TypeError: expected string or bytes-like object

[ZhuJD-China]

trafficstars

Pick a name for the new index

index_name = 'wikipedia-articles'

Check whether the index with the same name already exists - if so, delete it

if index_name in pinecone.list_indexes(): pinecone.delete_index(index_name)

Creates new index

pinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0])) index = pinecone.Index(index_name=index_name)

Confirm our index was created

pinecone.list_indexes()

[ZhuJD-China]

Using_vector_databases_for_embeddings_search

[AI-Expert-04]

The error message you mentioned, "TypeError: expected string or bytes-like object," typically occurs when a function or method expects a string or bytes-like object as input, but receives a different type of data instead. To resolve this issue, you should ensure that the input provided to the function is a string or can be converted to a string.

Based on the code snippet you provided, it appears that you are using the Pinecone API to create and manage indexes. The error you encountered might be related to how the index_name variable is defined or used in the code. Here's an example of how you can fix the issue:

# Pick a name for the new index
index_name = 'wikipedia-articles'

# Check whether the index with the same name already exists - if so, delete it
if index_name in pinecone.list_indexes():
    pinecone.delete_index(index_name)

# Creates new index
pinecone.create_index(name=index_name, dimension=len(article_df['content_vector'][0]))
index = pinecone.Index(index_name=index_name)

# Confirm our index was created
print(pinecone.list_indexes())

Make sure that the pinecone.list_indexes() function returns a list of strings representing the existing indexes. If the error persists, please provide additional information or context about the pinecone library you are using and the specific error traceback.