▸case-01 I need to build a function-calling agent using the GPT-4 API for a knowledge base QA system. Instead of writing a custom loop or using LangChain's agent executor, what specific agent class from the standard RAG framework should I instantiate to leverage native API function calling? | pass→pass | 10,739 | 4,505 | -58% | 1 | 1 | 0% | 1,686 | 1,158 | -31% | 0 | 0 | — |
▸case-02 I am deploying a GPT-4 function-calling agent for my RAG pipeline. Besides the core framework package, what specific extension package must I add to my `requirements.txt` to enable this native integration? | pass→pass | 5,618 | 2,682 | -52% | 1 | 1 | 0% | 1,111 | 600 | -46% | 0 | 0 | — |
▸case-03 I am building a reasoning loop with an open-weight model (Mistral) that lacks native API function calling. I need an agent that outputs a thought, action, and observation sequence. Which specific agent class should I use to implement this pattern? | pass→pass | 10,452 | 6,044 | -42% | 1 | 1 | 0% | 2,023 | 1,406 | -30% | 0 | 0 | — |
▸case-04 For a complex multi-step research task, a standard ReAct loop gets lost. I need an architecture that first generates a comprehensive step-by-step plan and then executes it. What specific agent class provides this plan-and-execute style? | fail→pass | 13,994 | 7,527 | -46% | 1 | 1 | 0% | 2,135 | 1,692 | -21% | 0 | 0 | — |
▸case-05 Users often ask comparative questions like 'Compare the Q3 revenue of Apple and Microsoft'. Instead of a standard semantic search, I need a component that breaks this down into 'What is Apple's Q3 revenue?' and 'What is Microsoft's Q3 revenue?' and routes them. What specific engine class implements this complex query decomposition? | pass→pass | 7,985 | 5,639 | -29% | 1 | 1 | 0% | 1,579 | 1,206 | -24% | 0 | 0 | — |
▸case-06 I am setting up a basic semantic search pipeline over a set of PDF manuals. What is the standard index class I should use to chunk these documents and generate embeddings for retrieval? | pass→pass | 7,731 | 5,812 | -25% | 1 | 1 | 0% | 1,807 | 1,154 | -36% | 0 | 0 | — |
▸case-07 I need to build a feature that generates an executive overview of an entire 50-page financial report. A standard embedding search only retrieves isolated chunks. What specific index type should I use to synthesize the entire document? | pass→pass | 14,012 | 11,411 | -19% | 1 | 1 | 0% | 2,457 | 2,361 | -4% | 0 | 0 | — |
▸case-08 My dataset consists of complex relationships between corporate entities, subsidiaries, and executives. Semantic search fails to capture these explicit links. What specific engine type should I configure to traverse these entities and relationships? | pass→pass | 12,735 | 7,418 | -42% | 1 | 1 | 0% | 2,136 | 1,589 | -26% | 0 | 0 | — |
▸case-09 I need my QA system to answer questions based on a PostgreSQL database containing user transaction records, rather than unstructured text. What specific engine type should I implement to translate natural language into database queries? | pass→pass | 12,105 | 7,171 | -41% | 1 | 1 | 0% | 2,080 | 1,613 | -22% | 0 | 0 | — |
▸case-10 I am converting a single-turn QA script into an interactive chat interface. When the user asks 'What about the other one?', the system loses context. What specific configuration must I add to the initialization to support multi-turn conversations? | fail→pass | 9,568 | 7,042 | -26% | 1 | 1 | 0% | 1,888 | 1,621 | -14% | 0 | 0 | — |
▸case-11 During development of my ReAct agent, it keeps selecting the wrong tool, but I only see the final output. What specific configuration setting should I enable on the agent or engine to trace its internal reasoning steps and tool selections? | pass→pass | 9,820 | 5,932 | -40% | 1 | 1 | 0% | 1,594 | 1,387 | -13% | 0 | 0 | — |
▸case-12 Users often submit poorly phrased or incomplete questions to the RAG pipeline, leading to bad retrieval. Before the query hits the index, what specific architectural modules should I configure to rewrite or expand the user's input? | pass→pass | 15,037 | 16,040 | +7% | 1 | 1 | 0% | 2,645 | 2,849 | +8% | 0 | 0 | — |
