Amazon Bedrock Embeddings
Amazon Bedrock Embeddings turns text into a numeric fingerprint of its meaning using the models of Amazon Bedrock, the AI service of Amazon Web Services (AWS). With that fingerprint, NappAI can find the documents that say something similar to what you are looking for, even when they use different words, for example in a knowledge base.
You connect the component to a vector database, which uses it both to store your documents and to search them. Use it if your company works with AWS and wants the text to be processed within its own account.
When to use it
- Give an agent the knowledge in your manuals, contracts or FAQs by storing them in a vector database.
- Build an internal search that finds documents by their meaning, not only by exact words.
- Your company already uses AWS and you want to use that same account.
Configuration
The AWS access keys and the region are stored in the credential, so you do not type them in the component.
| Field | What it does |
|---|---|
| Credential (required) | The credential of the service Amazon Bedrock API. Pick one with Choose an option or create one with Add new credential. See Credentials. |
| Model Id | The Amazon Bedrock model that calculates the fingerprints. Default: amazon.titan-embed-text-v1, the only option in the list. |
Advanced options
| Field | What it does |
|---|---|
| Endpoint URL | A connection address for Amazon Bedrock other than the usual one. Leave it empty unless the person who manages your AWS account gives you one. |
Batch processing
With the Batch Mode switch on, the component runs once for each item of the list you connect to Batch Input and returns all the results together as a list. Parallel Executions sets how many items are processed at the same time (1 to 5). Skip if Empty Batch Input skips the component when the list arrives empty, and Skip Branch if Empty Output stops the following components when the result is empty. More in Key concepts.
Output
- Embeddings: the model that calculates the fingerprints, ready to use. Connect it to the Embedding input of a vector database: Astra DB, Cassandra, Chroma DB, Couchbase, Elasticsearch, FAISS, Milvus, MongoDB Vector Store, PGVector, Pinecone, Qdrant, Redis, Supabase Vector Store, Upstash, Vectara, Weaviate Cloud (v4). Other components with an input of this type accept it too, such as Semantic Text Splitter or Text Embedder.
Example
A logistics company, Transportes Duero, wants its assistant to answer using its internal warehouse procedures.
- In a loading flow, split the documents into chunks with Recursive Character Text Splitter and pass them to Chroma DB with Operation set to
Add. - Add Amazon Bedrock Embeddings and connect its Embeddings output to the Embedding input of Chroma DB.
- In the assistant flow, use the same Chroma DB collection with the same Amazon Bedrock Embeddings component to find the chunks that answer each question.