Prerequisites for using a vector store you created for a knowledge base

1. To configure permissions and create a vector search collection in Amazon OpenSearch Serverless in the AWS Management Console, follow steps 1 and 2 at Working with vector search collections in the Amazon OpenSearch Service Developer Guide. Note the following considerations while setting up your collection:

   1. Give the collection a name and description of your choice.

   2. To make your collection private, select Standard create for the Security section. Then, in the Network access settings section, select VPC as the Access type and choose a VPC endpoint. For more information about setting up a VPC endpoint for an Amazon OpenSearch Serverless collection, see Access Amazon OpenSearch Serverless using an interface endpoint (AWS PrivateLink) in the Amazon OpenSearch Service Developer Guide.

2. Once the collection is created, take note of the Collection ARN for when you create the knowledge base.

3. In the left navigation pane, select Collections under Serverless. Then select your vector search collection.

4. Select the Indexes tab. Then choose Create vector index.

5. In the Vector index details section, enter a name for your index in the Vector index name field.

6. In the Vector fields section, choose Add vector field. Amazon Bedrock stores the vector embeddings for your data source in this field. Provide the following configurations:

   • Vector field name – Provide a name for the field (for example, embeddings).

   • Engine – The vector engine used for search. Select faiss.

   • Dimensions – The number of dimensions in the vector. Refer to the following table to determine how many dimensions the vector should contain:

     Model	Dimensions
     Titan G1 Embeddings - Text	1,536
     Titan V2 Embeddings - Text	1,024, 512, and 256
     Cohere Embed English	1,024
     Cohere Embed Multilingual	1,024

   • Distance metric – The metric used to measure the similarity between vectors. We recommend using Euclidean for floating-point vector embeddings.

7. Expand the Metadata management section and add two fields to configure the vector index to store additional metadata that a knowledge base can retrieve with vectors. The following table describes the fields and the values to specify for each field:

   Field description	Mapping field	Data type	Filterable
   Amazon Bedrock chunks the raw text from your data and stores the chunks in this field.	Name of your choice (for example, text)	String	True
   Amazon Bedrock stores metadata related to your knowledge base in this field.	Name of your choice (for example, bedrock-metadata)	String	False

8. Take note of the names you choose for the vector index name, vector field name, and metadata management mapping field names for when you create your knowledge base. Then choose Create.

1. To create a domain and vector index in OpenSearch Cluster in the AWS Management Console, follow the steps described in Creating and managing OpenSearch Service domains in the Amazon OpenSearch Service Developer Guide.

   Note the following considerations while setting up your domain:

   1. Give the domain a name of your choice.

   2. We recommend that you use the Easy create option to get started quickly with creating your domain.

      Note

      This option gives you a domain with a low throughput. If you have larger workloads that require a higher throughput, choose the Standard Create option. You can adjust the capacity later as required. With this option, you can start with the lowest capacity, which can then be modified later as needed.

   3. For Network, you must choose Public access. OpenSearch domains that are behind a VPC are not supported for your Knowledge Base.

   4. For Version, if you're using binary vector embeddings, Amazon Bedrock Knowledge Bases requires an Engine version of 2.16 or later. In addition, a version of 2.13 or higher is required to create a k-nn index. For more information, see K-NN Search in the Amazon OpenSearch Service developer guide.

   5. We recommend that you use the Dual-stack mode.

   6. We recommend that you enable Fine-grained access control to protect the data in your domain, and further control the permissions that grants your Knowledge base service role access to the OpenSearch domain and make requests.

   7. Leave all other settings to their default values and choose Create to create your domain.

2. Once the domain is created, click it to take note of the Domain ARN and Domain endpoint for when you create the knowledge base.

3. After you've created the domain, you can create a vector index by running the following commands on an OpenSearch dashboard or using curl commands. For more information, see the OpenSearch documentation.

   When running the command:

   • Provide a name for the vector field (for example, embeddings).

   • Make sure that the vector used for search is faiss. nmslib is not supported.

   • For the number of dimensions in the vector, refer to the following table to determine how many dimensions the vector should contain:

     Note

     The Titan V2 Embeddings - Text model supports multiple dimensions. It can also be 256 or 512.

     Model	Dimensions
     Titan G1 Embeddings - Text	1,536
     Titan V2 Embeddings - Text	1,024, 512, and 256
     Cohere Embed English	1,024
     Cohere Embed Multilingual	1,024

   • You can add two fields to configure the vector index to store additional metadata that a knowledge base can retrieve with vectors. The following table describes the fields and the values to specify for each of them.

     Field description	Mapping field
     Amazon Bedrock chunks the raw text from your data and stores the chunks in this field.	Specified as an object, for example, AMAZON_BEDROCK_TEXT_CHUNK.
     Amazon Bedrock stores metadata related to your knowledge base in this field.	Specified as an object, for example, AMAZON_BEDROCK_METADATA.

   PUT /<index-name>
   {
       "settings": {
           "index": {
               "knn": true
           }
       },
       "mappings": {
           "properties": {
               "<vector-name>": {
                   "type": "knn_vector",
                   "dimension": <embedding-dimension>,
                   "data_type": "binary",          # Only needed for binary embeddings
                   "space_type": "l2" | "hamming", # Use l2 for float embeddings and hamming for binary embeddings
                   "method": {
                       "name": "hnsw",
                       "engine": "faiss",
                       "parameters": {
                           "ef_construction": 128,
                           "m": 24
                       }
                   }
               },
   
               "AMAZON_BEDROCK_METADATA": {
                   "type": "text",
                   "index": "false"
               },
               "AMAZON_BEDROCK_TEXT_CHUNK": {
                   "type": "text",
                   "index": "true"            
               }
           }
       }
   }

4. Take note of the domain ARN and endpoint, and the names you choose for the vector index name, vector field name, and metadata management mapping field names for when you create your knowledge base.

