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chore: fix typos across repo #4497

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4 changes: 2 additions & 2 deletions ...ativeai/llm-workshop/chatbot-apps/build_cahtbot_applications_using_rag_on_sagemaker.ipynb
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Expand Up @@ -509,7 +509,7 @@
"\n",
"To achieve that, we will do following.\n",
"\n",
"1. **Generate embedings for each of document in the knowledge library with Huggingface all-MiniLM-L6-v2 embedding model.**\n",
"1. **Generate embeddings for each of document in the knowledge library with Huggingface all-MiniLM-L6-v2 embedding model.**\n",
"2. **Identify top K most relevant documents based on user query.**\n",
" - 2.1 **For a query of your interest, generate the embedding of the query using the same embedding model.**\n",
" - 2.2 **Search the indexes of top K most relevant documents in the embedding space using in-memory Faiss search.**\n",
Expand Down Expand Up @@ -689,7 +689,7 @@
"id": "cfe4a131-9b09-4141-96c5-6a13751e99ff",
"metadata": {},
"source": [
"We generate embedings for each of document in the knowledge library with Huggingface all-MiniLM-L6-v2 embedding model.documents"
"We generate embeddings for each of document in the knowledge library with Huggingface all-MiniLM-L6-v2 embedding model.documents"
]
},
{
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10 changes: 5 additions & 5 deletions ...wering_retrieval_augmented_generation/question_answering_Cohere+langchain_jumpstart.ipynb
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Expand Up @@ -253,7 +253,7 @@
"source": [
"## Step 2. Ask a question to LLM without providing the context\n",
"\n",
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and anwering problem. Let's directly ask the model a question and see how they respond."
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and answering problem. Let's directly ask the model a question and see how they respond."
]
},
{
Expand Down Expand Up @@ -390,7 +390,7 @@
"\n",
"To achieve that, we will do following.\n",
"\n",
"1. **Generate embedings for each of document in the knowledge library with Cohere Multilingual embedding model.**\n",
"1. **Generate embeddings for each of document in the knowledge library with Cohere Multilingual embedding model.**\n",
"2. **Identify top K most relevant documents based on user query.**\n",
" - 2.1 **For a query of your interest, generate the embedding of the query using the same embedding model.**\n",
" - 2.2 **Search the indexes of top K most relevant documents in the embedding space using in-memory Faiss search.**\n",
Expand Down Expand Up @@ -741,7 +741,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Firstly, we **generate embedings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**"
"Firstly, we **generate embeddings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**"
]
},
{
Expand Down Expand Up @@ -788,7 +788,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Print out the top 3 most relevant docuemnts as below."
"Print out the top 3 most relevant documents as below."
]
},
{
Expand Down Expand Up @@ -839,7 +839,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Send the top 3 most relevant docuemnts and question into LLM to get a answer."
"Send the top 3 most relevant documents and question into LLM to get a answer."
]
},
{
Expand Down
12 changes: 6 additions & 6 deletions .../question_answering_retrieval_augmented_generation/question_answering_jumpstart_knn.ipynb
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Expand Up @@ -195,7 +195,7 @@
"source": [
"## Step 2. Ask a question to LLM without providing the context\n",
"\n",
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and anwering problem. Let's directly ask the model a question and see how they respond."
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and answering problem. Let's directly ask the model a question and see how they respond."
