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)
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
## 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.
- 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
- 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
- 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
- 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
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
