- 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
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
