79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
#
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#
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import os
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from dotenv import load_dotenv
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from llama_index.core import VectorStoreIndex, Document, Settings
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from llama_index.instrumentation.agentops import AgentOpsHandler
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.llms.huggingface import HuggingFaceLLM
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handler = AgentOpsHandler(tags=["llamaindex", "rag", "agentops-example"])
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handler.init()
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load_dotenv()
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os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_agentops_api_key_here")
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
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Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
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Settings.llm = HuggingFaceLLM(model_name="microsoft/DialoGPT-medium")
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print("Using local HuggingFace embeddings and LLM")
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print("🚀 Starting LlamaIndex AgentOps Integration Example")
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print("=" * 50)
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documents = [
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Document(text="LlamaIndex is a framework for building context-augmented generative AI applications with LLMs."),
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Document(
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text="AgentOps provides observability into your AI applications, tracking LLM calls, performance metrics, and more."
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),
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Document(
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text="The integration between LlamaIndex and AgentOps allows you to monitor your RAG applications seamlessly."
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),
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Document(
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text="Vector databases are used to store and retrieve embeddings for similarity search in RAG applications."
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),
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Document(
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text="Context-augmented generation combines retrieval and generation to provide more accurate and relevant responses."
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),
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]
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print("📚 Creating vector index from sample documents...")
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index = VectorStoreIndex.from_documents(documents)
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print("✅ Vector index created successfully")
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query_engine = index.as_query_engine()
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print("🔍 Performing queries...")
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queries = [
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"What is LlamaIndex?",
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"How does AgentOps help with AI applications?",
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"What are the benefits of using vector databases in RAG?",
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]
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for i, query in enumerate(queries, 1):
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print(f"\n📝 Query {i}: {query}")
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response = query_engine.query(query)
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print(f"💬 Response: {response}")
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print("\n" + "=" * 50)
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print("🎉 Example completed successfully!")
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print("📊 Check your AgentOps dashboard to see the recorded session with LLM calls and operations.")
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print("🔗 The session link should be printed above by AgentOps.")
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# Let's check programmatically that spans were recorded in AgentOps
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print("\n" + "=" * 50)
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print("Now let's verify that our LLM calls were tracked properly...")
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try:
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import agentops
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agentops.validate_trace_spans(trace_context=None)
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print("\n✅ Success! All LLM spans were properly recorded in AgentOps.")
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except agentops.ValidationError as e:
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print(f"\n❌ Error validating spans: {e}")
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raise
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