code
stringlengths 24
2.07M
| docstring
stringlengths 25
85.3k
| func_name
stringlengths 1
92
| language
stringclasses 1
value | repo
stringlengths 5
64
| path
stringlengths 4
172
| url
stringlengths 44
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|---|---|---|---|---|---|---|---|
async isValidChatCompletionModel(_modelName = "") {
return true;
}
|
Stubbed method for compatibility with LLM interface.
|
isValidChatCompletionModel
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const systemMessageContent = `${systemPrompt}${this.#appendContext(contextTexts)}`;
let messages = [];
// Handle system prompt (either real or simulated)
if (this.noSystemPromptModels.includes(this.model)) {
if (systemMessageContent.trim().length > 0) {
this.#log(
`Model ${this.model} doesn't support system prompts; simulating.`
);
messages.push(
{
role: "user",
content: this.#generateContent({
userPrompt: systemMessageContent,
}),
},
{ role: "assistant", content: [{ text: "Okay." }] }
);
}
} else if (systemMessageContent.trim().length > 0) {
messages.push({
role: "system",
content: this.#generateContent({ userPrompt: systemMessageContent }),
});
}
// Add chat history
messages = messages.concat(
chatHistory.map((msg) => ({
role: msg.role,
content: this.#generateContent({
userPrompt: msg.content,
attachments: Array.isArray(msg.attachments) ? msg.attachments : [],
}),
}))
);
// Add final user prompt
messages.push({
role: "user",
content: this.#generateContent({
userPrompt: userPrompt,
attachments: Array.isArray(attachments) ? attachments : [],
}),
});
return messages;
}
|
Constructs the complete message array in the format expected by the Bedrock Converse API.
@param {object} params
@param {string} params.systemPrompt - The system prompt text.
@param {string[]} params.contextTexts - Array of context text snippets.
@param {Array<{role: 'user' | 'assistant', content: string, attachments?: Array<{contentString: string, mime: string}>}>} params.chatHistory - Previous messages.
@param {string} params.userPrompt - The latest user prompt text.
@param {Array<{contentString: string, mime: string}>} params.attachments - Attachments for the latest user prompt.
@returns {Array<object>} The formatted message array for the API call.
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature }) {
if (!messages?.length)
throw new Error(
"AWSBedrock::getChatCompletion requires a non-empty messages array."
);
const hasSystem = messages[0]?.role === "system";
const systemBlock = hasSystem ? messages[0].content : undefined;
const history = hasSystem ? messages.slice(1) : messages;
const maxTokensToSend = this.getMaxOutputTokens();
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.bedrockClient
.send(
new ConverseCommand({
modelId: this.model,
messages: history,
inferenceConfig: {
maxTokens: maxTokensToSend,
temperature: temperature ?? this.defaultTemp,
},
system: systemBlock,
})
)
.catch((e) => {
this.#log(
`Bedrock Converse API Error (getChatCompletion): ${e.message}`,
e
);
if (
e.name === "ValidationException" &&
e.message.includes("maximum tokens")
) {
throw new Error(
`AWSBedrock::getChatCompletion failed. Model ${this.model} rejected maxTokens value of ${maxTokensToSend}. Check model documentation for its maximum output token limit and set AWS_BEDROCK_LLM_MAX_OUTPUT_TOKENS if needed. Original error: ${e.message}`
);
}
throw new Error(`AWSBedrock::getChatCompletion failed. ${e.message}`);
}),
messages,
false
);
const response = result.output;
if (!response?.output?.message) {
this.#log(
"Bedrock response missing expected output.message structure.",
response
);
return null;
}
const latencyMs = response?.metrics?.latencyMs;
const outputTokens = response?.usage?.outputTokens;
const outputTps =
latencyMs > 0 && outputTokens ? outputTokens / (latencyMs / 1000) : 0;
return {
textResponse: this.#parseReasoningFromResponse(response.output.message),
metrics: {
prompt_tokens: response?.usage?.inputTokens ?? 0,
completion_tokens: outputTokens ?? 0,
total_tokens: response?.usage?.totalTokens ?? 0,
outputTps: outputTps,
duration: result.duration,
},
};
}
|
Sends a request for chat completion (non-streaming).
@param {Array<object> | null} messages - Formatted message array from constructPrompt.
@param {object} options - Request options.
@param {number} options.temperature - Sampling temperature.
