Prasad Chalasani pchalasani

Run like this from the langroid repo:

uv run examples/basic/chat-search.py -m groq/deepseek-r1-distill-llama-70b

Example screenshot:

Run like this from the langroid-examples repo after setting up the env

uv run examples/basic/tool-extract-short-example.py -m ollama/mistral-small

Sample output:

Install Langroid and run this doc-chat example script from the examples/ folder:

uv run examples/docqa/chat.py https://arxiv.org/pdf/2501.12948

Example output:

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="refresh" content="2">
	<title>LLM-Agent - Langroid Task Log</title>
	<style>
	body {
	background-color: #1e1e1e;

	# Simple example of getting bsky data via `atproto` python client
	#
	# First install: atproto, python-dotenv

	# See python client docs here:
	# https://github.com/MarshalX/atproto
	#
	from atproto import Client, IdResolver

	import os

	// Zed settings
	//
	// For information on how to configure Zed, see the Zed
	// documentation: https://zed.dev/docs/configuring-zed
	//
	// To see all of Zed's default settings without changing your
	// custom settings, run the `open default settings` command
	// from the command palette or from `Zed` application menu.
	{
	// The settings for slash commands.

	from typing import Any, Callable, cast, Optional, Union
	import textwrap
	import chainlit as cl
	from datetime import datetime
	from chainlit.sync import run_sync
	from chainlit.message import AskMessageBase, AskUserMessage, MessageStepType
	from chainlit.types import AskSpec
	from chainlit.step import StepDict
	from chainlit.config import config
	from chainlit.telemetry import trace_event

	/Users/pchalasani/Git/langroid-examples/.venv/lib/python3.11/site-packages/langroid/parsing/docu │
	│ ment_parser.py:301 in iterate_pages │
	│ │
	│ 298 │ def iterate_pages(self) -> Generator[Tuple[int, Any], None, None]: # type: ignore │
	│ 299 │ │ from unstructured.partition.pdf import partition_pdf │
	│ 300 │ │ │
	│ ❱ 301 │ │ elements = partition_pdf(file=self.doc_bytes, include_page_breaks=True) │
	│ 302 │ │ for i, el in enumerate(elements): │
	│ 303 │ │ │ yield i, el │
	│ 304 │

	import blankly
	from blankly import StrategyState
	import numpy as np


	def trade(state: StrategyState):
	'''
	Example of how to decide a trade based on ALL assets
	'''
	symbols = state.symbol

	'''
	Example of using blankly to decide trade based on all asset prices using a global state-list
	'''
	import blankly
	import numpy as np

	# global list of StrategyStates, one per symbol (price_event)
	states = []

	def trade():