Earlier khoj could technically only answer questions existential
questions, i.e question that would terminate once any relevant note to
answer that question was found.
This change enables khoj to answer universal questions, i.e questions
that require searching through all notes or finding all instances.
It enables more thorough retrieval from user's knowledge base by
combining semantic search, regex search, view and list files tools.
For more development details including motivation, see live coding
session 1.1 at https://www.youtube.com/live/-2s_qi4hd2k
Allow getting a map of user's knowledge base under specified path.
This enables more thorough retrieval from user's knowledge base by
combining search, view and list files tools.
Why
---
Previously researcher had a uniform response schema to pick next tool,
scratchpad, query and tool. This didn't allow choosing different
arguments for the different tools being called. And the tool call,
result format passed by khoj was custom and static across all LLMs.
Passing the tools and their schemas directly to llm when picking next
tool allows passing multiple, tool specific arguments for llm to
select. For example, model can choose webpage urls to read or image
gen aspect ratio (apart from tool query) to pass to the specific tool.
Using the LLM tool calling paradigm allows model to see tool call,
tool result in a format that it understands best.
Using standard tool calling paradigm also allows for incorporating
community builts tools more easily via MCP servers, clients tools,
native llm api tools etc.
What
---
- Return ResponseWithThought from completion_with_backoff ai model
provider methods
- Show reasoning model thoughts in research mode train of thought.
For non-reasoning models do not show researcher train of thought.
As non-reasoning models don't (by default) think before selecing
tool. Showing tool call is lame and resembles tool's action shown in
next step.
- Store tool calls in standardized format.
- Specify tool schemas in tool for research llm definitions as well.
- Transform tool calls, tool results to standardized form for use
within khoj. Manage the following tool call, result transformations:
- Model provider tool_call -> standardized tool call
- Standardized tool call, result -> model specific tool call, result
- Make researcher choose webpages urls to read as well for the webpage
tool. Previously it would just decide the query but let the webpage
reader infer the query url(s). But researcher has better context on
which webpages it wants to have read to answer their query.
This should eliminate the webpage reader deciding urls to read step
and speed up webpage read tool use.
Handle unset response thoughts. Useful when retry on failed request
Previously resulted in unbound local variable response_thoughts error
Previously these models could use response schema but not tools use
capabilities provided by these AI model APIs.
This change allows chat actors to use the function calling feature to
specify which tools the LLM by these providers can call.
This should help simplify tool definition and structure context in
forms that these LLMs natively understand.
(i.e in tool_call - tool_result ~chatml format).
Gemini models, especially flash models, seems to have a tendency to go
into long, repetitive output tokens loop. Unsure why.
Tune temp, top_p as gemini api doesn't seem to allow setting frequency
or presence penalty, at least for reasoning models. Those would have
been a more direct mechanism to avoid model getting stuck in a loop.
This should help prevent partial updates to agent. Especially useful
for agent's with large knowledge bases being updated. Failing the call
should raise an exception. This will allow your to retry save instead
of losing your previous agent changes or saving only partial.
Running safety check isn't required if the agent persona wasn't
updated this time around as it would have passed safety check
previously.
This should speed up editing agents when agent persona isn't updated.
It seems to me that it would be useful to be able to be explicit about
where the embedded database should live - as well as where it _does_
live (via the info log), when not specifying.
Extract questions has chat history in prompt and in actual chat history.
Only pass in prompt for now. Later update prompts to pass chat history
in chat messages list for better truncation flexibility.
1. Due to the interaction of two changes:
- dedupe by corpus_id, where corpus_id tracks logical content blocks
like files, org/md headings.
- return compiled, not logical blocks, where compiled track smaller
content chunks that fit within search model, llm context windows.
When combined they showed only 1 hit compiled chunk per logical
block. Even if multiple chunks match within a logical content block.
Fix is to either dedupe by compiled text or to return deduped
logical content blocks (by corpus_id) corresponding to matched
compiled chunks. This commit fixes it by the first method.
2. Due to inferred query, search results zip which resulted in a
single search result being returned per query!
This silently cut down matching search results and went undetected.
The tailwing theme spacing of the scroll area surrounding chat history
on large screens was what was causing the large gap between chat input
box and chat history on some screen layouts.
This change reduces the spacing to a more acceptable level.
Previously summarizedResult would be unset when a tool call failed.
This caused research to fail due to ChatMessageModel failures when
constructing tool chat histories and would have caused similar errors
in other constructed chat histories.
Putting a failed iteration message in the summary prevents that while
letting the research agent continue its research.
All web search providers, like Jina/Searxng?, do not return a text
snippet. Making snippet optional allows processing search results by
these web search providers, without hitting validation errors.
This bug was introduced in 05d4e19cb, version 1.42.2, during migration
to save deeply typed ChatMessageModel. As the ChatMessageModel did
not use the right field name for organic results (since the start).
Previously it did not matter as it was storing to DB irrespective but
now the mapping of dictionary to ChatMessageModel drops that field
before save to conversation in DB.
This was resulting in organic context being lost on page reload and
only being shown on first response.
Not sure why but it some cases when interacting with o3 (which needs
non-streaming) the stream_options seems to be set.
Cannot reproduce but hopefully dropping the stream_options explicitly
should resolve this issue.
Related 985a98214
