* Uses entire file text and summarizer model to generate document summary.
* Uses the contents of the user's query to create a tailored summary.
* Integrates with File Filters #788 for a better UX.
# Major
- Disambiguate Text output mode to disambiguate from Default data source lookup
- Fix showing headings in intermediate step in generating chat response
- Remove "Path" prefix from org ancestor heading in compiled entry
# Minor
- Fix OpenAI chat actor, director unit tests
- The task scheduling actor was having trouble calculating the
timezone. Giving the actor a scratchpad to improve correctness by
thinking step by step
- Add more examples to reduce chances of the inferred query looping to
create another reminder instead of running the query and sharing
results with user
- Improve task scheduling chat actor test with more tests and
by ensuring unexpected words not present in response
There's a difference between running a scheduled task and notifying
the user about the results of running the scheduled task.
Decide to notify the user only when the results of running the
scheduled task satisfy the user's requirements.
Use sync version of send_message_to_model_wrapper for scheduled tasks
Previous cross-encoder model was a few years old, newer models should
have improved in quality. Model size increases by 50% compared to
previous for better performance, at least on benchmarks
Add process_single_plaintext_file func etc with similar signatures as
org_to_entries and markdown_to_entries processors
The standardization makes modifications, abstractions easier to create
## Major
- Parse markdown, org parent entries as single entry if fit within max tokens
- Parse a file as single entry if it fits with max token limits
- Add parent heading ancestry to extracted markdown entries for context
- Chunk text in preference order of para, sentence, word, character
## Minor
- Create wrapper function to get entries from org, md, pdf & text files
- Remove unused Entry to Jsonl converter from text to entry class, tests
- Dedupe code by using single func to process an org file into entries
Resolves#620
* Add support for using OAuth2.0 in the Notion integration
* Add notion to the admin page
* Remove unnecessary content_index and image search/setup references
* Trigger background job to start indexing Notion after user configures it
* Add a log line when a new Notion integration is setup
* Fix references to the configure_content methods
More content indexed per entry would result in an overall scores
lowering effect. Increase default search distance threshold to counter that
- Details
- Fix expected results post indexing updates
- Fix search with max distance post indexing updates
- Minor
- Remove openai chat actor test for after: operator as it's not expected anymore
- Major
- Do not split org file, entry if it fits within the max token limits
- Recurse down org file entries, one heading level at a time until
reach leaf node or the current parent tree fits context window
- Update `process_single_org_file' func logic to do this recursion
- Convert extracted org nodes with children into entries
- Previously org node to entry code just had to handle leaf entries
- Now it recieve list of org node trees
- Only add ancestor path to root org-node of each tree
- Indent each entry trees headings by +1 level from base level (=2)
- Minor
- Stop timing org-node parsing vs org-node to entry conversion
Just time the wrapping function for org-mode entry extraction
This standardizes what is being timed across at md, org etc.
- Move try/catch to `extract_org_nodes' from `parse_single_org_file'
func to standardize this also across md, org
These changes improve context available to the search model.
Specifically this should improve entry context from short knowledge trees,
that is knowledge bases with sparse, short heading/entry trees
Previously we'd always split markdown files by headings, even if a
parent entry was small enough to fit entirely within the max token
limits of the search model. This used to reduce the context available
to the search model to select appropriate entries for a query,
especially from short entry trees
Revert back to using regex to parse through markdown file instead of
using MarkdownHeaderTextSplitter. It was easier to implement the
logical split using regexes rather than bend MarkdowHeaderTextSplitter
to implement it.
- DFS traverse the markdown knowledge tree, prefix ancestry to each entry
These changes improve entry context available to the search model
Specifically this should improve entry context from short knowledge trees,
that is knowledge bases with small files
Previously we split all markdown files by their headings,
even if the file was small enough to fit entirely within the max token
limits of the search model. This used to reduce the context available
to select the appropriate entries for a given query for the search model,
especially from short knowledge trees
- Previous simplistic chunking strategy of splitting text by space
didn't capture notes with newlines, no spaces. For e.g in #620
- New strategy will try chunk the text at more natural points like
paragraph, sentence, word first. If none of those work it'll split
at character to fit within max token limit
- Drop long words while preserving original delimiters
Resolves#620
This was earlier used when the index was plaintext jsonl file. Now
that documents are indexed in a DB this func is not required.
