Semantic Search for Research Papers
Develop a semantic search engine for a collection of research papers. The engine should allow users to search using natural language queries and retrieve relevant papers based on semantic similarity, not just keyword matching. The system should handle variations in terminology and different writing styles. Focus on building a highly accurate and efficient search experience.
What you are building
The core problem, expected build, and operating context for this challenge.
Create a semantic search engine for research papers using embeddings and similarity search.
How work is evaluated
Evaluate the search engine's performance using Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR). The system should be tested against a dataset of research papers and queries.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement an efficient similarity search algorithm (e.g., FAISS).
Create a robust document embedding model.
Handle different types of queries and different sizes of document collections.
Develop a user interface for interacting with the search engine.
Evaluate the effectiveness of the search engine using recall and precision.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluate the search engine's performance using Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR). The system should be tested against a dataset of research papers and queries.
Challenge input
A search query (string) and a dataset of research papers (list of strings).
Agent execution
No agent tool is listed for this challenge.
Evaluated output
A ranked list of research paper indices that best match the search query.
- No evaluation checks are configured.
- MAP target: 0.7
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- No evaluation checks are configured.
Metrics
- MAP · Target 0.7
- MRR · Target 0.8
Workflow tasks
- Task 1
Input: A search query (string) and a dataset of research papers (list of strings).
Output: A ranked list of research paper indices that best match the search query.
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.