> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# RedisVectorStore integration
> Integrate with the RedisVectorStore using LangChain JavaScript.
**Compatibility**: Only available on Node.js.
[Redis](https://redis.io/) is a fast open source, in-memory data store. As part of the [Redis Stack](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/), [RediSearch](https://redis.io/docs/latest/develop/interact/search-and-query/) is the module that enables vector similarity semantic search, as well as many other types of searching.
This guide provides a quick overview for getting started with Redis [vector stores](/oss/javascript/integrations/vectorstores). For detailed documentation of all `RedisVectorStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-redis/RedisVectorStore).
## Overview
### Integration details
| Class | Package | [PY support](https://python.langchain.com/docs/integrations/vectorstores/redis/) | Downloads | Version |
| :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------- | :------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------: |
| [`RedisVectorStore`](https://reference.langchain.com/javascript/langchain-redis/RedisVectorStore) | [`@langchain/redis`](https://www.npmjs.com/package/@langchain/redis) | ✅ |  |  |
## Setup
To use Redis vector stores, set up a Redis Stack instance with RediSearch enabled and install `@langchain/redis` and `@langchain/core`. Install the [`redis`](https://www.npmjs.com/package/redis) Node.js client when you pass your own `createClient` instance to `RedisVectorStore`.
This guide uses [OpenAI embeddings](/oss/javascript/integrations/embeddings/openai) as an example. You can use [other supported embeddings models](/oss/javascript/integrations/embeddings) instead.
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/redis @langchain/core redis @langchain/openai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/redis @langchain/core redis @langchain/openai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/redis @langchain/core redis @langchain/openai
```
You can set up a Redis instance locally with Docker by following [these instructions](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/docker/#redisredis-stack).
### Credentials
Set the `REDIS_URL` environment variable:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
process.env.REDIS_URL = "your-redis-url";
```
If you are using OpenAI embeddings for this guide, set your OpenAI key as well:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
```
If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"
```
## Instantiation
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { RedisVectorStore } from "@langchain/redis";
import { OpenAIEmbeddings } from "@langchain/openai";
import { createClient } from "redis";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const client = createClient({
url: process.env.REDIS_URL ?? "redis://localhost:6379",
});
await client.connect();
const vectorStore = new RedisVectorStore(embeddings, {
redisClient: client,
indexName: "langchainjs-testing",
});
```
## Manage vector store
### Add items to vector store
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import type { Document } from "@langchain/core/documents";
const document1: Document = {
pageContent: "The powerhouse of the cell is the mitochondria",
metadata: { type: "example" },
};
const document2: Document = {
pageContent: "Buildings are made out of brick",
metadata: { type: "example" },
};
const document3: Document = {
pageContent: "Mitochondria are made out of lipids",
metadata: { type: "example" },
};
const document4: Document = {
pageContent: "The 2024 Olympics are in Paris",
metadata: { type: "example" },
};
const documents = [document1, document2, document3, document4];
await vectorStore.addDocuments(documents);
```
Top-level document ids are currently not supported, but you can delete documents by providing their IDs directly to the vector store.
## Query vector store
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
### Query directly
Performing a simple similarity search can be done as follows:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const similaritySearchResults = await vectorStore.similaritySearch(
"biology",
2
);
for (const doc of similaritySearchResults) {
console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
```
Filtering will currently look for any metadata key containing the provided string.
If you want to execute a similarity search and receive the corresponding scores you can run:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const similaritySearchWithScoreResults =
await vectorStore.similaritySearchWithScore("biology", 2);
for (const [doc, score] of similaritySearchWithScoreResults) {
console.log(
`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(
doc.metadata
)}]`
);
}
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
* [SIM=0.835] The powerhouse of the cell is the mitochondria [{"type":"example"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"type":"example"}]
```
### Query by turning into retriever
You can also transform the vector store into a [retriever](/oss/javascript/langchain/retrieval) for easier usage in your chains.