Simplify org,md,pdf,plaintext to entries tests by removing the entry
to jsonl conversion step
- Convert extract_org_entries function to actually extract org entries
Previously it was extracting intermediary org-node objects instead
Now it extracts the org-node objects from files and converts them
into entries
- Create separate, new function to extract_org_nodes from files
- Similarly create wrapper funcs for md, pdf, plaintext to entries
- Update org, md, pdf, plaintext to entries tests to use the new
simplified wrapper function to extract org entries
- Overview
- Extract more structured date variants (e.g with dot(.) & slash(/) separators, 2-digit year)
- Extract some natural, partial dates as well from entries
- Capability
Add ability to extract the following additional date forms:
- Natural Dates: 21st April 2000, February 29 2024
- Partial Natural Dates: March 24, Mar 2024
- Structured Dates: 20/12/24, 20.12.2024, 2024/12/20
Note: Previously only YYYY-MM-DD ISO-8601 structured date form was extracted for date filters
- Performance
Using regexes is MUCH faster than using the `dateparser' python library
It's a little crude but gives acceptable performance for large datasets
- Much faster than using dateparser
- It took 2x-4x for improved regex to extracts 1-15% more dates
- Whereas It took 33x to 100x for dateparser to extract 65% - 400% more dates
- Improve date extractor tests to test deduping dates, natural,
structured date extraction from content
- Extract some natural, partial dates and more structured dates
Using regex is much faster than using dateparser. It's a little
crude but should pay off in performance.
Supports dates of form:
- (Day-of-Month) Month|AbbreviatedMonth Year|2DigitYear
- Month|AbbreviatedMonth (Day-of-Month) Year|2DigitYear
Previously we just extracted dates in YYYY-MM-DD format from content
for date filterings during search.
Use dateparser to extract dates across locales and natural language
This should improve notes returned as context when chat searches
knowledge base with date filters
Fallback to regex for date parsing from content if dateparser fails
- Limit natural date extractor capabilities to improve performance
- Assume language is english
Language detection otherwise takes a REALLY long time
- Do not extract unix timestamps, timezone
- This isn't required, as just using date and approximating dates as UTC
- Benefits of moving to llama-cpp-python from gpt4all:
- Support for all GGUF format chat models
- Support for AMD, Nvidia, Mac, Vulcan GPU machines (instead of just Vulcan, Mac)
- Supports models with more capabilities like tools, schema
enforcement, speculative ddecoding, image gen etc.
- Upgrade default chat model, prompt size, tokenizer for new supported
chat models
- Load offline chat model when present on disk without requiring internet
- Load model onto GPU if not disabled and device has GPU
- Load model onto CPU if loading model onto GPU fails
- Create helper function to check and load model from disk, when model
glob is present on disk.
