Loan Advisory Agent using Pipecat

1
import os
2
from dotenv import load_dotenv
3
from loguru import logger
4
from pipecat.frames.frames import LLMRunFrame
5
from pipecat.pipeline.pipeline import Pipeline
6
from pipecat.pipeline.runner import PipelineRunner
7
from pipecat.pipeline.task import PipelineTask
8
from pipecat.processors.aggregators.llm_context import LLMContext
9
from pipecat.processors.aggregators.llm_response_universal import (
10
    LLMContextAggregatorPair,
11
)
12
from pipecat.runner.types import RunnerArguments
13
from pipecat.runner.utils import create_transport
14
from pipecat.services.sarvam.stt import SarvamSTTService
15
from pipecat.services.sarvam.tts import SarvamTTSService
16
from pipecat.services.openai.llm import OpenAILLMService
17
from pipecat.transports.base_transport import TransportParams
18
from pipecat.transports.daily.transport import DailyParams
19
20
load_dotenv(override=True)
21
22
async def bot(runner_args: RunnerArguments):
23
    """Main bot entry point."""
24
    
25
    # Create transport (supports both Daily and WebRTC)
26
    transport = await create_transport(
27
        runner_args,
28
        {
29
            "daily": lambda: DailyParams(audio_in_enabled=True, audio_out_enabled=True),
30
            "webrtc": lambda: TransportParams(
31
                audio_in_enabled=True, audio_out_enabled=True
32
            ),
33
        },
34
    )
35
36
    # Initialize AI services
37
    stt = SarvamSTTService(
38
        api_key=os.getenv("SARVAM_API_KEY"),
39
        language="unknown",  # Auto-detect for diverse customer base
40
        model="saaras:v3",
41
        mode="transcribe"
42
    )
43
    
44
    tts = SarvamTTSService(
45
        api_key=os.getenv("SARVAM_API_KEY"),
46
        target_language_code="en-IN",
47
        model="bulbul:v3",
48
        speaker="aditya"  # Professional and trustworthy voice
49
    )
50
    
51
    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
52
53
    # Set up conversation context with loan advisor personality
54
    messages = [
55
        {
56
            "role": "system",
57
            "content": """You are a professional and helpful loan advisory agent for a leading financial institution in India.
58
59
Your expertise covers:
60
61
**Loan Products:**
62
- Personal Loans: Unsecured loans for various personal needs
63
- Home Loans: For purchasing, constructing, or renovating homes
64
- Vehicle Loans: Car loans, two-wheeler loans
65
- Business Loans: For SMEs, startups, and working capital
66
- Education Loans: For domestic and international education
67
- Gold Loans: Secured loans against gold jewelry
68
- Loan Against Property (LAP): Secured loans using property as collateral
69
70
**Key Information to Provide:**
71
- Interest rates (mention that actual rates depend on profile and market conditions)
72
- Typical loan amounts and tenure options
73
- Eligibility criteria (age, income, credit score, employment type)
74
- Required documents (ID proof, address proof, income documents, etc.)
75
- Processing fees and other charges
76
- EMI calculation basics
77
- Prepayment and foreclosure options
78
79
**Communication Guidelines:**
80
- Be professional, trustworthy, and helpful
81
- Explain financial terms in simple language
82
- Never guarantee loan approval - mention that final approval depends on detailed assessment
83
- Always recommend customers to read terms and conditions carefully
84
- If asked about specific interest rates, provide indicative ranges and mention they vary
85
- Encourage customers to visit the branch or website for exact current rates
86
- Be transparent about fees and charges
87
- Help customers understand their EMI burden before recommending loan amounts
88
- Suggest customers maintain a good credit score
89
90
**Compliance Reminders:**
91
- Never make false promises about loan approval
92
- Always mention that loans are subject to eligibility and documentation
93
- Recommend customers to compare offers before deciding
94
- Remind about the importance of timely repayments
95
96
Start by greeting the customer professionally and asking how you can help them with their loan requirements.""",
97
        },
98
    ]
99
    context = LLMContext(messages)
100
    context_aggregator = LLMContextAggregatorPair(context)
101
102
    # Build pipeline
103
    pipeline = Pipeline(
104
        [
105
            transport.input(),
106
            stt,
107
            context_aggregator.user(),
108
            llm,
109
            tts,
110
            transport.output(),
111
            context_aggregator.assistant(),
112
        ]
113
    )
114
115
    task = PipelineTask(pipeline)
116
117
    @transport.event_handler("on_client_connected")
118
    async def on_client_connected(transport, client):
119
        logger.info("Customer connected")
120
        messages.append(
121
            {"role": "system", "content": "Greet the customer professionally and ask how you can help them with their loan requirements."}
122
        )
123
        await task.queue_frames([LLMRunFrame()])
124
125
    @transport.event_handler("on_client_disconnected")
126
    async def on_client_disconnected(transport, client):
127
        logger.info("Customer disconnected")
128
        await task.cancel()
129
130
    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
131
    await runner.run(task)
132
133
if __name__ == "__main__":
134
    from pipecat.runner.run import main
135
    main()

1
stt = SarvamSTTService(
2
    api_key=os.getenv("SARVAM_API_KEY"),
3
    language="hi-IN",  # Hindi
4
    model="saaras:v3",
5
    mode="transcribe"
6
)
7
8
tts = SarvamTTSService(
9
    api_key=os.getenv("SARVAM_API_KEY"),
10
    target_language_code="hi-IN",
11
    model="bulbul:v3",
12
    speaker="anand"  # Professional male voice
13
)
14
15
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

1
stt = SarvamSTTService(
2
    api_key=os.getenv("SARVAM_API_KEY"),
3
    language="ta-IN",
4
    model="saaras:v3",
5
    mode="transcribe"
6
)
7
8
tts = SarvamTTSService(
9
    api_key=os.getenv("SARVAM_API_KEY"),
10
    target_language_code="ta-IN",
11
    model="bulbul:v3",
12
    speaker="priya"
13
)
14
15
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

1
stt = SarvamSTTService(
2
    api_key=os.getenv("SARVAM_API_KEY"),
3
    language="unknown",  # Auto-detects language
4
    model="saaras:v3",
5
    mode="transcribe"
6
)
7
8
tts = SarvamTTSService(
9
    api_key=os.getenv("SARVAM_API_KEY"),
10
    target_language_code="en-IN",
11
    model="bulbul:v3",
12
    speaker="aditya"
13
)
14
15
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

1
# Customer speaks Hindi/Tamil/etc. → Saaras converts to English → LLM processes
2
3
stt = SarvamSTTService(
4
    api_key=os.getenv("SARVAM_API_KEY"),
5
    model="saaras:v3",  # Speech-to-English translation
6
    mode="translate"
7
)
8
9
tts = SarvamTTSService(
10
    api_key=os.getenv("SARVAM_API_KEY"),
11
    target_language_code="en-IN",
12
    model="bulbul:v3",
13
    speaker="aditya"
14
)
15
16
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

1
tts = SarvamTTSService(
2
    api_key=os.getenv("SARVAM_API_KEY"),
3
    target_language_code="en-IN",
4
    model="bulbul:v3",
5
    speaker="aditya",
6
    pace=1.0,            # Normal pace for clear communication
7
    speech_sample_rate=24000  # 8000, 16000, 22050, 24000 Hz (default). v3 REST API also supports 32000, 44100, 48000 Hz
8
)