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\n
llm-polyglot
\n
\n \n\n
> Universal client for LLM providers with OpenAI-compatible interface
\n\n`llm-polyglot` extends the OpenAI SDK to provide a consistent interface across different LLM providers. Use the same familiar OpenAI-style API with Anthropic, Google, and others.\n\n## Provider Support\n\n**Native API Support Status:**\n\n| Provider API | Status | Chat | Basic Stream | Functions/Tool calling | Function streaming | Notes |\n|-------------|---------|------|--------------|---------------------|-----------------|--------|\n| OpenAI | ✅ | ✅ | ✅ | ✅ | ✅ | Direct SDK proxy |\n| Anthropic | ✅ | ✅ | ✅ | ❌ | ❌ | Claude models |\n| Google | ✅ | ✅ | ✅ | ✅ | ❌ | Gemini models + context caching |\n| Azure | 🚧 | ✅ | ✅ | ❌ | ❌ | OpenAI model hosting |\n| Cohere | ❌ | - | - | - | - | Not supported |\n| AI21 | ❌ | - | - | - | - | Not supported |\n\nStream Types:\n\n- **Basic Stream**: Simple text streaming\n- **Partial JSON Stream**: Progressive JSON object construction during streaming\n- **Function Stream**: Streaming function/tool calls and their results\n\n \n\n**OpenAI-Compatible Hosting Providers:**\n\nThese providers use the OpenAI SDK format, so they work directly with the OpenAI client configuration:\n\n| Provider | How to Use | Available Models |\n|----------|------------|------------------|\n| Together | Use OpenAI client with Together base URL | Mixtral, Llama, OpenChat, Yi, others |\n| Anyscale | Use OpenAI client with Anyscale base URL | Mistral, Llama, others |\n| Perplexity | Use OpenAI client with Perplexity base URL | pplx-* models |\n| Replicate | Use OpenAI client with Replicate base URL | Various open models |\n\n## Installation\n\n```bash\n# Base installation\nnpm install llm-polyglot openai\n\n# Provider-specific SDKs (as needed)\nnpm install @anthropic-ai/sdk # For Anthropic\nnpm install @google/generative-ai # For Google/Gemini\n```\n\n## Basic Usage\n\n```typescript\nimport { createLLMClient } from \"llm-polyglot\";\n\n// Initialize provider-specific client\nconst client = createLLMClient({\n provider: \"anthropic\" // or \"google\", \"openai\", etc.\n});\n\n// Use consistent OpenAI-style interface\nconst completion = await client.chat.completions.create({\n model: \"claude-3-opus-20240229\",\n messages: [{ role: \"user\", content: \"Hello!\" }],\n max_tokens: 1000\n});\n```\n\nOpenAI client options can stay at the top level. Use `clientOptions` when an OpenAI 6 option\ncollides with llm-polyglot's `provider` selector, including the SDK's third-party provider option:\n\n```typescript\nconst client = createLLMClient({\n provider: \"openai\",\n clientOptions: {\n apiKey: async () => getRotatedApiKey(),\n logLevel: \"off\"\n }\n});\n```\n\n## Provider-Specific Features\n\n### Anthropic\n\nThe llm-polyglot library provides support for Anthropic's API, including standard chat completions, streaming chat completions, and function calling. Both input paramaters and responses match exactly those of the OpenAI SDK - for more detailed documentation please see the OpenAI docs: [https://platform.openai.com/docs/api-reference](https://platform.openai.com/docs/api-reference)\n\nThe anthropic sdk is required when using the anthropic provider - we only use the types provided by the sdk.\n\n```bash\n bun add @anthropic-ai/sdk\n```\n\n```typescript\nconst client = createLLMClient({ provider: \"anthropic\" });\n\n// Standard completion\nconst response = await client.chat.completions.create({\n model: \"claude-3-opus-20240229\",\n messages: [{ role: \"user\", content: \"Hello!\" }]\n});\n\n// Streaming\nconst stream = await client.chat.completions.create({\n model: \"claude-3-opus-20240229\",\n messages: [{ role: \"user\", content: \"Hello!