1. Create an Amazon Aurora database (DB) cluster, schema, and table by following the steps at Using Aurora PostgreSQL as a knowledge base. When you create the table, configure it with the following columns and data types. You can use column names of your liking instead of the ones listed in the following table. Take note of the column names you choose so that you can provide them during knowledge base setup.

   Column name	Data type	Corresponding field in knowledge base setup (Console)	Corresponding field in knowledge base setup (API)	Description
   id	UUID primary key	Primary key	primaryKeyField	Contains unique identifiers for each record.
   embedding	Vector	Vector field	vectorField	Contains the vector embeddings of the data sources.
   chunks	Text	Text field	textField	Contains the chunks of raw text from your data sources.
   metadata	JSON	Bedrock-managed metadata field	metadataField	Contains metadata required to carry out source attribution and to enable data ingestion and querying

2. (Optional) If you added metadata to your files for filtering, you must also create a column for each metadata attribute in your files and specify the data type (text, number, or boolean). For example, if the attribute genre exists in your data source, you would add a column named genre and specifytext as the data type. During data ingestion, these columns will be populated with the corresponding attribute values.

3. Configure an AWS Secrets Manager secret for your Aurora DB cluster by following the steps at Password management with Amazon Aurora and AWS Secrets Manager.

4. Take note of the following information after you create your DB cluster and set up the secret.

   Field in knowledge base setup (Console)	Field in knowledge base setup (API)	Description
   Amazon Aurora DB Cluster ARN	resourceArn	The ARN of your DB cluster.
   Database name	databaseName	The name of your database
   Table name	tableName	The name of the table in your DB cluster
   Secret ARN	credentialsSecretArn	The ARN of the AWS Secrets Manager key for your DB cluster

1. To create a graph and vector store in Neptune Analytics in the AWS Management Console, follow the steps described in Vector indexing in Neptune Analytics in the Neptune Analytics User Guide.

   Note

   To use Neptune GraphRAG, create an empty Neptune Analytics graph with a vector search index. The vector search index can only be created when the graph is created. When you create a Neptune Analytics graph in the console, you specify the index dimension under Vector search settings near the end of the process.

   Note the following considerations while creating the graph:

   1. Give the graph a name of your choice.

   2. Under Data source, choose Create empty graph, and specify the number of m-NCUs to be allocated. Each m-NCU has around one GiB of memory capacity and corresponding compute and networking.

      Note

      The capacity of your graph can be modified later. We recommend that you start with the smallest instance and later choose a different instance, if needed.

   3. You can leave the default availability settings, and under Network and Security, you must enable public access. Neptune Analytics graphs behind a VPC are not supported.

   4. Under Vector search settings, choose Use vector dimension and specify the number of dimensions in each vector.

      Note

      The number of dimensions in each vector must match the vector dimensions in the embeddings model. Refer to the following table to determine how many dimensions the vector should contain:

      Model	Dimensions
      Titan G1 Embeddings - Text	1,536
      Titan V2 Embeddings - Text	1,024, 512, and 256
      Cohere Embed English	1,024
      Cohere Embed Multilingual	1,024

   5. Leave all other settings to their default and create the graph.

2. Once the graph is created, click it to take note of the Resource ARN and Vector dimensions for when you create the knowledge base.

3. Expand the Metadata management section and add two fields to configure the vector index to store additional metadata that's managed by Amazon Bedrock. The following table describes the fields and the values to specify for each field:

   Field description	Mapping field	Data type	Filterable
   Amazon Bedrock chunks the raw text from your data and stores the chunks in this field.	Name of your choice (for example, text)	String	True
   Amazon Bedrock stores metadata related to your knowledge base in this field, such as the Amazon S3 location of the file that contains this text.	Name of your choice (for example, bedrock-metadata)	String	False

4. Take note of the names you choose for the vector index name, vector field name, and metadata management mapping field names for when you create your knowledge base. Then choose Create.

To set up a secret for your Pinecone configuration

1. Follow the steps at Create an AWS Secrets Manager secret, setting the key as apiKey and the value as the API key to access your Pinecone index.

2. To find your API key, open your Pinecone console and select API Keys.

3. After you create the secret, take note of the ARN of the KMS key.

4. Attach permissions to your service role to decrypt the ARN of the KMS key by following the steps in Permissions to decrypt an AWS Secrets Manager secret for the vector store containing your knowledge base.

5. Later, when you create your knowledge base, enter the ARN in the Credentials secret ARN field.

To set up a secret for your Redis Enterprise Cloud configuration

1. Enable TLS to use your database with Amazon Bedrock by following the steps at Transport Layer Security (TLS).

2. Follow the steps at Create an AWS Secrets Manager secret. Set up the following keys with the appropriate values from your Redis Enterprise Cloud configuration in the secret:

   • username – The username to access your Redis Enterprise Cloud database. To find your username, look under the Security section of your database in the Redis Console.

   • password – The password to access your Redis Enterprise Cloud database. To find your password, look under the Security section of your database in the Redis Console.

   • serverCertificate – The content of the certificate from the Redis Cloud Certificate authority. Download the server certificate from the Redis Admin Console by following the steps at Download certificates.

   • clientPrivateKey – The private key of the certificate from the Redis Cloud Certificate authority. Download the server certificate from the Redis Admin Console by following the steps at Download certificates.

   • clientCertificate – The public key of the certificate from the Redis Cloud Certificate authority. Download the server certificate from the Redis Admin Console by following the steps at Download certificates.

3. After you create the secret, take note of its ARN. Later, when you create your knowledge base, enter the ARN in the Credentials secret ARN field.