custom tool selection for agents in #930
Expose the functionality to select which tools a given agent has access to. By default, they have all. Can limit both information sources and output modes.
Add new tools to the agent modification form
## Overview
Add user country code as context for doing online search with serper.dev API.
This should find more user relevant results from online searches by Khoj
## Details
### Major
- Default to using system clock to infer user timezone on js clients
- Infer country from timezone when only timezone received by chat API
- Localize online search results to user country when location available
### Minor
- Add `__str__` func to `LocationData` class to deduplicate location string generation
Make all the scroll actions just use requestAnimationFrame instead of
setTimeout. It better aligns with browser rendering loop, so better
for UX changes than setTimeout
Using system clock to infer user timezone on clients makes Khoj
more robust to provide location aware responses.
Previously only ip based location was used to infer timezone via API.
This didn't provide any decent fallback when calls to ipapi failed or
Khoj was being run in offline mode
Timezone is easier to infer using clients system clock. This can be
used to infer user country name, country code, even if ip based
location cannot be inferred.
This makes using location data to contextualize Khoj's responses more
robust. For example, online search results are retrieved for user's
country, even if call to ipapi.co for ip based location fails
Get country code to server chat api from i.p location check on clients.
Use country code to get country specific online search results via Serper.dev API
Previously the location string from location data was being generated
wherever it was being used.
By adding a __str__ representation to LocationData class, we can
dedupe and simplify the code to get the location string
- Use tabs for GPU/CPU type khoj being install on
- Update CMAKE flags to use to install Khoj with correct GPU support
Previous flags used DLLAMA, this has been updated to use DGGML now
in llama.cpp
Remove unnecessary "Inferred Query" heading prefix to image generation prompt
used by Khoj. The inferred query in chat message has a heading of it's
own, so avoid two headings for the image prompt
The problem was the tool tip was visible on hover, but it was slow, so before the tool tip popped up, the user would click on the button and this stopped the tool tip from popping up.
So i reduced the popup delay to 10ms. now as soon as user hovers over the button, they will see that its a feature coming soon!
Improve Scrolling on Chat page of Web app
- Details
1. Only auto scroll Khoj's streamed response when scroll is near bottom of page
Allows scrolling to other messages in conversation while Khoj is formulating and streaming its response
2. Add button to scroll to bottom of the chat page
3. Scroll to most recent conversation turn on conversation first load
It's a better default to anchor to most recent conversation turn (i.e most recent user message)
4. Smooth scroll when Khoj's chat response is streamed
Previously the scroll would jitter during response streaming
5. Anchor scroll position when fetch and render older messages in conversation
Allow users to keep their scroll position when older messages are fetched from server and rendered
Resolves#758
* Update the conversation_id primary key field to be a uuid
- update associated API endpoints
- this is to improve the overall application health, by obfuscating some information about the internal database
- conversation_id type is now implicitly a string, rather than an int
- ensure automations are also migrated in place, such that the conversation_ids they're pointing to are now mapped to the new IDs
* Update client-side API calls to correctly query with a string field
* Allow modifying of conversation properties from the chat title
* Improve drag and drop file experience for chat input area
* Use a phosphor icon for the copy to clipboard experience for code snippets
* Update conversation_id parameter to be a str type
* If django_apscheduler is not in the environment, skip the migration script
* Fix create automation flow by storing conversation id as string
The new UUID used for conversation id can't be directly serialized.
Convert to string for serializing it for later execution
---------
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
## Improve
- Intelligently initialize a decent default set of chat model options
- Create non-interactive mode. Auto set default server configuration on first run via Docker
## Fix
- Make RapidOCR dependency optional as flaky requirements causing docker build failures
- Set default openai text to image model correctly during initialization
## Details
Improve initialization flow during first run to remove need to configure Khoj:
- Set Google, Anthropic Chat models too
Previously only Offline, Openai chat models could be set during init
- Add multiple chat models for each LLM provider
Interactively set a comma separated list of models for each provider
- Auto add default chat models for each provider in non-interactive
model if the `{OPENAI,GEMINI,ANTHROPIC}_API_KEY' env var is set
- Used when server run via Docker as user input cannot be processed to configure server during first run
- Do not ask for `max_tokens', `tokenizer' for offline models during
initialization. Use better defaults inferred in code instead
- Explicitly set default chat model to use
If unset, it implicitly defaults to using the first chat model.
Make it explicit to reduce this confusion
Resolves#882
The chat model initialize interaction flow is fairly similar across
the chat model providers.
This should simplify adding new chat model providers and reduce
chances of bugs in the interactive chat model initialization flow.
This is an initial pass to add documentation for all the knobs
available on the Khoj Admin panel.
It should shed some light onto what each admin setting is for and how
they can be customized when self hosting.
Resolves#831
- Improve Self Hosting Docker Instructions
- Ask to Install Docker Desktop to not require separate
docker-compose install and unify the instruction across OS
- To Self Host on Windows, ask to use Docker Desktop with WSL2 backend
- Use nested Tab grouping to split Docker vs Pip Self Host Instructions
- Reduce Self Host Setup Steps in Documentation after code simplification
- First run now avoids need to configure Khoj via admin panel
- So move the chat model config steps into optional post setup
config section
- Improve Instructions to Configure chat models on First Run
- Compress configuring chat model providers into a Tab Group
- Add Documentation for Remote Access under Advanced Self Hosting
Given the LLM landscape is rapidly changing, providing a good default
set of options should help reduce decision fatigue to get started
Improve initialization flow during first run
- Set Google, Anthropic Chat models too
Previously only Offline, Openai chat models could be set during init
- Add multiple chat models for each LLM provider
Interactively set a comma separated list of models for each provider
- Auto add default chat models for each provider in non-interactive
model if the {OPENAI,GEMINI,ANTHROPIC}_API_KEY env var is set
- Do not ask for max_tokens, tokenizer for offline models during
initialization. Use better defaults inferred in code instead
- Explicitly set default chat model to use
If unset, it implicitly defaults to using the first chat model.
Make it explicit to reduce this confusion
Resolves#882
This should configure Khoj with decent default configurations via
Docker and avoid needing to configure Khoj via admin page to start
using dockerized Khoj
Update default max prompt size set during khoj initialization
as online chat model are cheaper and offline chat models have larger
context now
RapidOCR depends on OpenCV which by default requires a bunch of GUI
paramters. This system package dependency set (like libgl1) is flaky
Making the RapidOCR dependency optional should allow khoj to be more
resilient to setup/dependency failures
Trade-off is that OCR for documents may not always be available and
it'll require looking at server logs to find out when this happens
This reverts commit c9665fb20b.
Revert "Fix handling for new conversation in agents page"
This reverts commit 3466f04992.
Revert "Add a unique_id field for identifiying conversations (#914)"
This reverts commit ece2ec2d90.
- This allows triggering khoj chat from the browser addressbar
- So now if you add Khoj to your browser bookmark with
- URL: https://app.khoj.dev/?q=%s
- Keyword: khoj
- Then you can type "khoj what is the news today" to trigger Khoj to
quickly respond to your query. This avoids having to open the Khoj web
app before asking your question
* Add a unique_id field to the conversation object
- This helps us keep track of the unique identity of the conversation without expose the internal id
- Create three staged migrations in order to first add the field, then add unique values to pre-fill, and then set the unique constraint. Without this, it tries to initialize all the existing conversations with the same ID.
* Parse and utilize the unique_id field in the query parameters of the front-end view
- Handle the unique_id field when creating a new conversation from the home page
- Parse the id field with a lightweight parameter called v in the chat page
- Share page should not be affected, as it uses the public slug
* Fix suggested card category
Previously Khoj would stop in the middle of response generation when
the safety filters got triggered at default thresholds. This was
confusing as it felt like a service error, not expected behavior.
Going forward Khoj will
- Only block responding to high confidence harmful content detected by
Gemini's safety filters instead of using the default safety settings
- Show an explanatory, conversational response (w/ harm category)
when response is terminated due to Gemini's safety filters
- Support using image generation models like Flux via Replicate
- Modularize the image generation code
- Make generate better image prompt chat actor add composition details
- Generate vivid images with DALLE-3
Enables using any image generation model on Replicate's Predictions
API endpoints.
The server admin just needs to add text-to-image model on the
server/admin panel in organization/model_name format and input their
Replicate API key with it
Create db migration (including merge)
Set sender email using `RESEND_EMAIL` environment variable for magic link sent via Resend API for authentication . It was previously hard-coded. This prevented hosting Khoj on other domains.
Resolves#908
- Major
- The new O1 series doesn't seem to support streaming, response_format enforcement,
stop words or temperature currently.
- Remove any markdown json codeblock in chat actors expecting json responses
- Minor
- Override block display styling of links by Katex in chat messages
Strip any json md codeblock wrapper if exists before processing
response by output mode, extract questions chat actor. This is similar
to what is already being done by other chat actors
Useful for succesfully interpreting json output in chat actors when
using non (json) schema enforceable models like o1 and gemma-2
Use conversation helper function to centralize the json md codeblock
removal code
This happens sometimes when LLM respons contains [\[1\]] kind of links
as reference. Both markdown-it and katex apply styling.
Katex's span uses display: block which makes the rendering of these
references take up a whole line by themselves.
Override block styling of spans within an `a' element to prevent such
chat message styling issues
* Add functions to chat with Google's gemini model series
* Gracefully close thread when there's an exception in the gemini llm thread
* Use enums for verifying the chat model option type
* Add a migration to add the gemini chat model type to the db model
* Fix chat model selection verification and math prompt tuning
* Fix extract questions method with gemini. Enforce json response in extract questions.
* Add standard stop sequence for Gemini chat response generation
---------
Co-authored-by: sabaimran <narmiabas@gmail.com>
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
Additional logging was enabled to debug automation failures in
production since migration chat API to use POST request method (from
earlier GET).
Redirect from http to https was default to use GET instead of POST
method to call /api/chat on redirect. This has been resolved now
> To save time for both you and us, try follow these guidelines before submitting a new issue:
> 1. Check if there is an existing issue tracking your bug on our Github.
