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0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/connery.ts
|
import {
AsyncCaller,
AsyncCallerParams,
} from "@langchain/core/utils/async_caller";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { StructuredTool } from "@langchain/core/tools";
import { ZodOptional, ZodString, z } from "zod";
/**
* An object containing configuration parameters for the ConneryService class.
* @extends AsyncCallerParams
*/
export interface ConneryServiceParams extends AsyncCallerParams {
runnerUrl: string;
apiKey: string;
}
type ApiResponse<T> = {
status: "success";
data: T;
};
type ApiErrorResponse = {
status: "error";
error: {
message: string;
};
};
type Parameter = {
key: string;
title: string;
description: string;
type: string;
validation?: {
required?: boolean;
};
};
type Action = {
id: string;
key: string;
title: string;
description: string;
type: string;
inputParameters: Parameter[];
outputParameters: Parameter[];
pluginId: string;
};
type Input = Record<string, string | undefined>;
type Output = Record<string, string>;
type RunActionResult = {
output: Output;
used: {
actionId: string;
input: Input;
};
};
/**
* A LangChain Tool object wrapping a Connery action.
* ConneryAction is a structured tool that can be used only in the agents supporting structured tools.
* @extends StructuredTool
*/
export class ConneryAction extends StructuredTool {
name: string;
description: string;
schema: z.ZodObject<Record<string, ZodString | ZodOptional<ZodString>>>;
/**
* Creates a ConneryAction instance based on the provided Connery Action.
* @param _action The Connery Action.
* @param _service The ConneryService instance.
* @returns A ConneryAction instance.
*/
constructor(protected _action: Action, protected _service: ConneryService) {
super();
this.name = this._action.id;
this.description =
this._action.title +
(this._action.description ? `: ${this._action.description}` : "");
this.schema = this.createInputSchema();
}
/**
* Runs the Connery Action with the provided input.
* @param arg The input object expected by the action.
* @returns A promise that resolves to a JSON string containing the output of the action.
*/
protected _call(arg: z.output<typeof this.schema>): Promise<string> {
return this._service.runAction(this._action.id, arg);
}
/**
* Creates a Zod schema for the input object expected by the Connery action.
* @returns A Zod schema for the input object expected by the Connery action.
*/
protected createInputSchema(): z.ZodObject<
Record<string, ZodString | ZodOptional<ZodString>>
> {
const dynamicInputFields: Record<
string,
ZodString | ZodOptional<ZodString>
> = {};
this._action.inputParameters.forEach((param) => {
const isRequired = param.validation?.required ?? false;
let fieldSchema: ZodString | ZodOptional<ZodString> = z.string();
fieldSchema = isRequired ? fieldSchema : fieldSchema.optional();
const fieldDescription =
param.title + (param.description ? `: ${param.description}` : "");
fieldSchema = fieldSchema.describe(fieldDescription);
dynamicInputFields[param.key] = fieldSchema;
});
return z.object(dynamicInputFields);
}
}
/**
* A service for working with Connery Actions.
*/
export class ConneryService {
protected runnerUrl: string;
protected apiKey: string;
protected asyncCaller: AsyncCaller;
/**
* Creates a ConneryService instance.
* @param params A ConneryServiceParams object.
* If not provided, the values are retrieved from the CONNERY_RUNNER_URL
* and CONNERY_RUNNER_API_KEY environment variables.
* @returns A ConneryService instance.
*/
constructor(params?: ConneryServiceParams) {
const runnerUrl =
params?.runnerUrl ?? getEnvironmentVariable("CONNERY_RUNNER_URL");
const apiKey =
params?.apiKey ?? getEnvironmentVariable("CONNERY_RUNNER_API_KEY");
if (!runnerUrl || !apiKey) {
throw new Error(
"CONNERY_RUNNER_URL and CONNERY_RUNNER_API_KEY environment variables must be set."
);
}
this.runnerUrl = runnerUrl;
this.apiKey = apiKey;
this.asyncCaller = new AsyncCaller(params ?? {});
}
/**
* Returns the list of Connery Actions wrapped as a LangChain StructuredTool objects.
* @returns A promise that resolves to an array of ConneryAction objects.
*/
async listActions(): Promise<ConneryAction[]> {
const actions = await this._listActions();
return actions.map((action) => new ConneryAction(action, this));
}
/**
* Returns the specified Connery action wrapped as a LangChain StructuredTool object.
* @param actionId The ID of the action to return.
* @returns A promise that resolves to a ConneryAction object.
*/
async getAction(actionId: string): Promise<ConneryAction> {
const action = await this._getAction(actionId);
return new ConneryAction(action, this);
}
/**
* Runs the specified Connery action with the provided input.
* @param actionId The ID of the action to run.
* @param input The input object expected by the action.
* @returns A promise that resolves to a JSON string containing the output of the action.
*/
async runAction(actionId: string, input: Input = {}): Promise<string> {
const result = await this._runAction(actionId, input);
return JSON.stringify(result);
}
/**
* Returns the list of actions available in the Connery runner.
* @returns A promise that resolves to an array of Action objects.
*/
protected async _listActions(): Promise<Action[]> {
const response = await this.asyncCaller.call(
fetch,
`${this.runnerUrl}/v1/actions`,
{
method: "GET",
headers: this._getHeaders(),
}
);
await this._handleError(response, "Failed to list actions");
const apiResponse: ApiResponse<Action[]> = await response.json();
return apiResponse.data;
}
/**
* Returns the specified action available in the Connery runner.
* @param actionId The ID of the action to return.
* @returns A promise that resolves to an Action object.
* @throws An error if the action with the specified ID is not found.
