Spike: Adding Llamaindex llm instrumentation

[shuningc]

Core Implementation:

_handle_llm_start(): Captures model name, input messages, creates LLMInvocation entity
_handle_llm_end(): Extracts response content, token usage, finalizes span
Handles LlamaIndex-specific message formats (ChatMessage objects, blocks[0].text structure)
Supports both dict and object response types
Token extraction from response.raw.usage (OpenAI format)
Testing:

test_llm_instrumentation.py: Live OpenAI API integration test
Validates span attributes, message content, token counts
Verifies metrics emission
Documentation:

Quick start guide with code example
Expected span attributes and metrics output
Key differences vs LangChain (event-based callbacks, CBEventType enum)
Span Attributes (Gen AI Semantic Conventions)
Metrics:

gen_ai.client.operation.duration (histogram)
gen_ai.client.token.usage (counter)