@returns {Promise<object | null>} Response object with textResponse and metrics, or null.
@throws {Error} If the API call fails or validation errors occur.
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature }) {
if (!Array.isArray(messages) || messages.length === 0) {
throw new Error(
"AWSBedrock::streamGetChatCompletion requires a non-empty messages array."
);
}
const hasSystem = messages[0]?.role === "system";
const systemBlock = hasSystem ? messages[0].content : undefined;
const history = hasSystem ? messages.slice(1) : messages;
const maxTokensToSend = this.getMaxOutputTokens();
try {
// Attempt to initiate the stream
const stream = await this.bedrockClient.send(
new ConverseStreamCommand({
modelId: this.model,
messages: history,
inferenceConfig: {
maxTokens: maxTokensToSend,
temperature: temperature ?? this.defaultTemp,
},
system: systemBlock,
})
);
// If successful, wrap the stream with performance monitoring
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
stream,
messages,
false // Indicate it's not a function call measurement
);
return measuredStreamRequest;
} catch (e) {
// Catch errors during the initial .send() call (e.g., validation errors)
this.#log(
`Bedrock Converse API Error (streamGetChatCompletion setup): ${e.message}`,
e
);
if (
e.name === "ValidationException" &&
e.message.includes("maximum tokens")
) {
throw new Error(
`AWSBedrock::streamGetChatCompletion failed during setup. Model ${this.model} rejected maxTokens value of ${maxTokensToSend}. Check model documentation for its maximum output token limit and set AWS_BEDROCK_LLM_MAX_OUTPUT_TOKENS if needed. Original error: ${e.message}`
);
}
throw new Error(
`AWSBedrock::streamGetChatCompletion failed during setup. ${e.message}`
);
}
}
|
Sends a request for streaming chat completion.
@param {Array<object> | null} messages - Formatted message array from constructPrompt.
@param {object} options - Request options.
@param {number} [options.temperature] - Sampling temperature.
@returns {Promise<import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream>} The monitored stream object.
@throws {Error} If the API call setup fails or validation errors occur.
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
let hasUsageMetrics = false;
let usage = { prompt_tokens: 0, completion_tokens: 0, total_tokens: 0 };
return new Promise(async (resolve) => {
let fullText = "";
let reasoningText = "";
// Abort handler for client closing connection
const handleAbort = () => {
this.#log(`Client closed connection for stream ${uuid}. Aborting.`);
stream?.endMeasurement(usage); // Finalize metrics
clientAbortedHandler(resolve, fullText); // Resolve with partial text
};
response.on("close", handleAbort);
try {
// Process stream chunks
for await (const chunk of stream.stream) {
if (!chunk) {
this.#log("Stream returned null/undefined chunk.");
continue;
}
const action = Object.keys(chunk)[0];
switch (action) {
case "metadata": // Contains usage metrics at the end
if (chunk.metadata?.usage) {
hasUsageMetrics = true;
usage = {
// Overwrite with final metrics
prompt_tokens: chunk.metadata.usage.inputTokens ?? 0,
completion_tokens: chunk.metadata.usage.outputTokens ?? 0,
total_tokens: chunk.metadata.usage.totalTokens ?? 0,
};
}
break;
case "contentBlockDelta": {
// Contains text or reasoning deltas
const delta = chunk.contentBlockDelta?.delta;
if (!delta) break;
const token = delta.text;
const reasoningToken = delta.reasoningContent?.text;
if (reasoningToken) {
// Handle reasoning text
if (reasoningText.length === 0) {
// Start of reasoning block
const startTag = "<think>";
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: startTag + reasoningToken,
close: false,
error: false,
});
reasoningText += startTag + reasoningToken;
} else {
// Continuation of reasoning block
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: reasoningToken,
close: false,
error: false,
});
reasoningText += reasoningToken;
}
} else if (token) {
// Handle regular text
if (reasoningText.length > 0) {
// If reasoning was just output, close the tag
const endTag = "</think>";
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: endTag,
close: false,
error: false,
});
fullText += reasoningText + endTag; // Add completed reasoning to final text
reasoningText = ""; // Reset reasoning buffer
}
fullText += token; // Append regular text
if (!hasUsageMetrics) usage.completion_tokens++; // Estimate usage if no metrics yet
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
break;
}
case "messageStop": // End of message event
if (chunk.messageStop?.usage) {
// Check for final metrics here too
hasUsageMetrics = true;
usage = {
// Overwrite with final metrics if available
prompt_tokens:
chunk.messageStop.usage.inputTokens ?? usage.prompt_tokens,
completion_tokens:
chunk.messageStop.usage.outputTokens ??
