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Implement LlamaIndex for Advanced RAG

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Linked challenge: Graph-Based Strategic Market Intelligence with Gemini 2.5 Pro & LangGraph

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Linked challenge

Graph-Based Strategic Market Intelligence with Gemini 2.5 Pro & LangGraph

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Develop the LlamaIndex module to perform advanced RAG. This should include indexing diverse simulated data sources (e.g., market reports, patent filings, news articles) and a robust retrieval strategy. Agents within your LangGraph workflow will use this module to gather information.

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Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Graph-Based Strategic Market Intelligence with Gemini 2.5 Pro & LangGraph

For organizations pivoting into new, complex domains (e.g., AI infrastructure, inspired by Bitfarms' news), deep strategic market intelligence is paramount. This challenge involves building a sophisticated multi-agent system that leverages Gemini 2.5 Pro's 'Deep Think' mode within a LangGraph workflow to conduct comprehensive market research, competitive analysis, and strategic planning. The system will incorporate advanced RAG with LlamaIndex for synthesizing information from diverse, unstructured data sources. You will implement a graph-based workflow where agents communicate using an Agent-to-Agent (A2A) Protocol, allowing for dynamic task allocation, iterative refinement of hypotheses, and collaborative synthesis of insights. The system should exhibit extended thinking capabilities with adaptive reasoning budgets, enabling it to tackle ambiguous and data-intensive strategic questions.

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