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OpenAI from \"openai\";\nimport type { EmbedFn, ChatFn } from \"./types.js\";\n\n/**\n * Get OpenAI API key from environment\n * @throws Error if OPENAI_API_KEY is not set\n */\nfunction getOpenAIKey(): string {\n const key = process.env.OPENAI_API_KEY;\n if (!key) {\n throw new Error(\n \"OPENAI_API_KEY environment variable is required. Get your key from https://platform.openai.com/api-keys\"\n );\n }\n return key;\n}\n\nlet openaiClient: OpenAI | null = null;\n\n/**\n * Get or create OpenAI client instance\n */\nfunction getClient(): OpenAI {\n if (!openaiClient) {\n openaiClient = new OpenAI({\n apiKey: getOpenAIKey(),\n });\n }\n return openaiClient;\n}\n\n/**\n * Create an embedding function using OpenAI's text-embedding-3-small model.\n * Returns 1536-dimensional vectors by default.\n *\n * @param model - The embedding model to use (default: \"text-embedding-3-small\")\n * @returns An embedding function\n *\n * @example\n * ```ts\n * const embed = createOpenAIEmbed();\n * const vectors = await embed([\"hello\", \"world\"]);\n * // vectors.length === 2, vectors[0].length === 1536\n * ```\n */\nexport function createOpenAIEmbed(\n model = \"text-embedding-3-small\"\n): EmbedFn {\n return async (texts: string[]): Promise => {\n if (texts.length === 0) {\n return [];\n }\n\n const client = getClient();\n\n try {\n const response = await client.embeddings.create({\n model,\n input: texts,\n });\n\n return response.data.map((item) => item.embedding);\n } catch (error) {\n throw new Error(`OpenAI embedding failed: ${error}`);\n }\n };\n}\n\n/**\n * Create a chat function using OpenAI's chat completion API.\n *\n * @returns A chat function\n *\n * @example\n * ```ts\n * const chat = createOpenAIChat();\n * const answer = await chat({\n * model: \"gpt-4o-mini\",\n * system: \"You are a helpful assistant.\",\n * user: \"What is 2+2?\"\n * });\n * ```\n */\nexport function createOpenAIChat(): ChatFn {\n return async ({ model, system, user }): Promise => {\n const client = getClient();\n\n try {\n const response = await client.chat.completions.create({\n model,\n messages: [\n { role: \"system\", content: system },\n { role: \"user\", content: user },\n ],\n temperature: 0.1, // Low temperature for more consistent, factual responses\n });\n\n return response.choices[0]?.message?.content || \"\";\n } catch (error) {\n throw new Error(`OpenAI chat completion failed: ${error}`);\n }\n };\n}\n\n","import { Pinecone } from \"@pinecone-database/pinecone\";\nimport type { UpsertFn, QueryFn, VectorRecord } from \"./types.js\";\n\n/**\n * Get Pinecone configuration from environment\n * @throws Error if required environment variables are not set\n */\nfunction getPineconeConfig(): {\n apiKey: string;\n indexName: string;\n environment: string;\n} {\n const apiKey = process.env.PINECONE_API_KEY;\n const indexName = process.env.PINECONE_INDEX || \"context-window\";\n const environment = process.env.PINECONE_ENVIRONMENT || \"us-east-1\";\n\n if (!apiKey) {\n throw new Error(\n \"PINECONE_API_KEY environment variable is required. Get your key from https://app.pinecone.io/\"\n );\n }\n\n return { apiKey, indexName, environment };\n}\n\nlet pineconeClient: Pinecone | null = null;\n\n/**\n * Get or create Pinecone client instance\n */\nfunction getClient(): Pinecone {\n if (!pineconeClient) {\n const { apiKey } = getPineconeConfig();\n pineconeClient = new Pinecone({ apiKey });\n }\n return pineconeClient;\n}\n\n/**\n * Ensure a Pinecone index exists. Creates it if it doesn't exist.