storing-and-querying-vectors

Imported from aws/agent-toolkit-for-aws (plugins/aws-data-analytics/skills/storing-and-querying-vectors/SKILL.md) via skills.sh. Install upstream with npx skills add aws/agent-toolkit-for-aws --skill storing-and-querying-vectors. Copyright stays with the author.

Store and Query Vectors with Amazon S3 Vectors

Overview

Amazon S3 Vectors is a cost-effective AWS service for storing and querying vector embeddings at scale. Optimized for long-term storage with subsecond latency for cold queries, as low as 100ms for warm queries.

Decision Guide

• Hundreds/thousands of sustained queries per second (QPS): Wrong tool. Recommend OpenSearch.
• Hybrid search, aggregations, faceted search: Recommend OpenSearch with S3 Vectors as storage engine. For OpenSearch integration, search AWS docs for "Using S3 Vectors with OpenSearch Service".
• Tiered (bulk + hot): S3 Vectors for storage + OpenSearch Serverless for real-time. See references/limits-and-patterns.md.
• Cost-effective storage, infrequent queries, RAG: S3 Vectors is the right fit. Proceed.

For latest guidance, search AWS docs for "S3 Vectors best practices".

Common Tasks

Classify the request before starting:

• Simple query: Existing index, skip to Step 6
• Standard: You MUST list existing indexes first and suggest reusing if relevant. Else, new index + store vectors, follow Steps 2-6
• Migration or multi-tenant: Read references/limits-and-patterns.md first, then Steps 2-6

You MUST execute commands using AWS MCP server tools when connected. Fall back to AWS CLI only if AWS MCP is unavailable. You MUST explain each step to the user before executing.

1. Verify Dependencies

Constraints:

• You MUST check whether AWS MCP tools or AWS CLI is available and inform user if missing
• You MUST confirm target AWS region

2. Create a Vector Bucket

You MUST confirm bucket name with user. Names: 3-63 chars, lowercase letters, numbers, hyphens only. Encryption (SSE-S3 default or SSE-KMS for compliance) is immutable after creation.

aws s3vectors create-vector-bucket \
  --vector-bucket-name <BUCKET_NAME>

Constraints:

• You MUST explain encryption cannot be changed after creation
• For SSE-KMS, KMS key policy MUST grant kms:GenerateDataKey and kms:Decrypt to the S3 Vectors service principal indexing.s3vectors.amazonaws.com. You MUST use full KMS key ARN (not alias). See references/limits-and-patterns.md for command example.

3. Create a Vector Index

Every parameter is immutable after creation.

Pre-flight checklist (confirm ALL with user):

1. Dimension (required, integer 1-4096) -- MUST match embedding model output
2. Distance metric (required) -- cosine or euclidean. Use embedding model's recommended metric;
3. Non-filterable metadata keys (optional, max 10, 1-63 chars) -- Declare at creation or lose forever. For Bedrock Knowledge Bases integration, search AWS docs for "S3 Vectors Bedrock Knowledge Bases prerequisites" to get the required key names.
4. Encryption (optional) -- Inherits from bucket. Override per-index if needed.

aws s3vectors create-index \
  --vector-bucket-name <BUCKET_NAME> \
  --index-name <INDEX_NAME> \
  --dimension <DIM> \
  --distance-metric <cosine|euclidean> \
  --data-type float32 \
  --metadata-configuration '{"nonFilterableMetadataKeys":["<KEY1>","<KEY2>"]}'

Omit --metadata-configuration if no non-filterable keys are needed.

Index names: 3-63 chars, lowercase, numbers, hyphens, dots. Unique within bucket. Filterable metadata: 2 KB limit. Total metadata (filterable + non-filterable combined): 40 KB. See references/metadata-filtering.md.

4. Generate Embeddings (if needed)

Skip to Step 5 (store) or Step 6 (query) if user already has embeddings.

Constraints:

• You MUST ask which embedding model to use if not specified
• You MUST NOT assume a default model
• Dimension MUST match Step 3
• You MUST use the same model for both storing and querying

Generate embeddings with Bedrock invoke-model:

aws bedrock-runtime invoke-model \
  --model-id <MODEL_ID> \
  --content-type application/json \
  --cli-binary-format raw-in-base64-out \
  --body '{"inputText": "your text"}' \
  invoke-model-output.json

You MUST use --cli-binary-format raw-in-base64-out for CLI v2. Output file is required for CLI. The response key is model-dependent (e.g., embedding for Titan, embeddings for Cohere). For Titan, parse with json.load(open('invoke-model-output.json'))['embedding']. Use embedding array as float32 in put-vectors or query-vectors. For batch embedding generation, use AWS SDK or CLI.

5. Put Vectors

aws s3vectors put-vectors \
  --vector-bucket-name <BUCKET_NAME> \
  --index-name <INDEX_NAME> \
  --vectors '[{"key":"<ID>","data":{"float32":[<EMBEDDING>]},"metadata":{"topic":"science"}}]'

Constraints:

• You MUST NOT exceed 500 vectors per call
• You SHOULD batch vectors for cost optimization
• For bulk operations, You SHOULD use an SDK instead of CLI -- vector payloads may be too large for shell arguments
• You MUST implement retry with backoff on 429 TooManyRequestsException
• See references/limits-and-patterns.md for batch patterns

6. Query Vectors

Generate embedding if needed (Step 4), then query:

aws s3vectors query-vectors \
  --vector-bucket-name <BUCKET_NAME> \
  --index-name <INDEX_NAME> \
  --query-vector '{"float32":[<EMBEDDING>]}' \
  --top-k 10 \
  --return-distance

Optional: add --return-metadata and/or --filter '{"topic":{"$eq":"science"}}' (both require GetVectors permission). See references/metadata-filtering.md.

Example response body: {"vectors": [{"key": "id1", "distance": 0.45, "metadata": {"topic": "science"}}, ...], "distanceMetric": "cosine"}

Constraints:

• Using --filter or --return-metadata requires both s3vectors:QueryVectors AND s3vectors:GetVectors IAM permissions. Without GetVectors, these options return 403.

Troubleshooting

Additional Resources

• limits-and-patterns.md -- Multi-tenant patterns, batch ingestion, SSE-KMS, migration
• metadata-filtering.md -- Filter operators, non-filterable metadata, Bedrock KB keys