*/
protected async _getAction(actionId: string): Promise<Action> {
const actions = await this._listActions();
const action = actions.find((a) => a.id === actionId);
if (!action) {
throw new Error(
`The action with ID "${actionId}" was not found in the list of available actions in the Connery runner.`
);
}
return action;
}
/**
* Runs the specified Connery action with the provided input.
* @param actionId The ID of the action to run.
* @param input The input object expected by the action.
* @returns A promise that resolves to a RunActionResult object.
*/
protected async _runAction(
actionId: string,
input: Input = {}
): Promise<Output> {
const response = await this.asyncCaller.call(
fetch,
`${this.runnerUrl}/v1/actions/${actionId}/run`,
{
method: "POST",
headers: this._getHeaders(),
body: JSON.stringify({
input,
}),
}
);
await this._handleError(response, "Failed to run action");
const apiResponse: ApiResponse<RunActionResult> = await response.json();
return apiResponse.data.output;
}
/**
* Returns a standard set of HTTP headers to be used in API calls to the Connery runner.
* @returns An object containing the standard set of HTTP headers.
*/
protected _getHeaders(): Record<string, string> {
return {
"Content-Type": "application/json",
"x-api-key": this.apiKey,
};
}
/**
* Shared error handler for API calls to the Connery runner.
* If the response is not ok, an error is thrown containing the error message returned by the Connery runner.
* Otherwise, the promise resolves to void.
* @param response The response object returned by the Connery runner.
* @param errorMessage The error message to be used in the error thrown if the response is not ok.
* @returns A promise that resolves to void.
* @throws An error containing the error message returned by the Connery runner.
*/
protected async _handleError(
response: Response,
errorMessage: string
): Promise<void> {
if (response.ok) return;
const apiErrorResponse: ApiErrorResponse = await response.json();
throw new Error(
`${errorMessage}. Status code: ${response.status}. Error message: ${apiErrorResponse.error.message}`
);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/brave_search.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
/**
* Interface for the parameters required to instantiate a BraveSearch
* instance.
*/
export interface BraveSearchParams {
apiKey?: string;
}
/**
* Class for interacting with the Brave Search engine. It extends the Tool
* class and requires an API key to function. The API key can be passed in
* during instantiation or set as an environment variable named
* 'BRAVE_SEARCH_API_KEY'.
*/
export class BraveSearch extends Tool {
static lc_name() {
return "BraveSearch";
}
name = "brave-search";
description =
"a search engine. useful for when you need to answer questions about current events. input should be a search query.";
apiKey: string;
constructor(
fields: BraveSearchParams = {
apiKey: getEnvironmentVariable("BRAVE_SEARCH_API_KEY"),
}
) {
super();
if (!fields.apiKey) {
throw new Error(
`Brave API key not set. Please pass it in or set it as an environment variable named "BRAVE_SEARCH_API_KEY".`
);
}
this.apiKey = fields.apiKey;
}
/** @ignore */
async _call(input: string): Promise<string> {
const headers = {
"X-Subscription-Token": this.apiKey,
Accept: "application/json",
};
const searchUrl = new URL(
`https://api.search.brave.com/res/v1/web/search?q=${encodeURIComponent(
input
)}`
);
const response = await fetch(searchUrl, { headers });
if (!response.ok) {
throw new Error(`HTTP error ${response.status}`);
}
const parsedResponse = await response.json();
const webSearchResults = parsedResponse.web?.results;
const finalResults = Array.isArray(webSearchResults)
? webSearchResults.map(
(item: { title?: string; url?: string; description?: string }) => ({
title: item.title,
link: item.url,
snippet: item.description,
})
)
: [];
return JSON.stringify(finalResults);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/searchapi.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
type JSONPrimitive = string | number | boolean | null;
type JSONValue = JSONPrimitive | JSONObject | JSONArray;
interface JSONObject {
[key: string]: JSONValue;
}
interface JSONArray extends Array<JSONValue> {}
function isJSONObject(value: JSONValue): value is JSONObject {
return value !== null && typeof value === "object" && !Array.isArray(value);
}
/**
* SearchApiParameters Type Definition.
*
* For more parameters and supported search engines, refer specific engine documentation:
* Google - https://www.searchapi.io/docs/google
* Google News - https://www.searchapi.io/docs/google-news
* Google Scholar - https://www.searchapi.io/docs/google-scholar
* YouTube Transcripts - https://www.searchapi.io/docs/youtube-transcripts
* and others.
*
*/
export type SearchApiParameters = {
[key: string]: JSONValue;
};
/**
* SearchApi Class Definition.
*
* Provides a wrapper around the SearchApi.
*
* Ensure you've set the SEARCHAPI_API_KEY environment variable for authentication.
* You can obtain a free API key from https://www.searchapi.io/.
* @example
* ```typescript
* const searchApi = new SearchApi("your-api-key", {
* engine: "google_news",
* });
* const agent = RunnableSequence.from([
* ChatPromptTemplate.fromMessages([
* ["ai", "Answer the following questions using a bulleted list markdown format.""],
* ["human", "{input}"],
* ]),
* new ChatOpenAI({ temperature: 0 }),
* (input: BaseMessageChunk) => ({
* log: "test",
* returnValues: {
* output: input,
* },
* }),
* ]);
* const executor = AgentExecutor.fromAgentAndTools({
* agent,
* tools: [searchApi],
* });
* const res = await executor.invoke({
* input: "What's happening in Ukraine today?"",
* });
* console.log(res);
* ```
*/
export class SearchApi extends Tool {
static lc_name() {
return "SearchApi";
}
/**
* Converts the SearchApi instance to JSON. This method is not implemented
* and will throw an error if called.
* @returns Throws an error.
