What is a Vector Store?
Vector Stores allow you to upload documents, PDFs, and text files that provide context and knowledge to your agents. When agents need information, they can retrieve relevant content from your vector store using RAG (Retrieval-Augmented Generation), enabling them to answer questions and make decisions based on your uploaded knowledge base.Vector Stores use semantic search to find the most relevant information from
your uploaded files based on the agent’s query. This allows agents to access
specific knowledge without needing to process entire documents.
How It Works
Upload files to a vector store and provide a clear description of what information it contains. When you connect a vector store to an agent, the agent can automatically retrieve relevant content from your files to answer questions, make informed decisions, and provide accurate responses based on your knowledge base.Add Files
Add documents, PDFs, and text files to build your knowledge base
Semantic Search
Agents automatically find relevant content using semantic search
Provide Context
Give agents access to specific knowledge domains and information
Creating a Vector Store
1
Open Agent Studio
Navigate to Agent Studio and click the Create button to open the “Create new item” modal.
2
Select Vector Store
Choose the Vector Store option from the creation modal.
3
Add a Description
In the vector store editor, provide a clear description of what information
this vector store contains. This description helps the agent understand when
to retrieve information from this vector store.
4
Rename Your Vector Store
Change the default name to something specific that describes what the vector
store contains. This makes it easier to identify and manage multiple vector
stores.
5
Add Files
Click the Add files button to add documents to your vector store.
Supported file types include documents, PDFs, and text files. The files will be processed and indexed for semantic search.
Using Vector Stores in Agents
Add vector stores to agent steps to give agents access to your knowledge base. You can use managed vector stores that you create in Narada, or connect to external vector stores using Amazon Bedrock.1
Open the Add Vector Store Modal
In any agent step, find the Vector Stores section and click to open the “Add vector store” modal.
2
Create New or Add Existing
Choose Create new to create a new vector store, or Add existing to select
from your existing vector stores.
3
Select Vector Store Type
Choose the type of vector store you want to add:
- Managed: Select from your existing vector stores created in Narada. These are vector stores you’ve created and uploaded files to.
- Amazon Bedrock: Connect by providing the required connection information and credentials.
When selecting an external vector store type, you’ll need to provide the connection details specific to that service, such as API keys, region information, and other configuration required to connect to the external vector store.
4
Complete the Form
Fill in the required information based on your vector store type and selection, then click Add vector store or Create vector store to complete the process.
You can add multiple vector stores to a single agent step, giving your agent
access to different knowledge domains simultaneously.
Best Practices
Clear Descriptions
Write detailed descriptions that explain what information is stored. The agent uses this to determine when to retrieve from the vector store.
Organized Files
Upload related files to the same vector store. Group documents by topic or
domain for better organization.
Descriptive Names
Use specific, descriptive names for your vector stores to make them easy to
identify and manage.
Quality Content
Upload well-structured documents with clear, relevant information. Better source material leads to better agent responses.
Common Use Cases
- Research & Documentation
- Company Knowledge
- Product Information
Store research papers, technical documentation, or reference materials that agents can consult when answering questions or performing research tasks.Example: A vector store containing research papers about machine learning that agents can reference when discussing ML concepts.