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Matched embeddings contain contextually relevant metadata.\n\n## Installation\n\n```shell\nnpm install @ai-refiner/refiner-js\n```\n\n## OpenAI and Pinecone API Keys.\n\nYou'll need API keys for OpenAI and Pinecone.\n\nOnce you have your API keys, you can either set local ENV variables in a shell:\n\n```shell\n# Required variables\nexport PINECONE_API_KEY=\"API_KEY\"\nexport PINECONE_ENVIRONMENT_NAME=\"ENV_NAME\"\nexport OPENAI_API_KEY=\"API_KEY\"\n\n# Optional variables\nexport OPENAI_ADA_200_DEFAULT_DIMENSIONS=\"1536\"\nexport OPENAI_TEXT_EMBEDDING_MODEL=\"text-embedding-ada-002\"\n```\n\nor you can create a `.env` (dotenv) config file and pass in the file path when initializing the Embeddings class:\n\n```typescript\nimport { Embeddings } from \"@ai-refiner/refiner-js\";\nconst embeddings = new Embeddings(\"/path/to/.env\");\n```\n\nYour .env file should follow key/value format:\n\n```shell\nPINECONE_API_KEY=\"API_KEY\"\nPINECONE_ENVIRONMENT_NAME=\"ENV_NAME\"\nOPENAI_API_KEY=\"API_KEY\"\nOPENAI_ADA_200_DEFAULT_DIMENSIONS=\"1536\"\nOPENAI_TEXT_EMBEDDING_MODEL=\"text-embedding-ada-002\"\n```\n\n## Create Index\n\n```typescript\nconst indexes = new Indexes(\"./.env\");\nconst index = await indexes.createIndex(\"new-index\");\n// creating index: new-index\n\n// Index is being created... Please wait 30 seconds before trying to store embeddings.\n// { success: true }\n```\n\n## Create Embeddings\n\n```typescript\nconst embeddings = new Embeddings(\"/path/to/.env\");\nconst payload: PayloadItem[] = [\n  { id: \"1\", text: \"hello world\", metadata: { key: \"value\" } },\n];\nembeddings.create(payload, \"test-index\");\n// {'upserted_count': 1}\n```\n\n## Semantic Search\n\n```typescript\nconst embeddings = new Embeddings(\"/path/to/.env\");\nconst limit = 10;\nembeddings.search(\"hello\", \"test-index\", limit);\n// {'matches': [...]}\n```\n\n## Loaders - getDocumentFromURL\n\n```typescript\n// Get web page content from a URL and create a document.\nlet loaders = new Loaders();\nlet data = await loaders.getDocumentFromURL(\"https://news.yahoo.com/\");\n// [\n//  Document {\n//    pageContent:\n```\n\n## Loaders - getDocumentFromPDF\n\n```typescript\n// Get web page content from a PDF filepath or blob and create a document.\nlet path = \"/path/to/PDF/example.pdf\";\nlet data = await loader.getDocumentFromPDF(path);\n//  Document {\n//    pageContent:\n```\n\n## Transformers\n\n```typescript\n// Split the document text and create embeddings\nconst embeddings = new Embeddings(\"/path/to/.env\");\nlet transformers = new Transformers();\nlet texts = await transformers.splitText(data);\nlet vectors = [] as any;\ntexts.map(async (item, index) => {\n    // remove loc from metadata. It's not needed and throws a PineconeError when used.\n    if (\"loc\" in item.metadata) delete item.metadata.loc;\n\n    let vector = {\n        id: `id-${index}`,\n        text: item.pageContent,\n        metadata: { ...item.metadata, pageContent: item.pageContent },\n    };\n\n    vectors.push(vector);\n});\nconst created = await embeddings.create(vectors, \"test-index\");\n// { upsertedCount: 251 }\n...\n```\n\n## Document Chatbot Example\n\n```typescript\n// Ask the document questions. Format a Q/A style prompt.\n// Stream the completion results\nimport {\n  ChatCompletionCreateParams,\n  EmbeddingCreateParams,\n} from \"openai/resources\";\nconst question = \"What are the headlines of todays news?\";\n\nlet results = await embeddings.search(question, \"test-index\", 10);\nconst openaiClient = new RefinerOpenAIClient(\"OPENAI_API_KEY\");\n\nconst document = results.matches.map((m) => m.metadata?.pageContent).join(\"\\n\");\n\nconst prompt = `\n  Q: ${question}\\n\n  Using this document answer the question as a friendly chatbot that knows about the details in the document.\n  You can answer questions only with the information in the documents you've been trained on.\n  ${document}\\n\n  A:\n  `;\n\nconst payload: ChatCompletionCreateParams = {\n  model: \"gpt-3.5-turbo\",\n  messages: [{ role: \"user\", content: \"Hello, world!\" }],\n  stream: true,\n};\n\nconst stream = await openaiClient.createCompletion(\n  payload as ChatCompletionCreateParams\n);\nfor await (const part of stream as any) {\n  console.log(part.choices[0].delta);\n}\n```\n\n## CLI\n\nYou can install the [CLI](https://pypi.org/project/refiner-cli/) to `create` and `search` your vectors.\n\n```shell\npip install refiner-cli\n```\n\nThe --help option can be used to learn about the create and search commands.\n\n```shell\nrefiner --help\nrefiner create --help\nrefiner search --help\n```\n","readmeFilename":"README.md"}