*/
toJSON() {
return this.toJSONNotImplemented();
}
protected apiKey: string;
protected params: Partial<SearchApiParameters>;
constructor(
apiKey: string | undefined = getEnvironmentVariable("SEARCHAPI_API_KEY"),
params: Partial<SearchApiParameters> = {}
) {
super(...arguments);
if (!apiKey) {
throw new Error(
"SearchApi requires an API key. Please set it as SEARCHAPI_API_KEY in your .env file, or pass it as a parameter to the SearchApi constructor."
);
}
this.apiKey = apiKey;
this.params = params;
}
name = "search";
/**
* Builds a URL for the SearchApi request.
* @param parameters The parameters for the request.
* @returns A string representing the built URL.
*/
protected buildUrl(searchQuery: string): string {
const preparedParams: [string, string][] = Object.entries({
engine: "google",
api_key: this.apiKey,
...this.params,
q: searchQuery,
})
.filter(
([key, value]) =>
value !== undefined && value !== null && key !== "apiKey"
)
.map(([key, value]) => [key, `${value}`]);
const searchParams = new URLSearchParams(preparedParams);
return `https://www.searchapi.io/api/v1/search?${searchParams}`;
}
/** @ignore */
/**
* Calls the SearchAPI.
*
* Accepts an input query and fetches the result from SearchApi.
*
* @param {string} input - Search query.
* @returns {string} - Formatted search results or an error message.
*
* NOTE: This method is the core search handler and processes various types
* of search results including Google organic results, videos, jobs, and images.
*/
async _call(input: string) {
const resp = await fetch(this.buildUrl(input));
const json = await resp.json();
if (json.error) {
throw new Error(
`Failed to load search results from SearchApi due to: ${json.error}`
);
}
// Google Search results
if (json.answer_box?.answer) {
return json.answer_box.answer;
}
if (json.answer_box?.snippet) {
return json.answer_box.snippet;
}
if (json.knowledge_graph?.description) {
return json.knowledge_graph.description;
}
// Organic results (Google, Google News)
if (json.organic_results) {
const snippets = json.organic_results
.filter((r: JSONObject) => r.snippet)
.map((r: JSONObject) => r.snippet);
return snippets.join("\n");
}
// Google Jobs results
if (json.jobs) {
const jobDescriptions = json.jobs
.slice(0, 1)
.filter((r: JSONObject) => r.description)
.map((r: JSONObject) => r.description);
return jobDescriptions.join("\n");
}
// Google Videos results
if (json.videos) {
const videoInfo = json.videos
.filter((r: JSONObject) => r.title && r.link)
.map((r: JSONObject) => `Title: "${r.title}" Link: ${r.link}`);
return videoInfo.join("\n");
}
// Google Images results
if (json.images) {
const image_results = json.images.slice(0, 15);
const imageInfo = image_results
.filter(
(r: JSONObject) =>
r.title && r.original && isJSONObject(r.original) && r.original.link
)
.map(
(r: JSONObject) =>
`Title: "${r.title}" Link: ${(r.original as JSONObject).link}`
);
return imageInfo.join("\n");
}
return "No good search result found";
}
description =
"a search engine. useful for when you need to answer questions about current events. input should be a search query.";
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/aiplugin.ts
|
import { Tool, type ToolParams } from "@langchain/core/tools";
/**
* Interface for parameters required to create an instance of
* AIPluginTool.
*/
export interface AIPluginToolParams extends ToolParams {
name: string;
description: string;
apiSpec: string;
}
/**
* Class for creating instances of AI tools from plugins. It extends the
* Tool class and implements the AIPluginToolParams interface.
*/
export class AIPluginTool extends Tool implements AIPluginToolParams {
static lc_name() {
return "AIPluginTool";
}
private _name: string;
private _description: string;
apiSpec: string;
get name() {
return this._name;
}
get description() {
return this._description;
}
constructor(params: AIPluginToolParams) {
super(params);
this._name = params.name;
this._description = params.description;
this.apiSpec = params.apiSpec;
}
/** @ignore */
async _call(_input: string) {
return this.apiSpec;
}
/**
* Static method that creates an instance of AIPluginTool from a given
* plugin URL. It fetches the plugin and its API specification from the
* provided URL and returns a new instance of AIPluginTool with the
* fetched data.
* @param url The URL of the AI plugin.
* @returns A new instance of AIPluginTool.
*/
static async fromPluginUrl(url: string) {
const aiPluginRes = await fetch(url);
if (!aiPluginRes.ok) {
throw new Error(
`Failed to fetch plugin from ${url} with status ${aiPluginRes.status}`
);
}
const aiPluginJson = await aiPluginRes.json();
const apiUrlRes = await fetch(aiPluginJson.api.url);
if (!apiUrlRes.ok) {
throw new Error(
`Failed to fetch API spec from ${aiPluginJson.api.url} with status ${apiUrlRes.status}`
);
}
const apiUrlJson = await apiUrlRes.text();
return new AIPluginTool({
name: aiPluginJson.name_for_model,
description: `Call this tool to get the OpenAPI spec (and usage guide) for interacting with the ${aiPluginJson.name_for_human} API. You should only call this ONCE! What is the ${aiPluginJson.name_for_human} API useful for? ${aiPluginJson.description_for_human}`,
apiSpec: `Usage Guide: ${aiPluginJson.description_for_model}
OpenAPI Spec in JSON or YAML format:\n${apiUrlJson}`,
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/wikipedia_query_run.ts
|
import { Tool } from "@langchain/core/tools";
/**
* Interface for the parameters that can be passed to the
* WikipediaQueryRun constructor.
*/
export interface WikipediaQueryRunParams {
topKResults?: number;
maxDocContentLength?: number;
baseUrl?: string;
}
/**
* Type alias for URL parameters. Represents a record where keys are
* strings and values can be string, number, boolean, undefined, or null.
*/
type UrlParameters = Record<
string,
string | number | boolean | undefined | null
>;
/**
* Interface for the structure of search results returned by the Wikipedia
* API.
