> ## Documentation Index > Fetch the complete documentation index at: https://www.edenai.co/docs/llms.txt > Use this file to discover all available pages before exploring further. > Build conversational AI applications using Eden AI's OpenAI-compatible chat completions endpoint. # Chat completions export const TechArticleSchema = ({title, description, path, articleSection, about, proficiencyLevel = "Beginner", dependencies, keywords = [], datePublished, dateModified, image, inLanguage = "en"}) => { const baseUrl = "https://www.edenai.co/docs"; const canonicalUrl = `${baseUrl}/${path}`.replace(/\/+$/, ""); const ogParams = new URLSearchParams({ division: articleSection || "", title: title || "", description: description || "" }); const resolvedImage = image || `https://edenai.mintlify.app/_mintlify/api/og?${ogParams.toString()}`; const data = { "@context": "https://schema.org", "@type": "TechArticle", "@id": `${canonicalUrl}#techarticle`, mainEntityOfPage: { "@type": "WebPage", "@id": canonicalUrl }, headline: title, name: title, description: description, url: canonicalUrl, inLanguage: inLanguage, isPartOf: { "@type": "WebSite", name: "Eden AI Documentation", url: baseUrl }, author: [{ "@type": "Organization", name: "Eden AI", url: "https://www.edenai.co/" }], publisher: { "@type": "Organization", name: "Eden AI", url: "https://www.edenai.co/", logo: { "@type": "ImageObject", url: "https://www.edenai.co/assets/logo.png" } } }; if (articleSection) data.articleSection = articleSection; if (about) data.about = { "@type": "Thing", name: about }; if (proficiencyLevel) data.proficiencyLevel = proficiencyLevel; if (dependencies) data.dependencies = dependencies; if (keywords && keywords.length) data.keywords = keywords; if (datePublished) data.datePublished = datePublished; if (dateModified) data.dateModified = dateModified; data.image = Array.isArray(resolvedImage) ? resolvedImage : [resolvedImage]; const json = JSON.stringify(data); const schemaId = `techarticle-${canonicalUrl}`; React.useEffect(() => { if (typeof document === "undefined") return; document.querySelectorAll(`script[data-schema-id="${schemaId}"]`).forEach(n => n.remove()); const script = document.createElement("script"); script.type = "application/ld+json"; script.dataset.schemaId = schemaId; script.textContent = json; document.head.appendChild(script); return () => script.remove(); }, [json, schemaId]); return null; }; Build conversational AI applications using Eden AI's OpenAI-compatible chat completions endpoint. ## Overview Eden AI V3 provides full OpenAI API compatibility with multi-provider support. The endpoint follows OpenAI's exact format, making it a drop-in replacement. **Endpoint:** ``` POST /v3/chat/completions ``` **Note:** Streaming is optional. When enabled, responses are delivered via Server-Sent Events (SSE). See [Streaming](/v3/llms/streaming) for streaming examples. ## Model Format Use the simplified model string format for LLM: ``` provider/model ``` **Examples:** * `openai/gpt-4` * `anthropic/claude-sonnet-4-5` * `google/gemini-2.5-flash` * `cohere/command-r-plus` ## Basic Chat Completion ```python Python theme={null} import requests url = "https://api.edenai.run/v3/chat/completions" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "openai/gpt-4", "messages": [ {"role": "user", "content": "Hello! How are you?"} ] } response = requests.post(url, headers=headers, json=payload) result = response.json() print(result["choices"][0]["message"]["content"]) ``` ```javascript JavaScript theme={null} const url = 'https://api.edenai.run/v3/chat/completions'; const headers = { 'Authorization': 'Bearer YOUR_API_KEY', 'Content-Type': 'application/json' }; const payload = { model: 'openai/gpt-4', messages: [{role: 'user', content: 'Hello!'}] }; const response = await fetch(url, { method: 'POST', headers: headers, body: JSON.stringify(payload) }); const result = await response.json(); console.log(result.choices[0].message.content); ``` ```bash cURL theme={null} curl -X POST https://api.edenai.run/v3/chat/completions \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai/gpt-4", "messages": [{"role": "user", "content": "Hello!"}] }' ``` ## Multi-Turn Conversations Build conversations with message history: ```python Python theme={null} import requests url = "https://api.edenai.run/v3/chat/completions" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "anthropic/claude-sonnet-4-5", "messages": [ {"role": "user", "content": "What is the capital of France?"}, {"role": "assistant", "content": "The capital of France is Paris."}, {"role": "user", "content": "What's the population?"} ] } response = requests.post(url, headers=headers, json=payload) result = response.json() print(result["choices"][0]["message"]["content"]) ``` ## System Messages Guide the model's behavior with system messages: ```python Python theme={null} import requests url = "https://api.edenai.run/v3/chat/completions" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "openai/gpt-4", "messages": [ { "role": "system", "content": "You are a helpful assistant that speaks like a pirate." }, { "role": "user", "content": "Tell me about artificial intelligence." } ] } response = requests.post(url, headers=headers, json=payload) result = response.json() print(result["choices"][0]["message"]["content"]) ``` ## Temperature and Parameters Control response creativity and behavior: ```python Python theme={null} import requests url = "https://api.edenai.run/v3/chat/completions" headers = { "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json" } payload = { "model": "openai/gpt-4", "messages": [ {"role": "user", "content": "Write a creative story about a robot."} ], "temperature": 0.9, # Higher = more creative (0-2) "max_tokens": 500 # Limit response length } response = requests.post(url, headers=headers, json=payload) result = response.json() print(result["choices"][0]["message"]["content"]) ``` ## Available Parameters | Parameter | Type | Default | Description | | ------------------- | ------- | -------- | ------------------------------------- | | `model` | string | Required | Model string (e.g., `openai/gpt-4`) | | `messages` | array | Required | Conversation messages | | `stream` | boolean | false | Enable streaming (uses SSE when true) | | `temperature` | float | 1.0 | Randomness (0-2) | | `max_tokens` | integer | - | Maximum response tokens | | `top_p` | float | 1.0 | Nucleus sampling threshold | | `frequency_penalty` | float | 0.0 | Penalize repeated tokens (-2 to 2) | | `presence_penalty` | float | 0.0 | Penalize topic repetition (-2 to 2) | | `fallbacks` | array | - | Backup models tried if `model` fails. | For details on the `fallbacks` field, see [Fallback](/v3/general/fallback). ## Response Format Standard JSON response: ```json theme={null} { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-4", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "Hello! I'm doing well, thank you for asking." }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 12, "completion_tokens": 15, "total_tokens": 27 } } ``` ## Available Models For the full list of supported models and their capabilities (PDF support, reasoning, web search, tool calling), see [List LLM Models](/v3/llms/listing-models). ## OpenAI Python SDK Integration Use Eden AI with the OpenAI SDK: ```python Python theme={null} from openai import OpenAI # Point to Eden AI endpoint client = OpenAI( api_key="YOUR_EDEN_AI_API_KEY", base_url="https://api.edenai.run/v3" ) # Use any provider through OpenAI SDK response = client.chat.completions.create( model="anthropic/claude-sonnet-4-5", messages=[{"role": "user", "content": "Hello!"}] ) print(response.choices[0].message.content) ``` ## Next Steps Try OCR, image, and audio features with the Expert Models endpoint Understand when to use each endpoint