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Fixing minor typos in 02_qa_w_rag_claude_opensearch.ipynb #239

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Fixing minor typos in 02_qa_w_rag_claude_opensearch.ipynb #239

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4 changes: 2 additions & 2 deletions ...examples/01_Langchain_KnowledgeBases_and_RAG_examples/02_qa_w_rag_claude_opensearch.ipynb
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Expand Up @@ -374,7 +374,7 @@
"source": [
"Following the similar pattern embeddings could be generated for the entire corpus and stored in a vector store.\n",
"\n",
"Firt of all we have to create a vector store. In this workshop we will use ***Amazon OpenSearch serverless.***\n",
"First of all we have to create a vector store. In this workshop we will use ***Amazon OpenSearch serverless.***\n",
"\n",
"Amazon OpenSearch Serverless is a serverless option in Amazon OpenSearch Service. As a developer, you can use OpenSearch Serverless to run petabyte-scale workloads without configuring, managing, and scaling OpenSearch clusters. You get the same interactive millisecond response times as OpenSearch Service with the simplicity of a serverless environment. Pay only for what you use by automatically scaling resources to provide the right amount of capacity for your application—without impacting data ingestion. "
]
Expand Down Expand Up @@ -513,7 +513,7 @@
"source": [
"#### We can use the similarity search method to make a query and return the chunks of text without any LLM generating the response.\n",
"\n",
"It takes a few seconds to make documents availible in index. If you will get an empty output in a next cell, just wait a little bit and retry. "
"It takes a few seconds to make documents available in index. If you will get an empty output in a next cell, just wait a little bit and retry. "
]
},
{
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