*/
interface SearchResults {
query: {
search: Array<{
title: string;
}>;
};
}
/**
* Interface for the structure of a page returned by the Wikipedia API.
*/
interface Page {
pageid: number;
ns: number;
title: string;
extract: string;
}
/**
* Interface for the structure of a page result returned by the Wikipedia
* API.
*/
interface PageResult {
batchcomplete: string;
query: {
pages: Record<string, Page>;
};
}
/**
* Wikipedia query tool integration.
*
* Setup:
* Install `@langchain/community`. You'll also need an API key.
*
* ```bash
* npm install @langchain/community
* ```
*
* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.tools_wikipedia_query_run.WikipediaQueryRun.html#constructor)
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia_query_run";
*
* const tool = new WikipediaQueryRun({
* topKResults: 3,
* maxDocContentLength: 4000,
* });
* ```
* </details>
*
* <br />
*
* <details>
*
* <summary><strong>Invocation</strong></summary>
*
* ```typescript
* await tool.invoke("what is the current weather in sf?");
* ```
* </details>
*
* <br />
*
* <details>
*
* <summary><strong>Invocation with tool call</strong></summary>
*
* ```typescript
* // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
* const modelGeneratedToolCall = {
* args: {
* input: "what is the current weather in sf?",
* },
* id: "tool_call_id",
* name: tool.name,
* type: "tool_call",
* };
* await tool.invoke(modelGeneratedToolCall);
* ```
*
* ```text
* ToolMessage {
* "content": "...",
* "name": "wikipedia-api",
* "additional_kwargs": {},
* "response_metadata": {},
* "tool_call_id": "tool_call_id"
* }
* ```
* </details>
*/
export class WikipediaQueryRun extends Tool {
static lc_name() {
return "WikipediaQueryRun";
}
name = "wikipedia-api";
description =
"A tool for interacting with and fetching data from the Wikipedia API.";
protected topKResults = 3;
protected maxDocContentLength = 4000;
protected baseUrl = "https://en.wikipedia.org/w/api.php";
constructor(params: WikipediaQueryRunParams = {}) {
super();
this.topKResults = params.topKResults ?? this.topKResults;
this.maxDocContentLength =
params.maxDocContentLength ?? this.maxDocContentLength;
this.baseUrl = params.baseUrl ?? this.baseUrl;
}
async _call(query: string): Promise<string> {
const searchResults = await this._fetchSearchResults(query);
const summaries: string[] = [];
for (
let i = 0;
i < Math.min(this.topKResults, searchResults.query.search.length);
i += 1
) {
const page = searchResults.query.search[i].title;
const pageDetails = await this._fetchPage(page, true);
if (pageDetails) {
const summary = `Page: ${page}\nSummary: ${pageDetails.extract}`;
summaries.push(summary);
}
}
if (summaries.length === 0) {
return "No good Wikipedia Search Result was found";
} else {
return summaries.join("\n\n").slice(0, this.maxDocContentLength);
}
}
/**
* Fetches the content of a specific Wikipedia page. It returns the
* extracted content as a string.
* @param page The specific Wikipedia page to fetch its content.
* @param redirect A boolean value to indicate whether to redirect or not.
* @returns The extracted content of the specific Wikipedia page as a string.
*/
public async content(page: string, redirect = true): Promise<string> {
try {
const result = await this._fetchPage(page, redirect);
return result.extract;
} catch (error) {
throw new Error(`Failed to fetch content for page "${page}": ${error}`);
}
}
/**
* Builds a URL for the Wikipedia API using the provided parameters.
* @param parameters The parameters to be used in building the URL.
* @returns A string representing the built URL.
*/
protected buildUrl<P extends UrlParameters>(parameters: P): string {
const nonUndefinedParams: [string, string][] = Object.entries(parameters)
.filter(([_, value]) => value !== undefined)
.map(([key, value]) => [key, `${value}`]);
const searchParams = new URLSearchParams(nonUndefinedParams);
return `${this.baseUrl}?${searchParams}`;
}
private async _fetchSearchResults(query: string): Promise<SearchResults> {
const searchParams = new URLSearchParams({
action: "query",
list: "search",
srsearch: query,
format: "json",
});
const response = await fetch(`${this.baseUrl}?${searchParams.toString()}`);
if (!response.ok) throw new Error("Network response was not ok");
const data: SearchResults = await response.json();
return data;
}
private async _fetchPage(page: string, redirect: boolean): Promise<Page> {
const params = new URLSearchParams({
action: "query",
prop: "extracts",
explaintext: "true",
redirects: redirect ? "1" : "0",
format: "json",
titles: page,
});
const response = await fetch(`${this.baseUrl}?${params.toString()}`);
if (!response.ok) throw new Error("Network response was not ok");
const data: PageResult = await response.json();
const { pages } = data.query;
const pageId = Object.keys(pages)[0];
return pages[pageId];
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/serper.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
/**
* Defines the parameters that can be passed to the Serper class during
* instantiation. It includes `gl` and `hl` which are optional.
*/
export type SerperParameters = {
gl?: string;
hl?: string;
};
/**
* Wrapper around serper.
*
* You can create a free API key at https://serper.dev.
*
* To use, you should have the SERPER_API_KEY environment variable set.
*/
export class Serper extends Tool {
static lc_name() {
return "Serper";
}
/**
* Converts the Serper instance to JSON. This method is not implemented
* and will throw an error if called.
* @returns Throws an error.
*/
toJSON() {
return this.toJSONNotImplemented();
}
protected key: string;
protected params: Partial<SerperParameters>;
constructor(
apiKey: string | undefined = getEnvironmentVariable("SERPER_API_KEY"),
params: Partial<SerperParameters> = {}
) {
super();
if (!apiKey) {
throw new Error(
"Serper API key not set. You can set it as SERPER_API_KEY in your .env file, or pass it to Serper."
