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framework for connecting your data to large language models","keywords":["framework","large language model","data","retrieval agumented generation","rag","llm","generative ai","vector database","pinecone","pgvector","epsilla","gpt","gpt-4","openai","ingestion","models"],"author":{"name":"Axflow","url":"https://axflow.dev"},"license":"MIT","readme":"# Axgen ![Github CI](https://github.com/axflow/axflow/workflows/Github%20CI/badge.svg)\n\nAxgen is a framework for connecting your data to large language models.\n\nIngest, structure, and query your data with ease using the latest vector databases and LLMs.\n\n```bash\nnpm i axgen\n```\n\nWe built an open source [demo UI](https://github.com/axflow/original-demo-ui) for axgen, with a [short video](https://www.loom.com/share/458f9b6679b740f0a5c78a33fffee3dc) that shows the features.\n\n### Goals\n\nAxgen's goal is to break down the various concepts of working with LLMs into components with well-defined interfaces.\nThese interfaces are important as we cannot provide out-of-the-box components for every use case. Some components are\nprovided (with more coming soon) but the interfaces enable you to extend the framework with your own implementations\nto satisfy arbitrary use cases.\n\nAxgen aims to be a foundational framework from which you can construct higher-level, declarative workflows.\n\n## Example\n\nThis example showcases some core concepts of Axgen by 1) ingesting markdown files into the Pinecone vector database\nand 2) using retrieval augemented generation to answer a question about the contents of the data.\n\n```ts\nimport {\n  Ingestion,\n  Pinecone,\n  FileSystem,\n  MarkdownSplitter,\n  OpenAIEmbedder,\n  RAG,\n  Retriever,\n  PromptWithContext,\n  OpenAICompletion,\n} from 'axgen';\n\nconst { OPENAI_API_KEY, PINECONE_API_KEY } = process.env;\n\n// OpenAI's embedding model (defaults to text-embedding-ada-002)\nconst embedder = new OpenAIEmbedder({ apiKey: OPENAI_API_KEY });\n\n//////////////////////////////////\n// Connect to your vector store //\n//////////////////////////////////\nconst pinecone = new Pinecone({\n  index: 'mdindex',\n  namespace: 'default',\n  environment: 'us-west1-gcp-free',\n  apiKey: PINECONE_API_KEY,\n});\n\n/////////////////////////////////\n// Ingest local markdown files //\n/////////////////////////////////\nawait new Ingestion({\n  store: pinecone,\n  source: new FileSystem({ path: '../path/to/sales/data', glob: '**/*.md' }),\n  splitter: new MarkdownSplitter({ chunkSize: 1000 }),\n  embedder: embedder,\n}).run();\n\n///////////////////////////////////////////////////////////\n// Use retrieval augmented generation to query your data //\n///////////////////////////////////////////////////////////\nconst template = `Context information is below.\n---------------------\n{context}\n---------------------\nGiven the context information and not prior knowledge, answer the question: {query}\n`;\n\nconst rag = new RAG({\n  embedder: embedder,\n  model: new OpenAICompletion({\n    model: 'text-davinci-003',\n    max_tokens: 256,\n    apiKey: OPENAI_API_KEY,\n  }),\n  prompt: new PromptWithContext({ template }),\n  retriever: new Retriever({ store: pinecone, topK: 3 }),\n});\n\n// stream the response\nconst { result, info } = rag.stream(\n  'What were our biggest sales in Q4 of this year and who were the customers?'\n);\n\nfor await (const chunk of result) {\n  process.stdout.write(chunk);\n}\n\nprocess.stdout.write('\\n');\n\n// Information about what results were used from the vector database.\nconsole.log(info);\n```\n\n## Overview\n\nThe main components of the API are as follows:\n\n- **Vector stores** persist your data embeddings which can later be queried.\n- **Data sources** are documents pulled from arbitrary locations, e.g., a PDF from your local file system, documents from Notion, a wikipedia page, etc.\n- **Data splitters** split documents from a data source into smaller chunks. The embeddings of those chunks can be persisted in a vector store and later queried by similarity.\n- **Data embedders** create embeddings from chunks of text.\n- **Data retrievers** query vector stores for chunks of text similar to an input.\n- **Prompts and prompt templates** are used to construct the instructions sent to the LLM.\n- **Models (LLMs)** perform calls to generate e.g. completions or chat completions.\n\nAdditionally, there are two higher-level component types that create workflows out of the above components:\n\n1. **Ingestion** constructs a data ingestion pipeline from a data source, splitter, embedder, and vector store.\n2. **Generation** construct data generation pipelines (e.g., chat completion over your custom data) from some input, an embedder, prompts, and a model.\n\n## Supported Models\n\nWe currently support\n\n* OpenAI models\n* Anthropic models\n* Google models (e.g., text-bison, chat-bison, textembedding-gecko) through vertexai.\n* Cohere generate models\n\nHugging Face inference and more coming soon!\n\n## Documentation\n\nWe're working on new documentation website. In the meantime, please check out the following (runnable) examples:\n\n### Vector Stores\n\nThese implement CRUD operations for the supported vector stores.\n\n* [prepare](src/cmd/vector_store/prepare.ts). Run with `npm run vector_store:prepare -- <options>`\n* [teardown](src/cmd/vector_store/teardown.ts). Run with `npm run vector_store:teardown -- <options>`\n* [upload](src/cmd/vector_store/upload.ts). Run with `npm run vector_store:upload -- <options>`\n* [delete](src/cmd/vector_store/delete.ts). Run with `npm run vector_store:delete -- <options>`\n\n### Models / RAG pipelines\n\nThese implement basic LLM queries as well as RAG queries with all supported models.\n\n* [openai completion](src/cmd/openai/completion.ts). Run with `npm run openai:completion -- <options>`\n* [openai chat completion](src/cmd/openai/chat-completion.ts). Run with `npm run openai:chat-completion -- <options>`\n* [vertexai text](src/cmd/vertexai/text.ts). Run with `npm run vertexai:text -- <options>`\n* [vertexai chat](src/cmd/vertexai/chat.ts). Run with `npm run vertexai:chat -- <options>`\n* [anthropic](src/cmd/anthropic.ts). Run with `npm run anthropic -- <options>`\n* [cohere](src/cmd/cohere.ts). Run with `npm run cohere -- <options>`\n\n\n## Development\n\nSee the [development docs](docs/development.md).\n\n## License\n\n[MIT](LICENSE.md)\n","readmeFilename":"README.md","homepage":"https://github.com/axflow/axflowtree/main/packages/axgen","repository":{"type":"git","url":"git+https://github.com/axflow/axflow.git"},"bugs":{"url":"https://github.com/axflow/axflowissues"}}