usage.completion_tokens,
total_tokens:
chunk.messageStop.usage.totalTokens ?? usage.total_tokens,
};
}
// Ensure reasoning tag is closed if message stops mid-reasoning
if (reasoningText.length > 0) {
const endTag = "</think>";
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: endTag,
close: false,
error: false,
});
fullText += reasoningText + endTag;
reasoningText = "";
}
break;
// Ignore other event types for now
case "messageStart":
case "contentBlockStart":
case "contentBlockStop":
break;
default:
this.#log(`Unhandled stream action: ${action}`, chunk);
}
} // End for await loop
// Final cleanup for reasoning tag in case stream ended abruptly
if (reasoningText.length > 0 && !fullText.endsWith("</think>")) {
const endTag = "</think>";
if (!response.writableEnded) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: endTag,
close: false,
error: false,
});
}
fullText += reasoningText + endTag;
}
// Send final closing chunk to signal end of stream
if (!response.writableEnded) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
}
} catch (error) {
// Handle errors during stream processing
this.#log(
`\x1b[43m\x1b[34m[STREAMING ERROR]\x1b[0m ${error.message}`,
error
);
if (response && !response.writableEnded) {
writeResponseChunk(response, {
uuid,
type: "abort",
textResponse: null,
sources,
close: true,
error: `AWSBedrock:streaming - error. ${
error?.message ?? "Unknown error"
}`,
});
}
} finally {
response.removeListener("close", handleAbort);
stream?.endMeasurement(usage);
resolve(fullText); // Resolve with the accumulated text
}
});
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
handleAbort = () => {
this.#log(`Client closed connection for stream ${uuid}. Aborting.`);
stream?.endMeasurement(usage); // Finalize metrics
clientAbortedHandler(resolve, fullText); // Resolve with partial text
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
handleAbort = () => {
this.#log(`Client closed connection for stream ${uuid}. Aborting.`);
stream?.endMeasurement(usage); // Finalize metrics
clientAbortedHandler(resolve, fullText); // Resolve with partial text
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Handles the stream response from the AWS Bedrock API ConverseStreamCommand.
Parses chunks, handles reasoning tags, and estimates token usage if not provided.
@param {object} response - The HTTP response object to write chunks to.
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - The monitored stream object from streamGetChatCompletion.
@param {object} responseProps - Additional properties for the response chunks.
@param {string} responseProps.uuid - Unique ID for the response.
@param {Array} responseProps.sources - Source documents used (if any).
@returns {Promise<string>} A promise that resolves with the complete text response when the stream ends.
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/index.js
|
MIT
|
function getImageFormatFromMime(mimeType = "") {
if (!mimeType) return null;
const parts = mimeType.toLowerCase().split("/");
if (parts?.[0] !== "image") return null;
let format = parts?.[1];
if (!format) return null;
// Remap jpg to jpeg
switch (format) {
case "jpg":
format = "jpeg";
break;
default:
break;
}
if (!SUPPORTED_BEDROCK_IMAGE_FORMATS.includes(format)) return null;
return format;
}
|
Parses a MIME type string (e.g., "image/jpeg") to extract and validate the image format
supported by Bedrock Converse. Handles 'image/jpg' as 'jpeg'.
@param {string | null | undefined} mimeType - The MIME type string.
@returns {string | null} The validated image format (e.g., "jpeg") or null if invalid/unsupported.
|
getImageFormatFromMime
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/utils.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/utils.js
|
MIT
|
function base64ToUint8Array(base64String) {
try {
const binaryString = atob(base64String);
const len = binaryString.length;
const bytes = new Uint8Array(len);
for (let i = 0; i < len; i++) bytes[i] = binaryString.charCodeAt(i);
return bytes;
} catch (e) {
console.error(
`[AWSBedrock] Error decoding base64 string with atob: ${e.message}`
);
return null;
}
}
|
Decodes a pure base64 string (without data URI prefix) into a Uint8Array using the atob method.
This approach matches the technique previously used by Langchain's implementation.
@param {string} base64String - The pure base64 encoded data.
