Track research and operator results at each nested iteration step
using python object references + async events bubbled up from nested
iterators.
Instantiates operator with interrupted operator messages from research
or normal mode.
Reflects actual interaction trajectory as closely as possible to agent
including conversation history, partial operator trajectory and new
query for fine grained, corrigible steerability.
Research mode continues with operator tool directly if previous
iteration was an interrupted operator run.
Since partial state reload after interrupt drops Khoj messages. The
assumption that there will always be a Khoj message after a user
message is broken. That is, there can now be multiple user messages
preceding a Khoj user message now.
This change allow for user queries to still be extracted for chat
history even if no khoj message follow.
Minor logic update to only include non image inferred queries for
gemini, anthropic models as well instead of just for openai models.
Apart from that the extracted function should be functionally same.
We were passing operator results as a simple dictionary. Strongly
typing it makes sense as operator results becomes more complex.
Storing operator results with trajectory on interrupts will allow
restarting interrupted operator run with agent messages of interrupted
trajectory loaded into operator agents
This allows:
- Each operator agent to own its summarization prompt. That it can
tune if it wants
- The outer operator loop to pass an override summarize prompt when it
invokes the summarize func but it does not have to
It had become broken at some point due to refactoring. The cache
control was getting added and removed right after in add_action_results
What we actually wanted to do is clear the old cache breakpoint and
put a new one at the latest operator tool result message.
This should improve operator speed and lower costs with anthropic
models.
- Generalize building pyautogui into executable python code snippet.
This should work across docker and local. And should be easier to
extend to operate a remote computer over the network as well.
- Create dockerfile for pyautogui operate-able containerized computer
Previously it could only operate a (playwright) browser. Now
- The operator logic and naming has been updated assuming
multiple environment types can be operated
- The operator entrypoint is now at __init__.py to simplify imports
and the entrypoint function is called operate_environment
- All operator agents have been updated to select their system prompts
and tools based on the environment they'll operate
- Khoj can now save and restore research from partial state
This triggers an interrupt that saves the partial research, then
when a new query is sent it loads the previous partial research as
context and continues utilizing with the new user query to orient
its future research
- Support natural interrupt and send query behavior from web app
This triggers an abort and send when a user sends a chat message
while khoj is in the middle of some previous research.
This interrupt mechanism enables a more natural, interactive
research flow
- Just send your new query. If a query was running previously it'd
be interrupted and new query would start processing. This improves on
the previous 2 click interrupt and send ux.
- Utilizes partial research for interrupted query, so you can now
redirect khoj's research direction. This is useful if you need to
share more details, change khoj's research direction in anyway or
complete research. Khoj's train of thought can be helpful for this.
- Track operator, research context in ChatMessage
- Track query field in (document) context field of ChatMessage
This allows validating chat message before inserting into DB
These seem to be a new class of errors showing up. Explicitly using
django timezone functions to add awareness to date time files stored
in DB seems to mitigate the issue.
Related #1180
- Engage reasoning when using claude 4 models
- Allow claude 4 models as monolithic operator agents
- Ease identifying which anthropic models can reason, operate GUIs
- Track costs, set default context window of claude 4 models
- Handle stop reason on calls to new claude 4 models
- Normalize conversation_id type to str instead of str or UUID
- Do not pass conversation_id to agenerate_chat_response as
the associated conversation is also being passed. So can get its id
directly.
## Overview
1. Create base framework to compose different operators and environments
for Khoj to operate.
2. Enable Khoj to operate a web browser using anthropic, openai, gemini
or open-source models
**Note**: *This is an alpha level feature release. It is meant for local
testing by contributors and self-hosters.*
## Capabilities
- Have Khoj operate a web browser to complete tasks that require actions
and visual feedback.
- Experiment with any vision model as operator. Khoj supports monolithic
and binary operator
- Monolithic operators rely on a single models like claude, openai to
both reason and ground operator actions
- Binary operators allow bootstrapping a fully local operator. It can
use any vision model for visual reasoning when paired with a capable
visual grounding model.
## Limitations
- In general, it is slower, more expensive and less comprehensive than
standard Khoj for research
## Setup
1. Install Khoj with playwright by either
- running `pip install khoj[local]`
- installing playwright separately via `pip install playwright` and
`playwright install chromium`
2. Set `KHOJ_OPERATOR_ENABLED` env var to true (i.e
`KHOJ_OPERATOR_ENABLED=true`)
3. Start Khoj (e.g `USE_EMBEDDED_DB="true" khoj --anonymous-mode -vv`)
4. Add the necessary chat model(s) with `vision enabled` via your [Khoj
Admin Panel](http://localhost:42110/server/admin)
- To use Anthropic claude: `claude-3.7-sonnet*` chat model is required
with vision enabled
- To use Openai operator: `gpt-4o` chat model is required with vision
enabled
- For other operator configurations: a chat model named `ui-tars-1.5` is
required with vision enabled
This can technically be any visual grounding model served via an openai
compatible api. I've just tested with ui-tars-1.5-7b deployed to an HF
inference endpoint for now. See [deployment
instructions](https://github.com/bytedance/UI-TARS/blob/main/README_deploy.md)
5. Set your desired vision chat model via [user
settings](http://localhost:42110/settings) to use as operator.
6. Run your queries with either the `/operator` slash command or by just
asking Khoj in your query to use the operator tool. You can combine run
operator in research mode a well
### Advanced Usage
- Reuse Browser Session
- Why: Have Khoj operate web services you've logged into. E.g manage
your gmail, github, social media etc.
- Setup
1. Start Chromium or Edge in Remote Debugging mode. For example, on Mac
you can start Edge by running the following in your terminal:
`/Applications/Microsoft\ Edge.app/Contents/MacOS/Microsoft\ Edge
--remote-debugging-port=9222`
4. Connect Khoj to that browser instance by setting the environment
variable `KHOJ_CDP_URL` to its URL.
By default you'd set `KHOJ_CDP_URL="http://localhost:9222"`
## Architecture
### Operator Agents
| Type | Design |
|----- |-----|
| Monolithic | <img
src="https://github.com/user-attachments/assets/7a96440f-1732-482b-9bd9-0920cb0c60890"
width=400> |
| Binary | <img
src="https://github.com/user-attachments/assets/c5d101c0-3475-43c2-a301-daa943cde190"
width=400> |
The generic grounding agent has not been tested properly but at least
it should be aligned with the interface being used by the ui-tars
grounding agent which has been tested.
