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LangServe 🦜️🔗
Overview
LangServe helps developers deploy LangChain runnables and chains as a REST API.
This library is integrated with FastAPI and uses pydantic for data validation.
In addition, it provides a client that can be used to call into runnables deployed on a server. A javascript client is available in LangChainJS.
Features
- Input and Output schemas automatically inferred from your LangChain object, and enforced on every API call, with rich error messages
- API docs page with JSONSchema and Swagger (insert example link)
- Efficient
/invoke,/batchand/streamendpoints with support for many concurrent requests on a single server /stream_logendpoint for streaming all (or some) intermediate steps from your chain/agent- Built-in (optional) tracing to LangSmith, just add your API key (see Instructions])
- All built with battle-tested open-source Python libraries like FastAPI, Pydantic, uvloop and asyncio.
- Use the client SDK to call a LangServe server as if it was a Runnable running locally (or call the HTTP API directly)
Limitations
- Client callbacks are not yet supported for events that originate on the server
- Does not work with pydantic v2 yet
LangChain CLI 🛠️
Use the LangChain CLI to bootstrap a LangServe project quickly.
To use the langchain CLI make sure that you have a recent version of langchain installed
and also typer. (pip install langchain typer or pip install "langchain[cli]")
langchain ../path/to/directory
And follow the instructions...
Examples
For more examples, see the examples directory.
Server
Here's a server that deploys an OpenAI chat model, an Anthropic chat model, and a chain that uses the Anthropic model to tell a joke about a topic.
#!/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
app = FastAPI(
title="LangChain Server",
version="1.0",
description="A simple api server using Langchain's Runnable interfaces",
)
add_routes(
app,
ChatOpenAI(),
path="/openai",
)
add_routes(
app,
ChatAnthropic(),
path="/anthropic",
)
model = ChatAnthropic()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
add_routes(
app,
prompt | model,
path="/chain",
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000)
Docs
If you've deployed the server above, you can view the generated OpenAPI docs using:
curl localhost:8000/docs
Client
Python SDK
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" }])
In TypeScript (requires LangChain.js version 0.0.166 or later):
import { RemoteRunnable } from "langchain/runnables/remote";
const chain = new RemoteRunnable({ url: `http://localhost:8000/chain/invoke/` });
const result = await chain.invoke({
"topic": "cats",
});
Python using requests:
import requests
response = requests.post(
"http://localhost:8000/chain/invoke/",
json={'input': {'topic': 'cats'}}
)
response.json()
You can also use curl:
curl --location --request POST 'http://localhost:8000/chain/invoke/' \
--header 'Content-Type: application/json' \
--data-raw '{
"input": {
"topic": "cats"
}
}'
Endpoints
The following code:
...
add_routes(
app,
runnable,
path="/my_runnable",
)
adds of these endpoints to the server:
POST /my_runnable/invoke- invoke the runnable on a single inputPOST /my_runnable/batch- invoke the runnable on a batch of inputsPOST /my_runnable/stream- invoke on a single input and stream the outputPOST /my_runnable/stream_log- invoke on a single input and stream the output, including output of intermediate steps as it's generatedGET /my_runnable/input_schema- json schema for input to the runnableGET /my_runnable/output_schema- json schema for output of the runnableGET /my_runnable/config_schema- json schema for config of the runnable
Installation
For both client and server:
pip install "langserve[all]"
or pip install "langserve[client]" for client code, and pip install "langserve[server]" for server code.
Legacy Chains
LangServe works with both Runnables (constructed via LangChain Expression Language) and legacy chains (inheriting from Chain).
However, some of the input schemas for legacy chains may be incomplete/incorrect, leading to errors.
This can be fixed by updating the input_schema property of those chains in LangChain.
If you encounter any errors, please open an issue on THIS repo, and we will work to address it.
Handling Authentication
If you need to add authentication to your server, please reference FastAPI's security documentation and middleware documentation.
Deployment
Deploy to GCP
You can deploy to GCP Cloud Run using the following command:
gcloud run deploy [your-service-name] --source . --port 8001 --allow-unauthenticated --region us-central1 --set-env-vars=OPENAI_API_KEY=your_key
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