> 2. When unsure if your issue is an actual bug, first discuss it on a [Github discussion](https://github.com/khoj-ai/khoj/discussions/new?category=q-a) or the **#bugs** channel of our [Discord server](https://discord.gg/b6gUdpKr).
> These steps avoid opening issues which are duplicate or not actual bugs.
- type:"checkboxes"
id:"server"
attributes:
label:"Server"
description:"With which Khoj server are you experiencing the issue? (select at least one)"
options:
- label:"Cloud (https://app.khoj.dev)"
required:false
- label:"Self-Hosted Docker"
required:false
- label:"Self-Hosted Python package"
required:false
- label:"Self-Hosted source code"
required:false
validations:
required:true
- type:"checkboxes"
id:"clients"
attributes:
label:"Clients"
description:"With which Khoj client(s) are you experiencing the issue? (select at least one)"
options:
- label:"Web browser"
required:false
- label:"Desktop/mobile app"
required:false
- label:"Obsidian"
required:false
- label:"Emacs"
required:false
- label:"WhatsApp"
required:false
validations:
required:true
- type:"checkboxes"
id:"os"
attributes:
label:"OS"
description:"On which operating system do you experience the issue? (select at least one)"
options:
- label:"Windows"
required:false
- label:"macOS"
required:false
- label:"Linux"
required:false
- label:"Android"
required:false
- label:"iOS"
required:false
validations:
required:true
- type:"input"
id:"version"
attributes:
label:"Khoj version"
description:"Use `/help` command on the chat page to find the server version.\n
If using the cloud service, you can input, latest.\n
If self-hosting - please make sure to run the latest version of Khoj before reporting any issues as it may have already been fixed."
validations:
required:true
- type:"textarea"
id:"description"
attributes:
label:"Describe the bug"
description:"What is the problem? A clear and concise description of the bug."
validations:
required:true
- type:"textarea"
id:"current"
attributes:
label:"Current Behavior"
description:"What actually happened?\n\n
Please include full errors, uncaught exceptions, stack traces, screenshots and other relevant logs."
validations:
required:true
- type:"textarea"
id:"expected"
attributes:
label:"Expected Behavior"
description:"What did you expect to happen?"
validations:
required:true
- type:"textarea"
id:"reproduction"
attributes:
label:"Reproduction Steps"
description:"Detail the steps needed to reproduce the issue. This can include a self-contained, concise snippet of
code, if applicable.\n\n
For more complex issues, provide a link to a repository with the smallest sample that reproduces
the bug.\n
If the issue can be replicated without code, please provide a clear, step-by-step description of
the actions or conditions necessary to reproduce it. Any screenshots are also appreciated.\n
Avoid including business logic or unrelated details, as this makes diagnosis more difficult.\n\n
Whether it's a sequence of actions, code samples, or specific conditions, ensure that the steps
are clear enough to be easily followed and replicated."
validations:
required:true
- type:"textarea"
id:"workaround"
attributes:
label:"Possible Workaround"
description:"If you find any workaround for this problem - please, provide it here."
validations:
required:false
- type:"textarea"
id:"context"
attributes:
label:"Additional Information"
description:"Anything else that might be relevant for troubleshooting this bug.\n
Providing context helps us come up with a solution that is most useful in the real-world use case.\n
For example if self-hosting, provide environment details like OS versin, Docker version etc."
validations:
required:false
- type:"input"
id:"discussion_link"
attributes:
label:"Link to Discord or Github discussion"
description:"Provide a link to the first message of bug's discussion on Discord or Github.\n
This will help to keep history of why this bug exists."
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[📚 Read Blog](https://blog.khoj.dev)
[💬 Discord](https://discord.gg/BDgyabRM6e)
<span> • </span>
[✍🏽 Blog](https://blog.khoj.dev)
</div>
<divalign="left">
***
### 🎁 New
* Start any message with `/research` to try out the experimental research mode with Khoj.
* Anyone can now [create custom agents](https://blog.khoj.dev/posts/create-agents-on-khoj/) with tunable personality, tools and knowledge bases.
* [Read](https://blog.khoj.dev/posts/evaluate-khoj-quality/) about Khoj's excellent performance on modern retrieval and reasoning benchmarks.
***
Khoj is an application that creates always-available, personal AI agents for you to extend your capabilities.
- You can share your notes and documents to extend your digital brain.
- Your AI agents have access to the internet, allowing you to incorporate realtime information.
- Khoj is accessible on Desktop, Emacs, Obsidian, Web and Whatsapp.
-You can share pdf, markdown, org-mode, notion files and github repositories.
-You'll get fast, accurate semantic search on top of your docs.
-Your agents can create deeply personal images and understand your speech.
## Overview
[Khoj](https://khoj.dev) is a personal AI app to extend your capabilities. It smoothly scales up from an on-device personal AI to a cloud-scale enterprise AI.
-Chat with any local or online LLM (e.g llama3, qwen, gemma, mistral, gpt, claude, gemini).
-Get answers from the internet and your docs (including image, pdf, markdown, org-mode, word, notion files).
-Access it from your Browser, Obsidian, Emacs, Desktop, Phone or Whatsapp.
- Create agents with custom knowledge, persona, chat model and tools to take on any role.
- Automate away repetitive research. Get personal newsletters and smart notifications delivered to your inbox.
- Find relevant docs quickly and easily using our advanced semantic search.
- Generate images, talk out loud, play your messages.
- Khoj is open-source, self-hostable. Always.
- Run it privately on [your computer](https://docs.khoj.dev/get-started/setup) or try it on our [cloud app](https://app.khoj.dev).
@@ -57,6 +67,10 @@ You can see the full feature list [here](https://docs.khoj.dev/category/features
To get started with self-hosting Khoj, [read the docs](https://docs.khoj.dev/get-started/setup).
## Enterprise
Khoj is available as a cloud service, on-premises, or as a hybrid solution. To learn more about Khoj Enterprise, [visit our website](https://khoj.dev/teams).
## Contributors
Cheers to our awesome contributors! 🎉
@@ -69,10 +83,3 @@ Made with [contrib.rocks](https://contrib.rocks).
### Interested in Contributing?
We are always looking for contributors to help us build new features, improve the project documentation, or fix bugs. If you're interested, please see our [Contributing Guidelines](https://docs.khoj.dev/contributing/development) and check out our [Contributors Project Board](https://github.com/orgs/khoj-ai/projects/4).
# Use the following line to use the latest version of khoj. Otherwise, it will build from source.
# Use the following line to use the latest version of khoj. Otherwise, it will build from source. Set this to ghcr.io/khoj-ai/khoj-cloud:latest if you want to use the prod image.
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.
# 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 official image.
# build:
# context: .
ports:
@@ -29,10 +40,13 @@ services:
# change the port in the args in the build section,
# as well as the port in the command section to match
# Use 0.0.0.0 to explicitly set the host ip for the service on the container. https://pythonspeed.com/articles/docker-connection-refused/
environment:
- POSTGRES_DB=postgres
@@ -44,14 +58,57 @@ services:
- KHOJ_DEBUG=False
- KHOJ_ADMIN_EMAIL=username@example.com
- KHOJ_ADMIN_PASSWORD=password
# Uncomment the following lines to make your instance publicly accessible.
# Replace the domain with your domain. Proceed with caution, especially if you are using anonymous mode.
# Default URL of Terrarium, the default Python sandbox used by Khoj to run code. Its container is specified above
- KHOJ_TERRARIUM_URL=http://sandbox:8080
# Uncomment line below to have Khoj run code in remote E2B code sandbox instead of the self-hosted Terrarium sandbox above. Get your E2B API key from https://e2b.dev/.
# - E2B_API_KEY=your_e2b_api_key
# Default URL of SearxNG, the default web search engine used by Khoj. Its container is specified above
- KHOJ_SEARXNG_URL=http://search:8080
# Uncomment line below to use with Ollama running on your local machine at localhost:11434.
# Change URL to use with other OpenAI API compatible providers like VLLM, LMStudio etc.
> Describes the Khoj settings configurable via the admin panel
By default, you admin panel is available at `http://localhost:42110/server/admin/`. You can access the admin panel by logging in with your admin credentials (this would be your `KHOJ_ADMIN_EMAIL` and `KHOJ_ADMIN_PASSWORD`). The admin panel allows you to configure various settings for your Khoj server.
## App Settings
### Agents
Add all the agents you want to use for your different use-cases like Writer, Researcher, Therapist etc.
-`Personality`: This is a prompt to tell the chat model how to tune the personality of the agent.
-`Chat model`: The chat model to use for the agent.
-`Name`: The name of the agent. This field helps give the agent a unique identity across the app.
-`Avatar`: Url to the agents profile picture. It help give the agent a unique visual identity across the app.
-`Style color`, `Style icon`: These fields help give the agent a unique, visually identifiable identity across the app.
-`Slug`: This is the agent name to use in urls.
-`Public`: Check this if the agent is expected to be visible to all users on this Khoj server.
-`Managed by admin`: Check this if the agent is managed by admin, not by any user.
-`Creator`: The user who created the agent.
-`Tools`: The list of tools available to this agent. Tools include notes, image, online. This field is not currently configurable and only supports all tools (i.e `["*"]`)
### Chat Model Options
Add all the chat models you want to try, use and switch between for your different use-cases. For each chat model you add:
-`Chat model`: The name of an [OpenAI](https://platform.openai.com/docs/models), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/models#model-names), [Gemini](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models) or [Offline](https://huggingface.co/models?pipeline_tag=text-generation&library=gguf) chat model.
-`Model type`: The chat model provider like `OpenAI`, `Offline`.
-`Vision enabled`: Set to `true` if your model supports vision. This is currently only supported for vision capable OpenAI models like `gpt-4o`
-`Max prompt size`, `Subscribed max prompt size`: These are optional fields. They are used to truncate the context to the maximum context size that can be passed to the model. This can help with accuracy and cost-saving.<br/>
-`Tokenizer`: This is an optional field. It is used to accurately count tokens and truncate context passed to the chat model to stay within the models max prompt size.

### Server Chat Settings
The server chat settings are used as:
1. The default chat models for subscribed (`Advanced` field) and unsubscribed (`Default` field) users.
2. The chat model for all intermediate steps like intent detection, web search etc. during chat response generation.
If a server chat setting is not added the first ChatModelOption in your config is used as the default chat model.
