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chore: import upstream snapshot with attribution
2026-07-13 12:08:12 +08:00

284 行
11 KiB
JavaScript

const { EModelEndpoint, ContentTypes } = require('librechat-data-provider');
const {
AIMessage,
ToolMessage,
HumanMessage,
SystemMessage,
} = require('@librechat/agents/langchain/messages');
/**
* Formats a message to OpenAI Vision API payload format.
*
* @param {Object} params - The parameters for formatting.
* @param {Object} params.message - The message object to format.
* @param {string} [params.message.role] - The role of the message sender (must be 'user').
* @param {string} [params.message.content] - The text content of the message.
* @param {EModelEndpoint} [params.endpoint] - Identifier for specific endpoint handling
* @param {Array<string>} [params.image_urls] - The image_urls to attach to the message.
* @returns {(Object)} - The formatted message.
*/
const formatVisionMessage = ({ message, image_urls, endpoint }) => {
if (endpoint === EModelEndpoint.anthropic) {
message.content = [...image_urls, { type: ContentTypes.TEXT, text: message.content }];
return message;
}
message.content = [{ type: ContentTypes.TEXT, text: message.content }, ...image_urls];
return message;
};
/**
* Formats a message to OpenAI payload format based on the provided options.
*
* @param {Object} params - The parameters for formatting.
* @param {Object} params.message - The message object to format.
* @param {string} [params.message.role] - The role of the message sender (e.g., 'user', 'assistant').
* @param {string} [params.message._name] - The name associated with the message.
* @param {string} [params.message.sender] - The sender of the message.
* @param {string} [params.message.text] - The text content of the message.
* @param {string} [params.message.content] - The content of the message.
* @param {Array<string>} [params.message.image_urls] - The image_urls attached to the message for Vision API.
* @param {string} [params.userName] - The name of the user.
* @param {string} [params.assistantName] - The name of the assistant.
* @param {string} [params.endpoint] - Identifier for specific endpoint handling
* @param {boolean} [params.langChain=false] - Whether to return a LangChain message object.
* @returns {(Object|HumanMessage|AIMessage|SystemMessage)} - The formatted message.
*/
const formatMessage = ({ message, userName, assistantName, endpoint, langChain = false }) => {
let { role: _role, _name, sender, text, content: _content, lc_id } = message;
if (lc_id && lc_id[2] && !langChain) {
const roleMapping = {
SystemMessage: 'system',
HumanMessage: 'user',
AIMessage: 'assistant',
};
_role = roleMapping[lc_id[2]];
}
const role = _role ?? (sender && sender?.toLowerCase() === 'user' ? 'user' : 'assistant');
const content = _content ?? text ?? '';
const formattedMessage = {
role,
content,
};
const { image_urls } = message;
if (Array.isArray(image_urls) && image_urls.length > 0 && role === 'user') {
return formatVisionMessage({
message: formattedMessage,
image_urls: message.image_urls,
endpoint,
});
}
if (_name) {
formattedMessage.name = _name;
}
if (userName && formattedMessage.role === 'user') {
formattedMessage.name = userName;
}
if (assistantName && formattedMessage.role === 'assistant') {
formattedMessage.name = assistantName;
}
if (formattedMessage.name) {
// Conform to API regex: ^[a-zA-Z0-9_-]{1,64}$
// https://community.openai.com/t/the-format-of-the-name-field-in-the-documentation-is-incorrect/175684/2
formattedMessage.name = formattedMessage.name.replace(/[^a-zA-Z0-9_-]/g, '_');
if (formattedMessage.name.length > 64) {
formattedMessage.name = formattedMessage.name.substring(0, 64);
}
}
if (!langChain) {
return formattedMessage;
}
if (role === 'user') {
return new HumanMessage(formattedMessage);
} else if (role === 'assistant') {
return new AIMessage(formattedMessage);
} else {
return new SystemMessage(formattedMessage);
}
};
/**
* Formats an array of messages for LangChain.
*
* @param {Array<Object>} messages - The array of messages to format.
* @param {Object} formatOptions - The options for formatting each message.
* @param {string} [formatOptions.userName] - The name of the user.
* @param {string} [formatOptions.assistantName] - The name of the assistant.
* @returns {Array<(HumanMessage|AIMessage|SystemMessage)>} - The array of formatted LangChain messages.
*/
const formatLangChainMessages = (messages, formatOptions) =>
messages.map((msg) => formatMessage({ ...formatOptions, message: msg, langChain: true }));
/**
* Formats a LangChain message object by merging properties from `lc_kwargs` or `kwargs` and `additional_kwargs`.
*
* @param {Object} message - The message object to format.
* @param {Object} [message.lc_kwargs] - Contains properties to be merged. Either this or `message.kwargs` should be provided.
* @param {Object} [message.kwargs] - Contains properties to be merged. Either this or `message.lc_kwargs` should be provided.
* @param {Object} [message.kwargs.additional_kwargs] - Additional properties to be merged.
*
* @returns {Object} The formatted LangChain message.
