@prexo/ai-chat-sdk/dist/context.cjs.map

AI Chat SDK for building AI chat applications with Context and History

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{ Index } from \"@upstash/vector\";\nimport { VectorDB } from \"./vector-db\";\nimport type {\n  AddContextPayload,\n  VectorPayload,\n  SaveOperationResult,\n} from \"../types\";\n\nexport type ExtVectorConfig = {\n  url: string;\n  token: string;\n};\n\nexport class ExtVector {\n  private vectorDB: VectorDB;\n  private namespace: string;\n\n  constructor(config: ExtVectorConfig, namespace: string) {\n    const index = new Index({\n      url: config.url,\n      token: config.token,\n    });\n    this.vectorDB = new VectorDB(index);\n    this.namespace = namespace;\n  }\n\n  async addContext(input: AddContextPayload): Promise<SaveOperationResult> {\n    // Always use the provided namespace\n    if (!input.options) input.options = {};\n    input.options.namespace = this.namespace;\n    return this.vectorDB.save(input);\n  }\n\n  async removeContext(ids: string[]): Promise<void> {\n    await this.vectorDB.delete({ ids, namespace: this.namespace });\n  }\n\n  async getContext<TMetadata>(\n    payload: Omit<VectorPayload, \"namespace\">,\n  ): Promise<{ data: string; id: string; metadata: TMetadata }[]> {\n    return this.vectorDB.retrieve<TMetadata>({\n      ...payload,\n      namespace: this.namespace,\n    });\n  }\n\n  async resetContext(): Promise<void> {\n    await this.vectorDB.reset({ namespace: this.namespace });\n  }\n}\n","// Constants for the AI Chat SDK\nexport const DEFAULT_CHAT_SESSION_ID = (id?: string) =>\n  `prexo-chat-session/${id ? id : \"default\"}`;\n\nexport const DEFAULT_HISTORY_LENGTH = 50;\nexport const DEFAULT_HISTORY_TTL = 86_400;\n\nexport const DEFAULT_MSG_ID = `msg-${Date.now().toString()}`;\nexport const DEFAULT_SIMILARITY_THRESHOLD = 0.5;\nexport const DEFAULT_TOP_K = 5;\n","import Firecrawl from '@mendable/firecrawl-js';\n\n// Lazily initialize Firecrawl only when an API key is available.\n// This avoids throwing during client-side bundles where process.env is not present.\nlet firecrawlClient: Firecrawl | undefined;\n\nfunction getFirecrawlApiKey(): string | undefined {\n  try {\n    // In Node/bundler environments, process may exist\n    if (typeof process !== 'undefined' && process.env && process.env.FIRECRAWL_API_KEY) {\n      return process.env.FIRECRAWL_API_KEY;\n    }\n  } catch {\n    // ignore\n  }\n  return undefined;\n}\n\nfunction getFirecrawl(): Firecrawl | undefined {\n  const key = getFirecrawlApiKey();\n  if (!key) return undefined;\n  if (!firecrawlClient) {\n    firecrawlClient = new Firecrawl({ apiKey: key });\n  }\n  return firecrawlClient;\n}\n\nexport async function scrapeContent(url: string) {\n  const client = getFirecrawl();\n  if (!client) {\n    throw new Error(\"Firecrawl is not configured. Set FIRECRAWL_API_KEY on the server to enable scraping.\");\n  }\n  const data = await client.scrape(url, { formats: ['markdown', 'summary'] });\n  return data;\n}","import type {\n  AddContextPayload,\n  ResetOptions,\n  SaveOperationResult,\n  VectorPayload,\n} from \"../types\";\nimport { DEFAULT_SIMILARITY_THRESHOLD, DEFAULT_TOP_K } from \"../lib/constants\";\nimport type { Index } from \"@upstash/vector\";\nimport { nanoid } from \"nanoid\";\nimport { scrapeContent } from \"../lib/firecrawl\";\n\n// Helper function to chunk text\nfunction chunkText(text: string, chunkSize = 500, overlap = 50): string[] {\n  const chunks: string[] = [];\n  for (let i = 0; i < text.length; i += chunkSize - overlap) {\n    chunks.push(text.slice(i, i + chunkSize));\n  }\n  return chunks;\n}\n\nexport class VectorDB {\n  private index: Index;\n  constructor(index: Index) {\n    this.index = index;\n  }\n\n  async reset(options?: ResetOptions) {\n    await this.index.reset({ namespace: options?.namespace });\n  }\n\n  async delete({ ids, namespace }: { ids: string[]; namespace?: string }) {\n    await this.index.delete(ids, { namespace });\n  }\n\n  /**\n   * A method that allows you to query the vector database with plain text.\n   * It takes care of the text-to-embedding conversion by itself.\n   * Additionally, it lets consumers pass various options to tweak the output.\n   */\n  // eslint-disable-next-line @typescript-eslint/no-unnecessary-type-parameters\n  async retrieve<TMetadata>({\n    question,\n    similarityThreshold = DEFAULT_SIMILARITY_THRESHOLD,\n    topK = DEFAULT_TOP_K,\n    namespace,\n    contextFilter,\n    queryMode,\n  }: VectorPayload): Promise<\n    { data: string; id: string; metadata: TMetadata }[]\n  > {\n    const index = this.index;\n    const result = await index.query<Record<string, string>>(\n      {\n        ...(typeof question === \"string\"\n          ? { data: question }\n          : { vector: question }),\n        topK,\n        includeData: true,\n        includeMetadata: true,\n        ...