To add a server chat setting:
- Set your preferred default chat models in the `Default` fields of your [ServerChatSettings](http://localhost:42110/server/admin/database/serverchatsettings/)
- The `Advanced` field doesn't need to be set when self-hosting. When unset, the `Default` chat model is used for all users and the intermediate steps.
### AI Model API
These settings configure APIs to interact with AI models.
For each AI Model API you [add](http://localhost:42110/server/admin/database/aimodelapi/add):
-`Api key`: Set to your [OpenAI](https://platform.openai.com/api-keys), [Anthropic](https://console.anthropic.com/account/keys) or [Gemini](https://aistudio.google.com/app/apikey) API keys.
-`Name`: Give the configuration any friendly name like `OpenAI`, `Gemini`, `Anthropic`.
-`Api base url`: Set the API base URL. This is only relevant to set if you're using another OpenAI-compatible proxy server like [Ollama](/advanced/ollama) or [LMStudio](/advanced/lmstudio).

### Search Model Configs
Search models are used to generate vector embeddings of your documents for natural language search and chat. You can choose any [embeddings models on HuggingFace](https://huggingface.co/models?pipeline_tag=sentence-similarity) to create vector embeddings of your documents for natural language search and chat.
<imgsrc="/img/example_search_model_admin_settings.png"alt="Example Search Model Settings"style={{width:500}}/>
### Text to Image Model Options
Add text to image generation models with these settings. Khoj currently supports text to image models available via OpenAI, Stability or Replicate API
-`api-key`: Set to your OpenAI, Stability or Replicate API key
-`model`: Set the model name available over the selected model provider
-`model-type`: Set to the appropriate model provider
-`openai-config`: For image generation models available via OpenAI (compatible) API you can set the appropriate OpenAI Processor Conversation Settings instead of specifying the `api-key` field above
### Speech to Text Model Options
Add speech to text models with these settings. Khoj currently only supports whisper speech to text model via OpenAI API or Offline
### Voice Model Options
Add text to speech models with these settings. Khoj currently supports models from [ElevenLabs](https://elevenlabs.io/).
### Reflective Questions
This is a static list of starter question suggestions for each user. It is not current used in any client app. It used to be shown on the web app home page. We may turn it into a dynamic list of starter questions personalized to each users, say based on their recent conversations or synced knowledge base.
## User Data
- Users, Entrys, Conversations, Subscriptions, Github configs, Notion configs, User search configs, User conversation configs, User voice configs
## Miscellaneous Data
- Process Locks: Persistent Locks for Automations
- Client Applications:
Client applications allow you to setup third party applications that can query your Khoj server using a client application ID + secret. The secret would go in a bearer token.
This is only helpful for self-hosted users or teams. If you're using [Khoj Cloud](https://app.khoj.dev), both Magic Links and Google OAuth work.
:::
By default, most of the instructions for self-hosting Khoj assume a single user, and so the default configuration is to run in anonymous mode. However, if you want to enable authentication, you can do so either with with [Magic Links](#using-magic-links) or [Google OAuth](#using-google-oauth) as shown below. This can be helpful to make Khoj securely accessible to you and your team.
:::tip[Note]
Remove the `--anonymous-mode` flag in your start up command to enable authentication.
:::
## Using Magic Links
The most secure way to do this is to integrate with [Resend](https://resend.com) by setting up an account and adding an environment variable for `RESEND_API_KEY`. You can get your API key [here](https://resend.com/api-keys). This will allow you to automatically send sign-in links to users who want to log in.
It's still possible to use the magic links feature without Resend, but you'll need to manually send the magic links to users who want to log in.
## Manually sending magic links
1. The user will have to enter their email address in the login form.
They'll click `Send Magic Link`. Without the Resend API key, this will just create an unverified account for them in the backend
<imgsrc="/img/magic_link.png"alt="Magic link login form"width="400"/>
2. You can get their magic link using the admin panel
Go to the [admin panel](http://localhost:42110/server/admin/database/khojuser/). You'll see a list of users. Search for the user you want to send a magic link to. Tick the checkbox next to their row, and use the action drop down at the top to 'Get email login URL'. This will generate a magic link that you can send to the user, which will appear at the top of the admin interface.
| Get email login URL | Retrieved login URL |
|---------------------|---------------------|
| <imgsrc="/img/admin_get_emali_login.png"alt="Get user magic sign in link"width="400"/>| <imgsrc="/img/admin_successful_login_url.png"alt="Successfully retrieved a login URL"width="400"/>|
3. Send the magic link to the user. They can click on it to log in.
Once they click on the link, they'll automatically be logged in. They'll have to repeat this process for every new device they want to log in from, but they shouldn't have to repeat it on the same device.
A given magic link can only be used once. If the user tries to use it again, they'll be redirected to the login page to get a new magic link.
## Using Google OAuth
To set up your self-hosted Khoj with Google Auth, you need to create a project in the Google Cloud Console and enable the Google Auth API.
To implement this, you'll need to:
1. You must use the `python` package or build from source, because you'll need to install additional packages for the google auth libraries (`prod`). The syntax to install the right packages is
```
pip install khoj[prod]
```
2. [Create authorization credentials](https://developers.google.com/identity/sign-in/web/sign-in) for your application.
3. Open your [Google cloud console](https://console.developers.google.com/apis/credentials) and create a configuration like below for the relevant `OAuth 2.0 Client IDs` project:
4. Configure these environment variables: `GOOGLE_CLIENT_SECRET`, and `GOOGLE_CLIENT_ID`. You can find these values in the Google cloud console, in the same place where you configured the authorized origins and redirect URIs.
That's it! That should be all you have to do. Now, when you reload Khoj without `--anonymous-mode`, you should be able to use your Google account to sign in.
By default, most of the instructions for self-hosting Khoj assume a single user, and so the default configuration is to run in anonymous mode. However, if you want to enable authentication, you can do so either with with [Magic Links](#using-magic-links) or [Google OAuth](#using-google-oauth) as shown below. This can be helpful to make Khoj securely accessible to you and your team.
:::tip[Note]
Remove the `--anonymous-mode` flag from your khoj start up command or docker-compose file to enable authentication.
:::
## Using Magic Links
The most secure way to do this is to integrate with [Resend](https://resend.com).
1. Setup your account at https://resend.com
2. Set an environment variable for `RESEND_API_KEY`. You can get your API key [here](https://resend.com/api-keys).
3. Set an environment variable for `RESEND_EMAIL`. This is the email address that will show up in your `from` field when sending magic links.
This will allow you to automatically send sign-in links to users who want to log in.
It's still possible to use the magic links feature without Resend, but you'll need to manually send the magic links to users who want to log in.
## Manually sending magic links
1. The user will have to enter their email address in the login page at http://localhost:42110/login.
They'll click `Get Login Link`. Without the Resend API key, this will just create an unverified account for them in the backend
<img src="/img/magic_link.png" alt="Magic link login form" width="400"/>
2. You can get their magic link using the admin panel
Go to the [admin panel](http://localhost:42110/server/admin/database/khojuser/). You'll see a list of users. Search for the user you want to send a magic link to. Tick the checkbox next to their row, and use the action drop down at the top to 'Get email login URL'. This will generate a magic link that you can send to the user, which will appear at the top of the admin interface.
| Get email login URL | Retrieved login URL |
|---------------------|---------------------|
| <img src="/img/admin_get_emali_login.png" alt="Get user magic sign in link" width="400" />| <img src="/img/admin_successful_login_url.png" alt="Successfully retrieved a login URL" width="400" />|
3. Send the magic link to the user. They can click on it to log in.
Once they click on the link, they'll automatically be logged in. They'll have to repeat this process for every new device they want to log in from, but they shouldn't have to repeat it on the same device.
A given magic link can only be used once. If the user tries to use it again, they'll be redirected to the login page to get a new magic link.
## Using Google OAuth
For this method, you'll need to use the prod version of the Khoj package. You can install it as below:
<Tabs groupId="server" queryString>
<TabItem value="docker" label="Docker">
Update your `docker-compose.yml` to use the prod image
```bash
image: ghcr.io/khoj-ai/khoj-cloud:latest
```
</TabItem>
<TabItem value="pip" label="Pip">
```bash
pip install khoj[prod]
```
</TabItem>
</Tabs>
To set up your self-hosted Khoj with Google Auth, you need to create a project in the Google Cloud Console and enable the Google Auth API.
To implement this, you'll need to:
1. [Create authorization credentials](https://developers.google.com/identity/sign-in/web/sign-in) for your application.
2. Open your [Google cloud console](https://console.developers.google.com/apis/credentials) and create a configuration like below for the relevant `OAuth 2.0 Client IDs` project:
3. Configure these environment variables: `GOOGLE_CLIENT_SECRET`, and `GOOGLE_CLIENT_ID`. You can find these values in the Google cloud console, in the same place where you configured the authorized origins and redirect URIs.
That's it! That should be all you have to do. Now, when you reload Khoj without `--anonymous-mode`, you should be able to use your Google account to sign in.
This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you can directly use any of the pre-configured AI models.
:::
Khoj can use Google's Gemini and Anthropic's Claude family of AI models from [Vertex AI](https://cloud.google.com/vertex-ai) on Google Cloud. Explore Anthropic and Gemini AI models available on Vertex AI's [Model Garden](https://console.cloud.google.com/vertex-ai/model-garden).
## Setup
1. Follow [these instructions](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#before_you_begin) to use models on GCP Vertex AI.
3. Create a new [API Model API](http://localhost:42110/server/admin/database/aimodelapi/add) on your Khoj admin panel.
- **Name**: `Google Vertex` (or whatever friendly name you prefer).
- **Api Key**: `base64 encoded json keyfile` from step 2.
- **Api Base Url**: `https://{MODEL_GCP_REGION}-aiplatform.googleapis.com/v1/projects/{YOUR_GCP_PROJECT_ID}`
- MODEL_GCP_REGION: A region the AI model is available in. For example `us-east5` works for [Claude](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude#regions).
