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engineering","chatgpt","machine learning","ml","openai","embeddings","vectorstores"],"repository":{"url":"git+ssh://git@github.com/langchain-ai/langchainjs.git","type":"git"},"description":"Old abstractions from LangChain.js","maintainers":[{"name":"lc-oss-admin","email":"oss-admin@langchain.dev"},{"name":"langchain-security","email":"infra@langchain.dev"}],"readme":"# @langchain/classic\n\nThis package contains functionality from LangChain v0.x that has been moved out of the main `langchain` package as part of the v1.0 release. It exists to provide backward compatibility for existing applications while the core `langchain` package focuses on the essential building blocks for modern agent development.\n\n## When to use this package\n\nUse `@langchain/classic` if you:\n\n- Have existing code that uses legacy chains (e.g., `LLMChain`, `ConversationalRetrievalQAChain`, `RetrievalQAChain`)\n- Use the indexing API\n- Depend on functionality from `@langchain/community` that was previously re-exported from `langchain`\n- Are maintaining an existing application and not yet ready to migrate to the new `createAgent` API\n\n## When NOT to use this package\n\n**For new projects, use `langchain` v1.0 instead.** The new APIs provide:\n\n- **`createAgent`**: A cleaner, more powerful way to build agents with middleware support\n- **Better performance**: Optimized for modern agent workflows\n- **Focused API surface**: Less complexity, easier to learn\n- **Active development**: New features and improvements will focus on v1.0 APIs\n\nSee the [LangChain v1.0 release notes](https://docs.langchain.com/oss/javascript/releases/langchain-v1) for more information.\n\n## Installation\n\n```bash npm2yarn\nnpm install @langchain/classic\n```\n\nThis package requires `@langchain/core` as a peer dependency:\n\n```bash npm2yarn\nnpm install @langchain/core\n```\n\n## What's included\n\n### Legacy Chains\n\nAll chain implementations from v0.x, including:\n\n- `LLMChain` - Basic chain for calling an LLM with a prompt template\n- `ConversationalRetrievalQAChain` - Chain for conversational question-answering over documents\n- `RetrievalQAChain` - Chain for question-answering over documents without conversation memory\n- `StuffDocumentsChain` - Chain for stuffing documents into a prompt\n- `MapReduceDocumentsChain` - Chain for map-reduce operations over documents\n- `RefineDocumentsChain` - Chain for iterative refinement over documents\n- And many more...\n\n### Indexing API\n\nThe `RecordManager` and related indexing functionality for managing document updates in vector stores.\n\n### Community Integrations\n\nRe-exports from `@langchain/community` that were previously available in the main `langchain` package.\n\n### Other Deprecated Functionality\n\nVarious utilities and abstractions that have been replaced by better alternatives in v1.0.\n\n## Migration\n\n### From `langchain` v0.x to `@langchain/classic`\n\nIf you're upgrading to `langchain` v1.0 but want to keep using legacy functionality:\n\n1. Install `@langchain/classic`:\n\n   ```bash npm2yarn\n   npm install @langchain/classic\n   ```\n\n2. Update your imports:\n\n   ```typescript\n   // Before (v0.x)\n   import { LLMChain } from \"langchain/chains\";\n   import { ConversationalRetrievalQAChain } from \"langchain/chains\";\n\n   // After (v1.0)\n   import { LLMChain } from \"@langchain/classic/chains\";\n   import { ConversationalRetrievalQAChain } from \"@langchain/classic/chains\";\n   ```\n\n   Or if you imported from the root:\n\n   ```typescript\n   // Before (v0.x)\n   import { LLMChain } from \"langchain\";\n\n   // After (v1.0)\n   import { LLMChain } from \"@langchain/classic\";\n   ```\n\n### From `@langchain/classic` to `langchain` v1.0\n\n**For new development, we recommend using `createAgent` instead of legacy chains.**\n\nExample migration from `LLMChain`:\n\n```typescript\n// Before (using LLMChain)\nimport { LLMChain } from \"@langchain/classic/chains\";\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { PromptTemplate } from \"@langchain/core/prompts\";\n\nconst model = new ChatOpenAI({ model: \"gpt-4\" });\nconst prompt = PromptTemplate.fromTemplate(\n  \"What is a good name for a company that makes {product}?\"\n);\nconst chain = new LLMChain({ llm: model, prompt });\nconst result = await chain.call({ product: \"colorful socks\" });\n\n// After (using createAgent)\nimport { createAgent } from \"langchain\";\n\nconst agent = createAgent({\n  model: \"openai:gpt-4\",\n  systemPrompt: \"You are a creative assistant that helps name companies.\",\n});\n\nconst result = await agent.invoke({\n  messages: [\n    {\n      role: \"user\",\n      content: \"What is a good name for a company that makes colorful socks?\",\n    },\n  ],\n});\n```\n\nFor more complex migrations, see the [migration guide](https://docs.langchain.com/oss/javascript/migrate/langchain-v1).\n\n## Support and Maintenance\n\n`@langchain/classic` will receive:\n\n- **Bug fixes**: Critical bugs will be fixed\n- **Security updates**: Security vulnerabilities will be patched\n- **No new features**: New functionality will focus on `langchain` v1.0 APIs\n\nThis package is in **maintenance mode**. For new features and active development, use `langchain` v1.0.\n\n## Examples\n\n### Using a legacy chain\n\n```typescript\nimport { LLMChain } from \"@langchain/classic/chains\";\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { PromptTemplate } from \"@langchain/core/prompts\";\n\nconst model = new ChatOpenAI({ model: \"gpt-4\" });\n\nconst prompt = PromptTemplate.fromTemplate(\n  \"Tell me a {adjective} joke about {content}.\"\n);\n\nconst chain = new LLMChain({ llm: model, prompt });\n\nconst result = await chain.call({\n  adjective: \"funny\",\n  content: \"chickens\",\n});\n\nconsole.log(result.text);\n```\n\n### Using ConversationalRetrievalQAChain\n\n```typescript\nimport { ConversationalRetrievalQAChain } from \"@langchain/classic/chains\";\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { OpenAIEmbeddings } from \"@langchain/openai\";\nimport { MemoryVectorStore } from \"langchain/vectorstores/memory\";\n\n// Create vector store with documents\nconst vectorStore = await MemoryVectorStore.fromTexts(\n  [\"Document 1 text...\", \"Document 2 text...\"],\n  [{ id: 1 }, { id: 2 }],\n  new OpenAIEmbeddings()\n);\n\n// Create chain\nconst model = new ChatOpenAI({ model: \"gpt-4\" });\nconst chain = ConversationalRetrievalQAChain.fromLLM(\n  model,\n  vectorStore.asRetriever()\n);\n\n// Use chain\nconst result = await chain.call({\n  question: \"What is in the documents?\",\n  chat_history: [],\n});\n\nconsole.log(result.text);\n```\n\n## Resources\n\n- [LangChain v1.0 Release Notes](https://docs.langchain.com/oss/javascript/releases/langchain-v1)\n- [Migration Guide](https://docs.langchain.com/oss/javascript/migrate/langchain-v1)\n- [LangChain v1.0 Documentation](https://docs.langchain.com/oss/javascript/langchain/agents)\n- [GitHub Repository](https://github.com/langchain-ai/langchainjs)\n\n## Support\n\nFor bug reports and issues, please open an issue on [GitHub](https://github.com/langchain-ai/langchainjs/issues).\n\nFor questions and discussions, join our [Discord community](https://discord.gg/langchain).\n\n## License\n\nThis package is licensed under the MIT License. See the [LICENSE](../../LICENSE) file for details.\n","readmeFilename":"README.md"}