(typeof contextFilter === \"string\" && { filter: contextFilter }),\n        queryMode,\n      },\n      { namespace },\n    );\n    console.log(\"Vector Retrive: \", result)\n    const allValuesUndefined = result.every(\n      (embedding) => embedding.data === undefined,\n    );\n\n    if (allValuesUndefined) {\n      console.error(\n        \"There is no answer for this question in the provided context.\",\n      );\n\n      return [\n        {\n          data: \"There is no answer for this question in the provided context.\",\n          id: \"error\",\n          metadata: {} as TMetadata,\n        },\n      ];\n    }\n\n    const facts = result\n      .filter((x) => x.score >= similarityThreshold)\n      .map((embedding) => ({\n        data: embedding.data ?? \"\",\n        id: embedding.id.toString(),\n        metadata: embedding.metadata as TMetadata,\n      }));\n\n    return facts;\n  }\n\n  /**\n   * A method that allows you to add various data types into a vector database.\n   * It supports plain text, embeddings, PDF, HTML, Text file and CSV. Additionally, it handles text-splitting for CSV, PDF and Text file.\n   */\n  async save(input: AddContextPayload): Promise<SaveOperationResult> {\n    const { namespace } = input.options ?? {};\n    if (input.type === \"text\") {\n      try {\n        const returnId = input.id ?? nanoid();\n        await this.index.upsert(\n          {\n            data: input.data,\n            id: returnId,\n            metadata: input.options?.metadata,\n          },\n          { namespace },\n        );\n\n        return { success: true, ids: [returnId.toString()] };\n      } catch (error) {\n        return {\n          success: false,\n          error: JSON.stringify(error, Object.getOwnPropertyNames(error)),\n        };\n      }\n    } else if (input.type === \"embedding\") {\n      try {\n        const returnId = input.id ?? nanoid();\n        await this.index.upsert(\n          {\n            vector: input.data,\n            data: input.text,\n            id: returnId,\n            metadata: input.options?.metadata,\n          },\n          { namespace },\n        );\n\n        return { success: true, ids: [returnId.toString()] };\n      } catch (error) {\n        return {\n          success: false,\n          error: JSON.stringify(error, Object.getOwnPropertyNames(error)),\n        };\n      }\n    } else {\n      try {\n        // Determine the file source (string path or URL)\n        let fileSource: string | undefined = undefined;\n        if (\"fileSource\" in input && typeof input.fileSource === \"string\") {\n          fileSource = input.fileSource;\n        } else if (\"source\" in input && typeof input.source === \"string\") {\n          fileSource = input.source;\n        }\n        if (!fileSource) {\n          throw new Error(\"No valid file source provided for extractText\");\n        }\n\n        // extractText should return a string (the extracted text)\n        const res = await scrapeContent(fileSource);\n        if (!res?.markdown && !res?.summary) {\n          throw new Error(\"Failed to extract data from the provided source\");\n        }\n\n        // chunk the extracted text\n        const transformArgs = \"config\" in input ? input.config : {};\n        // If transformArgs has chunkSize/overlap, use them, else default\n        const chunkSize =\n          (transformArgs && (transformArgs as any).chunkSize) || 500;\n        const overlap =\n          (transformArgs && (transformArgs as any).overlap) || 100;\n\n        // Prefer markdown for chunking; fallbacks for possible shapes\n        const markdown =\n          (res as any).markdown ??\n          (res as any)?.output?.markdown ??\n          (res as any)?.output?.txt ??\n          \"\";\n        const summary =\n          (res as any).summary ?? (res as any)?.output?.summary;\n\n        const chunks = chunkText(markdown, chunkSize, overlap);\n\n        // upsert each chunk\n        const ids: string[] = [];\n        for (const chunk of chunks) {\n          const id = nanoid();\n          await this.index.upsert(\n            {\n              data: chunk,\n              id,\n              metadata: {\n                ...(input.options?.metadata ?? {}),\n                ...(summary ? { summary } : {}),\n              },\n            },\n            { namespace },\n          );\n          ids.push(id);\n        }\n\n        return { success: true, ids };\n      } catch (error) {\n        console.error(error);\n        return {\n          success: false,\n          error: JSON.stringify(error, Object.getOwnPropertyNames(error)),\n        };\n      }\n    }\n  }\n}\n","\"use client\";\nimport type { BaseVectorContext, GetContextClientParams } from \"../types\";\nimport { ExtVector } from \"./ext-vector\";\n\nexport const getContextClient = (\n  params?: GetContextClientParams,\n): BaseVectorContext | undefined => {\n  const vectorUrl = params?.vector?.url;\n  const vectorToken = params?.vector?.token;\n  // Prefer container_id as namespace, fallback to provided vector.namespace\n  const namespace = params?.vector?.namespace;\n\n  if (vectorUrl && vectorToken && namespace) {\n    return new ExtVector({ url: vectorUrl, token: vectorToken }, namespace);\n  }\n};\n"]}