app.py

import gradio as gr
import os
import logging
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_community.graphs import Neo4jGraph
from typing import List, Tuple
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.runnables import (
    RunnableBranch,
    RunnableLambda,
    RunnablePassthrough,
    RunnableParallel,
)
from langchain_core.prompts.prompt import PromptTemplate

# Setup Neo4j
graph = Neo4jGraph(
    url="neo4j+s://6457770f.databases.neo4j.io",
    username="neo4j",
    password="Z10duoPkKCtENuOukw3eIlvl0xJWKtrVSr-_hGX1LQ4"
)

# Define entity extraction and retrieval functions
class Entities(BaseModel):
    names: List[str] = Field(
        ..., description="All the person, organization, or business entities that appear in the text"
    )

entity_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are extracting organization and person entities from the text."),
    ("human", "Use the given format to extract information from the following input: {question}"),
])

chat_model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key="OPENAI_API_KEY")
entity_chain = entity_prompt | chat_model.with_structured_output(Entities)

def remove_lucene_chars(input: str) -> str:
    return input.translate(str.maketrans({
        "\\": r"\\", "+": r"\+", "-": r"\-", "&": r"\&", "|": r"\|", "!": r"\!",
        "(": r"\(", ")": r"\)", "{": r"\{", "}": r"\}", "[": r"\[", "]": r"\]",
        "^": r"\^", "~": r"\~", "*": r"\*", "?": r"\?", ":": r"\:", '"': r'\"',
        ";": r"\;", " ": r"\ "
    }))

def generate_full_text_query(input: str) -> str:
    full_text_query = ""
    words = [el for el in remove_lucene_chars(input).split() if el]
    for word in words[:-1]:
        full_text_query += f" {word}~2 AND"
    full_text_query += f" {words[-1]}~2"
    return full_text_query.strip()

def structured_retriever(question: str) -> str:
    result = ""
    entities = entity_chain.invoke({"question": question})
    for entity in entities.names:
        response = graph.query(
            """CALL db.index.fulltext.queryNodes('entity', $query, {limit:2})
            YIELD node,score
            CALL {
              WITH node
              MATCH (node)-[r:!MENTIONS]->(neighbor)
              RETURN node.id + ' - ' + type(r) + ' -> ' + neighbor.id AS output
              UNION ALL
              WITH node
              MATCH (node)<-[r:!MENTIONS]-(neighbor)
              RETURN neighbor.id + ' - ' + type(r) + ' -> ' +  node.id AS output
            }
            RETURN output LIMIT 50
            """,
            {"query": generate_full_text_query(entity)},
        )
        result += "\n".join([el['output'] for el in response])
    return result

def retriever_neo4j(question: str):
    structured_data = structured_retriever(question)
    logging.debug(f"Structured data: {structured_data}")
    return structured_data

# Setup for condensing the follow-up questions
_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question,
in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""

CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)

def _format_chat_history(chat_history: list[tuple[str, str]]) -> list:
    buffer = []
    for human, ai in chat_history:
        buffer.append(HumanMessage(content=human))
        buffer.append(AIMessage(content=ai))
    return buffer

_search_query = RunnableBranch(
    (
        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
            run_name="HasChatHistoryCheck"
        ),
        RunnablePassthrough.assign(
            chat_history=lambda x: _format_chat_history(x["chat_history"])
        )
        | CONDENSE_QUESTION_PROMPT
        | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
        | StrOutputParser(),
    ),
    RunnableLambda(lambda x: x["question"]),
)

# Define the QA prompt template
template = """As an expert concierge known for being helpful and a renowned guide for Birmingham, Alabama, I assist visitors in discovering the best that the city has to offer. I also assist the visitors about various sports and activities. Given today's sunny and bright weather on {current_date}, I am well-equipped to provide valuable insights and recommendations without revealing specific locations. I draw upon my extensive knowledge of the area, including perennial events and historical context.
In light of this, how can I assist you today? Feel free to ask any questions or seek recommendations for your day in Birmingham. If there's anything specific you'd like to know or experience, please share, and I'll be glad to help. Remember, keep the question concise for a quick, short, crisp, and accurate response.
"It was my pleasure!"
{context}
Question: {question}
Helpful Answer:"""

qa_prompt = ChatPromptTemplate.from_template(template)

# Define the chain for Neo4j-based retrieval and response generation
chain_neo4j = (
    RunnableParallel(
        {
            "context": _search_query | retriever_neo4j,
            "question": RunnablePassthrough(),
        }
    )
    | qa_prompt
    | chat_model
    | StrOutputParser()
)

# Define the function to get the response
def get_response(question):
    try:
        return chain_neo4j.invoke({"question": question})
    except Exception as e:
        return f"Error: {str(e)}"

# Define the function to clear input and output
def clear_fields():
    return "", ""

# Create the Gradio Blocks interface
with gr.Blocks() as demo:
    question_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
    response_output = gr.Textbox(label="Response", placeholder="The response will appear here...", interactive=False)
    get_response_btn = gr.Button("Get Response")
    clean_btn = gr.Button("Clean")
    
    get_response_btn.click(fn=get_response, inputs=question_input, outputs=response_output)
    clean_btn.click(fn=clear_fields, inputs=[], outputs=[question_input, response_output])

# Launch the Gradio interface
demo.launch(show_error=True)