- YOUR_GCP_PROJECT_ID: Get your project id from the [Google cloud dashboard](https://console.cloud.google.com/home/dashboard)
4. Create a new [Chat Model](http://localhost:42110/server/admin/database/chatmodel/add) on your Khoj admin panel.
- **Name**: `claude-3-7-sonnet@20250219`. Any Claude or Gemini model on Vertex's Model Garden should work.
- **Model Type**: `Anthropic` or `Google`
- **Ai Model API**: *the Google Vertex Ai Model API you created in step 3*
- **Max prompt size**: `60000` (replace with the max prompt size of your model)
- **Tokenizer**: *Do not set*
5. Select the chat model on [your settings page](http://localhost:42110/settings) and start a conversation.
3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
- Name: `proxy-name`
- Api Key: `any string`
- Api Base Url: **URL of yourOpenaiProxy API**
4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
- Name: `llama3.1` (replace with the name of your local model)
Khoj does not work with LM Studio anymore. Khoj leverages [json mode](https://platform.openai.com/docs/guides/structured-outputs#json-mode) extensively but LMStudio's API seems to have dropped support for json mode. [1](https://x.com/lmstudio/status/1770135858709975547), [2](https://lmstudio.ai/docs/api/structured-output)
:::
:::info
This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you're limited to our first-party models.
:::
@@ -14,17 +18,14 @@ LM Studio can expose an [OpenAI API compatible server](https://lmstudio.ai/docs/
## Setup
1. Install [LM Studio](https://lmstudio.ai/) and download your preferred Chat Model
2. Go to the Server Tab on LM Studio, Select your preferred Chat Model and Click the green Start Server button
3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
- Name: `proxy-name`
- Api Key: `any string`
- Api Base Url: `http://localhost:1234/v1/` (default for LMStudio)
4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
- Name: `llama3.1` (replace with the name of your local model)
- Model Type: `Openai`
-Openai Config: `<the proxy config you created in step 3>`
- Max prompt size: `20000` (replace with the max prompt size of your model)
- Tokenizer: *Do not set for OpenAI, mistral, llama3 based models*
5.Create a new [Server Chat Setting](http://localhost:42110/server/admin/database/serverchatsettings/add/) on your Khoj admin panel
- Default model: `<name of chat model option you created in step 4>`
- Summarizer model: `<name of chat model option you created in step 4>`
6. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
3. Create a new [AI Model API](http://localhost:42110/server/admin/database/aimodelapi/add/) on your Khoj admin panel
-**Name**: `lmstudio`
-**Api Key**: `any string`
-**Api Base Url**: `http://localhost:1234/v1/` (default for LMStudio)
4. Create a new [Chat Model](http://localhost:42110/server/admin/database/chatmodel/add) on your Khoj admin panel.
-**Name**: `llama3.1` (replace with the name of your local model)
-**Model Type**: `Openai`
-**Ai Model Api**: *the lmstudio Ai Model Api you created in step 3*
-**Max prompt size**: `20000` (replace with the max prompt size of your model)
-**Tokenizer**: *Do not set for OpenAI, mistral, llama3 based models*
5.Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you're limited to our first-party models.
:::
:::info
Khoj natively supports local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). Using an OpenAI API proxy with Khoj maybe useful for ease of setup, trying new models or using commercial LLMs via API.
:::
Ollama allows you to run [many popular open-source LLMs](https://ollama.com/library) locally from your terminal.
For folks comfortable with the terminal, Ollama's terminal based flows can ease setup and management of chat models.
Ollama exposes a local [OpenAI API compatible server](https://github.com/ollama/ollama/blob/main/docs/openai.md#models). This makes it possible to use chat models from Ollama to create your personal AI agents with Khoj.
## Setup
1. Setup Ollama: https://ollama.com/
2. Start your preferred model with Ollama. For example,
```bash
ollama run llama3.1
```
3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
- Name: `ollama`
- Api Key: `any string`
- Api Base Url: `http://localhost:11434/v1/` (default for Ollama)
4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
- Name: `llama3.1` (replace with the name of your local model)
6. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
That's it! You should now be able to chat with your Ollama model from Khoj. If you want to add additional models running on Ollama, repeat step 6 for each model.
This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you can use our first-party supported models.
:::
:::info
Khoj can directly run local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). The integration with Ollama is useful to run Khoj on Docker and have the chat models use your GPU or to try new models via CLI.
:::
Ollama allows you to run [many popular open-source LLMs](https://ollama.com/library) locally from your terminal.
For folks comfortable with the terminal, Ollama's terminal based flows can ease setup and management of chat models.
Ollama exposes a local [OpenAI API compatible server](https://github.com/ollama/ollama/blob/main/docs/openai.md#models). This makes it possible to use chat models from Ollama with Khoj.
## Setup
:::info
Restart your Khoj server after first run or update to the settings below to ensure all settings are applied correctly.
:::
<Tabs groupId="type" queryString>
<TabItem value="first-run" label="First Run">
<Tabs groupId="server" queryString>
<TabItem value="docker" label="Docker">
1. Setup Ollama: https://ollama.com/
2. Download your preferred chat model with Ollama. For example,
```bash
ollama pull llama3.1
```
3. Uncomment `OPENAI_BASE_URL` environment variable in your downloaded Khoj [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml#:~:text=OPENAI_BASE_URL)
4. Start Khoj docker for the first time to automatically integrate and load models from the Ollama running on your host machine
```bash
# run below command in the directory where you downloaded the Khoj docker-compose.yml
docker-compose up
```
</TabItem>
<TabItem value="pip" label="Pip">
1. Setup Ollama: https://ollama.com/
2. Download your preferred chat model with Ollama. For example,
```bash
ollama pull llama3.1
```
3. Set `OPENAI_BASE_URL` environment variable to `http://localhost:11434/v1/` in your shell before starting Khoj for the first time
- Open access to the Khoj port (default: 42110) from your OS and Network firewall.
:::warning[Use HTTPS certificate]
To expose Khoj on a custom domain over the public internet, use of an SSL certificate is strongly recommended. You can use [Let's Encrypt](https://letsencrypt.org/) to get a free SSL certificate for your domain.
To disable HTTPS, set the `KHOJ_NO_HTTPS` environment variable to `True`. This can be useful if Khoj is only accessible behind a secure, private network.
:::
:::info[Try Tailscale]
You can use [Tailscale](https://tailscale.com/) for easy, secure access to your self-hosted Khoj over the network.
1. Set `KHOJ_DOMAIN` to your machines [tailscale ip](https://tailscale.com/kb/1452/connect-to-devices#identify-your-devices) or [fqdn on tailnet](https://tailscale.com/kb/1081/magicdns#fully-qualified-domain-names-vs-machine-names). E.g `KHOJ_DOMAIN=100.4.2.0` or `KHOJ_DOMAIN=khoj.tailfe8c.ts.net`
2. Access Khoj by opening `http://tailscale-ip-of-server:42110` or `http://fqdn-of-server:42110` from any device on your tailscale network
This is only helpful for secure cross-device access to **self-hosted** Khoj. You **do not** need this if you're using [Khoj Cloud](https://app.khoj.dev).
:::
[Tailscale](https://tailscale.com) simplifies creating a private VPN using [Wireguard](https://www.wireguard.com/) and OAuth. So you can host and access services on your devices from anywhere.
The instructions below are one way to simply and securely access your self-hosted Khoj from your phone, laptop etc.
### Minimal Setup
1. Setup khoj on your preferred machine following the [standard steps](/get-started/setup)
2. Sign-up to [Tailscale](https://tailscale.com) and install the app on machines you want to access Khoj from. This usually includes your khoj server, your phone, laptop. Note the tailscale i.p of your khoj server.
3. Start khoj on your server by including the flag `--host <your_server_tailscale_ip>`
4. Open `http://<your_server_tailscale_ip>:42110` to access khoj from any device on your tailscale network!
### HTTPS Certificate
:::info
Tailscale uses Wireguard to encrypt and route traffic between your machines. So HTTPS isn't required with Tailscale for secure access. HTTPS with Tailscale is only useful for browsers to not complain about security and block certain features like clipboard access unless HTTPS is enabled.
:::
1. Enable [MagicDNS](https://tailscale.com/kb/1081/magicdns#enabling-magicdns) and [HTTPS](https://tailscale.com/kb/1153/enabling-https) toggle on your tailscale admin console [DNS](https://login.tailscale.com/admin/dns) page. Note your unique tailscale domain name (usually ends with .ts.net)
2. Create an https certificate for your Khoj server by running the following command:
```bash
# Assuming the server is named, `server` and your tailnet is `black-forest.ts.net`
# Note path of the .crt and .key files generated
tailscale cert server.black-forest.ts.net
```
3. Start khoj to be served via https on standard port
```bash
sudo KHOJ_DOMAIN=server.black-forest.ts.net \
khoj \
--sslcert /path/to/your/tailscale.crt \
--sslkey path/to/your/tailscale.key \
--host=server.black-forest.ts.net \
--port 443
```
4. You should now be able to access khoj on `https://server.black-forest.ts.net` from any device on your private tailscale network!
@@ -11,8 +11,8 @@ This is only helpful for self-hosted users. If you're using [Khoj Cloud](https:/
Khoj natively supports local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). Using an OpenAI API proxy with Khoj maybe useful for ease of setup, trying new models or using commercial LLMs via API.
:::
Khoj can use any OpenAI API compatible server including [Ollama](/advanced/ollama), [LMStudio](/advanced/lmstudio) and [LiteLLM](/advanced/litellm).
Configuring this allows you to use non-standard, open or commercial, local or hosted LLM models for Khoj
Khoj can use any OpenAI API compatible server including local providers like [Ollama](/advanced/ollama), [LMStudio](/advanced/lmstudio) and [LiteLLM](/advanced/litellm) and commercial providers like [HuggingFace](https://huggingface.co/docs/api-inference/tasks/chat-completion#using-the-api), [OpenRouter](https://openrouter.ai/docs/quick-start) etc.
Configuring this allows you to use non-standard, open or commercial, local or hosted LLM models for Khoj.
Combine them with Khoj can turn your favorite LLM into an AI agent. Allowing you to chat with your docs, find answers from the internet, build custom agents and run automations.