);
}
this.key = apiKey;
this.params = params;
}
name = "search";
/** @ignore */
async _call(input: string) {
const options = {
method: "POST",
headers: {
"X-API-KEY": this.key,
"Content-Type": "application/json",
},
body: JSON.stringify({
q: input,
...this.params,
}),
};
const res = await fetch("https://google.serper.dev/search", options);
if (!res.ok) {
throw new Error(`Got ${res.status} error from serper: ${res.statusText}`);
}
const json = await res.json();
if (json.answerBox?.answer) {
return json.answerBox.answer;
}
if (json.answerBox?.snippet) {
return json.answerBox.snippet;
}
if (json.answerBox?.snippet_highlighted_words) {
return json.answerBox.snippet_highlighted_words[0];
}
if (json.sportsResults?.game_spotlight) {
return json.sportsResults.game_spotlight;
}
if (json.knowledgeGraph?.description) {
return json.knowledgeGraph.description;
}
if (json.organic?.[0]?.snippet) {
return json.organic[0].snippet;
}
return "No good search result found";
}
description =
"a search engine. useful for when you need to answer questions about current events. input should be a search query.";
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/dadjokeapi.ts
|
import { Tool } from "@langchain/core/tools";
/**
* The DadJokeAPI class is a tool for generating dad jokes based on a
* specific topic. It fetches jokes from an external API and returns a
* random joke from the results. If no jokes are found for the given
* search term, it returns a message indicating that no jokes were found.
*/
class DadJokeAPI extends Tool {
static lc_name() {
return "DadJokeAPI";
}
name = "dadjoke";
description =
"a dad joke generator. get a dad joke about a specific topic. input should be a search term.";
/** @ignore */
async _call(input: string): Promise<string> {
const headers = { Accept: "application/json" };
const searchUrl = `https://icanhazdadjoke.com/search?term=${input}`;
const response = await fetch(searchUrl, { headers });
if (!response.ok) {
throw new Error(`HTTP error ${response.status}`);
}
const data = await response.json();
const jokes = data.results;
if (jokes.length === 0) {
return `No dad jokes found about ${input}`;
}
const randomIndex = Math.floor(Math.random() * jokes.length);
const randomJoke = jokes[randomIndex].joke;
return randomJoke;
}
}
export { DadJokeAPI };
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/wolframalpha.ts
|
import { Tool, type ToolParams } from "@langchain/core/tools";
/**
* @example
* ```typescript
* const tool = new WolframAlphaTool({
* appid: "YOUR_APP_ID",
* });
* const res = await tool.invoke("What is 2 * 2?");
* ```
*/
export class WolframAlphaTool extends Tool {
appid: string;
name = "wolfram_alpha";
description = `A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.`;
constructor(fields: ToolParams & { appid: string }) {
super(fields);
this.appid = fields.appid;
}
static lc_name() {
return "WolframAlphaTool";
}
async _call(query: string): Promise<string> {
const url = `https://www.wolframalpha.com/api/v1/llm-api?appid=${
this.appid
}&input=${encodeURIComponent(query)}`;
const res = await fetch(url);
return res.text();
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/serpapi.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
/**
* This does not use the `serpapi` package because it appears to cause issues
* when used in `jest` tests. Part of the issue seems to be that the `serpapi`
* package imports a wasm module to use instead of native `fetch`, which we
* don't want anyway.
*
* NOTE: you must provide location, gl and hl or your region and language will
* may not match your location, and will not be deterministic.
*/
// Copied over from `serpapi` package
interface BaseParameters {
/**
* Parameter defines the device to use to get the results. It can be set to
* `desktop` (default) to use a regular browser, `tablet` to use a tablet browser
* (currently using iPads), or `mobile` to use a mobile browser (currently
* using iPhones).
*/
device?: "desktop" | "tablet" | "mobile";
/**
* Parameter will force SerpApi to fetch the Google results even if a cached
* version is already present. A cache is served only if the query and all
* parameters are exactly the same. Cache expires after 1h. Cached searches
* are free, and are not counted towards your searches per month. It can be set
* to `false` (default) to allow results from the cache, or `true` to disallow
* results from the cache. `no_cache` and `async` parameters should not be used together.
*/
no_cache?: boolean;
/**
* Specify the client-side timeout of the request. In milliseconds.
*/
timeout?: number;
}
export interface SerpAPIParameters extends BaseParameters {
/**
* Search Query
* Parameter defines the query you want to search. You can use anything that you
* would use in a regular Google search. e.g. `inurl:`, `site:`, `intitle:`. We
* also support advanced search query parameters such as as_dt and as_eq. See the
* [full list](https://serpapi.com/advanced-google-query-parameters) of supported
* advanced search query parameters.
*/
q: string;
/**
* Location
* Parameter defines from where you want the search to originate. If several
* locations match the location requested, we'll pick the most popular one. Head to
* [/locations.json API](https://serpapi.com/locations-api) if you need more
* precise control. location and uule parameters can't be used together. Avoid
* utilizing location when setting the location outside the U.S. when using Google
* Shopping and/or Google Product API.
*/
location?: string;
/**
* Encoded Location
* Parameter is the Google encoded location you want to use for the search. uule
* and location parameters can't be used together.
*/
uule?: string;
/**
* Google Place ID
* Parameter defines the id (`CID`) of the Google My Business listing you want to
* scrape. Also known as Google Place ID.
*/
ludocid?: string;
/**
* Additional Google Place ID
* Parameter that you might have to use to force the knowledge graph map view to
* show up. You can find the lsig ID by using our [Local Pack
* API](https://serpapi.com/local-pack) or [Local Places Results
* API](https://serpapi.com/local-results).
