python-agents-examples/docs/examples/large_context at main · summit09876/python-agents-examples · GitHub

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lib		lib
README.md		README.md
large_context.py		large_context.py

README.md

title

Large Context Window LLM

Prerequisites

Add a .env in this directory with your LiveKit credentials:

LIVEKIT_URL=your_livekit_url
LIVEKIT_API_KEY=your_api_key
LIVEKIT_API_SECRET=your_api_secret

Install dependencies:

pip install "livekit-agents[silero]" python-dotenv

Load configuration and logging

Load environment variables and set up logging.

import logging
from pathlib import Path
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, cli, Agent, AgentSession, AgentServer, inference
from livekit.plugins import silero

load_dotenv()

logger = logging.getLogger("google_llm")
logger.setLevel(logging.INFO)

server = AgentServer()

Read the book into memory

Load the War and Peace text from lib/war_and_peace.txt so it can be embedded directly into the prompt.

book_path = Path(__file__).parent / "lib" / "war_and_peace.txt"
try:
    with open(book_path, "r", encoding="utf-8") as f:
        war_and_peace_text = f.read()
except FileNotFoundError:
    logger.error(f"Could not find book at {book_path}")
    war_and_peace_text = "War and Peace text not found."

Prewarm VAD and Define Entrypoint

We preload the VAD model. Inside the session, we configure the AgentSession with Deepgram STT, Gemini LLM (for large context), and OpenAI TTS (with custom instructions for tone).

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

server.setup_fnc = prewarm

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    session = AgentSession(
        stt=deepgram.STT(),
        llm=google.LLM(model="gemini-2.5-flash"),
        tts=openai.TTS(instructions="You are a literary discussion assistant with a pleasant voice. Speak in a natural, conversational tone that conveys enthusiasm for literature."),
        vad=ctx.proc.userdata["vad"],
    )
    
    agent = Agent(
        instructions=f"""
            You are a War and Peace book club assistant. You help users discuss and understand Leo Tolstoy's novel "War and Peace."

            You can answer questions about the plot, characters, themes, historical context, and literary analysis of the book.

            Here is the complete text of the book that you can reference:

            {war_and_peace_text}

            Be concise but informative in your responses. If asked about specific passages, quote directly from the text.
        """,
    )
    # ...

Start the session

Greet the user and start the session.

    @session.on("session_start")
    def on_session_start():
        session.generate_reply("Welcome to the War and Peace book club! I'm here to discuss Leo Tolstoy's epic novel with you. What would you like to talk about?")

    await session.start(agent=agent, room=ctx.room)
    await ctx.connect()

if __name__ == "__main__":
    cli.run_app(server)

Run it

python large_context.py console

How it works

The entire novel is embedded into the prompt at startup.
Gemini 2.0 Flash handles the large context and can quote passages directly.
Deepgram STT captures user questions; the LLM responds with literary analysis; TTS speaks in a friendly tone.
Silero VAD manages turn-taking between user and agent.

Full example

import logging
from pathlib import Path
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, cli, Agent, AgentSession, AgentServer, inference
from livekit.plugins import openai, google, deepgram, silero

load_dotenv()

logger = logging.getLogger("google_llm")
logger.setLevel(logging.INFO)

# Load book text once
book_path = Path(__file__).parent / "lib" / "war_and_peace.txt"
try:
    with open(book_path, "r", encoding="utf-8") as f:
        war_and_peace_text = f.read()
except FileNotFoundError:
    logger.error(f"Could not find book at {book_path}")
    war_and_peace_text = "War and Peace text not found."

server = AgentServer()

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

server.setup_fnc = prewarm

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    session = AgentSession(
        stt=deepgram.STT(),
        llm=google.LLM(model="gemini-2.0-flash"),
        tts=openai.TTS(instructions="You are a literary discussion assistant with a pleasant voice. Speak in a natural, conversational tone that conveys enthusiasm for literature."),
        vad=ctx.proc.userdata["vad"],
    )
    
    agent = Agent(
        instructions=f"""
            You are a War and Peace book club assistant. You help users discuss and understand Leo Tolstoy's novel "War and Peace."

            You can answer questions about the plot, characters, themes, historical context, and literary analysis of the book.

            Here is the complete text of the book that you can reference:

            {war_and_peace_text}

            Be concise but informative in your responses. If asked about specific passages, quote directly from the text.
        """,
    )
    
    @session.on("session_start")
    def on_session_start():
        session.generate_reply("Welcome to the War and Peace book club! I'm here to discuss Leo Tolstoy's epic novel with you. What would you like to talk about?")

    await session.start(agent=agent, room=ctx.room)
    await ctx.connect()

if __name__ == "__main__":
    cli.run_app(server)

python-agents-examples/docs/examples/large_context at main · summit09876/python-agents-examples · GitHub

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