If you create a dataset from runs and run the same chain or llm on it
later, it usually works great.
If you have an agent dataset and want to run a different agent on it, or
have more complex schema, it's hard for us to automatically map these
values every time. This PR lets you pass in an input_mapper function
that converts the example inputs to whatever format your model expects
- Remove the client implementation (this breaks backwards compatibility
for existing testers. I could keep the stub in that file if we want, but
not many people are using it yet
- Add SDK as dependency
- Update the 'run_on_dataset' method to be a function that optionally
accepts a client as an argument
- Remove the langchain plus server implementation (you get it for free
with the SDK now)
We could make the SDK optional for now, but the plan is to use w/in the
tracer so it would likely become a hard dependency at some point.
Extract the methods specific to running an LLM or Chain on a dataset to
separate utility functions.
This simplifies the client a bit and lets us separate concerns of LCP
details from running examples (e.g., for evals)
Previously, the client expected a strict 'prompt' or 'messages' format
and wouldn't permit running a chat model or llm on prompts or messages
(respectively).
Since many datasets may want to specify custom key: string , relax this
requirement.
Also, add support for running a chat model on raw prompts and LLM on
chat messages through their respective fallbacks.
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### Refactor the BaseTracer
- Remove the 'session' abstraction from the BaseTracer
- Rename 'RunV2' object(s) to be called 'Run' objects (Rename previous
Run objects to be RunV1 objects)
- Ditto for sessions: TracerSession*V2 -> TracerSession*
- Remove now deprecated conversion from v1 run objects to v2 run objects
in LangChainTracerV2
- Add conversion from v2 run objects to v1 run objects in V1 tracer
## Change Chain argument in client to accept a chain factory
The `run_over_dataset` functionality seeks to treat each iteration of an
example as an independent trial.
Chains have memory, so it's easier to permit this type of behavior if we
accept a factory method rather than the chain object directly.
There's still corner cases / UX pains people will likely run into, like:
- Caching may cause issues
- if memory is persisted to a shared object (e.g., same redis queue) ,
this could impact what is retrieved
- If we're running the async methods with concurrency using local
models, if someone naively instantiates the chain and loads each time,
it could lead to tons of disk I/O or OOM
### Add on_chat_message_start to callback manager and base tracer
Goal: trace messages directly to permit reloading as chat messages
(store in an integration-agnostic way)
Add an `on_chat_message_start` method. Fall back to `on_llm_start()` for
handlers that don't have it implemented.
Does so in a non-backwards-compat breaking way (for now)
