---
# generated by https://github.com/hashicorp/terraform-plugin-docs
page_title: "litellm_vector_store Resource - terraform-provider-litellm"
subcategory: ""
description: |-
  Manages a LiteLLM vector store for storing and retrieving vector embeddings.
---

# litellm_vector_store (Resource)

Manages a LiteLLM vector store for storing and retrieving vector embeddings. Vector stores enable semantic search and retrieval-augmented generation (RAG) capabilities using officially supported providers including AWS Bedrock Knowledge Bases, OpenAI Vector Stores, Azure Vector Stores, Vertex AI RAG Engine, and PG Vector.

## Example Usage

### AWS Bedrock Knowledge Base

```terraform
resource "litellm_credential" "bedrock_cred" {
  credential_name = "bedrock-knowledge-base"
  
  credential_info = {
    provider = "bedrock"
    region   = "us-east-1"
  }
  
  credential_values = {
    aws_access_key_id     = var.aws_access_key_id
    aws_secret_access_key = var.aws_secret_access_key
    aws_region            = "us-east-1"
  }
}

resource "litellm_vector_store" "bedrock_kb" {
  vector_store_name        = "bedrock-litellm-website-knowledgebase"
  custom_llm_provider      = "bedrock"
  litellm_credential_name  = litellm_credential.bedrock_cred.credential_name
  
  vector_store_description = "Bedrock vector store for the LiteLLM website knowledgebase"
  
  vector_store_metadata = {
    source = "https://www.litellm.com/docs"
  }
  
  litellm_params = {
    vector_store_id = "T37J8R4WTM"
  }
}
```

### OpenAI Vector Store

```terraform
resource "litellm_credential" "openai_cred" {
  credential_name = "openai-vector-store"
  
  credential_info = {
    provider = "openai"
  }
  
  credential_values = {
    api_key = var.openai_api_key
  }
}

resource "litellm_vector_store" "openai_store" {
  vector_store_name        = "openai-knowledge-base"
  custom_llm_provider      = "openai"
  litellm_credential_name  = litellm_credential.openai_cred.credential_name
  
  vector_store_description = "OpenAI vector store for document search"
  
  vector_store_metadata = {
    environment = "production"
    purpose     = "file-search"
  }
  
  litellm_params = {
    vector_store_id = "vs_687ae3b2439881918b433cb99d10662e"
  }
}
```

### Azure Vector Store

```terraform
resource "litellm_credential" "azure_cred" {
  credential_name = "azure-vector-store"
  
  credential_info = {
    provider = "azure"
  }
  
  credential_values = {
    api_key      = var.azure_openai_key
    api_base     = var.azure_openai_endpoint
    api_version  = "2023-12-01-preview"
  }
}

resource "litellm_vector_store" "azure_store" {
  vector_store_name        = "azure-knowledge-base"
  custom_llm_provider      = "azure"
  litellm_credential_name  = litellm_credential.azure_cred.credential_name
  
  vector_store_description = "Azure vector store for enterprise search"
  
  vector_store_metadata = {
    environment = "production"
    team        = "enterprise"
  }
  
  litellm_params = {
    vector_store_id = "vs_azure_example_id"
  }
}
```

### Vertex AI RAG Engine

```terraform
resource "litellm_credential" "vertex_cred" {
  credential_name = "vertex-rag-engine"
  
  credential_info = {
    provider = "vertex_ai"
    project  = "your-gcp-project"
  }
  
  credential_values = {
    service_account_key = var.gcp_service_account_key
  }
}

resource "litellm_vector_store" "vertex_rag" {
  vector_store_name        = "vertex-rag-corpus"
  custom_llm_provider      = "vertex_ai"
  litellm_credential_name  = litellm_credential.vertex_cred.credential_name
  
  vector_store_description = "Vertex AI RAG Engine for enterprise knowledge"
  
  vector_store_metadata = {
    project     = "your-gcp-project"
    environment = "production"
  }
  
  litellm_params = {
    vector_store_id = "6917529027641081856"
  }
}
```