@returns {Uint8Array | null} The resulting byte array or null on decoding error.
|
base64ToUint8Array
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/bedrock/utils.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/bedrock/utils.js
|
MIT
|
async handleStream(response, stream, responseProps) {
return new Promise(async (resolve) => {
const { uuid = v4(), sources = [] } = responseProps;
let fullText = "";
let usage = {
prompt_tokens: 0,
completion_tokens: 0,
};
const handleAbort = () => {
writeResponseChunk(response, {
uuid,
sources,
type: "abort",
textResponse: fullText,
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream.endMeasurement(usage);
resolve(fullText);
};
response.on("close", handleAbort);
try {
for await (const chat of stream) {
if (chat.eventType === "stream-end") {
const usageMetrics = chat?.response?.meta?.tokens || {};
usage.prompt_tokens = usageMetrics.inputTokens || 0;
usage.completion_tokens = usageMetrics.outputTokens || 0;
}
if (chat.eventType === "text-generation") {
const text = chat.text;
fullText += text;
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: text,
close: false,
error: false,
});
}
}
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream.endMeasurement(usage);
resolve(fullText);
} catch (error) {
writeResponseChunk(response, {
uuid,
sources,
type: "abort",
textResponse: null,
close: true,
error: error.message,
});
response.removeListener("close", handleAbort);
stream.endMeasurement(usage);
resolve(fullText);
}
});
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
handleAbort = () => {
writeResponseChunk(response, {
uuid,
sources,
type: "abort",
textResponse: fullText,
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream.endMeasurement(usage);
resolve(fullText);
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
handleAbort = () => {
writeResponseChunk(response, {
uuid,
sources,
type: "abort",
textResponse: fullText,
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream.endMeasurement(usage);
resolve(fullText);
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Handles the stream response from the Cohere API.
@param {Object} response - the response object
@param {import('../../helpers/chat/LLMPerformanceMonitor').MonitoredStream} stream - the stream response from the Cohere API w/tracking
@param {Object} responseProps - the response properties
@returns {Promise<string>}
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/cohere/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/cohere/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`DeepSeek chat: ${this.model} is not valid for chat completion!`
);
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result?.output?.hasOwnProperty("choices") ||
result?.output?.choices?.length === 0
)
throw new Error(
`Invalid response body returned from DeepSeek: ${JSON.stringify(result.output)}`
);
return {
textResponse: this.#parseReasoningFromResponse(result.output.choices[0]),
metrics: {
prompt_tokens: result.output.usage.prompt_tokens || 0,
completion_tokens: result.output.usage.completion_tokens || 0,
total_tokens: result.output.usage.total_tokens || 0,
outputTps: result.output.usage.completion_tokens / result.duration,
duration: result.duration,
},
};
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`DeepSeek chat: ${this.model} is not valid for chat completion!`
);
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages,
false
);
return measuredStreamRequest;
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
let hasUsageMetrics = false;
let usage = {
completion_tokens: 0,
};
return new Promise(async (resolve) => {
let fullText = "";
let reasoningText = "";
// Establish listener to early-abort a streaming response
// in case things go sideways or the user does not like the response.
// We preserve the generated text but continue as if chat was completed
// to preserve previously generated content.
const handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
};
response.on("close", handleAbort);
try {
for await (const chunk of stream) {
const message = chunk?.choices?.[0];
const token = message?.delta?.content;
const reasoningToken = message?.delta?.reasoning_content;
if (
chunk.hasOwnProperty("usage") && // exists
!!chunk.usage && // is not null
Object.values(chunk.usage).length > 0 // has values
) {
if (chunk.usage.hasOwnProperty("prompt_tokens")) {
usage.prompt_tokens = Number(chunk.usage.prompt_tokens);
}
if (chunk.usage.hasOwnProperty("completion_tokens")) {
hasUsageMetrics = true; // to stop estimating counter
usage.completion_tokens = Number(chunk.usage.completion_tokens);
}
}
// Reasoning models will always return the reasoning text before the token text.
if (reasoningToken) {
// If the reasoning text is empty (''), we need to initialize it
// and send the first chunk of reasoning text.
if (reasoningText.length === 0) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: `<think>${reasoningToken}`,
close: false,
error: false,
});
reasoningText += `<think>${reasoningToken}`;
continue;
} else {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: reasoningToken,
close: false,
error: false,
});
reasoningText += reasoningToken;
}
}
// If the reasoning text is not empty, but the reasoning token is empty
// and the token text is not empty we need to close the reasoning text and begin sending the token text.
if (!!reasoningText && !reasoningToken && token) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: `</think>`,
close: false,
error: false,
});
fullText += `${reasoningText}</think>`;
reasoningText = "";
}
if (token) {
fullText += token;
// If we never saw a usage metric, we can estimate them by number of completion chunks
if (!hasUsageMetrics) usage.completion_tokens++;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
// LocalAi returns '' and others return null on chunks - the last chunk is not "" or null.