\n *\n * @param dimension - Vector dimension (default: 1536 for OpenAI embeddings)\n * @returns Promise that resolves when index is ready\n *\n * @example\n * ```ts\n * await ensurePineconeIndex(1536);\n * ```\n */\nexport async function ensurePineconeIndex(\n dimension = 1536\n): Promise {\n const client = getClient();\n const { indexName, environment } = getPineconeConfig();\n\n try {\n // Check if index exists\n const indexes = await client.listIndexes();\n const indexExists = indexes.indexes?.some(\n (idx) => idx.name === indexName\n );\n\n if (!indexExists) {\n // Create serverless index\n await client.createIndex({\n name: indexName,\n dimension,\n metric: \"cosine\",\n spec: {\n serverless: {\n cloud: \"aws\",\n region: environment,\n },\n },\n });\n\n // Wait for index to be ready\n let ready = false;\n let attempts = 0;\n const maxAttempts = 30;\n\n while (!ready && attempts < maxAttempts) {\n const description = await client.describeIndex(indexName);\n ready = description.status?.ready ?? false;\n\n if (!ready) {\n await new Promise((resolve) => setTimeout(resolve, 2000));\n attempts++;\n }\n }\n\n if (!ready) {\n throw new Error(\n `Index ${indexName} creation timed out after ${maxAttempts * 2} seconds`\n );\n }\n }\n } catch (error) {\n throw new Error(`Failed to ensure Pinecone index: ${error}`);\n }\n}\n\n/**\n * Create an upsert function for Pinecone.\n *\n * @returns An upsert function\n *\n * @example\n * ```ts\n * const upsert = createPineconeUpsert();\n * await upsert(records, \"my-namespace\");\n * ```\n */\nexport function createPineconeUpsert(): UpsertFn {\n return async (records: VectorRecord[], namespace: string): Promise => {\n if (records.length === 0) {\n return;\n }\n\n const client = getClient();\n const { indexName } = getPineconeConfig();\n\n try {\n const index = client.index(indexName);\n\n // Pinecone recommends batching upserts in chunks of 100\n const batchSize = 100;\n for (let i = 0; i < records.length; i += batchSize) {\n const batch = records.slice(i, i + batchSize);\n await index.namespace(namespace).upsert(batch);\n }\n } catch (error) {\n throw new Error(`Pinecone upsert failed: ${error}`);\n }\n };\n}\n\n/**\n * Create a query function for Pinecone.\n *\n * @returns A query function\n *\n * @example\n * ```ts\n * const query = createPineconeQuery();\n * const matches = await query({\n * vector: [0.1, 0.2, ...],\n * topK: 5,\n * namespace: \"my-namespace\",\n * scoreThreshold: 0.7\n * });\n * ```\n */\nexport function createPineconeQuery(): QueryFn {\n return async ({ vector, topK, namespace, scoreThreshold }) => {\n const client = getClient();\n const { indexName } = getPineconeConfig();\n\n try {\n const index = client.index(indexName);\n\n const response = await index.namespace(namespace).query({\n vector,\n topK,\n includeMetadata: true,\n });\n\n // Filter by score threshold and ensure metadata exists\n const matches = (response.matches || [])\n .filter((match) => match.score !== undefined && match.score >= scoreThreshold)\n .map((match) => ({\n id: match.id,\n score: match.score!,\n metadata: {\n text: (match.metadata?.text as string) || \"\",\n source: (match.metadata?.source as string) || \"\",\n },\n }));\n\n return matches;\n } catch (error) {\n throw new Error(`Pinecone query failed: ${error}`);\n }\n };\n}\n\n","import { readFile } from \"fs/promises\";\nimport { extname } from \"path\";\nimport pdf from \"pdf-parse\";\n\n/**\n * Read a file and extract its text content based on file type.\n *\n * Supported formats:\n * - .txt: Plain text\n * - .md: Markdown\n * - .pdf: PDF documents\n *\n * @param filepath - Path to the file to read\n * @returns Promise resolving to the extracted text\n * @throws Error if file type is unsupported or file cannot be read\n *\n * @example\n * ```ts\n * const text = await readAsText(\"./document.pdf\");\n * console.log(text);\n * ```\n */\nexport async function readAsText(filepath: string): Promise {\n const ext = extname(filepath).toLowerCase();\n\n try {\n switch (ext) {\n case \".txt\":\n case \".md\": {\n const content = await readFile(filepath, \"utf-8\");\n return content;\n }\n\n case \".pdf\": {\n const dataBuffer = await readFile(filepath);\n const pdfData = await pdf(dataBuffer);\n return pdfData.text;\n }\n\n default:\n throw new Error(\n `Unsupported file type: ${ext}. Supported types: .txt, .md, .pdf`\n );\n }\n } catch (error) {\n if (error instanceof Error && error.message.includes(\"Unsupported\")) {\n throw error;\n }\n throw new Error(`Failed to read file ${filepath}: ${error}`);\n }\n}\n\n/**\n * Check if a file is supported based on its extension.