### PG Vector Store

```terraform
resource "litellm_credential" "pgvector_cred" {
  credential_name = "pgvector-store"
  
  credential_info = {
    provider = "pgvector"
    host     = "your-pgvector-host.com"
  }
  
  credential_values = {
    api_key  = var.pgvector_api_key
    api_base = "https://your-pgvector-host.com"
  }
}

resource "litellm_vector_store" "pgvector_store" {
  vector_store_name        = "postgres-vector-store"
  custom_llm_provider      = "pgvector"
  litellm_credential_name  = litellm_credential.pgvector_cred.credential_name
  
  vector_store_description = "PostgreSQL vector store with pgvector extension"
  
  vector_store_metadata = {
    database    = "vector_db"
    table       = "embeddings"
    environment = "production"
  }
  
  litellm_params = {
    api_base = "https://your-pgvector-host.com"
  }
}
```

## Argument Reference

The following arguments are supported:

* `vector_store_name` - (Required) Name of the vector store.
* `custom_llm_provider` - (Required) The vector store provider. Supported values: "bedrock", "openai", "azure", "vertex_ai", "pgvector".
* `vector_store_description` - (Optional) Description of the vector store.
* `vector_store_metadata` - (Optional) Map of metadata associated with the vector store.
* `litellm_credential_name` - (Optional) Name of the LiteLLM credential to use for authentication.
* `litellm_params` - (Optional, Sensitive) Map of additional parameters specific to the vector store provider. Do not put API keys or other secrets here; this map is stored unencrypted in state. Store secrets in a `litellm_credential` and reference it via `litellm_credential_name`.

## Attributes Reference

In addition to all arguments above, the following attributes are exported:

* `vector_store_id` - The unique identifier of the vector store.
* `created_at` - Timestamp when the vector store was created.
* `updated_at` - Timestamp when the vector store was last updated.

## Supported Providers

The following vector store providers are officially supported by LiteLLM:

* **AWS Bedrock Knowledge Bases** - Managed knowledge bases on AWS Bedrock
* **OpenAI Vector Stores** - OpenAI's native vector store service
* **Azure Vector Stores** - Azure OpenAI vector store integration
* **Vertex AI RAG Engine** - Google Cloud's RAG API for vector search
* **PG Vector** - PostgreSQL with pgvector extension

## Provider-Specific Parameters

### AWS Bedrock Knowledge Base

```terraform
litellm_params = {
  vector_store_id = "T37J8R4WTM"  # Your Bedrock Knowledge Base ID
}
```

### OpenAI Vector Store

```terraform
litellm_params = {
  vector_store_id = "vs_687ae3b2439881918b433cb99d10662e"  # Your OpenAI Vector Store ID
}
```

### Azure Vector Store

```terraform
litellm_params = {
  vector_store_id = "vs_azure_example_id"  # Your Azure Vector Store ID
}
```

### Vertex AI RAG Engine

```terraform
litellm_params = {
  vector_store_id = "6917529027641081856"  # Your Vertex AI RAG Engine ID
}
```

### PG Vector

```terraform
litellm_params = {
  api_base = "https://your-pgvector-host.com"
}
```

## Import

Vector stores can be imported using their ID:

```shell
terraform import litellm_vector_store.example "vector-store-id"
```

## Notes

* Vector stores require appropriate credentials for the chosen provider.
* The `litellm_params` field allows provider-specific configuration.
* Some providers may require additional setup outside of Terraform (e.g., creating Knowledge Bases in AWS Bedrock, Vector Stores in OpenAI).
* Ensure your vector store provider is properly configured and accessible from your LiteLLM instance.
* Only the officially supported providers listed above are guaranteed to work with LiteLLM's vector store integration.
* For the most up-to-date list of supported providers, refer to the [LiteLLM documentation](https://docs.litellm.ai/docs/completion/knowledgebase).
