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# from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
# from langchain.agents import AgentExecutor
import openai
import json
import gradio as gr
import boto3
from langchain import OpenAI, AgentExecutor, Tool, load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.agents.tools import SQLAgent
from langchain.memory import ReadOnlySharedMemory
postgres_connection_str = os.environ['POSTGRES_CONNECTION_STR']
access_key = os.environ['AWS_ACCESS_KEY_ID']
secret_key = os.environ['AWS_SECRET_ACCESS_KEY']
openai_api_key = os.environ['OPENAI_API_KEY']
region= 'us-east-1'
db = SQLDatabase.from_uri(postgres_connection_str,schema='langchain_testing')
s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key,region_name=region)
llm = OpenAI(temperature=0, verbose=True, openai_api_key=openai_api_key)
# toolkit = SQLDatabaseToolkit(db=db, llm=llm)
# agent_executor = create_sql_agent(
# llm=OpenAI(temperature=0),
# toolkit=toolkit,
# verbose=True
# )
# def generate_response(question):
# prompt_template = """
# Keep in mind that any site, building, or property related question should be routed to the real estate portal.
# Any Local or city or city-sector incentives programs are asking about the local incentives program table.
# Any State incentives programs are asking about the state incentives program table.
# When the user asks about a state incentive, don't query the local_incentives_catalog table.
# When the user asks about a local/city incentive, don't query the local_incentives_catalog table.
# If you can't find the answer, make sure to look up the program field in the local and state incentives catalogs.
# If your final answer is "I don't know", then respond with "Please adjust your question and try asking again."
# """
# chain_response = agent_executor.run(question + prompt_template)
# # bucket_name = 'your-bucket-name'
# # file_name = 'flagged_text.txt'
# # s3_client.put_object(Body=input_text, Bucket=bucket_name, Key=file_name)
# return chain_response
# iface = gr.Interface(
# fn=generate_response,
# inputs=gr.inputs.Textbox(label='Enter your question:', default='What is the Neighborhood Credit Fund in New York City?'),
# outputs=gr.outputs.Textbox(label="EDai Analyst's Response:")
# )
# iface.launch(share=True)
# Load the language model and tools
llm = OpenAI()
tools = load_tools(["llama_index"], llm=llm)
# Define the agent
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
# Define the Gradio interface
def chatbot(user_name):
# Define the initial message
message = f"Hi {user_name}, how may I help you?"
# Define the memory
memory = ReadOnlySharedMemory()
# Define the SQL agent
sql_agent = SQLAgent(database="my_database.db", table="my_table")
# Loop until the user ends the conversation
while True:
# Get the user's input
user_input = gr.text_area(message, default_value="")
# If the user ends the conversation, break the loop
if user_input.lower() in ["bye", "goodbye", "exit"]:
break
# Get the answer from the SQL agent
answer = sql_agent.run(user_input, memory=memory)
# Ask if the user needs anything else
message = f"{answer}\n\nDo you need anything else?"
# Return the final message
return "Goodbye!"
# Define the Gradio interface
iface = gr.Interface(fn=chatbot, inputs=["text"], outputs="text", title="LangChain Chatbot", description="A chatbot powered by LangChain and Gradio.")
# Launch the interface
iface.launch() |