- Track input, output token usage and cost for interactions
via chat api with openai, anthropic and google chat models
- Get usage metadata from OpenAI using stream_options
- Handle openai proxies that do not support passing usage in response
- Add new usage, end response events returned by chat api.
- This can be optionally consumed by clients at a later point
- Update streaming clients to mark message as completed after new
end response event, not after end llm response event
- Ensure usage data from final response generation step is included
- Pass usage data after llm response complete. This allows gathering
token usage and cost for the final response generation step across
streaming and non-streaming modes
- Previously online chat actors, director tests only worked with openai.
This change allows running them for any supported onlnie provider
including Google, Anthropic and Openai.
- Enable online/offline chat actor, director in two ways:
1. Explicitly setting KHOJ_TEST_CHAT_PROVIDER environment variable to
google, anthropic, openai, offline
2. Implicitly by the first API key found from openai, google or anthropic.
- Default offline chat provider to use Llama 3.1 3B for faster, lower
compute test runs
- Set output mode to single string. Specify output schema in prompt
- Both thesee should encourage model to select only 1 output mode
instead of encouraging it in prompt too many times
- Output schema should also improve schema following in general
- Standardize variable, func name of io selector for readability
- Fix chat actors to test the io selector chat actor
- Make chat actor return sources, output separately for better
disambiguation, at least during tests, for now
- Evaluate khoj on random 200 questions from each of google frames and openai simpleqa benchmarks across *general*, *default* and *research* modes
- Run eval with Gemini 1.5 Flash as test giver and Gemini 1.5 Pro as test evaluator models
- Trigger eval workflow on release or manually
- Make dataset, khoj mode and sample size configurable when triggered via manual workflow
- Enable Web search, webpage read tools during evaluation
- JSON extract from LLMs is pretty decent now, so get the input tools and output modes all in one go. It'll help the model think through the full cycle of what it wants to do to handle the request holistically.
- Make slight improvements to tool selection indicators
Previously, we'd replace the generated message with an error message
when message generation stopped via stop button on chat page of web app.
So the partially generated message (which could be useful) gets lost.
This change just stops generation, while keeping the generated
response so any useful information from the partially generated
message can be retrieved.
- Allows managing chat models in the OpenAI proxy service like Ollama.
- Removes need to manually add, remove chat models from Khoj Admin Panel
for these OpenAI compatible API services when enabled.
- Khoj still mantains the chat models configs within Khoj, so they can
be configured via the Khoj admin panel as usual.
Previously Jina search didn't API key. Now that it does need API key,
we should re-use the API key set in the Jina web scraper config,
otherwise fallback to using JINA_API_KEY from environment variable, if
either is present.
Resolves#978
- Integrate with Ollama or other openai compatible APIs by simply
setting `OPENAI_API_BASE' environment variable in docker-compose etc.
- Update docs on integrating with Ollama, openai proxies on first run
- Auto populate all chat models supported by openai compatible APIs
- Auto set vision enabled for all commercial models
- Minor
- Add huggingface cache to khoj_models volume. This is where chat
models and (now) sentence transformer models are stored by default
- Reduce verbosity of yarn install of web app. Otherwise hit docker
log size limit & stops showing remaining logs after web app install
- Suggest `ollama pull <model_name>` to start it in background
- Update to latest initialize with new claude 3.5 sonnet and haiku models
- Update to set vision enabled for google and anthropic models by
default. Previously we didn't support but we've supported this for a
month or two now
- Just load the raw csv from OpenAI bucket. Normalize it into FRAMES format
- Improve docstring for frames datasets as well
- Log the load dataset perf timer at info level
- Explictly adding a slash command is a higher priority intent than
research mode being enabled in the background. Respect that for a
more intuitive UX flow.
- Explicit slash commands do not currently work in research mode.
You've to turn research mode off to use other slash commands. This
is strange, unnecessary given intent priority is clear.
- JSON extract from LLMs is pretty decent now, so get the input tools and output modes all in one go. It'll help the model think through the full cycle of what it wants to do to handle the request holistically.
- Make slight improvements to tool selection indicators
Previously errors would get eaten up but the model wouldn't see
anything. And the model wouldn't be allowed re-run the same query-tool
combination in the next iteration.
This update should give it insight into why it didn't get a result. So
it can make an informed (hopefully better) decision on what to do next.
And re-run the previous query if appropriate.
Previously when call to online search API etc. failed, it'd error out
of response to query in research mode. Khoj should skip tool use that
iteration but continue to try respond.
Previously chatml messages were just strings, now they can be list of
strings or list of dicts as well.
- Use json seriallization to manage their variations and truncate them
before printing for context.
- Put logic in single function for use across chat models
- Default to evaluation decision of None when either agent or
evaluator llm fails. This fixes accuracy calculations on errors
- Fix showing color for decision True
- Enable arg flags to specify output results file paths
Previously chatml messages were just strings.
Since gemini, anthropic models always have messages as list of
strings, truncate those strings instead of the list of message content
Removing binary data and truncating large data in output files
generated by code runs should improve speed and cost of research mode
runs with large or binary output files.
Previously binary data in code results was passed around in iteration
context during research mode. This made the context inefficient
because models have limited efficiency and reasoning capabilities over
b64 encoded image (and other binary) data and would hit context limits
leading to unnecessary truncation of other useful context
Also remove image data when logging output of code execution
