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
# 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 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
