LATS: AI Agent with LlamaIndex for Recommendation Systems

Unlock the Power of Systematic AI Reasoning with Language Agent Tree Search (LATS)

Imagine an AI assistant that not only answers your questions but also systematically solves problems, learns from its experiences, and strategically plans multiple steps ahead. Language Agent Tree Search (LATS) is a cutting-edge AI framework that combines the methodical reasoning of ReAct prompting with the strategic planning capabilities of Monte Carlo Tree Search (MCTS).

LATS builds a comprehensive decision tree, exploring multiple solutions concurrently, and refining its decision-making process through continuous learning. Focusing on Vertical AI Agents, this article explores the practical implementation of LATS Agents using LlamaIndex and SambaNova.AI.

Key Learning Objectives:

• Grasp the ReAct (Reasoning Acting) prompting framework and its thought-action-observation cycle.
• Understand the advancements LATS brings to the ReAct framework.
• Implement the LATS framework, leveraging MCTS and language model capabilities.
• Analyze the trade-offs between computational resources and optimized outcomes in LATS implementations.
• Build a recommendation engine using a LlamaIndex LATS Agent with SambaNova System as the LLM provider.

(This article is part of the Data Science Blogathon.)

Table of Contents:

• ReAct Agents Explained
• Understanding Language Agent Tree Search Agents
• LATS and ReAct: A Synergistic Approach
• Cost Considerations: When to Employ LATS
• Building a Recommendation System with LlamaIndex and LATS
• Conclusion
• Frequently Asked Questions

ReAct Agents Explained

ReAct (Reasoning Acting) is a prompting framework enabling language models to tackle tasks through a cyclical process of thought, action, and observation. Imagine an assistant thinking aloud, taking actions, and learning from feedback. The cycle is:

• Thought: Analyzing the current situation.
• Action: Choosing a course of action based on the analysis.
• Observation: Gathering feedback from the environment.
• Repeat: Using feedback to inform subsequent thoughts.

This structured approach allows language models to break down complex problems, make informed decisions, and adapt their strategies based on results. For example, in a multi-step mathematical problem, the model might identify relevant concepts, apply a formula, assess the result's logic, and adjust its approach accordingly. This mirrors human problem-solving, resulting in more reliable outcomes.

(Previously covered: Implementation of ReAct Agent using LlamaIndex and Gemini)

Understanding Language Agent Tree Search Agents

Language Agent Tree Search (LATS) is an advanced framework merging MCTS with language model capabilities for sophisticated decision-making and planning.

LATS operates through continuous exploration, evaluation, and learning, initiated by an input query. It maintains a long-term memory encompassing a search tree of past explorations and reflections, guiding future decisions.

LATS systematically selects promising paths, samples potential actions at each decision point, evaluates their merit using a value function, and simulates them to a terminal state to gauge effectiveness. The code demonstration will illustrate tree expansion and score evaluation.

LATS and ReAct: A Synergistic Approach

LATS integrates ReAct's thought-action-observation cycle into its tree search:

• Each node uses ReAct's thought generation, action selection, and observation collection.
• LATS enhances this by exploring multiple ReAct sequences simultaneously and using past experiences to guide exploration.

This approach, however, is computationally intensive. Let's examine when LATS is most beneficial.

Cost Considerations: When to Employ LATS

While LATS outperforms CoT, ReAct, and other methods in benchmarks, its computational cost is significant. Complex tasks generate numerous nodes, leading to multiple LLM calls, unsuitable for production environments. Real-time applications are especially challenging due to the latency of each API call. Organizations must carefully weigh LATS's superior decision-making against infrastructure costs, especially when scaling.

Use LATS when:

• The task is complex with multiple solutions (e.g., programming).
• Mistakes are costly, and accuracy is paramount (e.g., finance, medical diagnosis).
• Learning from past attempts is advantageous (e.g., complex product searches).

Avoid LATS when:

• Tasks are simple and require quick responses (e.g., basic customer service).
• Time sensitivity is critical (e.g., real-time trading).
• Resources are limited (e.g., mobile applications).
• High-volume, repetitive tasks are involved (e.g., content moderation).

Building a Recommendation System with LlamaIndex and LATS

Let's build a recommendation system using LATS and LlamaIndex.

Step 1: Environment Setup

Install necessary packages:

!pip install llama-index-agent-lats llama-index-core llama-index-readers-file duckduckgo-search llama-index-llms-sambanovasystems
import nest_asyncio; nest_asyncio.apply()

Step 2: Configuration and API Setup

Set up your SambaNova LLM API key (replace <your-api-key></your-api-key>):

import os
os.environ["SAMBANOVA_API_KEY"] = "<your-api-key>"

from llama_index.core import Settings
from llama_index.llms.sambanovasystems import SambaNovaCloud

llm = SambaNovaCloud(model="Meta-Llama-3.1-70B-Instruct", context_window=100000, max_tokens=1024, temperature=0.7, top_k=1, top_p=0.01)
Settings.llm = llm</your-api-key>

Step 3: Defining Tool-Search (DuckDuckGo)

from duckduckgo_search import DDGS
from llama_index.core.tools import FunctionTool

def search(query:str) -> str:
    """Searches DuckDuckGo for the given query."""
    req = DDGS()
    response = req.text(query,max_results=4)
    context = ""
    for result in response:
      context += result['body']
    return context

search_tool = FunctionTool.from_defaults(fn=search)

Step 4: LlamaIndex Agent Runner – LATS

from llama_index.agent.lats import LATSAgentWorker
from llama_index.core.agent import AgentRunner

agent_worker = LATSAgentWorker(tools=[search_tool], llm=llm, num_expansions=2, verbose=True, max_rollouts=3)
agent = AgentRunner(agent_worker)

Step 5: Execute Agent

query = "Looking for a mirrorless camera under 00 with good low-light performance"
response = agent.chat(query)
print(response.response)

Step 6: Error Handling (Example using agent.list_tasks()) - This section provides a method to handle cases where the agent returns "I am still thinking." The code is provided in the original input.

Conclusion

LATS significantly advances AI agent architectures. While powerful, its computational demands must be carefully considered.

Frequently Asked Questions

The FAQs section is provided in the original input. (Note: The statement about the media ownership remains unchanged.)

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