How To Create Custom Columns When Creating Embeddings Using Llamaindex In Postgres (with Pgvector Extension)
**How to Create Custom Columns when Creating Embeddings using LlamaIndex in Postgres (with pgvector extension)**
In this article, we will explore how to create custom columns when creating embeddings using LlamaIndex in Postgres with the pgvector extension. LlamaIndex is a powerful tool for creating vector embeddings and vector search indexes, and when combined with the pgvector extension, it provides a robust and efficient way to store and query vector data in Postgres.
What is LlamaIndex and pgvector?
LlamaIndex is a Python library that provides a simple and efficient way to create vector embeddings and vector search indexes. It uses the Hugging Face Transformers library to generate vector embeddings from text data, and it provides a range of algorithms for creating vector search indexes.
The pgvector extension is a Postgres extension that provides a way to store and query vector data in Postgres. It uses the same algorithms as LlamaIndex to create vector search indexes, but it provides a more efficient and scalable way to store and query vector data in a relational database.
Creating Custom Columns with LlamaIndex and pgvector
When creating embeddings using LlamaIndex and pgvector, you may want to create custom columns to store additional metadata about the vector embeddings. For example, you may want to store the timestamp when the vector embedding was created, or the user who created the vector embedding.
To create custom columns with LlamaIndex and pgvector, you can use the create_index method of the LlamaIndex class to specify the columns that you want to create. For example:
from llama_index import LlamaIndex
index = LlamaIndex()
index.create_index(
"my_index",
columns=[
"vector",
"timestamp",
],
timestamp_column="timestamp",
)
index.create_index(
"my_index",
columns=[
"vector",
"user",
],
user_column="user",
)
In this example, we create two custom columns: timestamp and user. The timestamp column stores the timestamp when the vector embedding was created, and the user column stores the user who created the vector embedding.
Using Custom Columns with LlamaIndex and pgvector
Once you have created custom columns with LlamaIndex and pgvector, you can use them to store and query vector data in Postgres. For example:
from llama_index import LlamaIndex
index = LlamaIndex()
index.create_index(
"my_index",
columns=[
"vector",
"timestamp",
],
timestamp_column="timestamp",
)
index.create_index(
"my_index",
columns=[
"vector",
"user",
],
user_column="user",
)
vector = index.create_vector("Hello, world!")
index.store_vector(vector, timestamp="2022-01-01 12:00:00", user="john")
results = index.query(
"Hello, world!",
columns=["vector", "timestamp", "user"],
)
for result in results:
print(result["vector"])
print(result["timestamp"])
print(result["user"])
In this example, we create a vector embedding and store it in the index with the custom columns timestamp and user. We then query the index to retrieve the vector embedding and its metadata.
In this article, we have explored how to create custom columns when creating embeddings using LlamaIndex in Postgres with the pgvector extension. We have shown how to create custom columns to store additional metadata about the vector embeddings, and how to use them to store and query vector data in Postgres.
Q: How do I create a custom column with LlamaIndex and pgvector?
A: You can create a custom column with LlamaIndex and pgvector by using the create_index method of the LlamaIndex class to specify the columns that you want to create.
Q: How do I use custom columns with LlamaIndex and pgvector?
A: You can use custom columns with LlamaIndex and pgvector by specifying the columns that you want to retrieve when querying the index.
Q: Can I create multiple custom columns with LlamaIndex and pgvector?
A: Yes, you can create multiple custom columns with LlamaIndex and pgvector by specifying multiple columns in the create_index method.
Q: How do I store metadata about the vector embeddings with LlamaIndex and pgvector?
A: You can store metadata about the vector embeddings with LlamaIndex and pgvector by using the store_vector method to store the metadata along with the vector embedding.
Q: How do I query the index to retrieve the vector embeddings and their metadata with LlamaIndex and pgvector?
A: You can query the index to retrieve the vector embeddings and their metadata with LlamaIndex and pgvector by using the query method to specify the columns that you want to retrieve.