- khoj-auto-setup controls whether to automatically check for and
setup khoj server from within Emacs
- extract install, start, configure sequence into public, interactive
method. Allows calling khoj-setup during package load via init.el
- Fix: Do not attempt to configure or wait for server ready if
user has said no to auto-setup request
- Fix logic to mark server started vs ready
- Previously the started/running vs ready variables defs were getting
intertwined
- Server started indicates server bootup has been triggered
- Server ready indicates server API ready to accept requests
- If khoj server started outside emacs, khoj--server-ready should be set
to true by khoj--server-running method (instead of waiting for proc msg)
- If khoj server is unconfigured the /config/types endpoint wouldn't
return anything. Using config/data/default allows checking khoj server
running status without requiring it to be configured as well
If the config hasn't changed there'll be no update. If config has
changed indexing will get triggered asynchronously. But user cannot
make query till indexing done
As easier to know when server ready to configure
- Use process filter, sentinel to mark when khoj server is ready or not
- Display server messages for visibility into server boot-up process
- Wait until server ready to open khoj transient menu in Emacs
Until then khoj features wouldn't work anyway, so avoids confusion
- Move completion and chat_completion into helper methods under utils.py
- Add retry with exponential backoff on OpenAI exceptions using
tenacity package. This is officially suggested and used by other
popular GPT based libraries
- Use tiktoken to count tokens for chat models
- Make conversation turns to add to prompt configurable via method
argument to generate_chatml_messages_with_context method
- Remove the need to split by magic string in emacs and chat interfaces
- Move compiling references into string as context for GPT to GPT layer
- Update setup in tests to use new style of setting references
- Name first argument to converse as more appropriate "references"
- Render references as superscript
- Show reference definitions on hover over reference links to ease access
- Truncate reference def shown on hover to 70 char
- Add continuation suffix, ..., when reference definition truncated
- Style Message as Org Entries instead of List
- Put khoj response as child of user query entry
- Improves color coding for readability
- Allows folding each back-n-forth
- Put timestamp of message received into property drawer
- Use standardized time format for new and old chat messages
- Generalize the render-chat-response method to handle rendering
history or chat response from chat API reponse
- Trigger rendering of khoj chat history if Khoj chat buffer not
created for this session yet
- Use org-insert-link method to improve link rendering robustness
Previous simple mechanism to crete org-links would result in links
escaping out of formating. Use a user-facing org-mode method to
remove/reduce probability of this
- Replace newlines with space to render reference notes as links
- Query khoj chat API to get Khoj Chat response to user message
- Render chat messages as a org-mode list in format:
- [sender-name]: *[message]*
- /[receive-date]/
- Add references as org links with context visible on hover,
but no jump to note
- Require dash library for khoj.el to simplify list manipulation.
Use `-map-indexed' method from dash
- Reasons:
- GPT can extract date aware search queries with date filters
better than ChatGPT given the same prompt.
- Need quality more than cost savings for now.
- Need to figure ways to improve prompt for ChatGPT before using it
Update Search Actor prompt with answers, more precise primer and
two more examples for context
Mark the 3 chat quality tests using answer as context to generate
queries as expected to pass. Verify that the 3 tests pass now, unlike
before when the Search Actor did not have the answers for context
- Keep inferred questions in logs
- Improve prompt to GPT to try use past questions as context
- Pass past user message and inferred questions as context to help GPT
extract complete questions
- This should improve search results quality
- Example Expected Inferred Questions from User Message using History:
1. "What is the name of Arun's daughter?"
=> "What is the name of Arun's daughter"
2. "Where does she study?" =>
=> "Where does Arun's daughter study?" OR
=> "Where does Arun's daughter, Reena study?"
The Search Actor allows for
1. Looking up multiple pieces of information from the notes
E.g "Is Bob older than Tom?" searches for age of Bob and Tom in 2 searches
2. Allow date aware user queries in Khoj chat
Answer time range based questions
Limit search to specified timeframe in question using date filter
E.g "What national parks did I visit last year?" adds
dt>="2022-01-01" dt<"2023-01-01" to Khoj search
Note: Temperature set to 0. Message to search queries should be deterministic
Create Rubric to Test Chat Quality and Capabilities
### Issues
- Previously the improvements in quality of Khoj Chat on changes was uncertain
- Manual testing on my evolving set of notes was slow and didn't assess all expected, desired capabilities
### Fix
1. Create an Evaluation Dataset to assess Chat Capabilities
- Create custom notes for a fictitious person (I'll publish a book with these soon 😅😋)
- Add a few of Paul Graham's more personal essays. *[Easy to get as markdown](https://github.com/ofou/graham-essays)*
2. Write Unit Tests to Measure Chat Capabilities
- Measure quality at 2 separate layers
- **Chat Actor**: These are the narrow agents made of LLM + Prompt. E.g `summarize`, `converse` in `gpt.py`
- **Chat Director**: This is the chat orchestration agent. It calls on required chat actors, search through user provided knowledge base (i.e notes, ledger, image) etc to respond appropriately to the users message. This is what the `/api/chat` API exposes.
- Mark desired but not currently available capabilities as expected to fail <br />
This still allows measuring the chat capability score/percentage while only failing capability tests which were passing before on any changes to chat
- Set conversation_log arg default to dict
- Increase default temperature to 0.2 for a little creativity in
answering
- Make GPT be more reliable in looking at past conversations for
forming response
