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You can build this RAG architecture with Terraform by storing source documents in S3, using Amazon Bedrock Knowledge Bases to manage ingestion and retrieval, and choosing OpenSearch Serverless as the vector store. The important caveat is that AWS’s published Terraform RAG pattern is not a ready-made implementation of that exact stack: its example uses LangChain with Aurora PostgreSQL-Compatible. Treat the Knowledge Bases and OpenSearch Serverless design as a separate implementation whose provider resources and arguments you must verify against the versions you pin.
How the S3-to-answer data path works
- Store source documents in S3. Configure the bucket or permitted object scope as the Knowledge Base data source.
- Configure the Knowledge Base. Bedrock Knowledge Bases manages the connected-data ingestion and retrieval flow. Its storage configuration identifies the OpenSearch Serverless collection, vector index, and field mappings.
- Ingest and index the content. The configured embedding model and index fields must agree with the Knowledge Base configuration. Field names and embedding setup are choices to make for the deployment, not universal constants.
- Retrieve relevant content for an application. The Knowledge Base uses the indexed data during retrieval. This article covers provisioning the infrastructure boundary, not an application’s prompt design or response evaluation.
Keep the S3 source, embedding configuration, index mapping, and Knowledge Base settings consistent. A mismatch in the index fields or embedding setup can prevent the intended documents from being indexed or retrieved as expected.
What Terraform should provision—and what AWS’s example does not provide
Terraform can describe the infrastructure for this design, but AWS’s published Terraform RAG pattern should not be copied as though it were an exact S3, Bedrock Knowledge Bases, and OpenSearch Serverless template. That pattern demonstrates LangChain with Aurora PostgreSQL-Compatible as its vector store; it identifies Bedrock Knowledge Bases and OpenSearch Service as alternatives.
| Implementation path | What it provides | What to plan for |
|---|---|---|
| AWS’s Terraform RAG example | A Terraform RAG pattern using LangChain and Aurora PostgreSQL-Compatible | It is not the exact managed Knowledge Bases and OpenSearch Serverless stack described here. |
| Bedrock Knowledge Bases with OpenSearch Serverless | Managed Knowledge Base ingestion and retrieval with OpenSearch Serverless as a supported vector-store option | Configure the collection, vector index, field mappings, service-role permissions, and network access for your deployment. |
Use the AWS provider documentation for the provider version you intend to pin before writing resource blocks. The available source material does not establish a complete Terraform module, resource argument set, or provider version for this exact combination, so presenting copy-and-run Terraform here would imply verification that has not been established.
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How to organize the infrastructure implementation
- Pin and validate the provider. Choose a provider version, then confirm its current resource documentation and supported arguments for each AWS service component you plan to manage.
- Define the data path first. Decide which S3 content is in scope, which embedding model the Knowledge Base will use, and how the vector index fields will map to the Knowledge Base configuration.
- Establish the collection controls. Design the OpenSearch Serverless encryption, network, and data access policies as separate controls. Decide whether the collection is private before writing its network policy.
- Grant the Bedrock service role only the access it needs. Its trust relationship must permit Bedrock to assume the role. Its permissions must cover the selected embedding model, S3 data source, and vector store.
- Validate policy alignment before ingestion. Check the role’s identity-based permissions, the OpenSearch Serverless data access policy, and the resource ARNs together. The OpenSearch data access policy must grant the service role the required index access.
- Deploy, ingest, and verify. Confirm that the Knowledge Base can access its source and vector store, then verify that the expected content is available for retrieval before connecting an application.
- Remove temporary resources when finished. For experiments, clean up the collection and its associated policies when they are no longer needed.
How to scope the Knowledge Base service role
There are two distinct permission relationships to get right. First, the role’s trust policy lets the Bedrock service assume the role. Second, permissions attached to the role let it perform the operations required for the selected embedding model, S3 data source, and vector store. An overly broad trust or permission policy is not a substitute for a correctly scoped one.
OpenSearch Serverless adds a separate data-access-policy requirement: grant the Bedrock service role the necessary access to the relevant index. Align that policy with the index and resource ARNs used in the Knowledge Base setup. A role can have identity-based permissions and still lack the collection’s required data access if the OpenSearch policy does not grant it.
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Choose public or private collection access deliberately
For a private OpenSearch Serverless collection, AWS documents access through a PrivateLink VPC endpoint and a network policy that permits Bedrock as a source service. That is a separate concern from the role’s permissions and the collection’s data access policy.
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- Encryption policy: controls encryption for the collection.
- Data access policy: grants the role the required collection or index operations.
An AWS tutorial demonstrates a public network policy in its example. That example should not be treated as a production default; choose a posture that matches your deployment’s access requirements.
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Plan for collection lifecycle costs
AWS’s OpenSearch Serverless and Knowledge Bases tutorial notes that idle collections accrue OCU-hour charges. The amount depends on current pricing in the deployment Region and the workload; no price figure is established here. Check current regional pricing before deploying, and remove temporary collections and policies after testing to avoid leaving unused resources running.
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