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To mock the PGVectorStore in LlamaIndex, you can create an instance of the PGVectorStore class with the necessary parameters. Here's an example of how to do this:
from llama_index.vector_stores.postgres import PGVectorStore
# Create PGVectorStore instance
vector_store = PGVectorStore.from_params(
database="vector_db",
host="localhost",
password="password",
port=5432,
user="postgres",
table_name="paul_graham_essay",
embed_dim=1536 # openai embedding dimension
)This code creates a mock PGVectorStore instance with a specified database, host, password, port, user, table name, and embedding dimension. You can replace these values with your own.
Please note that this is not a "mock" in the traditional sense used in unit testing. This is an actual instance of the PGVectorStore class. If you want to create a mock for unit testing, you would typically use a mocking library like
unittest.mock in Python, and the exact code would depend on what behavior you want to simulate in your tests.
For more details, you can refer to the
LlamaIndex documentation ().
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