fix vision chat

Author: wangzuo

README.md

@@ -11,7 +11,7 @@ fluent-ai is a lightweight, type-safe AI toolkit that seamlessly integrates mult
 ## Installation
 
 ```sh
-npm install fluent-ai zod
+npm install fluent-ai zod@next
 ```
 
 ## AI Service provider support
@@ -48,11 +48,11 @@ Each request to AI providers is wrapped in a `Job`. which can also serialized an
 ### Method chaining
 
 ```ts
-import { openai, userPrompt } from "fluent-ai";
+import { openai, user } from "fluent-ai";
 
 const job = openai()
   .chat("gpt-4o-mini")
-  .messages([userPrompt("Hi")])
+  .messages([user("Hi")])
   .temperature(0.5)
   .maxTokens(1024);
 ```
@@ -101,11 +101,11 @@ Chat completion, such as ChatGPT, is the most common AI service. It generates re
 ### Text generation
 
 ```ts
-import { openai, systemPrompt, userPrompt } from "fluent-ai";
+import { openai, system, user } from "fluent-ai";
 
 const job = openai()
   .chat("gpt-4o-mini")
-  .messages([systemPrompt("You are a helpful assistant"), userPrompt("Hi")]);
+  .messages([system("You are a helpful assistant"), user("Hi")]);
 
 const { text } = await job.run();
 ```
@@ -120,7 +120,7 @@ fluent-ai provides a consistent `jsonSchema()` function for all providers to gen
 
 ```ts
 import { z } from "zod";
-import { openai, userPrompt } from "fluent-ai";
+import { openai, user } from "fluent-ai";
 
 const personSchema = z.object({
   name: z.string(),
@@ -129,7 +129,7 @@ const personSchema = z.object({
 
 const job = openai()
   .chat("gpt-4o-mini")
-  .messages([userPrompt("generate a person with name and age in json format")])
+  .messages([user("generate a person with name and age in json format")])
   .jsonSchema(personSchema, "person");
 
 const { object } = await job.run();
@@ -161,7 +161,7 @@ To use the tool, add it to a chat job with a function-calling-enabled model, suc
 const job = openai().chat("gpt-4o-mini").tool(weatherTool);
 
 const { toolCalls } = await job
-  .messages([userPrompt("What is the weather in San Francisco?")])
+  .messages([user("What is the weather in San Francisco?")])
   .run();
 ```
 
@@ -172,7 +172,7 @@ Rather than waiting for the complete response, streaming enables the model to re
 ```ts
 const job = openai()
   .chat("gpt-4o-mini")
-  .messages([systemPrompt("You are a helpful assistant"), userPrompt("Hi")])
+  .messages([system("You are a helpful assistant"), user("Hi")])
   .stream();
 
 const { stream } = await job.run();
@@ -188,13 +188,11 @@ fluent-ai supports streaming text, object and tool calls on demand. For more det
 You can leverage chat models with vision capabilities by including an image URL in your prompt.
 
 ```ts
-import { openai, systemPrompt, userPrompt } from "fluent-ai";
+import { openai, system, user } from "fluent-ai";
 
 openai()
   .chat("gpt-4o-mini")
-  .messages([
-    userPrompt("Describe the image", { image: { url: "<image_url>" } }),
-  ]);
+  .messages([user("Describe the image", { image: { url: "<image_url>" } })]);
 ```
 
 ## Embedding

docs/chat-streaming.md

@@ -13,7 +13,7 @@ export interface StreamOptions {
 ```ts
 const { textStream } = await openai()
   .chat("gpt-4o-mini")
-  .messages([userPrompt("hi")])
+  .messages([user("hi")])
   .stream()
   .run();
 
@@ -28,9 +28,7 @@ for await (const text of textStream) {
 const { toolCallStream } = await openai()
   .chat("gpt-4o-mini")
   .tool(weatherTool)
-  .messages([
-    userPrompt("What's the weather like in Boston, Beijing, Tokyo today?"),
-  ])
+  .messages([user("What's the weather like in Boston, Beijing, Tokyo today?")])
   .stream()
   .run();
 