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const retriever = vectorStore.asRetriever({
k: 2,
});
await retriever.invoke("biology");
```
```javascript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
Document {
pageContent: 'The powerhouse of the cell is the mitochondria',
metadata: { type: 'example' },
id: undefined
},
Document {
pageContent: 'Mitochondria are made out of lipids',
metadata: { type: 'example' },
id: undefined
}
]
```
### Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
* [Build a RAG app with LangChain](/oss/javascript/langchain/rag).
* [Agentic RAG](/oss/javascript/langgraph/agentic-rag)
* [Retrieval docs](/oss/javascript/langchain/retrieval)
## Deleting documents
You can delete documents from the vector store in two ways:
### Delete all documents
You can delete an entire index and all its documents with the following command:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
await vectorStore.delete({ deleteAll: true });
```
### Delete specific documents by ID
You can also delete specific documents by providing their IDs. Note that the configured key prefix will be automatically added to the IDs you provide:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// The key prefix will be automatically added to each ID
await vectorStore.delete({ ids: ["doc1", "doc2", "doc3"] });
```
## Closing connections
Make sure you close the client connection when you are finished to avoid excessive resource consumption:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
await client.disconnect();
```
## Advanced features
### Custom schema and metadata filtering
The Redis vector store supports custom schema definitions for metadata fields, enabling more efficient filtering and searching. This feature allows you to define specific field types and validation rules for your metadata.
#### Defining a custom schema
You can define a custom schema when creating your vector store to specify field types, validation rules, and indexing options:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { RedisVectorStore } from "@langchain/redis";
import type { RedisVectorStoreConfig } from "@langchain/redis";
import { OpenAIEmbeddings } from "@langchain/openai";
import { SchemaFieldTypes } from "redis";
import { createClient } from "redis";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const client = createClient({
url: process.env.REDIS_URL ?? "redis://localhost:6379",
});
await client.connect();
// Define custom schema for metadata fields
const customSchema:RedisVectorStoreConfig["customSchema"] = {
userId: {
type: SchemaFieldTypes.TEXT,
required: true,
SORTABLE: true,
},
category: {
type: SchemaFieldTypes.TAG,
SORTABLE: true,
SEPARATOR: ",",
},
score: {
type: SchemaFieldTypes.NUMERIC,
SORTABLE: true,
},
tags: {
type: SchemaFieldTypes.TAG,
SEPARATOR: ",",
CASESENSITIVE: true,
},
description: {
type: SchemaFieldTypes.TEXT,
NOSTEM: true,
WEIGHT: 2.0,
},
};
const vectorStoreWithSchema = new RedisVectorStore(embeddings, {
redisClient: client,
indexName: "langchainjs-custom-schema",
customSchema,
});
```
#### Schema field types
The custom schema supports three main field types:
* **TEXT**: Full-text searchable fields with optional stemming, weighting, and sorting
* **TAG**: Categorical fields for exact matching, with support for multiple values and custom separators
* **NUMERIC**: Numeric fields supporting range queries and sorting
#### Field configuration options
Each field can be configured with various options:
* `required`: Whether the field must be present in metadata (default: false)
* `SORTABLE`: Enable sorting on this field (default: undefined)
* `SEPARATOR`: For TAG fields, specify the separator for multiple values (default: ",")
* `CASESENSITIVE`: For TAG fields, enable case-sensitive matching (Redis expects `true`, not boolean)
* `NOSTEM`: For TEXT fields, disable stemming (Redis expects `true`, not boolean)
* `WEIGHT`: For TEXT fields, specify search weight (default: 1.0)
#### Adding documents with schema validation
When using a custom schema, documents are automatically validated against the defined schema:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import type { Document } from "@langchain/core/documents";
const documentsWithMetadata: Document[] = [
{
pageContent: "Advanced JavaScript techniques for modern web development",
metadata: {
userId: "user123",
category: "programming",
score: 95,
tags: ["javascript", "web-development", "frontend"],
description: "Comprehensive guide to JavaScript best practices",
},
},
{
pageContent: "Machine learning fundamentals and applications",
metadata: {
userId: "user456",
category: "ai",
score: 88,
tags: ["machine-learning", "python", "data-science"],
