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Let's go ahead and talk about PyCon indexes.

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An index is the highest level

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organizational unit of vector data in PyCon.

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It accepts and stores vectors, serves

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queries over the vectors it contains and
does other vector operations over its contents.

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There are currently serverless indexes
and pod -based indexes.

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Pods are pre -configured units of
hardware for running a PyCon service.

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With serverless indexes, you don't
configure or manage any compute or

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storage resources.

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Instead, serverless indexes scale

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automatically based on usage and you pay
only for the amount of data stored and

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operations performed with no minimums.

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With pod -based indexes, you choose one

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or more pre -configured units of hardware
called pods.

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Depending on the pod type, pod size and
the number of pods used, you get a

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different amount of storage and higher or
lower latency and throughput.

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In this section, we'll use the free
starter plan which is suitable for trying

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out and for small applications.

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Currently, on the free starter plan, you

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get one project with up to 5 serverless
indexes and up to 100 namespaces per index.

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This allows you to store nearly 300 ,000
vectors which is enough for tasks and

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small projects.

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If you need more, you'll have to upgrade

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to a paid plan.

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We are authenticated to Pinecon, so let's

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see some information about all indexes.

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Call PC .ListIndexes to get a complete

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description of all indexes in a project.

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Note that this is a list of dictionaries

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and you can access the name of the first
index using PC .ListIndexes of 0.

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And you can access its name by accessing
the value of the name key.

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This is the name of my index.

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This is equivalent to PC .DescribeIndex

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and the name of the index, Langchain.

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To display all index names, you can also

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call the names method.

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PC .ListIndexes .Names.

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Let's go ahead and create a Pinecon index.

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I'll import the serverlessSpec class from

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the Pinecon library.

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It is necessary to deploy a serverless index.

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From Pinecon, import serverlessSpec.

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Since I am using the free plan, I am

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allowed to have up to 5 indexes.

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In the Pinecon web console, check that

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you don't have more.

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In this example, I will remove the

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existing index called Langchain and
recreate it.

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I will create a new index called Langchain.

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indexName equals Langchain.

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I will create it only if it doesn't
already exist.

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So if indexName not in PC .ListIndexes
.Names, I am printing a message for the user.

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It will be an F string.

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Creating index and indexName in curly braces.

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Now, I am calling PC .CreateIndex with
the following arguments.

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Name equals indexName.

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Dimension equals 1536.

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This is the default dimension for text
embedding 3small, one of the recommended

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OpenACE embedding models.

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Metric equals cosine.

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This is the algorithm used to calculate
the distance between vectors.

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And the spec equals serverlessSpec of
cloud equals AWS and the region equals US

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East 1.

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Check for other available regions in the

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Pinecon documentation.

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And I am printing the message index created.

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Now, the else branch.

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If the index already exists, print

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indexName in curly braces already exists.

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Very well.

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I am running the code.

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It is creating the index.

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The index was created and I can check it
on the Pinecon web console.

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I am refreshing the page.

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Take a look, the index is here.

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Let's go ahead and take a look at how to
delete an index.

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So, indexName equals lengthchain.

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First, I will check that the index

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already exists.

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If indexName in PC .list .indexes .names,

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I am printing a message and deleting the index.

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Deleting index and the indexName in curly braces.

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And I am calling the deleteIndex method
with the indexName as an argument.

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And done.

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Else, if the index doesn't exist, I am

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just printing a message.

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index, indexName does not exist.

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The name of the variable is index
underlined name.

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I am running the code.

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It is deleting the index.

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And I am refreshing the web page.

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There are no indexes available.

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I am creating the index again.

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It is creating it.

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To perform any operation with an index,
you must first select it.

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To select an index, use PC .index and
provide the indexName as an argument.

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This method will return an object of type index.

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Here, I am selecting the index and

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displaying some statistics about it,
including the dimension and total vector count.

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index .describeIndexStacks.

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This operation provides statistics about

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the contents of an index, such as the
vector count per namespace, the number of

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dimensions and the index fullness.

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Keep in mind that serverless indexes

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automatically scale as needed, so index
fullness mainly applies to pod -based indexes.

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That is all for now.

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In this video, you have learned how to

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work with PyCon indexes, creating,
deleting and retrieving information and stacks.

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Next up, we will dive into working with vectors.