Initialize LlamaIndex Agent Team for Trend Analysis

Set up a LlamaIndex `AgentRunner` with two main agents: a 'Trend Analyst' and a 'Sentiment Processor'. The 'Trend Analyst' will use a `QueryEngineTool` connected to an index built from a collection of tech news articles and forum posts. The 'Sentiment Processor' will use a custom tool to interact with Hume AI for emotional analysis of voice transcripts. Both agents should leverage Claude 4 Sonnet as their underlying LLM for reasoning and text generation.
```python
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool, QueryEngineTool, ToolMetadata
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.anthropic import Anthropic
from llama_index.llms.base import ChatMessage
import os # Initialize Claude 4 Sonnet LLM
# Ensure ANTHROPIC_API_KEY is set in your environment
claude4_sonnet_llm = Anthropic(model="claude-4-sonnet", api_key=os.getenv("ANTHROPIC_API_KEY")) # 1. Setup for Trend Analyst: Indexing tech news and forum posts
# Assuming 'data/tech_news' and 'data/forum_posts' contain relevant documents
# For simplicity, we'll use a mock directory. In real scenario, use LlamaIndex data connectors.
# documents = SimpleDirectoryReader(input_dir="./data/tech_news").load_data()
# forum_docs = SimpleDirectoryReader(input_dir="./data/forum_posts").load_data()
# all_docs = documents + forum_docs
# index = VectorStoreIndex.from_documents(all_docs, llm=claude4_sonnet_llm)
# query_engine = index.as_query_engine(llm=claude4_sonnet_llm) # trend_search_tool = QueryEngineTool(
# query_engine=query_engine,
# metadata=ToolMetadata(
# name="trend_search_engine",
# description="Searches indexed tech news and developer forums for AI trends and topics."
# )
# ) # 2. Setup for Sentiment Processor: Hume AI Tool (placeholder for actual implementation)
# def hume_ai_sentiment_analysis(text_or_audio_path: str) -> str:
# """Analyzes text or audio (via path) for emotions and sentiment using Hume AI."""
# # This function would call the Hume AI API, securely authenticated by Aembit
# # For now, return mock data
# if "thrilled" in text_or_audio_path or "exciting" in text_or_audio_path:
# return '{"sentiment": "positive", "emotions": [{"emotion": "joy", "score": 0.9}]}'
# return '{"sentiment": "neutral", "emotions": []}' # hume_sentiment_tool = FunctionTool.from_defaults(
# fn=hume_ai_sentiment_analysis,
# name="hume_ai_sentiment_analyzer",
# description="Analyzes input text or audio transcript for sentiment and emotional cues using Hume AI. Input can be a string of text or a path to an audio file."
# ) # Initialize the ReActAgent with the tools
# agent = ReActAgent.from_tools(
# [trend_search_tool, hume_sentiment_tool],
# llm=claude4_sonnet_llm,
# verbose=True
# ) # Example interaction:
# response = agent.chat("Analyze the latest AI trends in inference hardware and gather developer sentiment regarding recent announcements.")
# print(response)
```