@@ -20,18 +20,15 @@ For specific integrations, see our [Ollama](/advanced/ollama), [LMStudio](/advan
## General Setup
1. Start your preferred OpenAI API compatible app
3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
- Name: `proxy-name`
- Api Key: `any string`
- Api Base Url: **URL of your Openai Proxy API**
4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
- Name: `llama3` (replace with the name of your local model)
- Model Type: `Openai`
-Openai Config: `<the proxy config you created in step 3>`
- Max prompt size: `2000` (replace with the max prompt size of your model)
- Tokenizer: *Do not set for OpenAI, mistral, llama3 based models*
5.Create a new [Server Chat Setting](http://localhost:42110/server/admin/database/serverchatsettings/add/) on your Khoj admin panel
- Default model: `<name of chat model option you created in step 4>`
- Summarizer model: `<name of chat model option you created in step 4>`
6. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
1. Start your preferred OpenAI API compatible app locally or get API keys from commercial AI model providers.
3. Create a new [API Model API](http://localhost:42110/server/admin/database/aimodelapi/add) on your Khoj admin panel
-**Name**: `any name`
-**Api Key**: `any string`
-**Api Base Url**: *The URL of your Openai Compatible API*
3. Create a new [Chat Model](http://localhost:42110/server/admin/database/chatmodel/add) on your Khoj admin panel.
-**Name**: `llama3` (replace with the name of your local model)
-**Model Type**: `Openai`
-**Ai Model Api**: *The AI Model API you created in step 2*
-**Max prompt size**: `2000` (replace with the max prompt size of your model)
-**Tokenizer**: *Do not set for OpenAI, mistral, llama3 based models*
4.Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
- **Discover**: Find similar notes to the current one
## Setup
:::info[Self Hosting]
If you are self-hosting the Khoj server, update the Khoj Obsidian plugin settings step below:
- Set the `Khoj URL` field to your Khoj server URL. By default, use `http://127.0.0.1:42110`.
- Do not set the `Khoj API Key` field if your Khoj server runs in anonymous mode. For example, `khoj --anonymous-mode`
:::
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/settings#clients)
4. Set your Khoj API Key in the Khoj plugin settings in Obsidian
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/settings#clients)
4. Set your Khoj API Key in the Khoj plugin settings on Obsidian
5. (Optional) Click `Force Sync` in the Khoj plugin settings on Obsidian to immediately sync your Obsidian vault.
<br/>By default, your Obsidian vault is automatically synced periodically.
See the official [Obsidian Plugin Docs](https://help.obsidian.md/Extending+Obsidian/Community+plugins) for more details on installing Obsidian plugins.
@@ -41,7 +48,7 @@ To see other notes similar to the current one, run *Khoj: Find Similar Notes* fr
### 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
See [Khoj Search](/features/search) for more details. Use [query filters](/miscellaneous/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.
Go see it here --> [Khoj Cloud](https://app.khoj.dev).

## Features
- **Chat**
- **Faster answers**: Find answers quickly, from your private notes or the public internet
@@ -25,7 +29,7 @@ You can upload documents to Khoj from the web interface, one at a time. This is
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.
@@ -37,9 +41,3 @@ You can optionally install Khoj as a [Progressive Web App (PWA)](https://web.dev
Text [+1 (848) 800 4242](https://wa.me/18488004242) or scan the QQ code below on your phone to chat with Khoj on WhatsApp.
Text [+1 (848) 800 4242](https://wa.me/18488004242) or scan the QR code below 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/settings).
If you hit usage limits for the WhatsApp bot, upgrade to [a paid plan](https://khoj.dev/pricing) on Khoj Cloud.
If you hit usage limits for the WhatsApp bot, upgrade to [a paid plan](https://khoj.dev/#pricing) on Khoj Cloud.
<imgsrc="https://khoj-web-bucket.s3.amazonaws.com/khojwhatsapp.png"alt="WhatsApp QR Code"width="300"height="300"/>
@@ -27,4 +27,4 @@ We have more commands under development, including `/share` to uploading documen
## Source Code
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.
You can find all of the code for the WhatsApp bot in the [flint repository](https://github.com/khoj-ai/flint). As all of our code, it is open source and you can contribute to it.
You can optionally use `yarn dev` to start a development server for the front-end which will be available at http://localhost:3000. This is especially useful if you're making changes to the front-end code, but not necessary for running Khoj. Note that streaming does not work on the dev server due to how it is handled with SSR in Next.js.
Always run `yarn export` to test your front-end changes on http://localhost:42110 before creating a PR.
#### 4. Run
1. Start Khoj
```bash
khoj -vv
@@ -224,7 +236,7 @@ The core code for the Obsidian plugin is under `src/interface/obsidian`. The fil
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/), [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.
Follow the steps below to [release](https://github.com/khoj-ai/khoj/releases/) Khoj. This will create a stable release of Khoj on [Pypi](https://pypi.org/project/khoj/), [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
@@ -243,7 +255,7 @@ Follow the steps below to [release](https://github.com/debanjum/khoj/releases/)
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.
:::warning[Unmaintained]
The Github integration is not maintained. We are considering deprecating it. It doesn't seem used by many folks and its cumbersome for us to maintain.
:::
The Github integration allows you to index as many repositories as you want. It's currently default configured to index all Markdown/Org/Text files in each repository. For large repositories, this takes a fairly long time, but it works well for smaller projects.
# Configure your settings
@@ -9,6 +13,6 @@ The Github integration allows you to index as many repositories as you want. It'
## 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 [https://app.khoj.dev/settings#github](https://app.khoj.dev/settings#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/settings#github](https://app.khoj.dev/settings/content/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 [https://app.khoj.dev/](https://app.khoj.dev/) and start searching!
@@ -16,4 +16,4 @@ Go to https://app.khoj.dev/settings 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/settings#notion](http://localhost:42110/settings#notion). Click `Save`.
5. Click `Configure` in http://localhost:42110/settings to index your Notion workspace(s).
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).
That's it! You should be ready to start searching and chatting. Make sure you've configured your [chat settings](/get-started/setup#use-khoj).
There are several ways you can get started with sharing your data with the Khoj AI.
- Drag and drop your documents via [the web UI](/clients/web/#upload-documents). This is best if you have a one-off document you need to interact with.
- You can use the [search page](https://app.khoj.dev/search) to upload documents that become indexed & searchable by your second brain. Click on "Add Documents" to get started.
- Use the desktop app to [upload and sync your documents](/clients/desktop). This is best if you have a lot of documents on your computer or you need the docs to stay in sync.
- Setup the sync options for either [Obsidian](/clients/obsidian) or [Emacs](/clients/emacs) to automatically sync your documents with Khoj. This is best if you are already using these tools and want to leverage Khoj's AI capabilities.
- Configure your [Notion](/data-sources/notion_integration) or [Github](/data-sources/github_integration) to sync with Khoj. By providing your credentials, you can keep the data synced in the background.


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.
@@ -29,7 +29,7 @@ Khoj is available as a [Desktop app](/clients/desktop), [Emacs package](/clients

### Supported Data Sources
Khoj can understand your word, PDF, org-mode, markdown, plaintext files, [Github projects](/data-sources/github_integration) and [Notion pages](/data-sources/notion_integration).
Khoj can understand your word, PDF, org-mode, markdown, plaintext files, and [Notion pages](/data-sources/notion_integration).
[Automations](https://app.khoj.dev/automations) are a powerful feature within Khoj to schedule repeated tasks for information retrieval directly from your account. You can run them at a specific time and interval. This is still an experimental feature, so please report any issues you encounter.
[Automations](https://app.khoj.dev/automations) are a powerful feature within Khoj to schedule repeated jobs for Khoj to do on your behalf. Automations allow you run your query automatically at a scheduled time and frequency. Khoj will send its research to you as an email.
You can ask Khoj to create custom newsletters, summarize news, notify you of world events etc.
Khoj will use your local time zone to determine the scheduling localization. You can go back and configure the prompt any time you want from the automations page. You can also delete the automation if you no longer need it.
Khoj uses your local time zone for scheduling. You can configure, share and delete your automations from the [automations page](https://app.khoj.dev/automations).
:::danger[Note]
Automations will not deliver emails to self-hosted users out of the box. You'll have to have Resend and [Authentication](/advanced/authentication) setup to send emails.
:::info[Self Hosting]
Selfhosting users need to setup Resend and [Authentication](/advanced/authentication) setup to have Khoj send automation emails.
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. It acts as an excellent research assistant, search engine, or personal tutor.
<imgsrc="/img/khoj_chat_on_web.png"alt="Chat on Web"style={{width:'400px'}}/>
<imgsrc="https://assets.khoj.dev/vision_chat_example.png"alt="Chat on Web"style={{width:'400px'}}/>
### Overview
- Creates a personal assistant for you to inquire and engage with your notes or online information as needed
@@ -15,50 +15,24 @@ You can configure Khoj to chat with you about anything. When relevant, it'll use
- 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 [Llama 3.1 by Meta](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) by default. For machines with no or small GPU we recommend using [Gemma 2 2B](https://huggingface.co/bartowski/gemma-2-2b-it-GGUF) or [Phi 3.5 mini](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF)
:::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.
See [the setup guide](/get-started/setup.mdx) to configure your chat models.
### 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
2. Enter your queries to chat with Khoj. Use [slash commands](#commands) and [query filters](/miscellaneous/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
1. Your query is used to retrieve the most relevant notes, if any, using Khoj search using RAG.
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
#### Conversation File Filters
You can use conversation file filters to limit the notes used in the chat response. To do so, use the left panel in the web UI. Alternatively, you can also use [query filters](/miscellaneous/advanced#query-filters) to limit the notes used in the chat response.
You can use conversation file filters to limit the notes used in the chat response. To do so, use the left panel in the web UI. Alternatively, you can also use [query filters](/miscellaneous/query-filters) to limit the notes used in the chat response.
Khoj can generate and run simple Python code as well. This is useful if you want to have Khoj do some data analysis, generate plots and reports. LLMs by default aren't skilled at complex quantitative tasks. Code generation & execution can come in handy for such tasks.
Khoj automatically infers when to use the code tool. You can also tell it explicitly to use the code tool or use the `/code` [slash command](https://docs.khoj.dev/features/chat/#commands) in your chat.
