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@langchain/redis\n\nThis package contains the LangChain.js integrations for Redis through their SDK.\n\n## Installation\n\n```bash npm2yarn\nnpm install @langchain/redis @langchain/core\n```\n\n## Development\n\nTo develop the Redis package, you'll need to follow these instructions:\n\n### Install dependencies\n\n```bash\npnpm install\n```\n\n### Build the package\n\n```bash\npnpm build\n```\n\nOr from the repo root:\n\n```bash\npnpm build --filter @langchain/redis\n```\n\n### Run tests\n\nTest files should live within a `tests/` file in the `src/` folder. Unit tests should end in `.test.ts` and integration tests should\nend in `.int.test.ts`:\n\n```bash\n$ pnpm test\n$ pnpm test:int\n```\n\n### Lint & Format\n\nRun the linter & formatter to ensure your code is up to standard:\n\n```bash\npnpm lint && pnpm format\n```\n\n#### Adding new entrypoints\n\nIf 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.\n\n## Migration Guide: RedisVectorStore to FluentRedisVectorStore\n\nThe `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`.\n\n### Key Differences\n\n| Feature                            | RedisVectorStore                           | FluentRedisVectorStore                       |\n|------------------------------------|--------------------------------------------|----------------------------------------------|\n| **Metadata Schema Definition**     | `Record<string, CustomSchemaField>`        | `MetadataFieldSchema[]`                      |\n| **Inferred Metadata Schema**       | No, only custom schema supported           | Yes, based on metadata when adding documents |\n| **Pre-filter - Definition**        | String arrays or raw query strings         | Type-safe `FilterExpression` objects         |\n| **Pre-filter - Nested conditions** | All filters joined by single AND condition | AND, OR, nesting supported                   |\n| **Pre-filter - conditions types**  | Numeric, Tag and Text                      | Numeric, Tag, Text, Geo, Timestamp           |\n| **Metadata Storage**               | JSON blob + optional indexed fields        | Individual indexed fields (no JSON blob)     |\n\n### Step 1: Update Imports\n\n**Before (RedisVectorStore):**\n```typescript\nimport { RedisVectorStore } from \"@langchain/redis\";\n```\n\n**After (FluentRedisVectorStore):**\n```typescript\nimport { FluentRedisVectorStore, Tag, Num, Text, Geo } from \"@langchain/redis\";\n```\n\n### Step 2: Convert Metadata Schema\n\nThe schema format has changed from an object-based to an array-based structure.\n\n**Before (RedisVectorStore):**\n```typescript\nconst customSchema = {\n  userId: { type: SchemaFieldTypes.TAG, required: true },\n  price: { type: SchemaFieldTypes.NUMERIC, SORTABLE: true },\n  description: { type: SchemaFieldTypes.TEXT },\n  location: { type: SchemaFieldTypes.GEO }\n};\n```\n\n**After (FluentRedisVectorStore):**\n```typescript\nconst customSchema = [\n  { name: \"userId\", type: \"tag\" },\n  { name: \"price\", type: \"numeric\", options: { sortable: true } },\n  { name: \"description\", type: \"text\" },\n  { name: \"location\", type: \"geo\" }\n];\n```\n\n### Step 3: Update Configuration\n\n**Before:**\n```typescript\nconst vectorStore = await RedisVectorStore.fromDocuments(\n  documents,\n  embeddings,\n  {\n    redisClient: client,\n    indexName: \"products\",\n    customSchema: {\n      category: { type: SchemaFieldTypes.TAG },\n      price: { type: SchemaFieldTypes.NUMERIC, SORTABLE: true }\n    }\n  }\n);\n```\n\n**After:**\n```typescript\nconst vectorStore = await FluentRedisVectorStore.fromDocuments(\n  documents,\n  embeddings,\n  {\n    redisClient: client,\n    indexName: \"products\",\n    customSchema: [\n      { name: \"category\", type: \"tag\" },\n      { name: \"price\", type: \"numeric\", options: { sortable: true } }\n    ]\n  }\n);\n```\n\n### Step 4: Update Search Queries with Filters\n\nThe filtering API has changed significantly. Instead of passing metadata objects or string arrays, you now use fluent filter expressions.\n\n**Before (RedisVectorStore):**\n```typescript\n// Simple metadata filtering\nconst results = await vectorStore.similaritySearchVectorWithScoreAndMetadata(\n  queryVector,\n  5,\n  { category: \"electronics\", price: { min: 100, max: 1000 } }\n);\n\n// Or with string-based filters\nconst results = await vectorStore.similaritySearchVectorWithScore(\n  queryVector,\n  5,\n  [\"electronics\", \"gadgets\"]\n);\n```\n\n**After (FluentRedisVectorStore):**\n```typescript\n// Custom filter expression with the fluent API\nconst results = await vectorStore.similaritySearchVectorWithScore(\n  queryVector,\n  5,\n  Tag(\"category\").eq(\"electronics\").and(Num(\"price\").between(100,1000)\n  )\n);\n\n// Basic filter expression with the fluent API\nconst results = await vectorStore.similaritySearchVectorWithScore(\n  queryVector,\n  5,\n  Tag(\"metadata\").eq(\"electronics\", \"gadgets\")\n);\n```\n\n### Step 5: Database Schema Migration\n\nThe `FluentRedisVectorStore` only supports metadata stored in individual fields, alongside the vector data and content data. \nIt is not compatible with the implementation of the RedisVectorStore which stores metadata as a JSON blob in a single field.\nThe custom schema option of the `RedisVectorStore` could be migrated to the `FluentRedisVectorStore` following the instructions in step 2.\n\nTo avoid ambiguous results, it's recommended to create a new index with the updated schema and migrate data.\n\n### Step 6: Update Application Code\n\nReplace all instances of `RedisVectorStore` with `FluentRedisVectorStore` and update filter usage:\n\n**Before:**\n```typescript\nasync function searchProducts(query: string, category?: string) {\n  const results = await vectorStore.similaritySearchVectorWithScoreAndMetadata(\n    await embeddings.embedQuery(query),\n    5,\n    category ? { category } : undefined\n  );\n  return results;\n}\n```\n\n**After:**\n```typescript\nasync function searchProducts(query: string, category?: string) {\n  const filter = category ? Tag(\"category\").eq(category) : undefined;\n  const results = await vectorStore.similaritySearchVectorWithScore(\n    await embeddings.embedQuery(query),\n    5,\n    filter\n  );\n  return results;\n}\n```","users":{}}