▸case-13 I have a Python function `calculate_margin(revenue: float, cost: float)` that I want to provide to my agent. I could manually subclass BaseTool, but what specific factory method is the standard way to convert a Python function into a tool? | fail→pass | 4,799 | 3,311 | -31% | 1 | 1 | 0% | 993 | 867 | -13% | 0 | 0 | — |
▸case-14 I have a semantic search engine and a summarization engine. I could write an if-statement based on keyword matching to choose between them, but what specific LlamaIndex class should I use to dynamically route queries to the appropriate engine using an LLM? | pass→pass | 8,149 | 4,882 | -40% | 1 | 1 | 0% | 1,734 | 1,194 | -31% | 0 | 0 | — |
▸case-15 I want to trace query execution times and token usage in my LlamaIndex pipeline using Arize Phoenix. Instead of writing custom middleware, what specific global function should I call to register the observability handler? | pass→pass | 4,258 | 4,922 | +16% | 1 | 1 | 0% | 828 | 1,154 | +39% | 0 | 0 | — |
▸case-16 I have 10 different employee handbooks. Instead of dumping them all into one massive vector store, I want to create individual query engines for each handbook and have a top-level system route queries to the correct one. What specific architectural pattern does this describe? | pass→pass | 11,213 | 7,999 | -29% | 1 | 1 | 0% | 1,675 | 1,688 | +1% | 0 | 0 | — |
▸case-17 I need to build a conversational interface over my document index. Unlike a standard query engine that treats each request in isolation, what specific engine type should I implement to maintain conversational context? | pass→pass | 11,081 | 8,577 | -23% | 1 | 1 | 0% | 1,900 | 1,820 | -4% | 0 | 0 | — |
▸case-18 I have an existing `VectorStoreIndex` configured as a query engine. I want my ReAct agent to use this engine to look up facts. Instead of calling it directly in a custom loop, what specific wrapper class must I use to convert the query engine into an agent tool? | pass→pass | 3,311 | 3,241 | -2% | 1 | 1 | 0% | 687 | 842 | +23% | 0 | 0 | — |
▸case-19 I am starting a new Python project to build a knowledge base QA system with ReAct agents and vector stores. What is the primary base package I need to install via pip to get these core abstractions? | pass→pass | 6,594 | 4,448 | -33% | 1 | 1 | 0% | 1,134 | 998 | -12% | 0 | 0 | — |
▸case-20 I am building a LlamaIndex pipeline and need to ingest a folder of local text files. What is the standard built-in class used to load all documents from a specified directory? | pass→pass | 3,220 | 2,716 | -16% | 1 | 1 | 0% | 565 | 674 | +19% | 0 | 0 | — |
▸case-21 I want to override the default QA prompt in my LlamaIndex query engine. What specific class must I use to wrap my custom string so it can be passed as the `text_qa_template` argument? | pass→pass | 3,375 | 4,359 | +29% | 1 | 1 | 0% | 668 | 1,183 | +77% | 0 | 0 | — |
▸case-22 After loading documents in LlamaIndex, I need to split the text into chunks of 1024 tokens with an overlap of 20 tokens. What specific node parser class is the standard choice for this chunking? | pass→pass | 4,808 | 3,795 | -21% | 1 | 1 | 0% | 968 | 999 | +3% | 0 | 0 | — |
▸case-23 I am building an agent using `gpt-4-turbo`. I can either use a generic reasoning loop that parses text outputs, or I can use the model's native JSON function calling API. Which specific agent class is optimized to leverage the latter? | pass→pass | 11,313 | 5,938 | -48% | 1 | 1 | 0% | 2,249 | 1,302 | -42% | 0 | 0 | — |
▸case-24 A user asks 'How does the architecture of System A compare to System B?'. A standard semantic search might just return documents mentioning both. To explicitly generate two separate queries ('What is the architecture of System A?' and 'What is the architecture of System B?'), which specific engine class must I use? | pass→pass | 6,480 | 4,752 | -27% | 1 | 1 | 0% | 1,104 | 1,041 | -6% | 0 | 0 | — |