@juspay/neurolink
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Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio
294 lines • 13.4 kB
JavaScript
/**
* Message Builder Module
*
* Handles all message construction logic for AI providers.
* Extracted from BaseProvider to follow Single Responsibility Principle.
*
* Responsibilities:
* - Building messages from text generation options
* - Building messages from stream options
* - Multimodal input detection
* - Message format conversion (to ModelMessage[])
*
* @module core/modules/MessageBuilder
*/
import { tracers, ATTR, withSpan } from "../../telemetry/index.js";
import { logger } from "../../utils/logger.js";
import { buildMessagesArray, buildMultimodalMessagesArray, } from "../../utils/messageBuilder.js";
/**
* Compute total content length across all messages for span attributes.
*/
function computeTotalContentLength(messages) {
let total = 0;
for (const msg of messages) {
if (typeof msg.content === "string") {
total += msg.content.length;
}
else if (Array.isArray(msg.content)) {
for (const part of msg.content) {
if ("text" in part &&
typeof part.text === "string") {
total += part.text.length;
}
}
}
}
return total;
}
/**
* Check whether input contains multimodal content (images, files, PDFs, CSVs).
*/
function detectMultimodal(opts) {
const input = opts.input;
const hasImages = !!input?.images?.length;
const hasContent = !!input?.content?.length;
const hasCSVFiles = !!input?.csvFiles?.length;
const hasPdfFiles = !!input?.pdfFiles?.length;
const hasFiles = !!input?.files?.length;
return {
isMultimodal: hasImages || hasContent || hasCSVFiles || hasPdfFiles || hasFiles,
hasImages,
hasFiles: hasCSVFiles || hasPdfFiles || hasFiles,
};
}
/**
* MessageBuilder class - Handles message construction for AI providers
*/
export class MessageBuilder {
providerName;
modelName;
constructor(providerName, modelName) {
this.providerName = providerName;
this.modelName = modelName;
}
/**
* Build messages array for generation
* Detects multimodal input and routes to appropriate message builder
*/
async buildMessages(options) {
return withSpan({
name: "neurolink.message.build",
tracer: tracers.sdk,
attributes: {
[ATTR.NL_PROVIDER]: this.providerName,
[ATTR.NL_MODEL]: this.modelName,
},
}, async (span) => {
const { isMultimodal, hasImages, hasFiles } = detectMultimodal(options);
span.setAttribute(ATTR.MSG_IS_MULTIMODAL, isMultimodal);
let messages;
if (isMultimodal) {
if (process.env.NEUROLINK_DEBUG === "true") {
logger.debug("Detected multimodal input, using multimodal message builder");
}
const input = options.input;
const multimodalOptions = {
input: {
text: options.prompt || options.input?.text || "",
images: input?.images,
content: input?.content,
csvFiles: input?.csvFiles,
pdfFiles: input?.pdfFiles,
files: input?.files,
},
csvOptions: options.csvOptions,
provider: options.provider,
model: options.model,
temperature: options.temperature,
maxTokens: options.maxTokens,
systemPrompt: options.systemPrompt,
enableAnalytics: options.enableAnalytics,
enableEvaluation: options.enableEvaluation,
context: options.context,
conversationHistory: options.conversationMessages,
schema: options.schema,
output: options.output,
fileRegistry: options.fileRegistry,
};
messages = await buildMultimodalMessagesArray(multimodalOptions, this.providerName, this.modelName);
// Propagate any systemPrompt augmentation (e.g. inline-file
// handling guidance from processUnifiedFilesArray) back to the
// caller's options. Providers like GoogleVertex's native
// @google/genai stream path read `options.systemPrompt` directly
// — without this propagation the augmentation lives only on the
// local `multimodalOptions` clone and never reaches the model.
