Deepseek can index and search a wide range of data types, including structured, semi-structured, and unstructured data. This includes text-based formats like documents, code repositories, logs, and database records, as well as metadata and real-time streaming data. For example, it handles common formats such as JSON, XML, CSV, PDFs, and plain text files, making it versatile for developers working with diverse data sources. This flexibility allows teams to unify search across codebases, application logs, API responses, or even multimedia metadata.
The system processes these formats by extracting meaningful content and metadata. For text documents like PDFs or Word files, it performs optical character recognition (OCR) or text extraction to index the raw content. For semi-structured data like JSON or XML, it parses nested fields and key-value pairs, enabling granular searches (e.g., filtering API logs by status_code=500). Code repositories are indexed with syntax-aware parsing, allowing searches for specific functions, variables, or language-specific constructs. Structured data from SQL databases or NoSQL systems like MongoDB is mapped into searchable schemas, supporting queries that combine relational data with unstructured text.
Deepseek scales to handle large datasets, including real-time streams like Kafka topics or time-series databases. It integrates with version control systems (e.g., Git) to index commit histories and code changes, enabling searches across code evolution. For logs, it supports timestamp-based filtering and pattern matching (e.g., ERROR entries from Kubernetes pods). Developers can extend its capabilities via plugins for niche formats, such as indexing Jupyter notebooks or IoT sensor data. By combining these features, Deepseek provides a unified search layer for heterogeneous data common in modern development workflows.