Implementing RAG with Amazon Bedrock and Amazon Titan - Part 1

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In this tutorial, we will build a chatbot based on the Retrieval Augmented Context generation technique. Amazon OpenSearch Serverless is used as the vector database, Amazon Titan is used for generating text embeddings and as an LLM, and Amazon Bedrock API is used for invoking the Titan model.

In part 1, we provision a collection in Amazon OpenSearch Serverless to store the text embeddings that we create in the next step.

Janakiram MSV

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While invoking bedrok, I am getting error AttributeError: 'Bedrock' object has no attribute 'invoke_model', how to solve this

AbhishekPorwal-hx

When creating the index, why the dimention is set to 4096 where Titan embedding model has a dimension of 1536?

zd

At Initialize and configure OpenSearch client, how do I get the value for host? What do you mean by "value from AWS Management Console"?

mannfai

while creating the opensearch serverless I am getting 403 forbidden error. Any clue/suggestions?

contactrahulsingh

Please, help why I am getting "unable to locate credentials" from notebook. when i run this cell .

policy = """{
"Rules": [
{
"Resource": [

],
"ResourceType": "collection"
}
],
"AWSOwnedKey": true
}"""

try:
response =
description="oscars collection encryption security policy",
name="oscars-encryption-policy",
policy=policy,
type="encryption",
)
print(json.dumps(response, indent=2))
except Exception as ex:
print(ex)

kwfuurs

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