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Implement Custom LlamaIndex Loaders & Indexing

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Linked challenge: Document AI: Summarize & Extract from Enterprise Content

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Linked challenge
Document AI: Summarize & Extract from Enterprise Content

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Implement the custom document loaders and indexing logic for your LlamaIndex application. You'll need to parse PDF content (using libraries like `pypdf`) and simple markdown transcripts. Initialize LlamaIndex and configure `ServiceContext` for Gemini 2.5 Pro (e.g., `Settings.llm = Gemini(model="gemini-pro", api_key="YOUR_KEY")`). Set up your `VectorStoreIndex` using `MongoDBAtlasVectorSearch` as the vector store. Provide Python code snippets for initializing `LLM`, `EmbeddingModel`, `VectorStore`, and creating/persisting the index. Use Fireworks AI for embedding generation.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

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