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Core ConceptsPipelines

Pipelines in cognee

Cognee uses tasks  grouped into pipelines that efficiently populate graph and vector stores. These tasks  process the data in order to create a semantic layer that will improve the quality of answers produced by Large Language Models (LLMs).

You can think of tasks as Python functions and pipelines as groups of Python functions executing in an order. Tasks are managed and executed asynchronously using the run_tasks and run_tasks_parallel functions.

pipeline = run_tasks(tasks, documents)
 
async for result in pipeline:
    print(result)

Main pipeline: cognify

The main pipeline currently implemented in cognee is designed to process data in a structured way and populate the graph and vector stores.

cognify pipeline description

The pipeline consists of several tasks, each responsible for different parts of the processing:

  • classify_documents: Converts each document into a specific Document type: PdfDocument, AudioDocument, ImageDocument or TextDocument

  • check_permissions_on_documents: Checks if the user has the necessary permissions to access the documents

  • extract_chunks_from_documents: Extracts text chunks based on the document type

  • add_data_points: Creates nodes and edges from the chunks and their properties and adds them to the graph engine

  • extract_graph_from_data: Generates knowledge graphs from the document chunks

  • summarize_text: Extracts a summary for each chunk using an LLM

When to use it

Use cognify whenever you have “plain” content (PDFs, text, audio transcripts, etc.) and you want cognee to:

  1. Ingest the data safely,
  2. Index it for semantic and relational search,
  3. Summarise it so your LLM can answer faster and cheaper,
  4. Generate knowledge graph quickly

Out-of-the-box it gives you a populated graph store, fresh embeddings in the vector store, and summaries ready for improved RAG - no extra wiring required.

Cognee allows you to make your own custom pipelines you might need one.

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TasksData Ingestion