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from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
import gradio as gr
from dotenv import load_dotenv
from langchain.schema import HumanMessage, AIMessage
from langchain.prompts import ChatPromptTemplate
import os.path
from init_db import db_description
# Load environment variables
load_dotenv()
# Check if database exists
if not os.path.exists('estate.db'):
raise FileNotFoundError(
"Database file 'estate.db' not found. Please run 'uv run init_db.py' first."
)
# Initialize model and database
model = ChatOpenAI(model="gpt-4o-2024-08-06", streaming=True)
db = SQLDatabase.from_uri("sqlite:///estate.db")
# Set up SQL toolkit and tools
toolkit = SQLDatabaseToolkit(db=db, llm=model)
tools = toolkit.get_tools()
sql_distance_query = """
SELECT
id,
url,
structure->>'$.lat' AS lat,
structure->>'$.lng' AS lng,
(6371000 * acos(
cos(radians(50.08804)) * cos(radians(CAST(structure->>'$.lat' AS FLOAT))) *
cos(radians(CAST(structure->>'$.lng' AS FLOAT)) - radians(14.42076)) +
sin(radians(50.08804)) * sin(radians(CAST(structure->>'$.lat' AS FLOAT)))
)) AS distance
FROM project
WHERE structure->>'$.lat' IS NOT NULL
AND structure->>'$.lng' IS NOT NULL
ORDER BY distance ASC
LIMIT 5;
"""
# Get the OpenAI tools agent prompt
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant. You speak Czech. You can answer questions about real estate projects (novostavby) in Czech Republic. You have access to a database of real estate projects (translate output to Czech too). Some info about the structure of the database: " + db_description + "Note that some json values can be null so you must sometimes check with IS NOT NULL. For calculating you can adapt this query: " + sql_distance_query),
("placeholder", "{chat_history}"),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
# Create the agent with OpenAI tools format
agent = create_openai_tools_agent(
llm=model,
tools=tools,
prompt=prompt
)
# Create agent executor
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True
)
def chat_with_sql(message, history):
try:
history_langchain_format = []
for msg in history:
if msg['role'] == "user":
history_langchain_format.append(HumanMessage(content=msg['content']))
elif msg['role'] == "assistant":
history_langchain_format.append(AIMessage(content=msg['content']))
history_langchain_format.append(HumanMessage(content=message))
response = agent_executor.invoke({"input": message, "history": history_langchain_format})
for i in range(len(response["output"])):
yield response["output"][:i+1]
except Exception as e:
yield f"Error: {str(e)}"
# Create the Gradio interface
demo = gr.ChatInterface(
fn=chat_with_sql,
title="Estate Chat",
description="Zeptej se me na cokoli o novostavbách v ČR 🇨🇿",
examples=[
"Projekt s nejnižším vkladem",
"Nejlevnejsi byt v Praze",
],
type="messages"
)
if __name__ == "__main__":
demo.launch(share=False, debug=True) |