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7dd6b32991
This PR introduces the following Runnables:
1. BaseRateLimiter: an abstraction for specifying a time based rate
limiter as a Runnable
2. InMemoryRateLimiter: Provides an in-memory implementation of a rate
limiter
## Example
```python
from langchain_core.runnables import InMemoryRateLimiter, RunnableLambda
from datetime import datetime
foo = InMemoryRateLimiter(requests_per_second=0.5)
def meow(x):
print(datetime.now().strftime("%H:%M:%S.%f"))
return x
chain = foo | meow
for _ in range(10):
print(chain.invoke('hello'))
```
Produces:
```
17:12:07.530151
hello
17:12:09.537932
hello
17:12:11.548375
hello
17:12:13.558383
hello
17:12:15.568348
hello
17:12:17.578171
hello
17:12:19.587508
hello
17:12:21.597877
hello
17:12:23.607707
hello
17:12:25.617978
hello
```
## Interface
The rate limiter uses the following interface for acquiring a token:
```python
class BaseRateLimiter(Runnable[Input, Output], abc.ABC):
@abc.abstractmethod
def acquire(self, *, blocking: bool = True) -> bool:
"""Attempt to acquire the necessary tokens for the rate limiter.```
```
The flag `blocking` has been added to the abstraction to allow
supporting streaming (which is easier if blocking=False).
## Limitations
- The rate limiter is not designed to work across different processes.
It is an in-memory rate limiter, but it is thread safe.
- The rate limiter only supports time-based rate limiting. It does not
take into account the size of the request or any other factors.
- The current implementation does not handle streaming inputs well and
will consume all inputs even if the rate limit has been reached. Better
support for streaming inputs will be added in the future.
- When the rate limiter is combined with another runnable via a
RunnableSequence, usage of .batch() or .abatch() will only respect the
average rate limit. There will be bursty behavior as .batch() and
.abatch() wait for each step to complete before starting the next step.
One way to mitigate this is to use batch_as_completed() or
abatch_as_completed().
## Bursty behavior in `batch` and `abatch`
When the rate limiter is combined with another runnable via a
RunnableSequence, usage of .batch() or .abatch() will only respect the
average rate limit. There will be bursty behavior as .batch() and
.abatch() wait for each step to complete before starting the next step.
This becomes a problem if users are using `batch` and `abatch` with many
inputs (e.g., 100). In this case, there will be a burst of 100 inputs
into the batch of the rate limited runnable.
1. Using a RunnableBinding
The API would look like:
```python
from langchain_core.runnables import InMemoryRateLimiter, RunnableLambda
rate_limiter = InMemoryRateLimiter(requests_per_second=0.5)
def meow(x):
return x
rate_limited_meow = RunnableLambda(meow).with_rate_limiter(rate_limiter)
```
2. Another option is to add some init option to RunnableSequence that
changes `.batch()` to be depth first (e.g., by delegating to
`batch_as_completed`)
```python
RunnableSequence(first=rate_limiter, last=model, how='batch-depth-first')
```
Pros: Does not require Runnable Binding
Cons: Feels over-complicated
|
||
|---|---|---|
| .. | ||
| __init__.py | ||
| base.py | ||
| branch.py | ||
| config.py | ||
| configurable.py | ||
| fallbacks.py | ||
| graph_ascii.py | ||
| graph_mermaid.py | ||
| graph_png.py | ||
| graph.py | ||
| history.py | ||
| learnable.py | ||
| passthrough.py | ||
| rate_limiter.py | ||
| retry.py | ||
| router.py | ||
| schema.py | ||
| utils.py | ||