Fixed typo described in Issue #240 and corrected the notebook names/links in 02_KnowledgeBases_and_RAG/README.md

02_KnowledgeBases_and_RAG/README.md

@@ -5,9 +5,9 @@
 
 - [1_managed-rag-kb-retrieve-generate-api.ipynb](./1\_managed-rag-kb-retrieve-generate-api.ipynb) - Code sample for managed retrieval augmented generation (RAG) using `RetrieveAndGenerate` API from Knowledge Bases for Amazon Bedrock.
 
-- [2_customized-rag-retrieve-api-claude-v2.ipynb](./2\_customized-rag-retrieve-api-claude-v2.ipynb) - If you want to customize your RAG workflow, you can use the `retrieve` API provided by Knowledge Bases for Amazon Bedrock. Use this code sample as a starting point.
+- [2_Langchain-rag-retrieve-api-mistral-and-claude-v2.ipynb](./2\_Langchain-rag-retrieve-api-mistral-and-claude-v2.ipynb) - If you want to customize your RAG workflow, you can use the `retrieve` API provided by Knowledge Bases for Amazon Bedrock. Use this code sample as a starting point.
 
-- [3_customized-rag-retrieve-api-langchain-claude-v2.ipynb](./3\_customized-rag-retrieve-api-langchain-claude-v2.ipynb) - Code sample for using the `RetrieveQA` chain from LangChain and Amazon Knowledge Base as the retriever.
+- [3_Langchain-rag-retrieve-api-claude-3.ipynb](./3\_Langchain-rag-retrieve-api-claude-3.ipynb) - Code sample for using the `RetrieveQA` chain from LangChain and Amazon Knowledge Base as the retriever.
 
 
 Remember to use the [4_CLEAN_UP.ipynb](./4\_CLEAN_UP.ipynb)

03_Model_customization/03_continued_pretraining_titan_text.ipynb

@@ -449,7 +449,7 @@
  "\n",
  "while training_job_status == \"InProgress\":\n",
  " time.sleep(60)\n",
- " fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]\n",
+ " training_job_status = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]\n",
  " print (training_job_status)"
  ]
  },