## Setup (Self-Hosting)
### Terrarium Sandbox
Use [Cohere's Terrarium](https://github.com/cohere-ai/cohere-terrarium) to host the code sandbox locally on your machine for free.
To run with Docker, use our [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml) to automatically setup the Terrarium code sandbox, or start it manually like this:
```bash
docker pull ghcr.io/khoj-ai/terrarium:latest
docker run -d -p 8080:8080 ghcr.io/khoj-ai/terrarium:latest
```
To run from source, check [these instructions](https://github.com/khoj-ai/cohere-terrarium?tab=readme-ov-file#development).
#### Verify
Verify that it's running, by evaluating a simple Python expression:
```bash
curl -X POST -H "Content-Type: application/json"\
--url http://localhost:8080 \
--data-raw '{"code": "1 + 1"}'\
--no-buffer
```
### E2B Sandbox
[E2B](https://e2b.dev/) allows Khoj to run code on a remote but versatile sandbox with support for more python libraries. This is [not free](https://e2b.dev/pricing).
To have Khoj use E2B as the code sandbox:
1. Generate an API key on [their dashboard](https://e2b.dev/dashboard).
2. Set the `E2B_API_KEY` environment variable to it on the machine running your Khoj server.
- When using our [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml), uncomment and set the `E2B_API_KEY` env var in the `docker-compose.yml` file.
3. Now restart your Khoj server to switch to using the E2B code sandbox.
@@ -10,6 +10,17 @@ To generate images, you just need to provide a prompt to Khoj in which the image
## 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`.
You have a couple of image generation options.
### Image Generation Models
We support most state of the art image generation models, including Ideogram, Flux, and Stable Diffusion. These will run using [Replicate](https://replicate.com). Here's how to set them up:
1. Get a Replicate API key [here](https://replicate.com/account/api-tokens).
2. Create a new [Text to Image Model](http://localhost:42110/server/admin/database/texttoimagemodelconfig/). Set the `type` to `Replicate`. Use any of the model names you see [on this list](https://replicate.com/pricing#image-models). We recommend the `model name``black-forest-labs/flux-1.1-pro` from [Replicate](https://replicate.com/black-forest-labs/flux-1.1-pro).
### OpenAI
1. Get [an OpenAI API key](https://platform.openai.com/settings/organization/api-keys).
2. Setup your OpenAI API key, if you haven't already. See instructions [here](/get-started/setup#add-chat-models)
3. Create a text to image config at http://localhost:42110/server/admin/database/texttoimagemodelconfig/. Use `model name``dall-e-3` to use openai for image generation. Make sure to set the `Ai model api` field to the OpenAI AI model api you setup in step 2.
Oftentimes, having to leave your keyboard to move the mouse can break your flow. We want to make it as easy as possible for AI to flow with you as you are, so we've added some keyboard shortcuts to facilitate that.
@@ -14,8 +14,18 @@ Try it out yourself! https://app.khoj.dev
## Self-Hosting
Online search works out of the box even when self-hosting. Khoj uses [JinaAI's reader API](https://jina.ai/reader/) to search online and read webpages by default. No API key setup is necessary.
### Search
To improve online search, set the `SERPER_DEV_API_KEY` environment variable to your [Serper.dev](https://serper.dev/) API key. These search results include additional context like answer box, knowledge graph etc.
Online search can work even with self-hosting! You have a few options:
For advanced webpage reading, set the `OLOSTEP_API_KEY` environment variable to your [Olostep](https://www.olostep.com/) API key. This has a higher success rate at reading webpages than the default webpage reader.
- If you're using Docker, online search should work out of the box with [searxng](https://github.com/searxng/searxng) using our standard `docker-compose.yml`.
- For a non-local, free solution, you can use [JinaAI's reader API](https://jina.ai/reader/) to search online and read webpages. You can get a free API key via https://jina.ai/reader. Set the `JINA_API_KEY` environment variable to your Jina AI reader API key to enable online search.
- To get production-grade, fast online search, set the `SERPER_DEV_API_KEY` environment variable to your [Serper.dev](https://serper.dev/) API key. These search results include additional context like answer box, knowledge graph etc.
### Webpage Reading
Out of the box, you **don't have to do anything to enable webpage reading**. Khoj will automatically read webpages by using the `requests` library. To get more distributed and scalable webpage reading, you can use the following options:
- If you're using Jina AI's reader API for search, it should work automatically for webpage reading as well.
- For scalable webpage scraping, you can use [Firecrawl](https://www.firecrawl.dev/). Create a new [Webscraper](http://localhost:42110/server/admin/database/webscraper/add/). Set your Firecrawl API key to the Api Key field, and set the type to Firecrawl.
- For advanced webpage reading, you can use [Olostep](https://www.olostep.com/). This has a higher success rate at reading webpages than the default webpage readers. Create a new [Webscraper](http://localhost:42110/server/admin/database/webscraper/add/). Set your Olostep API key to the Api Key field, and set the type to Olostep.
@@ -11,7 +11,41 @@ Take advantage of super fast search to find relevant notes and documents from yo
- **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
2. Query using natural language to find relevant entries from your knowledge base. Use [query filters](/miscellaneous/query-filters) to limit entries to search
A bi-encoder models is used to create meaning vectors (aka vector embeddings) of your documents and search queries.
1. When you sync you documents with Khoj, it uses the bi-encoder model to create and store meaning vectors of (chunks of) your documents
2. When you initiate a natural language search the bi-encoder model converts your query into a meaning vector and finds the most relevant document chunks for that query by comparing their meaning vectors.
3. The slower but higher-quality cross-encoder model is than used to re-rank these documents for your given query.
### Setup (Self-Hosting)
You are **not required** to configure the search model config when self-hosting. Khoj sets up decent default local search model config for general use.
You may want to configure this if you need better multi-lingual search, want to experiment with different, newer models or the default models do not work for your use-case.
You can use bi-encoder models downloaded locally [from Huggingface](https://huggingface.co/models?library=sentence-transformers), served via the [HuggingFace Inference API](https://endpoints.huggingface.co/), OpenAI API, Azure OpenAI API or any OpenAI Compatible API like Ollama, LiteLLM etc. Follow the steps below to configure your search model:
1. Open the [SearchModelConfig](http://localhost:42110/server/admin/database/searchmodelconfig/) page on your Khoj admin panel.
2. Hit the Plus button to add a new model config or click the id of an existing model config to edit it.
3. Set the `biencoder` field to the name of the bi-encoder model supported [locally](https://huggingface.co/models?library=sentence-transformers) or via the API you configure.
4. Set the `Embeddings inference endpoint api key` to your OpenAI API key and `Embeddings inference endpoint type` to `OpenAI` to use an OpenAI embedding model.
5. Also set the `Embeddings inference endpoint` to your Azure OpenAI or OpenAI compatible API URL to use the model via those APIs.
6. Ensure the search model config you want to use is the **only one** that has `name` field set to `default`[^1].
7. Save the search model configs and restart your Khoj server to start using your new, updated search config.
:::info
You will need to re-index all your documents if you want to use a different bi-encoder model.
:::
:::info
You may need to tune the `Bi encoder confidence threshold` field for each bi-encoder to get appropriate number of documents for chat with your Knowledge base.
Confidence here is a normalized measure of semantic distance between your query and documents. The confidence threshold limits the documents returned to chat that fall within the distance specified in this field. It can take values between 0.0 (exact overlap) and 1.0 (no meaning overlap).
:::
[^1]: Khoj uses the first search model config named `default` it finds on startup as the search model config for that session
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/.
Click on the little mic icon to send your voice message to Khoj. It will send back what it heard via text. You can edit the message before sending it, if required. Try it at https://app.khoj.dev/.
## Voice Response
If you send a voice message, Khoj will automatically respond back with a voice message.
You can also click on the speaker icon next to any message to hear it out loud. The voice response feature is available only on the web view right now.


## 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).
1. Setup your OpenAI API key. See instructions [here](/get-started/setup#add-chat-models).
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 want to use the Text to Speech feature, you can set it up by following these steps:
1. Setup your account on [ElevenLabs.io](https://elevenlabs.io/).
2. Configure your API key in your environment variables with the key `ELEVEN_LABS_API_KEY`.
2. (Optional) Create a new [Voice model option](http://localhost:42110/server/admin/database/voicemodeloption/) with a specific voice ID from whichever voice you want to use. You can explore the options [here](https://elevenlabs.io/app/voice-library).
3. (Optional) Create a new [Voice model option](http://localhost:42110/server/admin/database/voicemodeloption/) with a specific voice ID from whichever voice you want to use. You can explore the options [here](https://elevenlabs.io/app/voice-library).
@@ -29,13 +26,12 @@ Welcome to the Khoj Docs! This is the best place to get setup and explore Khoj's
- Khoj is an open source, personal AI
- You can [chat](/features/chat) with it about anything. It'll use files you shared with it to respond, when relevant. It can also access information from the public internet.
- Quickly [find](/features/search) relevant notes and documents using natural language
- It understands pdf, plaintext, markdown, org-mode files, [notion pages](/data-sources/notion_integration) and [github repositories](/data-sources/github_integration)
- It understands pdf, plaintext, markdown, org-mode files, and [notion pages](/data-sources/notion_integration).
- Access it from your [Emacs](/clients/emacs), [Obsidian](/clients/obsidian), the [Khoj desktop app](/clients/desktop), or [any web browser](/clients/web)
- Use [cloud](https://app.khoj.dev/login) to access your Khoj anytime from anywhere, [self-host](/get-started/setup) on consumer hardware for privacy
- Use our [cloud](https://app.khoj.dev/login) instance to access your Khoj anytime from anywhere, [self-host](/get-started/setup) on consumer hardware for privacy
@@ -14,37 +14,108 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
```
## Setup
## Setup Khoj
These are the general setup instructions for self-hosted Khoj.
You can install the Khoj server using either Docker or Pip.
You can install the Khoj server using either [Docker](?server=docker) or [Pip](?server=pip).
:::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.
To use the offline chat model with your GPU, we recommend using the Docker setup with Ollama . You can also use the local Khoj setup via the Python package directly.
