[Question]: why PostgresKVStore table class does not match my postgres vector table schema? about llama_index HOT 2 OPEN

To align the PostgresKVStore class with your small_db vector database schema, you'll need to modify the column definitions in the AbstractData class within the get_data_model function. This involves adjusting the column names and types to match those in your small_db schema. Here's a step-by-step guide:

1. Modify Column Definitions: Adjust the Column definitions in AbstractData to reflect your small_db schema. For example, if your schema includes columns for text, metadata_, node_id, and embedding, you should ensure these are represented in the class.

2. Example Modification:

from sqlalchemy import Column, Integer, String
from sqlalchemy.dialects.postgresql import JSON, VECTOR  # Assuming VECTOR is a custom type

def get_data_model(base: Type, index_name: str, schema_name: str, use_jsonb: bool = False) -> Any:
    tablename = f"data_{index_name}"
    class_name = f"Data{index_name}"

    class AbstractData(base):
        __abstract__ = True
        id = Column(Integer, primary_key=True, autoincrement=True)
        text = Column(String, nullable=False)
        metadata_ = Column(JSON)
        node_id = Column(String)
        embedding = Column(VECTOR(1536))

    return type(
        class_name,
        (AbstractData,),
        {
            "__tablename__": tablename,
            "__table_args__": (
                Index(f"{tablename}_idx_text", "text"),
                {"schema": schema_name},
            ),
        },
    )

3. Adjust for Custom Types or Constraints: If your schema uses custom types (like VECTOR), ensure these are correctly defined or imported. You might need to define custom types if they're not directly supported by SQLAlchemy.

4. Integration with Existing Code: Make sure the modified get_data_model function is correctly integrated with your PostgresKVStore class, particularly in methods that interact with the database, such as put and get.

This approach requires you to closely align the class definitions with your database schema, ensuring that the column names and types in the AbstractData class match those in your small_db. For more detailed guidance, consider referring to the source code of the PostgresKVStore class in the LlamaIndex repository.

To continue the conversation, mention @dosu.

from llama_index.

The kvstore and vector store should be created as two different tables, with different table names and different schemas

It seems like you are querying the vector store thinking it has the same fields as the kv store?

from llama_index.