* lsig ID is also available via a redirect Google uses within [Google My
* Business](https://www.google.com/business/).
*/
lsig?: string;
/**
* Google Knowledge Graph ID
* Parameter defines the id (`KGMID`) of the Google Knowledge Graph listing you
* want to scrape. Also known as Google Knowledge Graph ID. Searches with kgmid
* parameter will return results for the originally encrypted search parameters.
* For some searches, kgmid may override all other parameters except start, and num
* parameters.
*/
kgmid?: string;
/**
* Google Cached Search Parameters ID
* Parameter defines the cached search parameters of the Google Search you want to
* scrape. Searches with si parameter will return results for the originally
* encrypted search parameters. For some searches, si may override all other
* parameters except start, and num parameters. si can be used to scrape Google
* Knowledge Graph Tabs.
*/
si?: string;
/**
* Domain
* Parameter defines the Google domain to use. It defaults to `google.com`. Head to
* the [Google domains page](https://serpapi.com/google-domains) for a full list of
* supported Google domains.
*/
google_domain?: string;
/**
* Country
* Parameter defines the country to use for the Google search. It's a two-letter
* country code. (e.g., `us` for the United States, `uk` for United Kingdom, or
* `fr` for France). Head to the [Google countries
* page](https://serpapi.com/google-countries) for a full list of supported Google
* countries.
*/
gl?: string;
/**
* Language
* Parameter defines the language to use for the Google search. It's a two-letter
* language code. (e.g., `en` for English, `es` for Spanish, or `fr` for French).
* Head to the [Google languages page](https://serpapi.com/google-languages) for a
* full list of supported Google languages.
*/
hl?: string;
/**
* Set Multiple Languages
* Parameter defines one or multiple languages to limit the search to. It uses
* `lang_{two-letter language code}` to specify languages and `|` as a delimiter.
* (e.g., `lang_fr|lang_de` will only search French and German pages). Head to the
* [Google lr languages page](https://serpapi.com/google-lr-languages) for a full
* list of supported languages.
*/
lr?: string;
/**
* as_dt
* Parameter controls whether to include or exclude results from the site named in
* the as_sitesearch parameter.
*/
as_dt?: string;
/**
* as_epq
* Parameter identifies a phrase that all documents in the search results must
* contain. You can also use the [phrase
* search](https://developers.google.com/custom-search/docs/xml_results#PhraseSearchqt)
* query term to search for a phrase.
*/
as_epq?: string;
/**
* as_eq
* Parameter identifies a word or phrase that should not appear in any documents in
* the search results. You can also use the [exclude
* query](https://developers.google.com/custom-search/docs/xml_results#Excludeqt)
* term to ensure that a particular word or phrase will not appear in the documents
* in a set of search results.
*/
as_eq?: string;
/**
* as_lq
* Parameter specifies that all search results should contain a link to a
* particular URL. You can also use the
* [link:](https://developers.google.com/custom-search/docs/xml_results#BackLinksqt)
* query term for this type of query.
*/
as_lq?: string;
/**
* as_nlo
* Parameter specifies the starting value for a search range. Use as_nlo and as_nhi
* to append an inclusive search range.
*/
as_nlo?: string;
/**
* as_nhi
* Parameter specifies the ending value for a search range. Use as_nlo and as_nhi
* to append an inclusive search range.
*/
as_nhi?: string;
/**
* as_oq
* Parameter provides additional search terms to check for in a document, where
* each document in the search results must contain at least one of the additional
* search terms. You can also use the [Boolean
* OR](https://developers.google.com/custom-search/docs/xml_results#BooleanOrqt)
* query term for this type of query.
*/
as_oq?: string;
/**
* as_q
* Parameter provides search terms to check for in a document. This parameter is
* also commonly used to allow users to specify additional terms to search for
* within a set of search results.
*/
as_q?: string;
/**
* as_qdr
* Parameter requests search results from a specified time period (quick date
* range). The following values are supported:
* `d[number]`: requests results from the specified number of past days. Example
* for the past 10 days: `as_qdr=d10`
* `w[number]`: requests results from the specified number of past weeks.
* `m[number]`: requests results from the specified number of past months.
* `y[number]`: requests results from the specified number of past years. Example
* for the past year: `as_qdr=y`
*/
as_qdr?: string;
/**
* as_rq
* Parameter specifies that all search results should be pages that are related to
* the specified URL. The parameter value should be a URL. You can also use the
* [related:](https://developers.google.com/custom-search/docs/xml_results#RelatedLinksqt)
* query term for this type of query.
*/
as_rq?: string;
/**
* as_sitesearch
* Parameter allows you to specify that all search results should be pages from a
* given site. By setting the as_dt parameter, you can also use it to exclude pages
* from a given site from your search resutls.
*/
as_sitesearch?: string;
/**
* Advanced Search Parameters
* (to be searched) parameter defines advanced search parameters that aren't
* possible in the regular query field. (e.g., advanced search for patents, dates,
* news, videos, images, apps, or text contents).
*/
tbs?: string;
/**
* Adult Content Filtering
* Parameter defines the level of filtering for adult content. It can be set to
* `active`, or `off` (default).
*/
safe?: string;
/**
* Exclude Auto-corrected Results
* Parameter defines the exclusion of results from an auto-corrected query that is
* spelled wrong. It can be set to `1` to exclude these results, or `0` to include
* them (default).
*/
nfpr?: string;
/**
* Results Filtering
* Parameter defines if the filters for 'Similar Results' and 'Omitted Results' are
* on or off. It can be set to `1` (default) to enable these filters, or `0` to
* disable these filters.
*/
filter?: string;
/**
* Search Type
* (to be matched) parameter defines the type of search you want to do.
