mirror of https://github.com/hwchase17/langchain synced 2024-10-29 17:07:25 +00:00

Adds LangServe package

* Integrate Runnables with Fast API creating Server and a RemoteRunnable
client
* Support multiple runnables for a given server
* Support sync/async/batch/abatch/stream/astream/astream_log on the
client side (using async implementations on server)
* Adds validation using annotations (relying on pydantic under the hood)
-- this still has some rough edges -- e.g., open api docs do NOT
generate correctly at the moment
* Uses pydantic v1 namespace

Known issues: type translation code doesn't handle a lot of types (e.g.,
TypedDicts)

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

2023-09-28 10:52:44 +01:00

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LangServe 🦜️🔗

Overview

LangServe is a library that allows developers to host their Langchain runnables / call into them remotely from a runnable interface.

Examples

For more examples, see the examples directory.

Server

#!/usr/bin/env python
from fastapi import FastAPI
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatAnthropic, ChatOpenAI
from langserve import add_routes
from typing_extensions import TypedDict


app = FastAPI(
  title="LangChain Server",
  version="1.0",
  description="A simple api server using Langchain's Runnable interfaces",
)


# Serve Open AI and Anthropic models
LLMInput = Union[List[Union[SystemMessage, HumanMessage, str]], str]

add_routes(
    app,
    ChatOpenAI(),
    path="/openai",
    input_type=LLMInput,
    config_keys=[],
)
add_routes(
    app,
    ChatAnthropic(),
    path="/anthropic",
    input_type=LLMInput,
    config_keys=[],
)

# Serve a joke chain
class ChainInput(TypedDict):
    """The input to the chain."""

    topic: str
    """The topic of the joke."""

model = ChatAnthropic()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
add_routes(app, prompt | model, path="/chain", input_type=ChainInput)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="localhost", port=8000)

Client


from langchain.schema import SystemMessage, HumanMessage
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnableMap
from langserve import RemoteRunnable

openai = RemoteRunnable("http://localhost:8000/openai/")
anthropic = RemoteRunnable("http://localhost:8000/anthropic/")
joke_chain = RemoteRunnable("http://localhost:8000/chain/")

joke_chain.invoke({"topic": "parrots"})

# or async
await joke_chain.ainvoke({"topic": "parrots"})

prompt = [
    SystemMessage(content='Act like either a cat or a parrot.'), 
    HumanMessage(content='Hello!')
]

# Supports astream
async for msg in anthropic.astream(prompt):
    print(msg, end="", flush=True)
    
prompt = ChatPromptTemplate.from_messages(
    [("system", "Tell me a long story about {topic}")]
)
    
# Can define custom chains
chain = prompt | RunnableMap({
    "openai": openai,
    "anthropic": anthropic,
})

chain.batch([{ "topic": "parrots" }, { "topic": "cats" }])

Installation

# pip install langserve[all] -- has not been published to pypi yet

or use client extra for client code, and server extra for server code.

Features

Deploy runnables with FastAPI
Client can use remote runnables almost as if they were local
- Supports async
- Supports batch
- Supports stream

Limitations

Chain callbacks cannot be passed from the client to the server

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