Create app.py

app.py

@@ -0,0 +1,53 @@
+from langchain_community.utilities import SQLDatabase
+from langchain_core.callbacks import BaseCallbackHandler
+from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
+from uuid import UUID
+from langchain_community.agent_toolkits import create_sql_agent
+from langchain_openai import ChatOpenAI
+from langchain_community.vectorstores import Chroma
+from langchain_core.example_selectors import SemanticSimilarityExampleSelector
+from langchain_openai import OpenAIEmbeddings
+from langchain.agents.agent_toolkits import create_retriever_tool
+from langchain_core.output_parsers import JsonOutputParser
+import os
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    FewShotPromptTemplate,
+    MessagesPlaceholder,
+    PromptTemplate,
+    SystemMessagePromptTemplate,
+)
+import ast
+import re
+from utils import query_as_list, get_answer
+import gradio as gr
+
+llm = ChatOpenAI(model="gpt-4o-mini", temperature=0, api_key=os.environ['API_KEY'])
+example_selector = SemanticSimilarityExampleSelector.from_examples(
+    examples,
+    OpenAIEmbeddings(model="text-embedding-3-small", api_key=os.environ['API_KEY']),
+    Chroma(persist_directory="data"),
+    # Chroma,
+    k=5,
+    input_keys=["input"],
+)
+
+db = SQLDatabase.from_uri("sqlite:///attendance_system.db")
+
+employee = query_as_list(db, "SELECT FullName FROM Employee")
+
+vector_db = Chroma.from_texts(employee, OpenAIEmbeddings(model="text-embedding-3-small", api_key=os.environ['API_KEY']))
+retriever = vector_db.as_retriever(search_kwargs={"k": 15})
+description = """Use to look up values to filter on. Input is an approximate spelling of the proper noun, output is \
+valid proper nouns. Use the noun most similar to the search."""
+retriever_tool = create_retriever_tool(
+    retriever,
+    name="search_proper_nouns",
+    description=description,
+)
+
+
+if __name__ == "__main__":
+    demo = gr.Interface(fn=get_answer, inputs="text", outputs="text")
+    demo.launch()
+