*/
const formatFromLangChain = (message) => {
const { additional_kwargs, ...message_kwargs } = message.lc_kwargs ?? message.kwargs;
return {
...message_kwargs,
...additional_kwargs,
};
};
/**
* Formats an array of messages for LangChain, handling tool calls and creating ToolMessage instances.
*
* @param {Array<Partial<TMessage>>} payload - The array of messages to format.
* @returns {Array<(HumanMessage|AIMessage|SystemMessage|ToolMessage)>} - The array of formatted LangChain messages, including ToolMessages for tool calls.
*/
const formatAgentMessages = (payload) => {
const messages = [];
for (const message of payload) {
if (typeof message.content === 'string') {
message.content = [{ type: ContentTypes.TEXT, [ContentTypes.TEXT]: message.content }];
}
if (message.role !== 'assistant') {
messages.push(formatMessage({ message, langChain: true }));
continue;
}
let currentContent = [];
let lastAIMessage = null;
/**
* Every AIMessage produced from this TMessage that received `tool_calls`,
* in order. Multi-step tool turns (where the agent loop cycles the LLM
* multiple times with intervening tool results) produce one AIMessage per
* cycle, each owning a different `tool_call_id`. We attach persisted
* Vertex Gemini 3 thought signatures (`metadata.thoughtSignatures`,
* keyed by `tool_call_id`) onto each one so every step has its right
* signature on resume — Vertex validates per-step, not per-turn
* (issue #13006 follow-up).
*/
const toolBearingAIMessages = [];
let hasReasoning = false;
for (const part of message.content) {
if (part.type === ContentTypes.TEXT && part.tool_call_ids) {
/*
If there's pending content, it needs to be aggregated as a single string to prepare for tool calls.
For Anthropic models, the "tool_calls" field on a message is only respected if content is a string.
*/
if (currentContent.length > 0) {
let content = currentContent.reduce((acc, curr) => {
if (curr.type === ContentTypes.TEXT) {
return `${acc}${curr[ContentTypes.TEXT]}\n`;
}
return acc;
}, '');
content = `${content}\n${part[ContentTypes.TEXT] ?? ''}`.trim();
lastAIMessage = new AIMessage({ content });
messages.push(lastAIMessage);
currentContent = [];
continue;
}
// Create a new AIMessage with this text and prepare for tool calls
lastAIMessage = new AIMessage({
content: part.text || '',
});
messages.push(lastAIMessage);
} else if (part.type === ContentTypes.TOOL_CALL) {
if (!lastAIMessage) {
throw new Error('Invalid tool call structure: No preceding AIMessage with tool_call_ids');
}
// Note: `tool_calls` list is defined when constructed by `AIMessage` class, and outputs should be excluded from it
const { output, args: _args, ...tool_call } = part.tool_call;
// TODO: investigate; args as dictionary may need to be provider-or-tool-specific
let args = _args;
try {
args = JSON.parse(_args);
} catch (_e) {
if (typeof _args === 'string') {
args = { input: _args };
}
}
tool_call.args = args;
lastAIMessage.tool_calls.push(tool_call);
if (toolBearingAIMessages[toolBearingAIMessages.length - 1] !== lastAIMessage) {
toolBearingAIMessages.push(lastAIMessage);
}
// Add the corresponding ToolMessage
messages.push(
new ToolMessage({
tool_call_id: tool_call.id,
name: tool_call.name,
content: output || '',
}),
);
} else if (part.type === ContentTypes.THINK) {
hasReasoning = true;
continue;
} else if (part.type === ContentTypes.ERROR || part.type === ContentTypes.AGENT_UPDATE) {
continue;
} else {
currentContent.push(part);
}
}
if (hasReasoning) {
currentContent = currentContent
.reduce((acc, curr) => {
if (curr.type === ContentTypes.TEXT) {
return `${acc}${curr[ContentTypes.TEXT]}\n`;
}
return acc;
}, '')
.trim();
}
if (currentContent.length > 0) {
messages.push(new AIMessage({ content: currentContent }));
}
/**
* Restore signatures per-step. The persisted shape is
* `{ [tool_call_id]: signature }`; for each tool-bearing AIMessage we
* build a position-aligned `additional_kwargs.signatures` array (empty
* placeholders for tool_calls without a stored signature). Agents'
* `fixThoughtSignatures` then dispatches the non-empty entries to
* functionCall parts in order — order matches because non-empty
* signatures and tool_calls share their original parts ordering.
*/
const sigsByCallId = message.metadata?.thoughtSignatures;
if (sigsByCallId && typeof sigsByCallId === 'object' && toolBearingAIMessages.length > 0) {
for (const aiMsg of toolBearingAIMessages) {
const sigs = aiMsg.tool_calls.map((tc) => sigsByCallId[tc.id] ?? '');
if (sigs.some((s) => typeof s === 'string' && s.length > 0)) {
aiMsg.additional_kwargs ??= {};
aiMsg.additional_kwargs.signatures = sigs;
}
}
}
}
return messages;
};
module.exports = {
formatMessage,
formatFromLangChain,
formatAgentMessages,
formatLangChainMessages,
};