// Either way, the key `finish_reason` must be present to determine ending chunk.
if (
message?.hasOwnProperty("finish_reason") && // Got valid message and it is an object with finish_reason
message.finish_reason !== "" &&
message.finish_reason !== null
) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream?.endMeasurement(usage);
resolve(fullText);
break; // Break streaming when a valid finish_reason is first encountered
}
}
} catch (e) {
console.log(`\x1b[43m\x1b[34m[STREAMING ERROR]\x1b[0m ${e.message}`);
writeResponseChunk(response, {
uuid,
type: "abort",
textResponse: null,
sources: [],
close: true,
error: e.message,
});
stream?.endMeasurement(usage);
resolve(fullText); // Return what we currently have - if anything.
}
});
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/deepseek/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/deepseek/index.js
|
MIT
|
static parseBasePath(providedBasePath = process.env.DPAIS_LLM_BASE_PATH) {
try {
const baseURL = new URL(providedBasePath);
const basePath = `${baseURL.origin}/v1/openai`;
return basePath;
} catch (e) {
return null;
}
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
parseBasePath
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
log(text, ...args) {
console.log(`\x1b[36m[${this.constructor.name}]\x1b[0m ${text}`, ...args);
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
log
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
streamingEnabled
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
static promptWindowLimit(_modelName) {
const limit = process.env.DPAIS_LLM_MODEL_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No Dell Pro AI Studio token context limit was set.");
return Number(limit);
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
promptWindowLimit
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
promptWindowLimit() {
const limit = process.env.DPAIS_LLM_MODEL_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No Dell Pro AI Studio token context limit was set.");
return Number(limit);
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
promptWindowLimit
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async isValidChatCompletionModel(_ = "") {
return true;
}
|
Parse the base path for the Dell Pro AI Studio API
so we can use it for inference requests
@param {string} providedBasePath
@returns {string}
|
isValidChatCompletionModel
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
_attachments = [], // not used for Dell Pro AI Studio - `attachments` passed in is ignored
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, _attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.model)
throw new Error(
`Dell Pro AI Studio chat: ${this.model} is not valid or defined model for chat completion!`
);
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.dpais.chat.completions.create({
model: this.model,
messages,
temperature,
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage?.prompt_tokens || 0,
completion_tokens: result.output.usage?.completion_tokens || 0,
total_tokens: result.output.usage?.total_tokens || 0,
outputTps: result.output.usage?.completion_tokens / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.model)
throw new Error(
`Dell Pro AI Studio chat: ${this.model} is not valid or defined model for chat completion!`
);
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.dpais.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/dellProAiStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/dellProAiStudio/index.js
|
MIT
|
get supportsSystemPrompt() {
return !NO_SYSTEM_PROMPT_MODELS.includes(this.model);
}
|
Checks if the model supports system prompts
This is a static list of models that are known to not support system prompts
since this information is not available in the API model response.
@returns {boolean}
|
supportsSystemPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
static cacheIsStale() {
const MAX_STALE = 8.64e7; // 1 day in MS
if (!fs.existsSync(path.resolve(cacheFolder, ".cached_at"))) return true;
const now = Number(new Date());
const timestampMs = Number(
fs.readFileSync(path.resolve(cacheFolder, ".cached_at"))
);
return now - timestampMs > MAX_STALE;
}
|
Checks if the model supports system prompts
This is a static list of models that are known to not support system prompts
since this information is not available in the API model response.
@returns {boolean}
|
cacheIsStale
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
|
Checks if the model supports system prompts
This is a static list of models that are known to not support system prompts
since this information is not available in the API model response.
