@langchain/redis

This package contains the LangChain.js integrations for Redis through their SDK.

Installation

npm install @langchain/redis @langchain/core

Development

To develop the Redis package, you'll need to follow these instructions:

Install dependencies

pnpm install

Build the package

pnpm build

Or from the repo root:

pnpm build --filter @langchain/redis

Run tests

Test files should live within a tests/ file in the src/ folder. Unit tests should end in .test.ts and integration tests should end in .int.test.ts:

$ pnpm test
$ pnpm test:int

Lint & Format

Run the linter & formatter to ensure your code is up to standard:

pnpm lint && pnpm format

Adding new entrypoints

If you add a new file to be exported, either import & re-export from src/index.ts, or add it to the exports field in the package.json file and run pnpm build to generate the new entrypoint.

Migration Guide: RedisVectorStore to FluentRedisVectorStore

The FluentRedisVectorStore is the recommended approach for new projects. It provides a more powerful and type-safe filtering API with support for complex metadata queries. This guide helps you migrate from the legacy RedisVectorStore to FluentRedisVectorStore.

Key Differences

| Feature | RedisVectorStore | FluentRedisVectorStore | |------------------------------------|--------------------------------------------|----------------------------------------------| | Metadata Schema Definition | Record<string, CustomSchemaField> | MetadataFieldSchema[] | | Inferred Metadata Schema | No, only custom schema supported | Yes, based on metadata when adding documents | | Pre-filter - Definition | String arrays or raw query strings | Type-safe FilterExpression objects | | Pre-filter - Nested conditions | All filters joined by single AND condition | AND, OR, nesting supported | | Pre-filter - conditions types | Numeric, Tag and Text | Numeric, Tag, Text, Geo, Timestamp | | Metadata Storage | JSON blob + optional indexed fields | Individual indexed fields (no JSON blob) |

Step 1: Update Imports

Before (RedisVectorStore):

import { RedisVectorStore } from "@langchain/redis";

After (FluentRedisVectorStore):

import { FluentRedisVectorStore, Tag, Num, Text, Geo } from "@langchain/redis";

Step 2: Convert Metadata Schema

The schema format has changed from an object-based to an array-based structure.

Before (RedisVectorStore):

const customSchema = {
  userId: { type: SchemaFieldTypes.TAG, required: true },
  price: { type: SchemaFieldTypes.NUMERIC, SORTABLE: true },
  description: { type: SchemaFieldTypes.TEXT },
  location: { type: SchemaFieldTypes.GEO }
};

After (FluentRedisVectorStore):

const customSchema = [
  { name: "userId", type: "tag" },
  { name: "price", type: "numeric", options: { sortable: true } },
  { name: "description", type: "text" },
  { name: "location", type: "geo" }
];

Step 3: Update Configuration

Before:

const vectorStore = await RedisVectorStore.fromDocuments(
  documents,
  embeddings,
  {
    redisClient: client,
    indexName: "products",
    customSchema: {
      category: { type: SchemaFieldTypes.TAG },
      price: { type: SchemaFieldTypes.NUMERIC, SORTABLE: true }
    }
  }
);

After:

const vectorStore = await FluentRedisVectorStore.fromDocuments(
  documents,
  embeddings,
  {
    redisClient: client,
    indexName: "products",
    customSchema: [
      { name: "category", type: "tag" },
      { name: "price", type: "numeric", options: { sortable: true } }
    ]
  }
);

Step 4: Update Search Queries with Filters

The filtering API has changed significantly. Instead of passing metadata objects or string arrays, you now use fluent filter expressions.

Before (RedisVectorStore):

// Simple metadata filtering
const results = await vectorStore.similaritySearchVectorWithScoreAndMetadata(
  queryVector,
  5,
  { category: "electronics", price: { min: 100, max: 1000 } }
);

// Or with string-based filters
const results = await vectorStore.similaritySearchVectorWithScore(
  queryVector,
  5,
  ["electronics", "gadgets"]
);

After (FluentRedisVectorStore):

// Custom filter expression with the fluent API
const results = await vectorStore.similaritySearchVectorWithScore(
  queryVector,
  5,
  Tag("category").eq("electronics").and(Num("price").between(100,1000)
  )
);

// Basic filter expression with the fluent API
const results = await vectorStore.similaritySearchVectorWithScore(
  queryVector,
  5,
  Tag("metadata").eq("electronics", "gadgets")
);

Step 5: Database Schema Migration

The FluentRedisVectorStore only supports metadata stored in individual fields, alongside the vector data and content data. It is not compatible with the implementation of the RedisVectorStore which stores metadata as a JSON blob in a single field. The custom schema option of the RedisVectorStore could be migrated to the FluentRedisVectorStore following the instructions in step 2.

To avoid ambiguous results, it's recommended to create a new index with the updated schema and migrate data.

Step 6: Update Application Code

Replace all instances of RedisVectorStore with FluentRedisVectorStore and update filter usage:

Before:

async function searchProducts(query: string, category?: string) {
  const results = await vectorStore.similaritySearchVectorWithScoreAndMetadata(
    await embeddings.embedQuery(query),
    5,
    category ? { category } : undefined
  );
  return results;
}

After:

async function searchProducts(query: string, category?: string) {
  const filter = category ? Tag("category").eq(category) : undefined;
  const results = await vectorStore.similaritySearchVectorWithScore(
    await embeddings.embedQuery(query),
    5,
    filter
  );
  return results;
}