:::
### 1A. Install Method 1: Docker
:::info[First Run]
Restart your Khoj server after the first run to ensure all settings are applied correctly.
:::
#### 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/).
- *Option 1*: Click here to install [Docker Desktop](https://docs.docker.com/desktop/install/mac-install/). Make sure you also install the [Docker Compose](https://docs.docker.com/desktop/install/mac-install/) tool.
#### Setup
- *Option 2*: Use [Homebrew](https://brew.sh/) to install Docker and Docker Compose.
```shell
brew install --cask docker
brew install docker-compose
```
<h3>Setup</h3>
1. Download the Khoj docker-compose.yml file [from Github](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml)
2. Configure the environment variables in the `docker-compose.yml`
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
- Uncomment `OPENAI_BASE_URL` to use [Ollama](/advanced/ollama?type=first-run&server=docker#setup) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
```shell
cd ~/.khoj
docker-compose up
```
</TabItem>
<TabItem value="windows" label="Windows">
<h3>Prerequisites</h3>
1. Install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and restart your machine
```shell
# Run in PowerShell
wsl --install
```
2. Install [Docker Desktop](https://docs.docker.com/desktop/install/windows-install/) with **[WSL2 backend](https://docs.docker.com/desktop/wsl/#turn-on-docker-desktop-wsl-2)** (default)
1. Get the sample docker-compose file [from Github](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml).
2. Configure the environment variables in the docker-compose.yml to your choosing.<br />
Note: *Your admin account will automatically be created based on the admin credentials in that file, so pay attention to those.*
3. Now start the container by running the following command in the same directory as your docker-compose.yml file. This will automatically setup the database and run the Khoj server.
```shell
docker-compose up
```
<h3>Setup</h3>
1. Download the Khoj docker-compose.yml file [from Github](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml)
```shell
# Windows users should use their WSL2 terminal to run these commands
2. Configure the environment variables in the `docker-compose.yml`
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
- Uncomment `OPENAI_BASE_URL` to use [Ollama](/advanced/ollama) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
```shell
# Windows users should use their WSL2 terminal to run these commands
2. Configure the environment variables in the `docker-compose.yml`
- Set `KHOJ_ADMIN_PASSWORD`, `KHOJ_DJANGO_SECRET_KEY` (and optionally the `KHOJ_ADMIN_EMAIL`) to something secure. This allows you to customize Khoj later via the admin panel.
- Set `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, or `GEMINI_API_KEY` to your API key if you want to use OpenAI, Anthropic or Gemini commercial chat models respectively.
- Uncomment `OPENAI_BASE_URL` to use [Ollama](/advanced/ollama) running on your host machine. Or set it to the URL of your OpenAI compatible API like vLLM or [LMStudio](/advanced/lmstudio).
3. Start Khoj by running the following command in the same directory as your docker-compose.yml file.
```shell
cd ~/.khoj
docker-compose up
```
</TabItem>
</Tabs>
### 1B. Install Method 2: Pip
#### Prerequisites
##### Install Postgres (with PgVector)
:::info[Remote Access]
By default Khoj is only accessible on the machine it is running. To access Khoj from a remote machine see [Remote Access Docs](/advanced/remote).
:::
Your setup is complete once you see `🌖 Khoj is ready to use` in the server logs on your terminal.
</TabItem>
<TabItem value="pip" label="Pip">
<h3>1. Install Postgres (with PgVector)</h3>
Khoj uses Postgres DB for all server configuration and to scale to multi-user setups. It uses the pgvector package in Postgres to manage your document embeddings. Both Postgres and pgvector need to be installed for Khoj to work.
@@ -66,198 +137,292 @@ Install [Postgres.app](https://postgresapp.com/). This comes pre-installed with
</TabItem>
</Tabs>
##### Create the Khoj database
<h3>2. Create the Khoj database</h3>
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.
Make sure to update the `POSTGRES_HOST`, `POSTGRES_PORT`, `POSTGRES_USER`, `POSTGRES_DB` or `POSTGRES_PASSWORD` environment variables to match any customizations in your Postgres configuration.
:::
##### Install Khoj Server
- *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
<h3>3. Install Khoj Server</h3>
- 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://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#supported-backends) if you hit any llama-cpp issues with the installation
Run the following command in your terminal to install the Khoj server.
Run the following command from your terminal to start the Khoj backend and open Khoj in your browser.
Run the following command from your terminal to start the Khoj service.
```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.
`--anonymous-mode` allows access to Khoj without requiring login. This is usually fine for local only, single user setups. If you need authentication follow the [authentication setup docs](/advanced/authentication).
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`)
<h4>First Run</h4>
On the first run of the above command, you will be prompted to:
1. Create an admin account with an email and secure password
2. Customize the chat models to enable
- Keep your [OpenAI](https://platform.openai.com/api-keys), [Anthropic](https://console.anthropic.com/account/keys), [Gemini](https://aistudio.google.com/app/apikey) API keys and [OpenAI](https://platform.openai.com/docs/models), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/models#model-names), [Gemini](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models), [Offline](https://huggingface.co/models?pipeline_tag=text-generation&library=gguf) chat model names handy to set any of them up during first run.
3. Your setup is complete once you see `🌖 Khoj is ready to use` in the server logs on your terminal!
### 2. Configure
#### Login to the Khoj Admin Panel
:::tip[Auto Start]
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`)
:::
</TabItem>
</Tabs>
## Use Khoj
You can now open the web app at http://localhost:42110 and start interacting!<br />
Nothing else is necessary, but you can customize your setup further by following the steps below.
:::info[First Message to Offline Chat Model]
The offline chat model gets downloaded when you first send a message to it. The download can take a few minutes! Subsequent messages should be faster.
:::
### Add Chat Models
<h4>Login to the Khoj Admin Panel</h4>
Go to http://localhost:42110/server/admin and login with the admin credentials you setup during installation.
:::info[CSRF Error]
Ensure you are using **localhost, not 127.0.0.1**, to access the admin panel to avoid the CSRF error.
:::
:::info[DISALLOWED HOST Error]
:::info[CSRF Trusted Origin or Unset Cookie Error]
If using a load balancer/reverse_proxy in front of your Khoj server: Set the environment variable KHOJ_ALLOWED_DOMAIN=your-internal-ip-or-domain to avoid this error.
If unset, it defaults to KHOJ_DOMAIN.
:::
:::info[DISALLOWED HOST or Bad Request (400) Error]
You may hit this if you try access Khoj exposed on a custom domain (e.g. 192.168.12.3 or example.com) or over HTTP.
Set the environment variables KHOJ_DOMAIN=your-domain and KHOJ_NO_HTTPS=false if required to avoid this error.
Set the environment variables KHOJ_DOMAIN=your-external-ip-or-domain and KHOJ_NO_HTTPS=True if required to avoid this error.
:::
:::tip[Note]
Using Safari on Mac? You might not be able to login to the admin panel. Try using Chrome or Firefox instead.
:::
#### Configure Chat Model
<h4>Configure Chat Model</h4>
Setup which chat model you'd want to use. Khoj supports local and online chat models.
:::tip[Multiple Chat Models]
Add a `ServerChatSettings` with `Default` and `Summarizer` fields set to your preferred chat model via [the admin panel](http://localhost:42110/server/admin/database/serverchatsettings/add/). Otherwise Khoj defaults to use the first chat model in your [ChatModelOptions](http://localhost:42110/server/admin/database/chatmodeloptions/) for all non chat response generation tasks.
:::
##### Configure OpenAI Chat
<Tabs groupId="chatmodel" queryString>
<TabItem value="openai" label="OpenAI">
:::info[Ollama Integration]
Using Ollama? See the [Ollama Integration](/advanced/ollama) section for more custom setup instructions.
:::
1. Go to the [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 and server API base URL. The API base URL is optional - it's only relevant if you're using another OpenAI-compatible proxy server.
2. Go over to configure your [chat model options](http://localhost:42110/server/admin/database/chatmodeloptions/). Set the `chat-model` field to a supported chat model[^1] of your choice. For example, you can specify `gpt-4o` if you're using OpenAI.
1. Create a new [AI Model Api](http://localhost:42110/server/admin/database/aimodelapi/add) in the server admin settings.
- Add your [OpenAI API key](https://platform.openai.com/api-keys)
- Give the configuration a friendly name like `OpenAI`
- (Optional) Set the API base URL. It is only relevant if you're using another OpenAI-compatible proxy server like [Ollama](/advanced/ollama) or [LMStudio](/advanced/lmstudio).<br />

2. Create a new [chat model](http://localhost:42110/server/admin/database/chatmodel/add)
- Set the `chat-model` field to an [OpenAI chat model](https://platform.openai.com/docs/models). Example: `gpt-4o`.
- Make sure to set the `model-type` field to `OpenAI`.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only if you're sure of the tokenizer or token limit for the model you're using. Contact us if you're unsure what to do here.
- If your model supports vision, set the `vision enabled` field to `true`. This is currently only supported for OpenAI models with vision capabilities.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only if you're sure of the tokenizer or token limit for the model you're using. Contact us if you're unsure what to do here.<br />

</TabItem>
<TabItem value="anthropic" label="Anthropic">
1. Create a new [AI Model API](http://localhost:42110/server/admin/database/aimodelapi/add) in the server admin settings.
- Add your [Anthropic API key](https://console.anthropic.com/account/keys)
- Give the configuration a friendly name like `Anthropic`. Do not configure the API base url.
2. Create a new [chat model](http://localhost:42110/server/admin/database/chatmodel/add)
- Set the `chat-model` field to an [Anthropic chat model](https://docs.anthropic.com/en/docs/about-claude/models#model-names). Example: `claude-3-5-sonnet-20240620`.
- Set the `model-type` field to `Anthropic`.
- Set the `ai model api` field to the Anthropic AI Model API you created in step 1.
</TabItem>
<TabItem value="gemini" label="Gemini">
1. Create a new [AI Model API](http://localhost:42110/server/admin/database/aimodelapi/add) in the server admin settings.
- Add your [Gemini API key](https://aistudio.google.com/app/apikey)
- Give the configuration a friendly name like `Gemini`. Do not configure the API base url.