\n *\n * @param filepath - Path to check\n * @returns True if the file type is supported\n *\n * @example\n * ```ts\n * if (isSupportedFile(\"doc.pdf\")) {\n * // Process file\n * }\n * ```\n */\nexport function isSupportedFile(filepath: string): boolean {\n const ext = extname(filepath).toLowerCase();\n return [\".txt\", \".md\", \".pdf\"].includes(ext);\n}\n\n","/**\n * Chunk configuration\n */\nexport interface ChunkOptions {\n size: number;\n overlap: number;\n}\n\n/**\n * Split text into overlapping chunks using a sliding window approach.\n *\n * @param text - The text to chunk\n * @param options - Chunking options (size and overlap)\n * @returns Array of text chunks\n *\n * @example\n * ```ts\n * const chunks = chunkText(\"Long document text...\", { size: 1000, overlap: 150 });\n * // Returns array of overlapping text chunks\n * ```\n */\nexport function chunkText(\n text: string,\n options: ChunkOptions = { size: 1000, overlap: 150 }\n): string[] {\n const { size, overlap } = options;\n\n // Handle empty or very short text\n if (!text || text.length === 0) {\n return [];\n }\n\n if (text.length <= size) {\n return [text];\n }\n\n const chunks: string[] = [];\n let start = 0;\n\n while (start < text.length) {\n const end = start + size;\n const chunk = text.slice(start, end);\n chunks.push(chunk);\n\n // If we've reached the end, break\n if (end >= text.length) {\n break;\n }\n\n // Move forward by (size - overlap) to create overlapping chunks\n start += size - overlap;\n }\n\n return chunks;\n}\n\n","import { createHash } from \"crypto\";\n\n/**\n * Generate a stable SHA-1 hash for a given string.\n * Used for creating idempotent chunk IDs.\n *\n * @param input - The string to hash\n * @returns The hexadecimal hash string\n *\n * @example\n * ```ts\n * const id = stableHash(\"my-file.txt#0:first 64 chars...\");\n * // => \"a1b2c3d4e5f6...\"\n * ```\n */\nexport function stableHash(input: string): string {\n return createHash(\"sha1\").update(input, \"utf8\").digest(\"hex\");\n}\n\n","import { stat, readdir } from \"fs/promises\";\nimport { join, basename } from \"path\";\nimport { readAsText, isSupportedFile } from \"./readers.js\";\nimport { chunkText } from \"../util/chunker.js\";\nimport { stableHash } from \"../util/hash.js\";\nimport type { EmbedFn } from \"../adapters/ai/types.js\";\nimport type { UpsertFn, VectorRecord } from \"../adapters/store/types.js\";\n\n/**\n * Options for ingestion\n */\nexport interface IngestOptions {\n /** Input paths (files or directories) */\n inputs: string[];\n /** Vector store namespace */\n namespace: string;\n /** Chunk size */\n chunkSize: number;\n /** Chunk overlap */\n chunkOverlap: number;\n /** Embedding function */\n embed: EmbedFn;\n /** Upsert function */\n upsert: UpsertFn;\n}\n\n/**\n * Recursively collect all supported files from input paths.\n *\n * @param paths - Array of file or directory paths\n * @returns Array of file paths\n */\nasync function collectFiles(paths: string[]): Promise {\n const files: string[] = [];\n\n for (const path of paths) {\n const stats = await stat(path);\n\n if (stats.isFile()) {\n if (isSupportedFile(path)) {\n files.push(path);\n }\n } else if (stats.isDirectory()) {\n const entries = await readdir(path, { withFileTypes: true });\n\n for (const entry of entries) {\n const fullPath = join(path, entry.name);\n\n if (entry.isDirectory()) {\n // Recursively process subdirectories\n const subFiles = await collectFiles([fullPath]);\n files.push(...subFiles);\n } else if (entry.isFile() && isSupportedFile(fullPath)) {\n files.push(fullPath);\n }\n }\n }\n }\n\n return files;\n}\n\n/**\n * Ingest files into the vector store.\n *\n * This function:\n * 1. Recursively collects all supported files from input paths\n * 2. Reads and chunks each file\n * 3. Generates stable IDs for idempotent ingestion\n * 4. Embeds chunks in batches\n * 5. Upserts to the vector store\n *\n * @param options - Ingestion options\n * @returns Promise that resolves when ingestion is complete\n *\n * @example\n * ```ts\n * await ingestPaths({\n * inputs: [\"./docs\"],\n * namespace: \"my-project\",\n * chunkSize: 1000,\n * chunkOverlap: 150,\n * embed: embedFn,\n * upsert: upsertFn\n * });\n * ```\n */\nexport async function ingestPaths(options: IngestOptions): Promise {\n const { inputs, namespace, chunkSize, chunkOverlap, embed, upsert } = options;\n\n // Collect all files\n const files = await collectFiles(inputs);\n\n if (files.length === 0) {\n console.warn(\"No supported files found to ingest\");\n return;\n }\n\n console.log(`Ingesting ${files.length} file(s)...