* It can be set to:
* `(no tbm parameter)`: regular Google Search,
* `isch`: [Google Images API](https://serpapi.com/images-results),
* `lcl` - [Google Local API](https://serpapi.com/local-results)
* `vid`: [Google Videos API](https://serpapi.com/videos-results),
* `nws`: [Google News API](https://serpapi.com/news-results),
* `shop`: [Google Shopping API](https://serpapi.com/shopping-results),
* or any other Google service.
*/
tbm?: string;
/**
* Result Offset
* Parameter defines the result offset. It skips the given number of results. It's
* used for pagination. (e.g., `0` (default) is the first page of results, `10` is
* the 2nd page of results, `20` is the 3rd page of results, etc.).
* Google Local Results only accepts multiples of `20`(e.g. `20` for the second
* page results, `40` for the third page results, etc.) as the start value.
*/
start?: number;
/**
* Number of Results
* Parameter defines the maximum number of results to return. (e.g., `10` (default)
* returns 10 results, `40` returns 40 results, and `100` returns 100 results).
*/
num?: string;
/**
* Page Number (images)
* Parameter defines the page number for [Google
* Images](https://serpapi.com/images-results). There are 100 images per page. This
* parameter is equivalent to start (offset) = ijn * 100. This parameter works only
* for [Google Images](https://serpapi.com/images-results) (set tbm to `isch`).
*/
ijn?: string;
}
type UrlParameters = Record<
string,
string | number | boolean | undefined | null
>;
/**
* Wrapper around SerpAPI.
*
* To use, you should have the `serpapi` package installed and the SERPAPI_API_KEY environment variable set.
*/
export class SerpAPI extends Tool {
static lc_name() {
return "SerpAPI";
}
toJSON() {
return this.toJSONNotImplemented();
}
protected key: string;
protected params: Partial<SerpAPIParameters>;
protected baseUrl: string;
constructor(
apiKey: string | undefined = getEnvironmentVariable("SERPAPI_API_KEY"),
params: Partial<SerpAPIParameters> = {},
baseUrl = "https://serpapi.com"
) {
super(...arguments);
if (!apiKey) {
throw new Error(
"SerpAPI API key not set. You can set it as SERPAPI_API_KEY in your .env file, or pass it to SerpAPI."
);
}
this.key = apiKey;
this.params = params;
this.baseUrl = baseUrl;
}
name = "search";
/**
* Builds a URL for the SerpAPI request.
* @param path The path for the request.
* @param parameters The parameters for the request.
* @param baseUrl The base URL for the request.
* @returns A string representing the built URL.
*/
protected buildUrl<P extends UrlParameters>(
path: string,
parameters: P,
baseUrl: string
): string {
const nonUndefinedParams: [string, string][] = Object.entries(parameters)
.filter(([_, value]) => value !== undefined)
.map(([key, value]) => [key, `${value}`]);
const searchParams = new URLSearchParams(nonUndefinedParams);
return `${baseUrl}/${path}?${searchParams}`;
}
/** @ignore */
async _call(input: string) {
const { timeout, ...params } = this.params;
const resp = await fetch(
this.buildUrl(
"search",
{
...params,
api_key: this.key,
q: input,
},
this.baseUrl
),
{
signal: timeout ? AbortSignal.timeout(timeout) : undefined,
}
);
const res = await resp.json();
if (res.error) {
throw new Error(`Got error from serpAPI: ${res.error}`);
}
const answer_box = res.answer_box_list
? res.answer_box_list[0]
: res.answer_box;
if (answer_box) {
if (answer_box.result) {
return answer_box.result;
} else if (answer_box.answer) {
return answer_box.answer;
} else if (answer_box.snippet) {
return answer_box.snippet;
} else if (answer_box.snippet_highlighted_words) {
return answer_box.snippet_highlighted_words.toString();
} else {
const answer: { [key: string]: string } = {};
Object.keys(answer_box)
.filter(
(k) =>
!Array.isArray(answer_box[k]) &&
typeof answer_box[k] !== "object" &&
!(
typeof answer_box[k] === "string" &&
answer_box[k].startsWith("http")
)
)
.forEach((k) => {
answer[k] = answer_box[k];
});
return JSON.stringify(answer);
}
}
if (res.events_results) {
return JSON.stringify(res.events_results);
}
if (res.sports_results) {
return JSON.stringify(res.sports_results);
}
if (res.top_stories) {
return JSON.stringify(res.top_stories);
}
if (res.news_results) {
return JSON.stringify(res.news_results);
}
if (res.jobs_results?.jobs) {
return JSON.stringify(res.jobs_results.jobs);
}
if (res.questions_and_answers) {
return JSON.stringify(res.questions_and_answers);
}
if (res.popular_destinations?.destinations) {
return JSON.stringify(res.popular_destinations.destinations);
}
if (res.top_sights?.sights) {
const sights: Array<{ [key: string]: string }> = res.top_sights.sights
.map((s: { [key: string]: string }) => ({
title: s.title,
description: s.description,
price: s.price,
}))
.slice(0, 8);
return JSON.stringify(sights);
}
if (res.shopping_results && res.shopping_results[0]?.title) {
return JSON.stringify(res.shopping_results.slice(0, 3));
}
if (res.images_results && res.images_results[0]?.thumbnail) {
return res.images_results
.map((ir: { thumbnail: string }) => ir.thumbnail)
.slice(0, 10)
.toString();
}
const snippets = [];
if (res.knowledge_graph) {
if (res.knowledge_graph.description) {
snippets.push(res.knowledge_graph.description);
}
const title = res.knowledge_graph.title || "";
Object.keys(res.knowledge_graph)
.filter(
(k) =>
typeof res.knowledge_graph[k] === "string" &&
k !== "title" &&
k !== "description" &&
!k.endsWith("_stick") &&
!k.endsWith("_link") &&
!k.startsWith("http")
)
.forEach((k) =>
snippets.push(`${title} ${k}: ${res.knowledge_graph[k]}`)
);
}
const first_organic_result = res.organic_results?.[0];
if (first_organic_result) {
