--- title: "Bedrock Knowledge Bases" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/bedrock-vector-store source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:08:18.777Z license: CC-BY-4.0 attribution: "Bedrock Knowledge Bases — Claudary (https://claudary.paisolsolutions.com/skills/bedrock-vector-store)" --- # Bedrock Knowledge Bases import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import Image from '@theme/IdealImage'; # Bedrock Knowledge Bases AWS Bedrock Knowledge Bases allows you to connect your LLM's to your organization's data, letting your models retrieve and reference information specific to your business. | Property | Details | |----------|---------| | Description | Bedrock Knowledge Bases connects your data to LLM's, enabling them to retrieve and reference your organization's information in their responses. | | Provider Route on LiteLLM | `bedrock` in the litellm vector_store_registry | | Provider Doc | [AWS Bedrock Knowledge Bases ↗](https://aws.amazon.com/bedrock/knowledge-bases/) | ## Quick Start ### LiteLLM Python SDK ```python showLineNumbers title="Example using LiteLLM Python SDK" import os import litellm from litellm.vector_stores.vector_store_registry import VectorStoreRegistry, LiteLLM_ManagedVectorStore # Init vector store registry with your Bedrock Knowledge Base litellm.vector_store_registry = VectorStoreRegistry( vector_stores=[ LiteLLM_ManagedVectorStore( vector_store_id="YOUR_KNOWLEDGE_BASE_ID", # KB ID from AWS Bedrock custom_llm_provider="bedrock" ) ] ) # Make a completion request using your Knowledge Base response = await litellm.acompletion( model="anthropic/claude-3-5-sonnet", messages=[{"role": "user", "content": "What does our company policy say about remote work?"}], tools=[ { "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"] } ], ) print(response.choices[0].message.content) ``` ### LiteLLM Proxy #### 1. Configure your vector_store_registry ```yaml model_list: - model_name: claude-3-5-sonnet litellm_params: model: anthropic/claude-3-5-sonnet api_key: os.environ/ANTHROPIC_API_KEY vector_store_registry: - vector_store_name: "bedrock-company-docs" litellm_params: vector_store_id: "YOUR_KNOWLEDGE_BASE_ID" custom_llm_provider: "bedrock" vector_store_description: "Bedrock Knowledge Base for company documents" vector_store_metadata: source: "Company internal documentation" ``` On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials. #### 2. Make a request with vector_store_ids parameter ```bash curl http://localhost:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer $LITELLM_API_KEY" \\ -d '{ "model": "claude-3-5-sonnet", "messages": [{"role": "user", "content": "What does our company policy say about remote work?"}], "tools": [ { "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"] } ] }' ``` ```python from openai import OpenAI # Initialize client with your LiteLLM proxy URL client = OpenAI( base_url="http://localhost:4000", api_key="your-litellm-api-key" ) # Make a completion request with vector_store_ids parameter response = client.chat.completions.create( model="claude-3-5-sonnet", messages=[{"role": "user", "content": "What does our company policy say about remote work?"}], tools=[ { "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"] } ] ) print(response.choices[0].message.content) ``` ## Filter Results Filter by metadata attributes. **Operators** (OpenAI-style, auto-translated): - `eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin` **AWS operators** (use directly): - `equals`, `notEquals`, `greaterThan`, `greaterThanOrEquals`, `lessThan`, `lessThanOrEquals`, `in`, `notIn`, `startsWith`, `listContains`, `stringContains` ```python response = await litellm.acompletion( model="anthropic/claude-3-5-sonnet", messages=[{"role": "user", "content": "What are the latest updates?"}], tools=[{ "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"], "filters": { "key": "category", "value": "updates", "operator": "eq" } }] ) ``` ```python response = await litellm.acompletion( model="anthropic/claude-3-5-sonnet", messages=[{"role": "user", "content": "What are the policies?"}], tools=[{ "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"], "filters": { "and": [ {"key": "category", "value": "policy", "operator": "eq"}, {"key": "year", "value": 2024, "operator": "gte"} ] } }] ) ``` ```python response = await litellm.acompletion( model="anthropic/claude-3-5-sonnet", messages=[{"role": "user", "content": "Show me technical docs"}], tools=[{ "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"], "filters": { "or": [ {"key": "category", "value": "api", "operator": "eq"}, {"key": "category", "value": "sdk", "operator": "eq"} ] } }] ) ``` ```python response = await litellm.acompletion( model="anthropic/claude-3-5-sonnet", messages=[{"role": "user", "content": "Find docs"}], tools=[{ "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"], "filters": { "and": [ {"key": "title", "value": "Guide", "operator": "stringContains"}, {"key": "tags", "value": "important", "operator": "listContains"} ] } }] ) ``` ```bash curl http://localhost:4000/v1/chat/completions \\ -H "Content-Type: application/json" \\ -H "Authorization: Bearer $LITELLM_API_KEY" \\ -d '{ "model": "claude-3-5-sonnet", "messages": [{"role": "user", "content": "What are our policies?"}], "tools": [{ "type": "file_search", "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"], "filters": { "and": [ {"key": "department", "value": "engineering", "operator": "eq"}, {"key": "type", "value": "policy", "operator": "eq"} ] } }] }' ``` ## Accessing Search Results See how to access vector store search results in your response: - [Accessing Search Results (Non-Streaming & Streaming)](../completion/knowledgebase#accessing-search-results-citations) ## Further Reading Vector Stores: - [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model) - [Listing available vector stores on litellm proxy](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores) - [How LiteLLM Vector Stores Work](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works) --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/bedrock-vector-store) · https://claudary.paisolsolutions.com