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# Chains
from langchain_core.pydantic_v1 import BaseModel, Field
# To serve the app
from fastapi import FastAPI
from langchain_core.messages import BaseMessage
from langserve import add_routes, CustomUserType
import dotenv
dotenv.load_dotenv()
from ingredients import script_db, woo_db, full_chain, compound_chain, agent_executor
## Type specifications (with unusual class-scope fields)
class StrInput(BaseModel):
input: str
class Input(BaseModel):
input: str
chat_history: list[BaseMessage] = Field(
...,
extra = dict(widget = dict(type = 'chat', input = 'location')),
)
class Output(BaseModel):
output: str
## App definition
# NOTE: The chat playground type has a web page issue (flashes and becomes white, hence non-interactable; this was supposedly solved in an issue late last year)
app = FastAPI(
title = 'Star Wars Expert',
version = '1.0',
description = 'A Star Wars expert chatbot',
)
# Basic retriever versions
# add_routes(app, script_db.as_retriever())
# add_routes(app, woo_db.as_retriever())
# History-aware retriever version
# add_routes(app, full_chain.with_types(input_type = StrInput, output_type = Output), playground_type = 'default')
# Agent version
# add_routes(app, agent_executor, playground_type = 'chat')
# add_routes(app, agent_executor.with_types(input_type = StrInput, output_type = Output))
# Non-agent chain-logic version
add_routes(app, compound_chain.with_types(input_type = StrInput))
# add_routes(app, compound_chain.with_types(input_type = Input), playground_type = 'chat')
if __name__ == '__main__':
import uvicorn
uvicorn.run(app, host = 'localhost', port = 8000)
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