Expired60% confidenceFactTime unknown
Qoder's built-in code retrieval engine can retrieve 100,000 code files at once, achieving project-level precise cognition through a hybrid retrieval technology combining semantic vector search, code graph, and real-time indexing.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
5/31/2026
First Seen
Valid until: 8/29/2026(expired)
Sources
Related Entities
Related Claims
UnverifiedQoder uses Retrieval-Augmented Generation (RAG) technology to create vector indexes of the codebase and dynamically retrieves the most relevant code snippets when users ask questions.74% similarUnverifiedQoder (Tongyi Lingma) has project-level code comprehension capabilities, able to understand the structure of an entire code repository and automatically analyze dependency relationships between modules.72% similarUnverifiedQoder 1.0 uses vectorized indexing of the codebase to quickly locate relevant files.71% similarUnverifiedZicoder's Agentic Search lets an Agent autonomously explore the codebase over multiple rounds, providing tools such as file list browsing, file content reading, grep search, and directory traversal.69% similarUnverifiedUsing structured documentation generated by OpenWiki as the knowledge source for a vector retrieval library can further improve AI retrieval accuracy in large codebase scenarios.68% similar
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