genkitx-qdrant/lib/index.mjs.map

Genkit AI framework plugin for the Qdrant vector database.

{"version":3,"sources":["../src/index.mts"],"sourcesContent":["import { EmbedderArgument } from '@genkit-ai/ai/embedder';\nimport {\n  CommonRetrieverOptionsSchema,\n  Document,\n  indexerRef,\n  retrieverRef,\n} from '@genkit-ai/ai/retriever';\nimport type { QdrantClientParams, Schemas } from '@qdrant/js-client-rest';\nimport { QdrantClient } from '@qdrant/js-client-rest';\nimport { z, type Genkit } from 'genkit';\nimport { genkitPlugin, type GenkitPlugin } from 'genkit/plugin';\nimport { v5 as uuidv5 } from 'uuid';\n\nconst FilterType: z.ZodType<Schemas['Filter']> = z.any();\n\nconst QdrantRetrieverOptionsSchema = CommonRetrieverOptionsSchema.extend({\n  k: z.number().default(10),\n  filter: FilterType.optional(),\n  scoreThreshold: z.number().optional(),\n});\n\nexport const QdrantIndexerOptionsSchema = z.null().optional();\n\nconst CONTENT_PAYLOAD_KEY = 'content';\nconst METADATA_PAYLOAD_KEY = 'metadata';\nconst CONTENT_TYPE_KEY = '_content_type';\n\n/**\n * Parameters for the Qdrant plugin.\n */\ninterface QdrantPluginParams<E extends z.ZodTypeAny = z.ZodTypeAny> {\n  /**\n   * Parameters for instantiating `QdrantClient`.\n   */\n  clientParams: QdrantClientParams;\n  /**\n   * Name of the Qdrant collection.\n   */\n  collectionName: string;\n  /**\n   * Embedder to use for the retriever and indexer.\n   */\n  embedder: EmbedderArgument<E>;\n  /**\n   * Addtional options for the embedder.\n   */\n  embedderOptions?: z.infer<E>;\n  /**\n   * Document content key in the Qdrant payload.\n   * Default is 'content'.\n   */\n  contentPayloadKey?: string;\n  /**\n   * Document metadata key in the Qdrant payload.\n   * Default is 'metadata'.\n   */\n  metadataPayloadKey?: string;\n  /**\n   * Document data type key in the Qdrant payload.\n   * Default is '_content_type'.\n   * This is used to store the type of content.\n   */\n  dataTypePayloadKey?: string;\n  /**\n   * Additional options when creating a collection.\n   */\n  collectionCreateOptions?: Schemas['CreateCollection'];\n}\n\n/**\n * qdrantRetrieverRef function creates a retriever for Qdrant.\n * @param params The params for the new Qdrant retriever\n * @param params.collectionName The collection name for the Qdrant retriever\n * @param params.displayName  A display name for the retriever. If not specified, the default label will be `Qdrant - <collectionName>`\n * @returns A reference to a Qdrant retriever.\n */\nexport const qdrantRetrieverRef = (\n  collectionName: string,\n  displayName: string | null = null,\n) => {\n  return retrieverRef({\n    name: `qdrant/${collectionName}`,\n    info: {\n      label: displayName ?? `Qdrant - ${collectionName}`,\n    },\n    configSchema: QdrantRetrieverOptionsSchema,\n  });\n};\n\n/**\n * qdrantIndexerRef function creates an indexer for Qdrant.\n * @param params The params for the new Qdrant indexer.\n * @param params.collectionName The collection name for the Qdrant indexer.\n * @param params.displayName  A display name for the indexer. If not specified, the default label will be `Qdrant - <collectionName>`\n * @returns A reference to a Qdrant indexer.\n */\nexport const qdrantIndexerRef = (\n  collectionName: string,\n  displayName: string | null = null,\n) => {\n  return indexerRef({\n    name: `qdrant/${collectionName}`,\n    info: {\n      label: displayName ?? `Qdrant - ${collectionName}`,\n    },\n    configSchema: QdrantIndexerOptionsSchema,\n  });\n};\n\n/**\n * Qdrant plugin that provides the Qdrant retriever\n * and indexer\n */\nexport function qdrant<EmbedderCustomOptions extends z.ZodTypeAny>(\n  params: QdrantPluginParams<EmbedderCustomOptions>[],\n): GenkitPlugin {\n  return genkitPlugin('qdrant', async (ai) => {\n    params.forEach((p) => configureQdrantRetriever(ai, p));\n    params.forEach((p) => configureQdrantIndexer(ai, p));\n  });\n}\n\nexport default qdrant;\n\nexport function configureQdrantRetriever<\n  EmbedderCustomOptions extends z.ZodTypeAny,\n>(ai: Genkit, params: QdrantPluginParams<EmbedderCustomOptions>) {\n  const {\n    embedder,\n    collectionName,\n    embedderOptions,\n    clientParams,\n    contentPayloadKey,\n    metadataPayloadKey,\n  } = params;\n  const client = new QdrantClient(clientParams);\n  const contentKey = contentPayloadKey ?? CONTENT_PAYLOAD_KEY;\n  const metadataKey = metadataPayloadKey ?? METADATA_PAYLOAD_KEY;\n  const dataTypeKey = params.dataTypePayloadKey ?? CONTENT_TYPE_KEY;\n  return ai.defineRetriever(\n    {\n      name: `qdrant/${collectionName}`,\n      configSchema: QdrantRetrieverOptionsSchema,\n    },\n    async (content, options) => {\n      await ensureCollection(params, false, ai);\n      const queryEmbeddings = await ai.embed({\n        embedder,\n        content,\n        options: embedderOptions,\n      });\n      const results = (\n        await client.query(collectionName, {\n          query: queryEmbeddings[0].embedding,\n          limit: options.k,\n          filter: options.filter,\n          score_threshold: options.scoreThreshold,\n          with_payload: [contentKey, metadataKey, dataTypeKey],\n          with_vector: false,\n        })\n      ).points;\n      const documents = results.map((result) => {\n        const content = result.payload?.