`Llama.from_pretrained' needs internet to get repo info from
HuggingFace. This isn't required, if the model is already downloaded
Didn't find any existing HF or llama.cpp method that looked for model
glob on disk without internet
* Initial pass at backend changes to support agents
- Add a db model for Agents, attaching them to conversations
- When an agent is added to a conversation, override the system prompt to tweak the instructions
- Agents can be configured with prompt modification, model specification, a profile picture, and other things
- Admin-configured models will not be editable by individual users
- Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications
* Customize default behaviors for conversations without agents or with default agents
* Add a new web client route for viewing all agents
* Use agent_id for getting correct agent
* Add web UI views for agents
- Add a page to view all agents
- Add slugs to manage agents
- Add a view to view single agent
- Display active agent when in chat window
- Fix post-login redirect issue
* Fix agent view
* Spruce up the 404 page and improve the overall layout for agents pages
* Create chat actor for directly reading webpages based on user message
- Add prompt for the read webpages chat actor to extract, infer
webpage links
- Make chat actor infer or extract webpage to read directly from user
message
- Rename previous read_webpage function to more narrow
read_webpage_at_url function
* Rename agents_page -> agent_page
* Fix unit test for adding the filename to the compiled markdown entry
* Fix layout of agent, agents pages
* Merge migrations
* Let the name, slug of the default agent be Khoj, khoj
* Fix chat-related unit tests
* Add webpage chat command for read web pages requested by user
Update auto chat command inference prompt to show example of when to
use webpage chat command (i.e when url is directly provided in link)
* Support webpage command in chat API
- Fallback to use webpage when SERPER not setup and online command was
attempted
- Do not stop responding if can't retrieve online results. Try to
respond without the online context
* Test select webpage as data source and extract web urls chat actors
* Tweak prompts to extract information from webpages, online results
- Show more of the truncated messages for debugging context
- Update Khoj personality prompt to encourage it to remember it's capabilities
* Rename extract_content online results field to webpages
* Parallelize simple webpage read and extractor
Similar to what is being done with search_online with olostep
* Pass multiple webpages with their urls in online results context
Previously even if MAX_WEBPAGES_TO_READ was > 1, only 1 extracted
content would ever be passed.
URL of the extracted webpage content wasn't passed to clients in
online results context. This limited them from being rendered
* Render webpage read in chat response references on Web, Desktop apps
* Time chat actor responses & chat api request start for perf analysis
* Increase the keep alive timeout in the main application for testing
* Do not pipe access/error logs to separate files. Flow to stdout/stderr
* [Temp] Reduce to 1 gunicorn worker
* Change prod docker image to use jammy, rather than nvidia base image
* Use Khoj icon when Khoj web is installed on iOS as a PWA
* Make slug required for agents
* Simplify calling logic and prevent agent access for unauthenticated users
* Standardize to use personality over tuning in agent nomenclature
* Make filtering logic more stringent for accessible agents and remove unused method:
* Format chat message query
---------
Co-authored-by: Debanjum Singh Solanky <debanjum@gmail.com>
### Overview
Khoj can now read website directly without needing to go through the search step first
### Details
- Parallelize simple webpage read and extractor
- Rename extract_content online results field to web pages
- Tweak prompts to extract information from webpages, online results
- Test select webpage as data source and extract web urls chat actors
- Render webpage read in chat response references on Web, Desktop apps
- Pass multiple webpages with their urls in online results context
- Support webpage command in chat API
- Add webpage chat command for read web pages requested by user
- Create chat actor for directly reading webpages based on user message
* Initial pass at backend changes to support agents
- Add a db model for Agents, attaching them to conversations
- When an agent is added to a conversation, override the system prompt to tweak the instructions
- Agents can be configured with prompt modification, model specification, a profile picture, and other things
- Admin-configured models will not be editable by individual users
- Add unit tests to verify agent behavior. Unit tests demonstrate imperfect adherence to prompt specifications
* Customize default behaviors for conversations without agents or with default agents
* Use agent_id for getting correct agent
* Merge migrations
* Simplify some variable definitions, add additional security checks for agents
* Rename agent.tuning -> agent.personality
Previously was assuming the system prompt is being always passed as
the first message. So expected there to be at least 2 messages in logs.
This broke chat actors querying with single long non system message.
A more robust way to extract system prompt is via the message role
instead
- Add prompt for the read webpages chat actor to extract, infer
webpage links
- Make chat actor infer or extract webpage to read directly from user
message
- Rename previous read_webpage function to more narrow
read_webpage_at_url function
- Remove stale tests
- Improve tests to pass across gpt-3.5 and gpt-4-turbo
- The haiku creation director was failing because of duplicate query in
instantiated prompt
- Time reading webpage, extract info from webpage steps for perf
analysis
- Deduplicate webpages to read gathered across separate google
searches
- Use aiohttp to make API requests non-blocking, pair with asyncio to
parallelize all the online search webpage read and extract calls