2. Create a new [chat model](http://localhost:42110/server/admin/database/chatmodel/add)
- Set the `chat-model` field to a [Google Gemini chat model](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-models). Example: `gemini-2.0-flash`.
- Set the `model-type` field to `Google`.
- Set the `ai model api` field to the Gemini AI Model API you created in step 1.
##### Configure Offline Chat
Any chat model on Huggingface in GGUF format can be used for local chat. Here's how you can set it up:
</TabItem>
<TabItem value="offline" label="Offline">
Offline chat stays completely private and can work without internet using any open-weights model.
1. No need to setup a conversation processor config!
2. Go over to configure your [chat model options](http://localhost:42110/server/admin/database/chatmodeloptions/). Set the `chat-model` field to a supported chat model[^1] of your choice. For example, we recommend `bartowski/Meta-Llama-3.1-8B-Instruct-GGUF`, but [any gguf model on huggingface](https://huggingface.co/models?library=gguf) should work.
- Make sure to set the `model-type` to `Offline`. Do not set `openai config`.
- The `tokenizer` and `max-prompt-size` fields are optional. You can set these for non-standard models (i.e not Mistral or Llama based models) or when you know the token limit of the model to improve context stuffing.
:::tip[System Requirements]
- Minimum 8 GB RAM. Recommend **16Gb VRAM**
- Minimum **5 GB of Disk** available
- A Nvidia, AMD GPU or a Mac M1+ machine would significantly speed up chat responses
:::
#### Share your data
You can sync your files and folders with Khoj using the [Desktop](/clients/desktop#setup), [Obsidian](/clients/obsidian#setup), or [Emacs](/clients/emacs#setup) clients or just drag and drop specific files on the [website](/clients/web#upload-documents). You can also directly sync your [Notion workspace](/data-sources/notion_integration).
1. Get the name of your preferred chat model from [HuggingFace](https://huggingface.co/models?pipeline_tag=text-generation&library=gguf). *Most GGUF format chat models are supported*.
2. Open the [create chat model page](http://localhost:42110/server/admin/database/chatmodel/add/) on the admin panel
3. Set the `chat-model` field to the name of your preferred chat model
- Make sure the `model-type` is set to `Offline`
4. Set the newly added chat model as your preferred model in your [User chat settings](http://localhost:42110/settings) and [Server chat settings](http://localhost:42110/server/admin/database/serverchatsettings/).
5. Restart the Khoj server and [start chatting](http://localhost:42110) with your new offline model!
</TabItem>
</Tabs>
[^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](/advanced/use-openai-proxy) on how to locally use OpenAI-format compatible proxy servers.
:::tip[Multiple Chat Models]
Set your preferred default chat model in the `Default`, `Advanced` fields of your [ServerChatSettings](http://localhost:42110/server/admin/database/serverchatsettings/).
Khoj uses these chat model for all intermediate steps like intent detection, web search etc.
:::
### 3. Use Khoj 🚀
:::info[Chat Model Fields]
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only if you're sure of the tokenizer or token limit for the model you're using. This improves context stuffing. Contact us if you're unsure what to do here.
- Only tick the `vision enabled` field for OpenAI models with vision capabilities like gpt-4o. Vision capabilities in other chat models is not currently utilized.
:::
Now open http://localhost:42110 to start interacting with Khoj!
### Sync your Knowledge
### 4. Install Khoj Clients (Optional)
Khoj exposes a web interface to search, chat and configure by default.<br />
You can install a Khoj client to sync your documents or to easily access Khoj from within Obsidian, Emacs or your OS.
- You can chat with your notes and documents using Khoj.
- Khoj can keep your files and folders synced using the Khoj [Desktop](/clients/desktop#setup), [Obsidian](/clients/obsidian#setup) or [Emacs](/clients/emacs#setup) clients.
- Your [Notion workspace](/data-sources/notion_integration) can be directly synced from the web app.
- You can also just drag and drop specific files you want to chat with on the [Web app](/clients/web#upload-documents).
- **Khoj Desktop**:<br />
[Install](/clients/desktop#setup) the Khoj Desktop app.
### Setup Khoj Clients
The Khoj web app is available by default to chat, search and configure Khoj.<br />
You can also install a Khoj client to easily access it from Obsidian, Emacs, Whatsapp or your OS and keep your documents synced with Khoj.
- **Khoj Obsidian**:<br />
[Install](/clients/obsidian#setup) the Khoj Obsidian plugin.
- **Khoj Emacs**:<br />
[Install](/clients/emacs#setup) khoj.el
#### Setup host URL
:::info[Note]
Set the host URL on your clients settings page to your Khoj server URL. By default, use `http://127.0.0.1:42110` or `http://localhost:42110`. Note that `localhost` may not work in all cases.
:::
<Tabs groupId="client" queryString>
<TabItem value="desktop" label="Desktop">
- Read the Khoj Desktop app [setup docs](/clients/desktop#setup).
</TabItem>
<TabItem value="emacs" label="Emacs">
- Read the Khoj Emacs package [setup docs](/clients/emacs#setup).
</TabItem>
<TabItem value="obsidian" label="Obsidian">
- Read the Khoj Obsidian plugin [setup docs](/clients/obsidian#setup).
</TabItem>
<TabItem value="whatsapp" label="Whatsapp">
- Read the Khoj Whatsapp app [setup docs](/clients/whatsapp).
</TabItem>
</Tabs>
## Upgrade
<Tabs groupId="environment">
<TabItem value="localsetup" label="Local Setup">
### Upgrade Server
<Tabs groupId="server" queryString>
<TabItem value="pip" label="Pip">
```shell
pip install --upgrade khoj
```
*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.
Run the commands below from the same directory where you have your `docker-compose.yml` file.
This will fetch the latest build and upgrade your server.
```shell
# Windows users should use their WSL2 terminal to run these commands
cd ~/.khoj # assuming your khoj docker-compose.yml file is here
docker-compose up --build
```
</TabItem>
</Tabs>
### Upgrade Clients
<Tabs groupId="client" queryString>
<TabItem value="desktop" label="Desktop">
- The Desktop app automatically updates to the latest released version on restart.
- You can manually download the latest version from the [Khoj Website](https://khoj.dev/downloads).
</TabItem>
<TabItem value="emacs" label="Emacs">
- Use your Emacs Package Manager to Upgrade
- See [khoj.el package setup](/clients/emacs#setup) for details
@@ -269,9 +434,9 @@ Set the host URL on your clients settings page to your Khoj server URL. By defau
</Tabs>
## Uninstall
<Tabs groupId="environment">
<TabItem value="localsetup" label="Local Setup">
### Uninstall Server
<Tabs groupId="server" queryString>
<TabItem value="pip" label="Pip">
```shell
# uninstall khoj server
pip uninstall khoj
@@ -281,19 +446,25 @@ Set the host URL on your clients settings page to your Khoj server URL. By defau
```
</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.
Run the command below from the same directory where you have your `docker-compose` file.
This will remove the server containers, networks, images and volumes.
```shell
docker-compose down --volumes
```
</TabItem>
</Tabs>
### Uninstall Clients
<Tabs groupId="client" queryString>
<TabItem value="desktop" label="Desktop">
Uninstall the Khoj Desktop client in the standard way from your OS.
</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.
Uninstall the Khoj Emacs package in the standard way from Emacs.
</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.
Uninstall via the Community plugins tab on the settings pane in the Obsidian app
</TabItem>
</Tabs>
@@ -310,7 +481,6 @@ Set the host URL on your clients settings page to your Khoj server URL. By defau
```
3. Now start `khoj` using the standard steps described earlier
#### Install fails while building Tokenizer dependency
- **Details**: `pip install khoj` fails while building the `tokenizers` dependency. Complains about Rust.
- **Fix**: Install Rust to build the tokenizers package. For example on Mac run:
@@ -321,18 +491,6 @@ Set the host URL on your clients settings page to your Khoj server URL. By defau
```
- **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)
## Advanced
### Self Host on Custom Domain
You can self-host Khoj behind 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[Without HTTPS certificate]
To expose Khoj on a custom domain over the public internet, use of an SSL certificate is strongly recommended. You can use [Let's Encrypt](https://letsencrypt.org/) to get a free SSL certificate for your domain.
To disable HTTPS, set the `KHOJ_NO_HTTPS` environment variable to `True`. This can be useful if Khoj is only accessible behind a secure, private network.
@@ -14,13 +14,6 @@ We don't send any personal information or any information from/about your conten
## Disable Telemetry
If you're self-hosting Khoj, you can opt out of telemetry at any time. To do so,
1. Open `~/.khoj/khoj.yml`
2. Add the following configuration:
```
app:
should-log-telemetry: false
```
3. Save the file and restart Khoj
If you're self-hosting Khoj, you can opt out of telemetry at any time by setting the `KHOJ_TELEMETRY_DISABLE` environment variable to `True` via shell or `docker-compose.yml`
If you have any questions or concerns, please reach out to us on [Discord](https://discord.gg/BDgyabRM6e).
{"name":"Khoj","short_name":"Khoj","display":"standalone","start_url":"/","description":"The open, personal AI for your digital brain. You can ask Khoj to draft a message, paint your imagination, find information on the internet and even answer questions from your documents.","theme_color":"#ffffff","background_color":"#ffffff","icons":[{"src":"/static/assets/icons/khoj_lantern_128x128.png","sizes":"128x128","type":"image/png"},{"src":"/static/assets/icons/khoj_lantern_256x256.png","sizes":"256x256","type":"image/png"}],"screenshots":[{"src":"/static/assets/samples/phone-remember-plan-sample.png","sizes":"419x900","type":"image/png","form_factor":"narrow","label":"Remember and Plan"},{"src":"/static/assets/samples/phone-browse-draw-sample.png","sizes":"419x900","type":"image/png","form_factor":"narrow","label":"Browse and Draw"},{"src":"/static/assets/samples/desktop-remember-plan-sample.png","sizes":"1260x742","type":"image/png","form_factor":"wide","label":"Remember and Plan"},{"src":"/static/assets/samples/desktop-browse-draw-sample.png","sizes":"1260x742","type":"image/png","form_factor":"wide","label":"Browse and Draw"}]}
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<resources>
<colorname="shortcut_background">#F5F5F5</color>
</resources>
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