@@ -44,7 +42,7 @@ for await (const toolCalls of toolCallStream) {
 ```ts
 const { objectStream } = await openai()
   .chat("gpt-4o-mini")
-  .messages([userPrompt("generate a person with name and age in json format")])
+  .messages([user("generate a person with name and age in json format")])
   .responseSchema(personSchema)
   .objectStream()
   .run();
@@ -61,7 +59,7 @@ The original chunk object from providers
 ```ts
 const { stream } = await openai()
   .chat("gpt-4o-mini")
-  .messages([userPrompt("hi")]);
+  .messages([user("hi")]);
 
 for await (const chunk of stream) {
   console.log(chunk);

examples/anthropic-chat.ts

@@ -1,8 +1,8 @@
-import { anthropic, userPrompt } from "../src";
+import { anthropic, user } from "../src";
 
 const job = anthropic()
   .chat("claude-3-5-sonnet-20241022")
   .maxTokens(1024)
-  .messages([userPrompt("Hello, world")]);
+  .messages([user("Hello, world")]);
 const result = await job.run();
 console.log(result.raw);

examples/deepseek-chat.ts

@@ -1,11 +1,8 @@
-import { deepseek, systemPrompt, userPrompt } from "../src";
+import { deepseek, system, user } from "../src";
 
 const job = deepseek()
   .chat("deepseek-chat")
-  .messages([
-    systemPrompt("you are a helpful assistant"),
-    userPrompt("who are you"),
-  ]);
+  .messages([system("you are a helpful assistant"), user("who are you")]);
 const result = await job.run();
 
 console.log(result);

examples/fal-image-stream.ts

@@ -6,6 +6,4 @@ const stream = await job.run();
 for await (const event of stream) {
   console.log(event);
 }
-
-const result = await stream.done();
 console.log(result);

examples/fireworks-chat.ts

@@ -1,8 +1,8 @@
-import { fireworks, systemPrompt, userPrompt } from "../src";
+import { fireworks, system, user } from "../src";
 
 const job = await fireworks()
   .chat("accounts/fireworks/models/llama-v3p1-70b-instruct")
-  .messages([systemPrompt("you are a helpful assistant"), userPrompt("hi")]);
+  .messages([system("you are a helpful assistant"), user("hi")]);
 const result = await job.run();
 
 console.log(result);

examples/google-chat.ts

@@ -1,8 +1,8 @@
-import { google, systemPrompt, userPrompt } from "../src";
+import { google, system, user } from "../src";
 
 const job = google()
   .chat("gemini-1.5-flash")
-  .messages([systemPrompt("you are a helpful assistant"), userPrompt("hi")]);
+  .messages([system("you are a helpful assistant"), user("hi")]);
 const result = await job.run();
 
 console.log(result);

examples/groq-chat.ts

@@ -1,14 +1,11 @@
-import { openai, systemPrompt, userPrompt } from "../src";
+import { openai, system, user } from "../src";
 
 const job = openai({
   apiKey: process.env.GROQ_API_KEY,
-  baseURL: "https://api.groq.com/openai/v1"
+  baseURL: "https://api.groq.com/openai/v1",
 })
   .chat("meta-llama/llama-4-scout-17b-16e-instruct")
-  .messages([
-    systemPrompt("you are a helpful assistant"),
-    userPrompt("who are you"),
-  ]);
+  .messages([system("you are a helpful assistant"), user("who are you")]);
 const result = await job.run();
 
 console.log(result);

examples/ollama-chat.ts

@@ -1,7 +1,7 @@
-import { ollama, systemPrompt, userPrompt } from "../src";
+import { ollama, system, user } from "../src";
 
 const job = ollama()
   .chat("llama3.2")
-  .messages([systemPrompt("you are a helpful assistant"), userPrompt("hi")]);
+  .messages([system("you are a helpful assistant"), user("hi")]);
 const result = await job.run();
 console.log(result);

examples/ollama-embedding.ts

@@ -1,6 +1,5 @@
 import { ollama } from "../src";
 
-const job = ollama().embedding("nomic-embed-text").input("hello");
+const job = ollama().embedding("nomic-embed-text").value("hello");
 const result = await job.run();
-
-console.log(JSON.stringify(result, null, 2));
+console.log(result);