description: "Introduction to ML concepts and practical applications",
},
},
{
pageContent: "Database optimization strategies for high performance",
metadata: {
userId: "user789",
category: "database",
score: 92,
tags: ["database", "optimization", "performance"],
description: "Advanced techniques for database performance tuning",
},
},
];
// This will validate each document's metadata against the custom schema
await vectorStoreWithSchema.addDocuments(documentsWithMetadata);
```
#### Advanced similarity search with metadata filtering
The custom schema enables powerful metadata filtering capabilities using the `similaritySearchVectorWithScoreAndMetadata` method:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Search with TAG filtering
const tagFilterResults =
await vectorStoreWithSchema.similaritySearchVectorWithScoreAndMetadata(
await embeddings.embedQuery("programming tutorial"),
3,
{
category: "programming", // Exact tag match
tags: ["javascript", "frontend"], // Multiple tag OR search
}
);
console.log("Tag filter results:");
for (const [doc, score] of tagFilterResults) {
console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent}`);
console.log(` Metadata: ${JSON.stringify(doc.metadata)}`);
}
```
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Search with NUMERIC range filtering
const numericFilterResults =
await vectorStoreWithSchema.similaritySearchVectorWithScoreAndMetadata(
await embeddings.embedQuery("high quality content"),
5,
{
score: { min: 90, max: 100 }, // Score between 90 and 100
category: ["programming", "ai"], // Multiple categories
}
);
console.log("Numeric filter results:");
for (const [doc, score] of numericFilterResults) {
console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent}`);
console.log(
` Score: ${doc.metadata.score}, Category: ${doc.metadata.category}`
);
}
```
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Search with TEXT field filtering
const textFilterResults =
await vectorStoreWithSchema.similaritySearchVectorWithScoreAndMetadata(
await embeddings.embedQuery("development guide"),
3,
{
description: "comprehensive guide", // Text search in description field
score: { min: 85 }, // Minimum score of 85
}
);
console.log("Text filter results:");
for (const [doc, score] of textFilterResults) {
console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent}`);
console.log(` Description: ${doc.metadata.description}`);
}
```
#### Numeric range query options
For numeric fields, you can specify various range queries:
```txt theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// Exact value match
{ score: 95 }
// Range with both min and max
{ score: { min: 80, max: 100 } }
// Only minimum value
{ score: { min: 90 } }
// Only maximum value
{ score: { max: 95 } }
```
#### Error handling and validation
The custom schema provides automatic validation with helpful error messages:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
try {
// This will fail validation - missing required userId field
const invalidDoc: Document = {
pageContent: "Some content without required metadata",
metadata: {
category: "test",
// Missing required userId field
},
};
await vectorStoreWithSchema.addDocuments([invalidDoc]);
} catch (error) {
console.log("Validation error:", error.message);
// Output: "Required metadata field 'userId' is missing"
}
try {
// This will fail validation - wrong type for score field
const wrongTypeDoc: Document = {
pageContent: "Content with wrong metadata type",
metadata: {
userId: "user123",
score: "not-a-number", // Should be number, not string
},
};
await vectorStoreWithSchema.addDocuments([wrongTypeDoc]);
} catch (error) {
console.log("Type validation error:", error.message);
// Output: "Metadata field 'score' must be a number, got string"
}
```
#### Performance benefits
Using custom schema provides several performance advantages:
1. **Indexed Metadata Fields**: Individual metadata fields are indexed separately, enabling fast filtering
2. **Type-Optimized Queries**: Numeric and tag fields use optimized query structures
3. **Reduced Data Transfer**: Only relevant fields are returned in search results
4. **Better Query Planning**: Redis can optimize queries based on field types and indexes
#### Backward compatibility
The custom schema feature is fully backward compatible. Existing Redis vector stores without custom schemas will continue to work exactly as before. You can gradually migrate to custom schemas for new indexes or when rebuilding existing ones.
***
## API reference
For detailed documentation of all `RedisVectorStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-redis/RedisVectorStore).
***
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