serve.py

# Implementing everything we built into a single chain.

from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_groq import ChatGroq
import os
from dotenv import load_dotenv
from langserve import add_routes

#Add routes helps us to create APIs. It is based on FastAPI

load_dotenv()

#Setting up the model.

groq_api_key = os.getenv("GROQ_API_KEY")
model = ChatGroq(model="Gemma2-9b-It", groq_api_key = groq_api_key)

#Creating the prompts.

system_template = "Translate the following into {language} in a conversational style. Don't keep it formal."
human_template = "{text}"

chat_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_template),
        ("human", human_template),
    ]
)

#Setting up Parser

parser = StrOutputParser()

#Setting up the chain.

chain = chat_prompt | model | parser

# App Definition

app = FastAPI(title='Langchain Translation using Groq',
              version='1.0.0',
              description="A simple API server using langchain rumble interfaces."
)

#Adding Routes

add_routes(
    app,
    chain,
    path='/chain'
)

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)
#