if (multimodalOptions.systemPrompt &&
multimodalOptions.systemPrompt !== options.systemPrompt) {
options.systemPrompt = multimodalOptions.systemPrompt;
}
}
else {
if (process.env.NEUROLINK_DEBUG === "true") {
logger.debug("No multimodal input detected, using standard message builder");
}
messages = await buildMessagesArray(options);
}
// Convert messages to Vercel AI SDK format
// Preserve providerOptions (e.g. Anthropic cache_control) through conversion
const coreMessages = messages.map((msg) => {
const providerOptions = msg
.providerOptions;
if (typeof msg.content === "string") {
return {
role: msg.role,
content: msg.content,
...(providerOptions && { providerOptions }),
};
}
else {
return {
role: msg.role,
content: msg.content.map((item) => {
const itemProviderOptions = item.providerOptions;
if (item.type === "text") {
return {
type: "text",
text: item.text || "",
...(itemProviderOptions && {
providerOptions: itemProviderOptions,
}),
};
}
else if (item.type === "image") {
return {
type: "image",
image: item.image || "",
...(itemProviderOptions && {
providerOptions: itemProviderOptions,
}),
};
}
return item;
}),
...(providerOptions && { providerOptions }),
};
}
});
span.setAttribute(ATTR.MSG_COUNT, coreMessages.length);
span.setAttribute(ATTR.MSG_HAS_IMAGES, hasImages);
span.setAttribute(ATTR.MSG_HAS_FILES, hasFiles);
span.setAttribute(ATTR.MSG_HAS_SYSTEM_PROMPT, !!options.systemPrompt);
span.setAttribute(ATTR.MSG_TOTAL_CONTENT_LENGTH, computeTotalContentLength(coreMessages));
return coreMessages;
});
}
/**
* Build messages array for streaming operations
* This is a protected helper method that providers can use to build messages
* with automatic multimodal detection, eliminating code duplication
*
* @param options - Stream options or text generation options
* @returns Promise resolving to ModelMessage array ready for AI SDK
*/
async buildMessagesForStream(options) {
return withSpan({
name: "neurolink.message.build_for_stream",
tracer: tracers.sdk,
attributes: {
[ATTR.NL_PROVIDER]: this.providerName,
[ATTR.NL_MODEL]: this.modelName,
},
}, async (span) => {
const { isMultimodal, hasImages, hasFiles } = detectMultimodal(options);
span.setAttribute(ATTR.MSG_IS_MULTIMODAL, isMultimodal);
let messages;
if (isMultimodal) {
if (process.env.NEUROLINK_DEBUG === "true") {
logger.debug(`${this.providerName}: Detected multimodal input, using multimodal message builder`);
}
const input = options.input;
const multimodalOptions = {
input: {
text: options.prompt ||
options.input?.text ||
"",
images: input?.images,
content: input?.content,
csvFiles: input?.csvFiles,
pdfFiles: input?.pdfFiles,
files: input?.files,
},
csvOptions: options.csvOptions,
provider: options.provider,
model: options.model,
temperature: options.temperature,
maxTokens: options.maxTokens,
systemPrompt: options.systemPrompt,
enableAnalytics: options.enableAnalytics,
enableEvaluation: options.enableEvaluation,
context: options.context,
conversationHistory: options
.conversationMessages,
schema: options.schema,
output: options.output,
fileRegistry: options.fileRegistry,
};
messages = await buildMultimodalMessagesArray(multimodalOptions, this.providerName, this.modelName);
// Propagate any systemPrompt augmentation (e.g. inline-file
// handling guidance from processUnifiedFilesArray) back to the
// caller's options. Providers like GoogleVertex's native
// @google/genai stream path read `options.systemPrompt` directly
// — without this propagation the augmentation lives only on the
// local `multimodalOptions` clone and never reaches the model.
if (multimodalOptions.systemPrompt &&
multimodalOptions.systemPrompt !== options.systemPrompt) {
options.systemPrompt = multimodalOptions.systemPrompt;
}
}
else {
if (process.env.NEUROLINK_DEBUG === "true") {
logger.debug(`${this.providerName}: No multimodal input detected, using standard message builder`);
}
messages = await buildMessagesArray(options);
}
// Convert messages to Vercel AI SDK format
// Preserve providerOptions (e.g. Anthropic cache_control) through conversion
const coreMessages = messages.map((msg) => {
const providerOptions = msg
.providerOptions;
if (typeof msg.content === "string") {
return {
role: msg.role,
content: msg.content,
...(providerOptions && { providerOptions }),
};
}
else {
return {
role: msg.role,
content: msg.content.map((item) => {
const itemProviderOptions = item.providerOptions;
if (item.type === "text") {
return {
type: "text",
text: item.text || "",
...(itemProviderOptions && {
providerOptions: itemProviderOptions,
}),
};
}
else if (item.type === "image") {
return {
type: "image",
image: item.image || "",
...(itemProviderOptions && {
providerOptions: itemProviderOptions,
}),
};
}
return item;
}),
...(providerOptions && { providerOptions }),
};
}
});
span.setAttribute(ATTR.MSG_COUNT, coreMessages.length);
span.setAttribute(ATTR.MSG_HAS_IMAGES, hasImages);
span.setAttribute(ATTR.MSG_HAS_FILES, hasFiles);
span.setAttribute(ATTR.MSG_HAS_SYSTEM_PROMPT, !!options.systemPrompt);
span.setAttribute(ATTR.MSG_TOTAL_CONTENT_LENGTH, computeTotalContentLength(coreMessages));
return coreMessages;
});
}
}
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