`);\n\n // Process each file\n const allRecords: VectorRecord[] = [];\n\n for (const filepath of files) {\n try {\n // Read file content\n const text = await readAsText(filepath);\n\n if (!text || text.trim().length === 0) {\n console.warn(`Skipping empty file: ${filepath}`);\n continue;\n }\n\n // Chunk the text\n const chunks = chunkText(text, { size: chunkSize, overlap: chunkOverlap });\n\n // Create records with stable IDs\n const source = basename(filepath);\n\n for (let i = 0; i < chunks.length; i++) {\n const chunk = chunks[i];\n // Use first 64 chars for ID stability\n const preview = chunk.slice(0, 64).replace(/\\s+/g, \" \");\n const id = stableHash(`${filepath}#${i}:${preview}`);\n\n // Note: embeddings will be added in batch below\n allRecords.push({\n id,\n values: [], // Placeholder, will be filled after embedding\n metadata: {\n text: chunk,\n source,\n },\n });\n }\n } catch (error) {\n console.error(`Failed to process ${filepath}:`, error);\n // Continue with other files\n }\n }\n\n if (allRecords.length === 0) {\n console.warn(\"No chunks generated from files\");\n return;\n }\n\n console.log(`Generated ${allRecords.length} chunk(s), embedding...`);\n\n // Embed in batches of 100 (to manage rate limits)\n const batchSize = 100;\n for (let i = 0; i < allRecords.length; i += batchSize) {\n const batch = allRecords.slice(i, i + batchSize);\n const texts = batch.map((r) => r.metadata.text);\n\n try {\n const embeddings = await embed(texts);\n\n // Assign embeddings to records\n for (let j = 0; j < batch.length; j++) {\n batch[j].values = embeddings[j];\n }\n } catch (error) {\n throw new Error(`Embedding batch failed: ${error}`);\n }\n }\n\n console.log(`Upserting ${allRecords.length} chunk(s) to vector store...`);\n\n // Upsert to vector store (the upsert function handles batching internally)\n await upsert(allRecords, namespace);\n\n console.log(\"Ingestion complete!\");\n}\n\n","import type {\n CreateContextWindowOptions,\n ContextWindow,\n AskResult,\n NormalizedOptions,\n} from \"../types.js\";\nimport { createOpenAIEmbed, createOpenAIChat } from \"../adapters/ai/openai.js\";\nimport {\n ensurePineconeIndex,\n createPineconeUpsert,\n createPineconeQuery,\n} from \"../adapters/store/pinecone.js\";\nimport { ingestPaths } from \"../io/ingest.js\";\n\n/**\n * Normalize and validate options with defaults\n */\nfunction normalizeOptions(opts: CreateContextWindowOptions): NormalizedOptions {\n const dataPaths = Array.isArray(opts.data) ? opts.data : [opts.data];\n const aiModel = opts.ai?.model || \"gpt-4o-mini\";\n const namespace = opts.vectorStore?.namespace || opts.namespace;\n const chunkSize = opts.chunk?.size || 1000;\n const chunkOverlap = opts.chunk?.overlap || 150;\n const topK = opts.limits?.topK || 8;\n const maxContextChars = opts.limits?.maxContextChars || 8000;\n const scoreThreshold = opts.limits?.scoreThreshold || 0;\n\n if (!namespace) {\n throw new Error(\"namespace is required\");\n }\n\n if (dataPaths.length === 0) {\n throw new Error(\"at least one data path is required\");\n }\n\n return {\n namespace,\n dataPaths,\n aiModel,\n chunkSize,\n chunkOverlap,\n topK,\n maxContextChars,\n scoreThreshold,\n };\n}\n\n/**\n * Pack context from query matches, respecting maxContextChars limit.\n * Returns the packed context text and unique source filenames.\n */\nfunction packContext(\n matches: Array<{ metadata: { text: string; source: string } }>,\n maxContextChars: number\n): { context: string; sources: string[] } {\n const chunks: string[] = [];\n const sources: string[] = [];\n const seenSources = new Set();\n let totalChars = 0;\n\n for (const match of matches) {\n const chunk = match.metadata.text;\n const source = match.metadata.source;\n\n // Check if adding this chunk would exceed the limit\n if (totalChars + chunk.length > maxContextChars) {\n break;\n }\n\n chunks.push(chunk);\n totalChars += chunk.length;\n\n // Track unique sources in order of appearance\n if (!seenSources.has(source)) {\n sources.push(source);\n seenSources.add(source);\n }\n }\n\n return {\n context: chunks.join(\"\\n\\n---\\n\\n\"),\n sources,\n };\n}\n\n/**\n * Build the system prompt for strict RAG\n */\nfunction buildSystemPrompt(context: string): string {\n return `You are a strict RAG assistant.