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Airweave - an open-source tool for turning any application into an agent knowledge base.

Airweave is an open-source tool that can synchronize data from any application (including APIs, databases, websites, etc.) to graph and vector databases, enabling data retrieval based on intelligent agents or search mechanisms. Airweave...

What is Airweave?

Airweave is an open-source tool that synchronizes data from any application (including APIs, databases, websites, etc.) to graph and vector databases, enabling data retrieval based on intelligent agents or search mechanisms. Airweave simplifies data retrieval and management processes with features such as data chunking, hash checking, and automatic synchronization. Airweave provides core functionalities such as no-code integration, multi-tenancy support, and multi-source data integration, and supports operation through the React front-end interface or FastAPI interface.

Airweave's main functions

  • No-code integrationUsers can quickly transform application data into searchable content without writing code.
  • Multi-tenancy supportSuitable for SaaS developers, it supports multi-tenant data synchronization based on OAuth2 while ensuring data privacy and security.
  • Data BlockingEach data source (such as a database, API, or file system) defines an async def generate_chunks() method, which is used to generate data chunks in a consistent format.
  • Automatic synchronizationSupports scheduled or on-demand data synchronization.
  • Version control and hashingBased on changes in hash detection data, only the modified parts in the vector storage are updated.
  • Multi-source supportIt supports connecting to multiple data sources and unifying them into a single queryable layer.
  • ScalabilitySupports local deployment based on Docker Compose (with future support for production-grade deployment using Kubernetes).

Airweave's technical principles

  • Data collectionThis feature connects to various data sources (such as APIs, databases, and file systems) to collect data. Each data source defines an `async def generate_chunks()` method to process data in chunks, ensuring that the data is output in a consistent format.
  • Data processing and partitioningAfter data is processed in chunks, text or other data types are converted into vector form using embedders. Vectors can then be efficiently stored and retrieved by vector databases.
  • Data storage:
    • Graph DatabaseUsed to store relationships between data.
    • Vector DatabaseExamples of similarity search tools include Chroma, Milvus, Pinecone, Qdrant, and Weaviate, which are used to store vectorized data and support efficient similarity search.
  • Data synchronization and updateBased on changes in hash-based detection data, only modified data blocks are updated, reducing unnecessary data synchronization. It supports scheduled synchronization and on-demand synchronization, allowing users to flexibly configure synchronization plans according to their needs.
  • Search and queryAfter data is stored, users can retrieve it using intelligent agents or search mechanisms. Airweave supports queries via the front-end interface or API, allowing users to quickly obtain the data they need.
  • Asynchronous task processingARQ Redis enables background task processing, supports asynchronous processing of large-scale data synchronization, and improves system performance and scalability.
  • Multi-tenancy and privacy protection: Based on OAuth2, it supports multi-tenant data synchronization, ensuring data isolation and privacy protection between different tenants.

Airweave project address

Airweave application scenarios

  • Enterprise developersBuild semantic search applications, synchronize enterprise data sources, and achieve efficient data retrieval and management.
  • Data AnalystQuickly retrieve and analyze data, improve data analysis efficiency with Airweave, and generate business intelligence reports.
  • SaaS platform developersProvides data retrieval services for multi-tenants, ensuring data isolation and privacy.
  • Content creators and managersQuickly retrieve and manage content, improving content management efficiency.
  • Intelligent Customer Service TeamQuickly retrieve customer support data, provide accurate answers, and improve customer satisfaction.