@returns {boolean}
|
streamingEnabled
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
static promptWindowLimit(modelName) {
try {
const cacheModelPath = path.resolve(cacheFolder, "models.json");
if (!fs.existsSync(cacheModelPath))
return MODEL_MAP.get("gemini", modelName) ?? 30_720;
const models = safeJsonParse(fs.readFileSync(cacheModelPath));
const model = models.find((model) => model.id === modelName);
if (!model)
throw new Error(
"Model not found in cache - falling back to default model."
);
return model.contextWindow;
} catch (e) {
console.error(`GeminiLLM:promptWindowLimit`, e.message);
return MODEL_MAP.get("gemini", modelName) ?? 30_720;
}
}
|
Checks if the model supports system prompts
This is a static list of models that are known to not support system prompts
since this information is not available in the API model response.
@returns {boolean}
|
promptWindowLimit
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
promptWindowLimit() {
try {
if (!fs.existsSync(this.cacheModelPath))
return MODEL_MAP.get("gemini", this.model) ?? 30_720;
const models = safeJsonParse(fs.readFileSync(this.cacheModelPath));
const model = models.find((model) => model.id === this.model);
if (!model)
throw new Error(
"Model not found in cache - falling back to default model."
);
return model.contextWindow;
} catch (e) {
console.error(`GeminiLLM:promptWindowLimit`, e.message);
return MODEL_MAP.get("gemini", this.model) ?? 30_720;
}
}
|
Checks if the model supports system prompts
This is a static list of models that are known to not support system prompts
since this information is not available in the API model response.
@returns {boolean}
|
promptWindowLimit
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
isExperimentalModel(modelName) {
if (
fs.existsSync(cacheFolder) &&
fs.existsSync(path.resolve(cacheFolder, "models.json"))
) {
const models = safeJsonParse(
fs.readFileSync(path.resolve(cacheFolder, "models.json"))
);
const model = models.find((model) => model.id === modelName);
if (!model) return false;
return model.experimental;
}
return modelName.includes("exp") || v1BetaModels.includes(modelName);
}
|
Checks if a model is experimental by reading from the cache if available, otherwise it will perform
a blind check against the v1BetaModels list - which is manually maintained and updated.
@param {string} modelName - The name of the model to check
@returns {boolean} A boolean indicating if the model is experimental
|
isExperimentalModel
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async isValidChatCompletionModel(modelName = "") {
const models = await this.fetchModels(process.env.GEMINI_API_KEY);
return models.some((model) => model.id === modelName);
}
|
Checks if a model is valid for chat completion (unused)
@deprecated
@param {string} modelName - The name of the model to check
@returns {Promise<boolean>} A promise that resolves to a boolean indicating if the model is valid
|
isValidChatCompletionModel
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [], // This is the specific attachment for only this prompt
}) {
let prompt = [];
if (this.supportsSystemPrompt) {
prompt.push({
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
});
} else {
this.#log(
`${this.model} - does not support system prompts - emulating...`
);
prompt.push(
{
role: "user",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
},
{
role: "assistant",
content: "Okay.",
}
);
}
return [
...prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature: temperature,
})
.catch((e) => {
console.error(e);
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage.prompt_tokens || 0,
completion_tokens: result.output.usage.completion_tokens || 0,
total_tokens: result.output.usage.total_tokens || 0,
outputTps: result.output.usage.completion_tokens / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature: temperature,
}),
messages,
true
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/gemini/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/gemini/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
max_tokens: this.maxTokens,
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: this.#parseReasoningFromResponse(result.output.choices[0]),
metrics: {
prompt_tokens: result.output?.usage?.prompt_tokens || 0,
completion_tokens: result.output?.usage?.completion_tokens || 0,
total_tokens: result.output?.usage?.total_tokens || 0,
outputTps:
(result.output?.usage?.completion_tokens || 0) / result.duration,
duration: result.duration,
},
};
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
max_tokens: this.maxTokens,
}),
messages
// runPromptTokenCalculation: true - There is not way to know if the generic provider connected is returning
// the correct usage metrics if any at all since any provider could be connected.
);
return measuredStreamRequest;
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
let hasUsageMetrics = false;
let usage = {
completion_tokens: 0,
};
return new Promise(async (resolve) => {
let fullText = "";
let reasoningText = "";
// Establish listener to early-abort a streaming response
// in case things go sideways or the user does not like the response.
