Build Summary & Knowledge Graph Query Engine

implementationChallenge

Prompt Content

Develop the LlamaIndex query engines for both document summarization and knowledge graph-enhanced querying. For summarization, consider using LlamaIndex's `ResponseSynthesizer` with a `tree_summarize` mode or a custom prompt for Gemini 2.5 Pro to generate a podcast-style script and key highlights. For knowledge graph queries, implement a `KnowledgeGraphQueryEngine` that can answer questions requiring relational understanding by leveraging your MongoDB Atlas Vector Search integration. Provide Python code for setting up these query engines and sample usage.

Try this prompt

Open the workspace to execute this prompt with free credits, or use your own API keys for unlimited usage.

Related Prompts

Explore similar prompts from our community

Design LlamaIndex RAG Pipeline Architecture

planning

Implement Custom LlamaIndex Loaders & Indexing

implementation

Integrate LangFuse for Observability

implementation

Usage Tips

Copy the prompt and paste it into your preferred AI tool (Claude, ChatGPT, Gemini)

Customize placeholder values with your specific requirements and context

For best results, provide clear examples and test different variations