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
|
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|---|---|---|
| .devcontainer | ||
| .github | ||
| cookbook | ||
| docker | ||
| docs | ||
| libs | ||
| scripts | ||
| templates | ||
| .gitattributes | ||
| .gitignore | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| LICENSE | ||
| Makefile | ||
| MIGRATE.md | ||
| poetry.lock | ||
| poetry.toml | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
🦜️🔗 LangChain
⚡ Build context-aware reasoning applications ⚡
Looking for the JS/TS library? Check out LangChain.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications. Fill out this form to speak with our sales team.
Quick Install
With pip:
pip install langchain
With conda:
conda install langchain -c conda-forge
🤔 What is LangChain?
LangChain is a framework for developing applications powered by large language models (LLMs).
For these applications, LangChain simplifies the entire application lifecycle:
- Open-source libraries: Build your applications using LangChain's open-source building blocks, components, and third-party integrations. Use LangGraph to build stateful agents with first-class streaming and human-in-the-loop support.
- Productionization: Inspect, monitor, and evaluate your apps with LangSmith so that you can constantly optimize and deploy with confidence.
- Deployment: Turn your LangGraph applications into production-ready APIs and Assistants with LangGraph Cloud.
Open-source libraries
langchain-core: Base abstractions and LangChain Expression Language.langchain-community: Third party integrations.- Some integrations have been further split into partner packages that only rely on
langchain-core. Examples includelangchain_openaiandlangchain_anthropic.
- Some integrations have been further split into partner packages that only rely on
langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.LangGraph: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
Productionization:
- LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
Deployment:
- LangGraph Cloud: Turn your LangGraph applications into production-ready APIs and Assistants.
🧱 What can you build with LangChain?
❓ Question answering with RAG
- Documentation
- End-to-end Example: Chat LangChain and repo
🧱 Extracting structured output
- Documentation
- End-to-end Example: SQL Llama2 Template
🤖 Chatbots
- Documentation
- End-to-end Example: Web LangChain (web researcher chatbot) and repo
And much more! Head to the Tutorials section of the docs for more.
🚀 How does LangChain help?
The main value props of the LangChain libraries are:
- Components: composable building blocks, tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
- Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
LangChain Expression Language (LCEL)
LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
- Overview: LCEL and its benefits
- Interface: The standard Runnable interface for LCEL objects
- Primitives: More on the primitives LCEL includes
- Cheatsheet: Quick overview of the most common usage patterns
Components
Components fall into the following modules:
📃 Model I/O
This includes prompt management, prompt optimization, a generic interface for chat models and LLMs, and common utilities for working with model outputs.
📚 Retrieval
Retrieval Augmented Generation involves loading data from a variety of sources, preparing it, then searching over (a.k.a. retrieving from) it for use in the generation step.
🤖 Agents
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a standard interface for agents, along with LangGraph for building custom agents.
📖 Documentation
Please see here for full documentation, which includes:
- Introduction: Overview of the framework and the structure of the docs.
- Tutorials: If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
- How-to guides: Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
- Conceptual guide: Conceptual explanations of the key parts of the framework.
- API Reference: Thorough documentation of every class and method.
🌐 Ecosystem
- 🦜🛠️ LangSmith: Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- 🦜🕸️ LangGraph: Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- 🦜🏓 LangServe: Deploy LangChain runnables and chains as REST APIs.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.