// We preserve the generated text but continue as if chat was completed
// to preserve previously generated content.
const handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
};
response.on("close", handleAbort);
try {
for await (const chunk of stream) {
const message = chunk?.choices?.[0];
const token = message?.delta?.content;
const reasoningToken = message?.delta?.reasoning_content;
if (
chunk.hasOwnProperty("usage") && // exists
!!chunk.usage && // is not null
Object.values(chunk.usage).length > 0 // has values
) {
if (chunk.usage.hasOwnProperty("prompt_tokens")) {
usage.prompt_tokens = Number(chunk.usage.prompt_tokens);
}
if (chunk.usage.hasOwnProperty("completion_tokens")) {
hasUsageMetrics = true; // to stop estimating counter
usage.completion_tokens = Number(chunk.usage.completion_tokens);
}
}
// Reasoning models will always return the reasoning text before the token text.
if (reasoningToken) {
// If the reasoning text is empty (''), we need to initialize it
// and send the first chunk of reasoning text.
if (reasoningText.length === 0) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: `<think>${reasoningToken}`,
close: false,
error: false,
});
reasoningText += `<think>${reasoningToken}`;
continue;
} else {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: reasoningToken,
close: false,
error: false,
});
reasoningText += reasoningToken;
}
}
// If the reasoning text is not empty, but the reasoning token is empty
// and the token text is not empty we need to close the reasoning text and begin sending the token text.
if (!!reasoningText && !reasoningToken && token) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: `</think>`,
close: false,
error: false,
});
fullText += `${reasoningText}</think>`;
reasoningText = "";
}
if (token) {
fullText += token;
// If we never saw a usage metric, we can estimate them by number of completion chunks
if (!hasUsageMetrics) usage.completion_tokens++;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
if (
message?.hasOwnProperty("finish_reason") && // Got valid message and it is an object with finish_reason
message.finish_reason !== "" &&
message.finish_reason !== null
) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
stream?.endMeasurement(usage);
resolve(fullText);
break; // Break streaming when a valid finish_reason is first encountered
}
}
} catch (e) {
console.log(`\x1b[43m\x1b[34m[STREAMING ERROR]\x1b[0m ${e.message}`);
writeResponseChunk(response, {
uuid,
type: "abort",
textResponse: null,
sources: [],
close: true,
error: e.message,
});
stream?.endMeasurement(usage);
resolve(fullText);
}
});
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
handleAbort = () => {
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Parses and prepends reasoning from the response and returns the full text response.
@param {Object} response
@returns {string}
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/genericOpenAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/genericOpenAi/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [], // This is the specific attachment for only this prompt
}) {
// NOTICE: SEE GroqLLM.#conditionalPromptStruct for more information on how attachments are handled with Groq.
return this.#conditionalPromptStruct({
systemPrompt,
contextTexts,
chatHistory,
userPrompt,
attachments,
});
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`GroqAI:chatCompletion: ${this.model} is not valid for chat completion!`
);
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage.prompt_tokens || 0,
completion_tokens: result.output.usage.completion_tokens || 0,
total_tokens: result.output.usage.total_tokens || 0,
outputTps:
result.output.usage.completion_tokens /
result.output.usage.completion_time,
duration: result.output.usage.total_time,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`GroqAI:streamChatCompletion: ${this.model} is not valid for chat completion!`
);
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages,
false
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/groq/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/groq/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
max_tokens: this.maxTokens,
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
const promptTokens = LLMPerformanceMonitor.countTokens(messages);
const completionTokens = LLMPerformanceMonitor.countTokens([
{ content: result.output.choices[0].message.content },
]);
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: promptTokens,
completion_tokens: completionTokens,
total_tokens: promptTokens + completionTokens,
outputTps: completionTokens / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
max_tokens: this.maxTokens,
}),
messages
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise(async (resolve) => {
let fullText = "";
let usage = {
prompt_tokens: LLMPerformanceMonitor.countTokens(stream.messages || []),
completion_tokens: 0,
};
const handleAbort = () => {
usage.completion_tokens = LLMPerformanceMonitor.countTokens([
{ content: fullText },
]);
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
};
response.on("close", handleAbort);
for await (const chunk of stream) {
const message = chunk?.choices?.[0];
const token = message?.delta?.content;
if (token) {
fullText += token;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
// KoboldCPP finishes with "length" or "stop"
if (
message.finish_reason !== "null" &&
(message.finish_reason === "length" ||
message.finish_reason === "stop")
) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
usage.completion_tokens = LLMPerformanceMonitor.countTokens([
