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Provides `AgentCCLLM` and `AgentCCEmbedding` classes that implement the LlamaIndex LLM and BaseEmbedding interfaces, routing all calls through the AgentCC gateway.\n\n## Install\n\n```bash\nnpm install @agentcc/llamaindex\n```\n\nPeer dependencies:\n\n```bash\nnpm install llamaindex@\">=0.3.0\" @agentcc/client@\">=0.1.0\"\n```\n\n## Usage\n\n### LLM\n\n```typescript\nimport { AgentCCLLM } from \"@agentcc/llamaindex\";\n\nconst llm = new AgentCCLLM({\n  agentccApiKey: process.env.AGENTCC_API_KEY,\n  agentccBaseUrl: \"https://gateway.futureagi.com/v1\",\n  model: \"gpt-4o\",\n  temperature: 0,\n});\n\n// Single-turn chat\nconst response = await llm.chat({\n  messages: [{ role: \"user\", content: \"What is RAG?\" }],\n});\nconsole.log(response.message.content);\n```\n\n### Streaming\n\n```typescript\nconst stream = llm.chatStream({\n  messages: [{ role: \"user\", content: \"Explain embeddings step by step.\" }],\n});\n\nfor await (const chunk of stream) {\n  process.stdout.write(chunk.delta);\n}\n```\n\n### Embeddings\n\n```typescript\nimport { AgentCCEmbedding } from \"@agentcc/llamaindex\";\n\nconst embedder = new AgentCCEmbedding({\n  agentccApiKey: process.env.AGENTCC_API_KEY,\n  agentccBaseUrl: \"https://gateway.futureagi.com/v1\",\n  model: \"text-embedding-3-small\",\n});\n\nconst vector = await embedder.getTextEmbedding(\"Hello, world\");\n```\n\nPass `llm` and `embedModel` to any LlamaIndex pipeline that accepts those options to route through the AgentCC gateway with routing, caching, and guardrails intact.\n\n## Documentation\n\n[https://docs.futureagi.com](https://docs.futureagi.com)\n\n## License\n\nApache 2.0 — see [LICENSE](../../../LICENSE).\n","readmeFilename":"README.md"}