search

torvalds-dev / weaviate

Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
https://weaviate.io/developers/weaviate/
BSD 3-Clause "New" or "Revised" License
0 stars 0 forks source link

what are the features of weaviate? #5

Open torvalds-dev opened 8 months ago

torvalds-dev commented 8 months ago

torvalds.dev is analyzing the ticket

torvalds-dev commented 8 months ago

The issue is about understanding the features of Weaviate. Weaviate is an open-source, GraphQL and RESTful API-enabled, knowledge graph that allows you to store, search, and retrieve data. It is powered by a vector search engine and can be used as a standalone database or in combination with other databases.

The files weaviate_utils.py and weaviate.py in the llama_index/vector_stores/ directory are relevant to this issue. These files contain Python code that interacts with Weaviate.

Here are some features of Weaviate as inferred from the code:

  1. Schema Creation and Validation: Weaviate allows you to create and validate schemas. Schemas define the structure of the data that you want to store in Weaviate. The validate_client, class_schema_exists, and create_default_schema functions in weaviate_utils.py are used for this purpose.

  2. Data Storage and Retrieval: You can add data to Weaviate and retrieve it. The add_node function in weaviate_utils.py and the add function in weaviate.py are used to add data. The parse_get_response function in weaviate_utils.py and the query function in weaviate.py are used to retrieve data.

  3. Batch Processing: Weaviate supports batch processing, which allows you to process multiple data objects at once. This is evident from the add function in weaviate.py.

  4. Vector Search: Weaviate uses a vector search engine to find the most similar nodes to a given query. This is evident from the query function in weaviate.py.

  5. Filtering: You can apply filters when querying data. The _to_weaviate_filter function in weaviate.py is used for this purpose.

  6. Hybrid Search: Weaviate supports hybrid search, which combines vector search and full-text search. This is evident from the query function in weaviate.py.

  7. Error Handling: The code includes error handling to manage issues that may arise when interacting with Weaviate. This is evident from the validate_client, parse_get_response, and class_schema_exists functions in weaviate_utils.py.

  8. Metadata Management: The code includes functions for managing metadata, such as metadata_dict_to_node and node_to_metadata_dict in weaviate_utils.py.

In terms of actions, if you want to use Weaviate, you should familiarize yourself with these features and understand how to use them in your application. You may also need to install the weaviate-client Python package, as suggested in the validate_client function in weaviate_utils.py.