Add example DAGs for LlamaIndex RAG pipelines

[vikramkoka]

• Adds example_llamaindex_rag.py with three example DAGs demonstrating RAG patterns using the new LlamaIndex operators

  • Full RAG pipeline: DocumentLoaderOperator → EmbeddingOperator → RetrievalOperator → LLMOperator
  • Separate index/query DAGs: weekly PDF indexing DAG + on-demand parameterized query DAG (production pattern)
  • Multi-source RAG: combines CSV and text files with metadata tagging, merges via @task, then embeds

  Dependencies

  Requires PR Add DocumentLoaderOperator to common.ai provider #67120 (DocumentLoaderOperator) and PR Add LlamaIndex operators to common.ai provider #67121 (LlamaIndex operators) to merge first.

  Test plan

  • Verify DAG file parses without errors after dependency PRs merge
  • Verify all three DAGs appear in the Airflow UI
  • Test full RAG pipeline end-to-end with sample text files and an OpenAI connection
  • Test parameterized query DAG with custom question parameter

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[kaxil]

Folded into #67121 as part of the comprehensive LlamaIndex rewrite -- closing in favor of that.

The example_llamaindex_rag.py file landed in apache/airflow:aip99-llamaindex at 82fa97b with the following adjustments to match the rewritten operator API:

• Uses the renamed LlamaIndexEmbeddingOperator / LlamaIndexRetrievalOperator classes (per Kaxil's r3267387604 -- generic *Operator names risked collision with future framework operators).
• Drops documents="{{ ti.xcom_pull(...) }}" Jinja templating -- template_fields was removed because list[dict] doesn't survive Jinja stringification and templating doc text would also expand {{ var.value.api_key }} tokens inside user documents (secret-leak vector). Binds via loader.output direct instead.
• LlamaIndex operators use the new llamaindex_default conn (was pydanticai_default); the synthesis-step LLMOperator keeps pydanticai_default because it's pydantic-ai-backed (different framework, intentional split documented in the module docstring).
• Adds explicit embed_model="text-embedding-3-small" to each embedding/retrieval call -- the rewritten operator validates that embed_model is set (either on the operator, or via extra["embed_model"] on the connection).
• Fixes the string-reference task chains (load >> "build_index" -> load >> build_index) which weren't valid task dependencies.

Thanks for the example DAGs -- they're the load-bearing demos for the LlamaIndex RAG story.