\n\nAnswer ONLY using the provided context. If not fully supported,\nsay: \"I don't know based on the uploaded files.\"\nCite sources like [filename]. Keep answers concise.\n\nContext:\n${context}`;\n}\n\n/**\n * Create a context window instance without ingesting data.\n * This is useful for connecting to an existing Pinecone namespace.\n *\n * @param namespace - The namespace name\n * @param options - Optional configuration overrides\n * @returns A ContextWindow instance\n *\n * @internal\n */\nexport function createContextWindowWithoutIngestion(\n namespace: string,\n options?: {\n aiModel?: string;\n namespace?: string;\n topK?: number;\n maxContextChars?: number;\n scoreThreshold?: number;\n }\n): ContextWindow {\n const config = {\n aiModel: options?.aiModel || \"gpt-4o-mini\",\n namespace: options?.namespace || namespace,\n topK: options?.topK || 8,\n maxContextChars: options?.maxContextChars || 8000,\n scoreThreshold: options?.scoreThreshold || 0,\n };\n\n // Create adapter functions\n const embed = createOpenAIEmbed();\n const chat = createOpenAIChat();\n const query = createPineconeQuery();\n\n // Return ContextWindow instance\n return {\n async ask(question: string): Promise {\n // Embed the question\n const [questionVector] = await embed([question]);\n\n // Query the vector store\n const matches = await query({\n vector: questionVector,\n topK: config.topK,\n namespace: config.namespace,\n scoreThreshold: config.scoreThreshold,\n });\n\n // Pack context and extract sources\n const { context, sources } = packContext(matches, config.maxContextChars);\n\n // If no context found, return \"I don't know\"\n if (!context || context.trim().length === 0) {\n return {\n text: \"I don't know based on the uploaded files.\",\n sources: [],\n };\n }\n\n // Build system prompt\n const systemPrompt = buildSystemPrompt(context);\n\n // Get answer from AI\n const answer = await chat({\n model: config.aiModel,\n system: systemPrompt,\n user: question,\n });\n\n return {\n text: answer,\n sources,\n };\n },\n };\n}\n\n/**\n * Create a context window instance.\n *\n * This is the main entry point for the library. It:\n * 1. Validates and normalizes options\n * 2. Ensures the Pinecone index exists\n * 3. Ingests the provided data files\n * 4. Returns an object with an ask() method for querying\n *\n * @param opts - Configuration options\n * @returns Promise resolving to a ContextWindow instance\n *\n * @example\n * ```ts\n * const cw = await createContextWindow({\n * namespace: \"my-book\",\n * data: [\"./my-book.pdf\"],\n * ai: { provider: \"openai\", model: \"gpt-4o-mini\" },\n * vectorStore: { provider: \"pinecone\" }\n * });\n *\n * const { text, sources } = await cw.ask(\"When was America founded?\");\n * console.log(text, sources);\n * ```\n */\nexport async function createContextWindow(\n opts: CreateContextWindowOptions\n): Promise {\n // Normalize options with defaults\n const config = normalizeOptions(opts);\n\n // Create adapter functions\n const embed = createOpenAIEmbed();\n const chat = createOpenAIChat();\n const upsert = createPineconeUpsert();\n const query = createPineconeQuery();\n\n // Ensure Pinecone index exists (1536 dims for OpenAI embeddings)\n await ensurePineconeIndex(1536);\n\n // Ingest data\n await ingestPaths({\n inputs: config.dataPaths,\n namespace: config.namespace,\n chunkSize: config.chunkSize,\n chunkOverlap: config.chunkOverlap,\n embed,\n upsert,\n });\n\n // Return ContextWindow instance\n return {\n async ask(question: string): Promise {\n // Embed the question\n const [questionVector] = await embed([question]);\n\n // Query the vector store\n const matches = await query({\n vector: questionVector,\n topK: config.topK,\n namespace: config.namespace,\n scoreThreshold: config.scoreThreshold,\n });\n\n // Pack context and extract sources\n const { context, sources } = packContext(matches, config.maxContextChars);\n\n // If no context found, return \"I don't know\"\n if (!context || context.trim().length === 0) {\n return {\n text: \"I don't know based on the uploaded files.