]
},
{
Expand Down Expand Up @@ -302,7 +302,7 @@
"\n",
"To achieve that, we will do following.\n",
"\n",
"* **Generate embedings for each of document in the knowledge library with the GPT-J-6B embedding model.**\n",
"* **Generate embeddings for each of document in the knowledge library with the GPT-J-6B embedding model.**\n",
"* **Identify top K most relevant documents based on user query.**\n",
" * **For a query of your interest, generate the embedding of the query using the same embedding model.**\n",
" * **Search the indexes of top K most relevant documents in the embedding space using the SageMaker KNN algorithm.**\n",
Expand Down Expand Up @@ -405,7 +405,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2. Generate embedings for each of document in the knowledge library with the GPT-J-6B embedding model.\n",
"### 4.2. Generate embeddings for each of document in the knowledge library with the GPT-J-6B embedding model.\n",
"\n",
"For the purpose of the demo we will use [Amazon SageMaker FAQs](https://aws.amazon.com/sagemaker/faqs/) as knowledge library. The data are formatted in a CSV file with two columns Question and Answer. We use **only** the Answer column as the documents of knowledge library, from which relevant documents are retrieved based on a query. \n",
"\n",
Expand Down Expand Up @@ -541,7 +541,7 @@
"1. Start a training job to index the embedding knowledge data. The underlying algorithm used to index the data is [Faiss](https://github.com/facebookresearch/faiss).\n",
"2. Start an endpoint to take the embedding of the query as input and return the top K nearest indexes of the documents.\n",
"\n",
"**Note.** For the KNN training job, the features are N by P matrix, where N is the number of documetns in the knowledge library, P is the embedding dimension, and each row corresponds to an embedding of a document. The labels are ordinal integers starting from 0. During inference, given an embedding of query, the labels of the top K nearest documents with respect to the query are used as indexes to retrieve the corresponded textual documents.\n",
"**Note.** For the KNN training job, the features are N by P matrix, where N is the number of documents in the knowledge library, P is the embedding dimension, and each row corresponds to an embedding of a document. The labels are ordinal integers starting from 0. During inference, given an embedding of query, the labels of the top K nearest documents with respect to the query are used as indexes to retrieve the corresponded textual documents.\n",
"\n",
"\n"
]
Expand Down Expand Up @@ -649,7 +649,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Deploy the KNN endpoint for retrieving indexes of top K most relevant docuemnts."
"Deploy the KNN endpoint for retrieving indexes of top K most relevant documents."
]
},
{
Expand Down Expand Up @@ -740,7 +740,7 @@
"context_embed_retrieve = construct_context(context_predictions_arr, df_knowledge[\"Answer\"])\n",
"\n",
"print(\n",
" f\"{newline}{bold}Elastic time for computing the embedding of a query and retrieved the top K most relevant docuemnts: {time.time() - start} seconds.{unbold}{newline}\"\n",
" f\"{newline}{bold}Elastic time for computing the embedding of a query and retrieved the top K most relevant documents: {time.time() - start} seconds.{unbold}{newline}\"\n",
")"
]
},
Expand Down
8 changes: 4 additions & 4 deletions ...ion_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
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Expand Up @@ -330,7 +330,7 @@
"\n",
"To achieve that, we will do following.\n",
"\n",
"1. **Generate embedings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**\n",
"1. **Generate embeddings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**\n",
"2. **Identify top K most relevant documents based on user query.**\n",
" - 2.1 **For a query of your interest, generate the embedding of the query using the same embedding model.**\n",
" - 2.2 **Search the indexes of top K most relevant documents in the embedding space using in-memory Faiss search.**\n",
Expand Down Expand Up @@ -670,7 +670,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Firstly, we **generate embedings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**"
"Firstly, we **generate embeddings for each of document in the knowledge library with SageMaker GPT-J-6B embedding model.**"
]
},
{
Expand Down Expand Up @@ -711,7 +711,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Print out the top 3 most relevant docuemnts as below."
"Print out the top 3 most relevant documents as below."
]
},
{
Expand Down Expand Up @@ -756,7 +756,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Send the top 3 most relevant docuemnts and question into LLM to get a answer."
"Send the top 3 most relevant documents and question into LLM to get a answer."
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions ...wering_retrieval_augmented_generation/question_answering_pinecone_llama-2_jumpstart.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -128,7 +128,7 @@
"source": [
"## Step 2. Ask a question to LLM without providing the context\n",
"\n",
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and anwering problem. Let's directly ask the model a question and see how they respond."
"To better illustrate why we need retrieval-augmented generation (RAG) based approach to solve the question and answering problem. Let's directly ask the model a question and see how they respond."
]
},
{
Expand Down Expand Up @@ -335,7 +335,7 @@
"\n",
"To achieve that, we will do following.\n",
"\n",
"* Generate embedings for each of document in the knowledge library with the MiniLM embedding model.\n",
"* Generate embeddings for each of document in the knowledge library with the MiniLM embedding model.\n",
"* Identify top K most relevant documents based on user query.\n",
" * For a query of your interest, generate the embedding of the query using the same embedding model.\n",
" * Search the indexes of top K most relevant documents in the embedding space using the SageMaker KNN algorithm.\n",
Expand Down