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engineering","chatgpt","machine learning","ml","openai","embeddings","vectorstores"],"repository":{"type":"git","url":"git+ssh://git@github.com/langchain-ai/langchainjs.git"},"description":"Core LangChain.js abstractions and schemas","maintainers":[{"name":"hwchase17","email":"hw.chase.17@gmail.com"},{"name":"jacoblee93","email":"jacoblee93@gmail.com"},{"name":"basproul","email":"braceasproul@gmail.com"},{"name":"eric_langchain","email":"eric@langchain.dev"},{"name":"andrewnguonly","email":"andrewnguonly@gmail.com"},{"name":"nfcampos","email":"nuno@boringbits.io"},{"name":"davidduong","email":"david@duong.cz"},{"name":"maddyadams","email":"madelyn.adams1@gmail.com"},{"name":"sam_noyes","email":"sam@langchain.dev"},{"name":"hntrl","email":"hunter@hntrl.io"}],"readme":"# 🦜🍎️ @langchain/core\n\n[![CI](https://github.com/langchain-ai/langchainjs/actions/workflows/ci.yml/badge.svg)](https://github.com/langchain-ai/langchainjs/actions/workflows/ci.yml) ![npm](https://img.shields.io/npm/dm/@langchain/core) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)\n\n`@langchain/core` contains the core abstractions and schemas of LangChain.js, including base classes for language models,\nchat models, vectorstores, retrievers, and runnables.\n\n## 💾 Quick Install\n\n```bash\n$ yarn add @langchain/core\n```\n\n## 🤔 What is this?\n\n`@langchain/core` contains the base abstractions that power the rest of the LangChain ecosystem.\nThese abstractions are designed to be as modular and simple as possible.\nExamples of these abstractions include those for language models, document loaders, embedding models, vectorstores, retrievers, and more.\nThe benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.\n\nFor example, you can install other provider-specific packages like this:\n\n```bash\n$ yarn add @langchain/openai\n```\n\nAnd use them as follows:\n\n```typescript\nimport { StringOutputParser } from \"@langchain/core/output_parsers\";\nimport { ChatPromptTemplate } from \"@langchain/core/prompts\";\nimport { ChatOpenAI } from \"@langchain/openai\";\n\nconst prompt = ChatPromptTemplate.fromTemplate(\n  `Answer the following question to the best of your ability:\\n{question}`\n);\n\nconst model = new ChatOpenAI({\n  model: \"gpt-4o-mini\",\n  temperature: 0.8,\n});\n\nconst outputParser = new StringOutputParser();\n\nconst chain = prompt.pipe(model).pipe(outputParser);\n\nconst stream = await chain.stream({\n  question: \"Why is the sky blue?\",\n});\n\nfor await (const chunk of stream) {\n  console.log(chunk);\n}\n\n/*\nThe\n sky\n appears\n blue\n because\n of\n a\n phenomenon\n known\n as\n Ray\nleigh\n scattering\n*/\n```\n\nNote that for compatibility, all used LangChain packages (including the base LangChain package, which itself depends on core!) must share the same version of `@langchain/core`.\nThis means that you may need to install/resolve a specific version of `@langchain/core` that matches the dependencies of your used packages.\n\n## 🔗 What is LangChain Expression Language?\n\nLangChain Core also contains LangChain Expression Language, or LCEL, a runtime that allows users to compose arbitrary sequences together and get several benefits that are important when building LLM applications.\nWe call these sequences “runnables”.\n\nAll runnables expose the same interface with single-invocation, batch, streaming and async methods.\nThis design is useful because it is not enough to have a single sync interface when building an LLM application.\nBatch is needed for efficient processing of many inputs.\nStreaming (and streaming of intermediate steps) is needed to show the user that progress is being made.\nAsync interfaces are nice when moving into production.\nRather than having to write multiple implementations for all of those, LCEL allows you to write a runnable once and invoke it in many different ways.\n\nFor more check out the [LCEL docs](https://js.langchain.com/docs/concepts/lcel).\n\n![LangChain Stack](../docs/core_docs/static/svg/langchain_stack_062024.svg)\n\n## 📕 Releases & Versioning\n\n`@langchain/core` is currently on version `0.3.x`.\n\nAs `@langchain/core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `@langchain/core/beta`. The reason for `@langchain/core/beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.\n\nMinor version increases will occur for:\n\n- Breaking changes for any public interfaces NOT in `@langchain/core/beta`\n\nPatch version increases will occur for:\n\n- Bug fixes\n- New features\n- Any changes to private interfaces\n- Any changes to `@langchain/core/beta`\n\n## 📦 Creating your own package\n\nOther LangChain packages should add this package as a dependency and extend the classes within.\nFor an example, see the [@langchain/anthropic](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-anthropic) in this repo.\n\nBecause all used packages must share the same version of core, packages should never directly depend on `@langchain/core`. Instead they should have core as a peer dependency and a dev dependency. We suggest using a tilde dependency to allow for different (backwards-compatible) patch versions:\n\n```json\n{\n  \"name\": \"@langchain/anthropic\",\n  \"version\": \"0.0.3\",\n  \"description\": \"Anthropic integrations for LangChain.js\",\n  \"type\": \"module\",\n  \"author\": \"LangChain\",\n  \"license\": \"MIT\",\n  \"dependencies\": {\n    \"@anthropic-ai/sdk\": \"^0.10.0\"\n  },\n  \"peerDependencies\": {\n    \"@langchain/core\": \"~0.3.0\"\n  },\n  \"devDependencies\": {\n    \"@langchain/core\": \"~0.3.0\"\n  }\n}\n```\n\nThis recommendation will change to a caret once a major version (1.x.x) release has occurred.\n\nWe suggest making all packages cross-compatible with ESM and CJS using a build step like the one in\n[@langchain/anthropic](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-anthropic), then running `yarn build` before running `npm publish`.\n\nWe will be exploring how to make this process easier in the future.\n\n## 💁 Contributing\n\nBecause `@langchain/core` is a low-level package whose abstractions will change infrequently, most contributions should be made in the higher-level LangChain package.\n\nBugfixes or suggestions should be made using the same guidelines as the main package.\nSee [here](https://github.com/langchain-ai/langchainjs/tree/main/CONTRIBUTING.md) for detailed information.\n\nPlease report any security issues or concerns following our [security guidelines](https://github.com/langchain-ai/langchainjs/tree/main/SECURITY.md).\n","readmeFilename":"README.md"}