\" }],\n stream: true\n});\n\nfor await (const chunk of stream) {\n process.stdout.write(chunk.choices[0]?.delta?.content ?? \"\");\n}\n\n// Tool/Function calling\nconst result = await client.chat.completions.create({\n model: \"claude-3-opus-20240229\",\n messages: [{ role: \"user\", content: \"Analyze this data\" }],\n tools: [{\n type: \"function\",\n function: {\n name: \"analyze\",\n parameters: {\n type: \"object\",\n properties: {\n sentiment: { type: \"string\" }\n }\n }\n }\n }]\n});\n```\n\n### Google (Gemini)\n\nThe llm-polyglot library provides support for Google's Gemini API including:\n\n- Standard chat completions with OpenAI-compatible interface\n- Streaming chat completions with delta updates\n- Function/tool calling with automatic schema conversion\n- Context caching for token optimization (requires paid API key)\n- Grounding support with Google Search integration\n- Safety settings and model generation config\n- Session management for stateful conversations\n- Automatic response transformation with source attribution\n\nThe Google generative-ai sdk is required when using the google provider:\n\n```bash\n bun add @google/generative-ai\n```\n\nTo use any of the above functionality, the schema matches OpenAI's format since we translate the OpenAI params spec into Gemini's model spec.\n\n#### Basic Usage\n\n```typescript\nconst client = createLLMClient({ provider: \"google\" });\n\n// Standard completion\nconst completion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-latest\",\n messages: [{ role: \"user\", content: \"Hello!\" }],\n max_tokens: 1000\n});\n\n// With grounding (Google Search)\nconst groundedCompletion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-latest\",\n messages: [{ role: \"user\", content: \"What are the latest AI developments?\" }],\n groundingThreshold: 0.7,\n max_tokens: 1000\n});\n\n// With safety settings\nconst safeCompletion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-latest\",\n messages: [{ role: \"user\", content: \"Tell me a story\" }],\n additionalProperties: {\n safetySettings: [{\n category: \"HARM_CATEGORY_HARASSMENT\",\n threshold: \"BLOCK_MEDIUM_AND_ABOVE\"\n }]\n }\n});\n\n// With session management\nconst sessionCompletion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-latest\",\n messages: [{ role: \"user\", content: \"Remember this: I'm Alice\" }],\n additionalProperties: {\n sessionId: \"user-123\"\n }\n});\n```\n\n#### Context Caching\n\n[Context Caching](https://ai.google.dev/gemini-api/docs/caching) is a feature specific to Gemini that helps cut down on duplicate token usage by allowing you to create a cache with a TTL:\n\n```typescript\n// Create a cache\nconst cache = await client.cacheManager.create({\n model: \"gemini-1.5-flash-8b\",\n messages: [{ role: \"user\", content: \"Context to cache\" }],\n ttlSeconds: 3600 // Cache for 1 hour\n});\n\n// Use the cached context\nconst completion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-8b\",\n messages: [{ role: \"user\", content: \"Follow-up question\" }],\n additionalProperties: {\n cacheName: cache.name\n }\n});\n```\n\n#### Function/Tool Calling\n\n```typescript\nconst completion = await client.chat.completions.create({\n model: \"gemini-1.5-flash-latest\",\n messages: [{ role: \"user\", content: \"Analyze this data\" }],\n tools: [{\n type: \"function\",\n function: {\n name: \"analyze\",\n parameters: {\n type: \"object\",\n properties: {\n sentiment: { type: \"string\" }\n }\n }\n }\n }],\n tool_choice: {\n type: \"function\",\n function: { name: \"analyze\" }\n }\n});\n```\n\n## Error Handling\n\n```\n","readmeFilename":"README.md"}
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