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Merge pull request #493 from ssinghgai/reinvent-kb-features
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Fixing utility error + more refactoring
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mani-aiml authored Feb 21, 2025
2 parents 0c2854a + 83e4dfa commit 0fbc92d
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Amazon Bedrock Knowledge Bases - End to end example using multiple data sources as data source(s)\n",
"# Amazon Bedrock Knowledge Bases - End to end example using multiple data source(s)\n",
"\n",
"This notebook provides sample code for building an empty OpenSearch Serverless (OSS) index,Amazon Bedrock Knowledge Bases and ingest documents into the index from various data sources (S3, Confluence, Sharepoint, Salesforce, and Web). Please note that you can add upto 5 data sources.\n",
"This notebook provides sample code for building an end-to-end example for building a RAG application using Amazon Bedrock Knowledge Bases and ingest documents into the index from various data sources (S3, Confluence, Sharepoint, Salesforce, and Web). Please note that you can add upto 5 data sources.\n",
"\n",
"\n",
"#### Notebook Walkthrough\n",
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"- Ingestion - Knowledge base will split them into smaller chunks (based on the strategy selected), generate embeddings and store it in the associated vectore store.\n",
"\n",
"<!-- ![data_ingestion.png](./images/data_ingestion.png) -->\n",
"<img src=\"../images/data_ingestion.png\" width=50% height=20% />\n",
"<img src=\"./images/data_ingestion.png\" width=50% height=20% />\n",
"\n",
"\n",
"#### Steps: \n",
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"source": [
"\n",
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note:</b> Please make sure to enable `Anthropic Claude 3 Sonnet` and, `Titan Text Embeddings V2` model access in Amazon Bedrock Console.\n",
"<b>Note:</b> Please make sure to enable `amazon.nova-micro-v1:0`, `Anthropic Claude 3 Sonnet`, `amazon.titan-text-express-v1`, `anthropic.claude-3-haiku-20240307-v1:0` and, `Titan Text Embeddings V2` model access in Amazon Bedrock Console.\n",
"<br> ------------------------------------------------------------------------------------------------------------------------------------------------------- </br>\n",
" \n",
"Please run the notebook cell by cell instead of using \"Run All Cells\" option.\n",
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},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install --force-reinstall -q -r ../requirements.txt"
"%pip install --upgrade pip --quiet\n",
"%pip install -r ../requirements.txt --no-deps --quiet\n",
"%pip install -r ../requirements.txt --upgrade --quiet"
]
},
{
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},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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"metadata": {},
"outputs": [],
"source": [
"foundation_model = \"anthropic.claude-3-sonnet-20240229-v1:0\"\n",
"foundation_model = \"amazon.nova-micro-v1:0\"\n",
"\n",
"response = bedrock_agent_runtime_client.retrieve_and_generate(\n",
" input={\n",
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},
"outputs": [],
"source": [
"%pip install --force-reinstall -q -r ../requirements.txt"
"%pip install --upgrade pip --quiet\n",
"%pip install -r ../requirements.txt --no-deps --quiet\n",
"%pip install -r ../requirements.txt --upgrade --quiet"
]
},
{
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},
"outputs": [],
"source": [
"# %store -r kb_id\n",
"kb_id = \"<<knowledge_base_id>>\" # Replace with your knowledge base id here."
"%store -r kb_id\n",
"# kb_id = \"<<knowledge_base_id>>\" # Replace with your knowledge base id here."
]
},
{
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],
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3",
"display_name": "bedrock_env",
"language": "python",
"name": "python3"
},
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"\n",
"\n",
"<!-- ![data_ingestion](./images/data_ingestion.png) -->\n",
"<img src=\"../images/data_ingestion.png\" width=50% height=20% />\n",
"<img src=\"./images/data_ingestion.png\" width=50% height=20% />\n",
"\n",
"\n",
"#### Notebook Walkthrough\n",
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"metadata": {},
"outputs": [],
"source": [
"%pip install --force-reinstall -q -r ../requirements.txt"
"%pip install --upgrade pip --quiet\n",
"%pip install -r ../requirements.txt --no-deps --quiet\n",
"%pip install -r ../requirements.txt --upgrade --quiet"
]
},
{
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],
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3 (Data Science 3.0)",
"display_name": "bedrock_env",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.13"
}
},
"nbformat": 4,
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"metadata": {},
"outputs": [],
"source": [
"%pip install --force-reinstall -q -r ../requirements.txt"
"%pip install --upgrade pip --quiet\n",
"%pip install -r ../requirements.txt --no-deps --quiet\n",
"%pip install -r ../requirements.txt --upgrade --quiet"
]
},
{
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