老范的自言自语 Walter's Solo

langchain quick start

September 26, 2024 by walter

Overview

LangChain is a framework to build with LLMs by chaining interoperable components. LangGraph is the framework for building controllable agentic workflows.

build an application

• install library

  pip install langchain
  pip install -qU langchain-openai

  example 1

#!/usr/bin/env python3
from typing import List
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

import os
from dotenv import load_dotenv
load_dotenv()

#os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["OPENAI_API_KEY"] = os.getenv("LLM_API_KEY")
os.environ["USER_AGENT"] = "waltertest"
#os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
#os.environ["OPENAI_API_KEY"] = getpass.getpass()

model = ChatOpenAI(
    model='deepseek-chat',
    openai_api_key=os.getenv("LLM_API_KEY"),
    openai_api_base=os.getenv("LLM_BASE_URL"),
    max_tokens=4096
)

system_template = "Translate the following into {language}:"
prompt_template = ChatPromptTemplate.from_messages(
    [("system", system_template), ("user", "{text}")]
)

parser = StrOutputParser()

#result = model.invoke(messages)
#parser.invoke(result)
#result = prompt_template.invoke({"language": "Chinese", "text": "hi"})

chain = prompt_template | model | parser

result = chain.invoke({"language": "chinese", "text": "As you sow, so shall you reap"})
print(result)

输出: 种瓜得瓜，种豆得豆

example 2

#!/usr/bin/env python
from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langserve import add_routes

import os, sys
from loguru import logger
from dotenv import load_dotenv

load_dotenv()

# 1. Create prompt template
system_template = "Translate the following into {language}:"
prompt_template = ChatPromptTemplate.from_messages([
    ('system', system_template),
    ('user', '{text}')
])

# 2. Create model
model = ChatOpenAI(
    model='deepseek-chat',
    openai_api_key=os.getenv("LLM_API_KEY"),
    openai_api_base=os.getenv("LLM_BASE_URL"),
    max_tokens=4096
)

# 3. Create parser
parser = StrOutputParser()

# 4. Create chain
chain = prompt_template | model | parser

# 4. App definition
app = FastAPI(
  title="LangChain Server",
  version="1.0",
  description="A simple API server using LangChain's Runnable interfaces",
)

# 5. Adding chain route
add_routes(
    app,
    chain,
    path="/chain",
)

if __name__ == "__main__":
    import uvicorn

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

• Call the above API

#!/usr/bin/env python
from langserve import RemoteRunnable

remote_chain = RemoteRunnable("http://localhost:8000/chain/")
result = remote_chain.invoke({"language": "chinese", "text": "As you sow, so shall you reap"})
print(result)

打印: 种瓜得瓜，种豆得豆

Reference

• https://python.langchain.com/v0.2/docs/tutorials/llm_chain/
• https://python.langchain.com/docs/concepts/

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