> ## Documentation Index > Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt > Use this file to discover all available pages before exploring further. # Python API Reference > Complete Python API reference for the cognee package # Python API Reference Complete reference for the `cognee` Python package. All functions are accessible directly from the top-level `cognee` module. ```python theme={null} import cognee # v1.0 workflow await cognee.remember("Your data here") results = await cognee.recall("Your query") ``` ## Core Operations These are the default v1.0 entry points for storing, querying, enriching, and deleting memory. Store data as permanent graph memory or fast session memory in one call. Query memory with auto-routing and session-aware retrieval. Enrich an existing graph and bridge session memory into permanent memory. Remove a data item, an entire dataset, or all memory for the current user. ## Legacy Operations The lower-level pipeline operations are still available when you need direct control over each step. Ingest text, files, or structured data into the knowledge base. Transform raw data into a structured knowledge graph. Query the knowledge graph with multiple search modes. Enrich an existing knowledge graph with custom extraction and enrichment tasks. ## Data Management List, create, and delete datasets. Update existing data items. Clean up data and system resources. Delete data items (deprecated — use datasets). Apply pending relational and vector database schema migrations. ## Session Management Use `session_id` and inspect stored conversation history with `get_session()`. Add and clear feedback on stored session Q\&A entries. ## Configuration & Utilities Configure LLM providers, databases, chunking, and more. All available search modes and when to use them. Execute custom task pipelines. Key types: DataPoint, Task, PipelineRunInfo, SearchResult.