\",\n sources: [],\n };\n }\n\n // Build system prompt\n const systemPrompt = buildSystemPrompt(context);\n\n // Get answer from AI\n const answer = await chat({\n model: config.aiModel,\n system: systemPrompt,\n user: question,\n });\n\n return {\n text: answer,\n sources,\n };\n },\n };\n}\n","import type { ContextWindow, CreateContextWindowOptions } from \"../types.js\";\nimport {\n createContextWindow,\n createContextWindowWithoutIngestion,\n} from \"./ContextWindow.js\";\n\n/**\n * Global registry to store context windows by namespace\n */\nconst registry = new Map();\n\n/**\n * Default options for getCtxWindow when connecting to existing namespace\n */\nexport interface GetContextWindowOptions {\n aiModel?: string;\n namespace?: string;\n topK?: number;\n maxContextChars?: number;\n scoreThreshold?: number;\n}\n\n/**\n * Create a context window and register it with the given namespace.\n * The context window can later be retrieved using getCtxWindow.\n *\n * @param opts - Configuration options including namespace\n * @returns Promise resolving to the created ContextWindow instance\n *\n * @example\n * ```ts\n * await createCtxWindow({\n * namespace: \"american-history\",\n * data: [\"./examples/sample/README.md\"],\n * ai: { provider: \"openai\", model: \"gpt-4o-mini\" },\n * vectorStore: { provider: \"pinecone\" }\n * });\n *\n * // Later, retrieve and use it\n * const cw = getCtxWindow(\"american-history\");\n * const result = await cw.ask(\"When was America founded?\");\n * ```\n */\nexport async function createCtxWindow(\n opts: CreateContextWindowOptions\n): Promise {\n const cw = await createContextWindow(opts);\n registry.set(opts.namespace, cw);\n return cw;\n}\n\n/**\n * Retrieve or create a context window by its namespace.\n *\n * If the context window is already in the registry, returns it immediately.\n * Otherwise, creates a new connection to the Pinecone namespace with the given name.\n * This allows you to reconnect to existing data without calling createCtxWindow.\n *\n * @param namespace - The namespace of the context window to retrieve\n * @param options - Optional configuration overrides\n * @returns The ContextWindow instance\n *\n * @example\n * ```ts\n * // Connect to existing Pinecone namespace\n * const cw = getCtxWindow(\"american-history\");\n * const result = await cw.ask(\"When was America founded?\");\n *\n * // With custom options\n * const cw2 = getCtxWindow(\"my-docs\", { topK: 10, aiModel: \"gpt-4\" });\n * ```\n */\nexport function getCtxWindow(\n namespace: string,\n options?: GetContextWindowOptions\n): ContextWindow {\n // Check if already in registry\n let cw = registry.get(namespace);\n\n if (!cw) {\n // Create a new connection to the existing Pinecone namespace\n cw = createContextWindowWithoutIngestion(namespace, options);\n registry.set(namespace, cw);\n }\n\n return cw;\n}\n\n/**\n * Check if a context window with the given namespace exists in the registry.\n *\n * @param namespace - The namespace to check\n * @returns true if the context window exists, false otherwise\n */\nexport function hasCtxWindow(namespace: string): boolean {\n return registry.has(namespace);\n}\n\n/**\n * Remove a context window from the registry.\n *\n * @param namespace - The namespace of the context window to remove\n * @returns true if removed, false if it didn't exist\n */\nexport function deleteCtxWindow(namespace: string): boolean {\n return registry.delete(namespace);\n}\n\n/**\n * Clear all context windows from the registry.\n */\nexport function clearCtxWindows(): void {\n registry.clear();\n}\n\n/**\n * Get all registered context window namespaces.\n *\n * @returns Array of namespace names\n */\nexport function listCtxWindows(): string[] {\n return Array.from(registry.keys());\n}\n\n"]}