[contentKey] ?? '';\n        const metadata = {\n          ...(result.payload?.[metadataKey] ?? {}),\n          _similarityScore: result.score,\n        } as Record<string, unknown>;\n        const dataType = result.payload?.[dataTypeKey] ?? 'text';\n        return Document.fromData(\n          content as string,\n          dataType as string,\n          metadata as Record<string, unknown>,\n        ).toJSON();\n      });\n      return {\n        documents,\n      };\n    },\n  );\n}\n\nexport function configureQdrantIndexer<\n  EmbedderCustomOptions extends z.ZodTypeAny,\n>(ai: Genkit, params: QdrantPluginParams<EmbedderCustomOptions>) {\n  const {\n    embedder,\n    collectionName,\n    embedderOptions,\n    clientParams,\n    contentPayloadKey,\n    metadataPayloadKey,\n  } = params;\n  const client = new QdrantClient(clientParams);\n  const contentKey = contentPayloadKey ?? CONTENT_PAYLOAD_KEY;\n  const metadataKey = metadataPayloadKey ?? METADATA_PAYLOAD_KEY;\n  const dataTypeKey = params.dataTypePayloadKey ?? CONTENT_TYPE_KEY;\n  return ai.defineIndexer(\n    {\n      name: `qdrant/${collectionName}`,\n      configSchema: QdrantIndexerOptionsSchema,\n    },\n    async (docs, options) => {\n      await ensureCollection(params, true, ai);\n      const embeddings = await Promise.all(\n        docs.map((doc) =>\n          ai.embed({\n            embedder,\n            content: doc,\n            options: embedderOptions,\n          }),\n        ),\n      );\n      const points = embeddings\n        .map((embeddingArr, i) => {\n          const doc = docs[i];\n          const embeddingDocs = doc.getEmbeddingDocuments(embeddingArr);\n          return embeddingArr.map((docEmbedding, j) => {\n            const embeddingDoc = embeddingDocs[j] || {};\n            const id = uuidv5(JSON.stringify(embeddingDoc), uuidv5.URL);\n            return {\n              id,\n              vector: docEmbedding.embedding,\n              payload: {\n                [contentKey]: embeddingDoc.data,\n                [metadataKey]: embeddingDoc.metadata,\n                [dataTypeKey]: embeddingDoc.dataType,\n              },\n            };\n          });\n        })\n        .reduce((acc, val) => acc.concat(val), []);\n      await client.upsert(collectionName, { points });\n    },\n  );\n}\n\n/**\n * Helper function for creating a Qdrant collection.\n */\nexport async function createQdrantCollection<\n  EmbedderCustomOptions extends z.ZodTypeAny,\n>(params: QdrantPluginParams<EmbedderCustomOptions>, ai) {\n  const { embedder, embedderOptions, clientParams, collectionName } = params;\n  const client = new QdrantClient(clientParams);\n  let collectionCreateOptions = params.collectionCreateOptions;\n  if (!collectionCreateOptions) {\n    const embeddings = await ai.embed({\n      embedder,\n      content: 'SOME_TEXT',\n      options: embedderOptions,\n    });\n    const vector = Array.isArray(embeddings)\n      ? embeddings[0].embedding\n      : embeddings.embedding;\n    collectionCreateOptions = {\n      vectors: {\n        size: vector.length,\n        distance: 'Cosine',\n      },\n    };\n  }\n  return await client.createCollection(collectionName, collectionCreateOptions);\n}\n\n/**\n * Helper function for deleting Qdrant collections.\n */\nexport async function deleteQdrantCollection(params: QdrantPluginParams) {\n  const client = new QdrantClient(params.clientParams);\n  return await client.deleteCollection(params.collectionName);\n}\n\n/**\n * Private helper for ensuring that a Qdrant collection exists.\n */\nasync function ensureCollection(\n  params: QdrantPluginParams,\n  createCollection = true,\n  ai?,\n) {\n  const { clientParams, collectionName } = params;\n  const client = new QdrantClient(clientParams);\n\n  if ((await client.collectionExists(collectionName)).exists) {\n    return;\n  }\n\n  if (createCollection) {\n    await createQdrantCollection(params, ai);\n  } else {\n    throw new Error(\n      `Collection ${collectionName} does not exist. 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