{ content: fullText },
]);
stream?.endMeasurement(usage);
resolve(fullText);
}
}
});
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
handleAbort = () => {
usage.completion_tokens = LLMPerformanceMonitor.countTokens([
{ content: fullText },
]);
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
handleAbort = () => {
usage.completion_tokens = LLMPerformanceMonitor.countTokens([
{ content: fullText },
]);
stream?.endMeasurement(usage);
clientAbortedHandler(resolve, fullText);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleAbort
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/koboldCPP/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/koboldCPP/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions
.create({
model: this.model,
messages,
temperature,
max_tokens: parseInt(this.maxTokens), // LiteLLM requires int
})
.catch((e) => {
throw new Error(e.message);
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage?.prompt_tokens || 0,
completion_tokens: result.output.usage?.completion_tokens || 0,
total_tokens: result.output.usage?.total_tokens || 0,
outputTps:
(result.output.usage?.completion_tokens || 0) / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
max_tokens: parseInt(this.maxTokens), // LiteLLM requires int
}),
messages
// runPromptTokenCalculation: true - We manually count the tokens because they may or may not be provided in the stream
// responses depending on LLM connected. If they are provided, then we counted for nothing, but better than nothing.
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/liteLLM/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/liteLLM/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.model)
throw new Error(
`LMStudio chat: ${this.model} is not valid or defined model for chat completion!`
);
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.lmstudio.chat.completions.create({
model: this.model,
messages,
temperature,
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: result.output.usage?.prompt_tokens || 0,
completion_tokens: result.output.usage?.completion_tokens || 0,
total_tokens: result.output.usage?.total_tokens || 0,
outputTps: result.output.usage?.completion_tokens / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.model)
throw new Error(
`LMStudio chat: ${this.model} is not valid or defined model for chat completion!`
);
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.lmstudio.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
function parseLMStudioBasePath(providedBasePath = "") {
try {
const baseURL = new URL(providedBasePath);
const basePath = `${baseURL.origin}/v1`;
return basePath;
} catch (e) {
return providedBasePath;
}
}
|
Parse the base path for the LMStudio API. Since the base path must end in /v1 and cannot have a trailing slash,
and the user can possibly set it to anything and likely incorrectly due to pasting behaviors, we need to ensure it is in the correct format.
@param {string} basePath
@returns {string}
|
parseLMStudioBasePath
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/lmStudio/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/lmStudio/index.js
|
MIT
|
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
attachments = [],
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [
prompt,
...formatChatHistory(chatHistory, this.#generateContent),
{
role: "user",
content: this.#generateContent({ userPrompt, attachments }),
},
];
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
constructPrompt
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`LocalAI chat: ${this.model} is not valid for chat completion!`
);
const result = await LLMPerformanceMonitor.measureAsyncFunction(
this.openai.chat.completions.create({
model: this.model,
messages,
temperature,
})
);
if (
!result.output.hasOwnProperty("choices") ||
result.output.choices.length === 0
)
return null;
const promptTokens = LLMPerformanceMonitor.countTokens(messages);
const completionTokens = LLMPerformanceMonitor.countTokens(
result.output.choices[0].message.content
);
return {
textResponse: result.output.choices[0].message.content,
metrics: {
prompt_tokens: promptTokens,
completion_tokens: completionTokens,
total_tokens: promptTokens + completionTokens,
outputTps: completionTokens / result.duration,
duration: result.duration,
},
};
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
getChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`LocalAi chat: ${this.model} is not valid for chat completion!`
);
const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
this.openai.chat.completions.create({
model: this.model,
stream: true,
messages,
temperature,
}),
messages
);
return measuredStreamRequest;
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
streamGetChatCompletion
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
handleStream(response, stream, responseProps) {
return handleDefaultStreamResponseV2(response, stream, responseProps);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
handleStream
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedTextInput
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
embedChunks
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
|
Construct the user prompt for this model.
@param {{attachments: import("../../helpers").Attachment[]}} param0
@returns
|
compressMessages
|
javascript
|
Mintplex-Labs/anything-llm
|
server/utils/AiProviders/localAi/index.js
|
https://github.com/Mintplex-Labs/anything-llm/blob/master/server/utils/AiProviders/localAi/index.js
|
MIT
|
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