Building an AI Agent for Commercial Real Estate

pip install flask twilio openai google-api-python-client langchain faiss-cpu

@app.route("/sms", methods=["POST"])
def sms_reply():
    incoming_msg = request.form.get('Body')
    resp = MessagingResponse()

    if incoming_msg:
        # Grounded AI Response Generation
        completion = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[{"role": "system", "content": "Answer as a helpful real estate agent."},
                      {"role": "user", "content": incoming_msg}],
            max_tokens=150
        )
        resp.message(completion.choices[0].message.content.strip())

    return str(resp)

@app.route("/voice", methods=["POST"])
def voice_call():
    resp = VoiceResponse()
    # Gather speech input from caller
    gather = resp.gather(input='speech', action='/voice_response', timeout=5)
    gather.say("How can I help you today?", voice='alice')
    return str(resp)

@app.route("/voice_response", methods=["POST"])
def voice_response():
    speech_text = request.values.get('SpeechResult', None)
    # [Insert AI Processing Logic per Step 2]
    # [...]
    return str(resp)

def query_realestate(query):
    # Vector DB Search on Property Embeddings
    docs = index.similarity_search(query, k=3)
    # Augment Prompt with relevant property data
    chain = VectorDBQA(llm=OpenAI(temperature=0.2), vectorstore=index)
    return chain.run(query)