if (first_organic_result.snippet) {
snippets.push(first_organic_result.snippet);
} else if (first_organic_result.snippet_highlighted_words) {
snippets.push(first_organic_result.snippet_highlighted_words);
} else if (first_organic_result.rich_snippet) {
snippets.push(first_organic_result.rich_snippet);
} else if (first_organic_result.rich_snippet_table) {
snippets.push(first_organic_result.rich_snippet_table);
} else if (first_organic_result.link) {
snippets.push(first_organic_result.link);
}
}
if (res.buying_guide) {
snippets.push(res.buying_guide);
}
if (res.local_results?.places) {
snippets.push(res.local_results.places);
}
if (snippets.length > 0) {
return JSON.stringify(snippets);
} else {
return "No good search result found";
}
}
description =
"a search engine. useful for when you need to answer questions about current events. input should be a search query.";
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/discord.ts
|
import {
Client,
TextChannel,
GatewayIntentBits,
Message,
ChannelType,
} from "discord.js";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
/**
* Base tool parameters for the Discord tools
*/
interface DiscordToolParams {
botToken?: string;
client?: Client;
}
/**
* Tool parameters for the DiscordGetMessagesTool
*/
interface DiscordGetMessagesToolParams extends DiscordToolParams {
messageLimit?: number;
}
/**
* Tool parameters for the DiscordSendMessageTool
*/
interface DiscordSendMessageToolParams extends DiscordToolParams {
channelId?: string;
}
/**
* Tool parameters for the DiscordChannelSearch
*/
interface DiscordChannelSearchParams extends DiscordToolParams {
channelId?: string;
}
/**
* A tool for retrieving messages from a discord channel using a bot.
* It extends the base Tool class and implements the _call method to
* perform the retrieve operation. Requires an bot token which can be set
* in the environment variables, and a limit on how many messages to retrieve.
* The _call method takes the discord channel ID as the input argument.
* The bot must have read permissions to the given channel. It returns the
* message content, author, and time the message was created for each message.
*/
export class DiscordGetMessagesTool extends Tool {
static lc_name() {
return "DiscordGetMessagesTool";
}
name = "discord-get-messages";
description = `A discord tool. useful for reading messages from a discord channel.
Input should be the discord channel ID. The bot should have read
permissions for the channel.`;
protected botToken: string;
protected messageLimit: number;
protected client: Client;
constructor(fields?: DiscordGetMessagesToolParams) {
super();
const {
botToken = getEnvironmentVariable("DISCORD_BOT_TOKEN"),
messageLimit = 10,
client,
} = fields ?? {};
if (!botToken) {
throw new Error(
"Environment variable DISCORD_BOT_TOKEN missing, but is required for DiscordGetMessagesTool."
);
}
this.client =
client ??
new Client({
intents: [GatewayIntentBits.Guilds, GatewayIntentBits.GuildMessages],
});
this.botToken = botToken;
this.messageLimit = messageLimit;
}
/** @ignore */
async _call(input: string): Promise<string> {
try {
await this.client.login(this.botToken);
const channel = (await this.client.channels.fetch(input)) as TextChannel;
if (!channel) {
return "Channel not found.";
}
const messages = await channel.messages.fetch({
limit: this.messageLimit,
});
await this.client.destroy();
const results =
messages.map((message: Message) => ({
author: message.author.tag,
content: message.content,
timestamp: message.createdAt,
})) ?? [];
return JSON.stringify(results);
} catch (err) {
await this.client.destroy();
return "Error getting messages.";
}
}
}
/**
* A tool for retrieving all servers a bot is a member of. It extends the
* base `Tool` class and implements the `_call` method to perform the retrieve
* operation. Requires a bot token which can be set in the environment
* variables.
*/
export class DiscordGetGuildsTool extends Tool {
static lc_name() {
return "DiscordGetGuildsTool";
}
name = "discord-get-guilds";
description = `A discord tool. Useful for getting a list of all servers/guilds the bot is a member of. No input required.`;
protected botToken: string;
protected client: Client;
constructor(fields?: DiscordToolParams) {
super();
const { botToken = getEnvironmentVariable("DISCORD_BOT_TOKEN"), client } =
fields ?? {};
if (!botToken) {
throw new Error(
"Environment variable DISCORD_BOT_TOKEN missing, but is required for DiscordGetGuildsTool."
);
}
this.client =
client ??
new Client({
intents: [GatewayIntentBits.Guilds],
});
this.botToken = botToken;
}
/** @ignore */
async _call(_input: string): Promise<string> {
try {
await this.client.login(this.botToken);
const guilds = await this.client.guilds.fetch();
await this.client.destroy();
const results =
guilds.map((guild) => ({
id: guild.id,
name: guild.name,
createdAt: guild.createdAt,
})) ?? [];
return JSON.stringify(results);
} catch (err) {
await this.client.destroy();
return "Error getting guilds.";
}
}
}
/**
* A tool for retrieving text channels within a server/guild a bot is a member
* of. It extends the base `Tool` class and implements the `_call` method to
* perform the retrieve operation. Requires a bot token which can be set in
* the environment variables. The `_call` method takes a server/guild ID
* to get its text channels.
*/
export class DiscordGetTextChannelsTool extends Tool {
static lc_name() {
return "DiscordGetTextChannelsTool";
}
name = "discord-get-text-channels";
description = `A discord tool. Useful for getting a list of all text channels in a server/guild. Input should be a discord server/guild ID.`;
protected botToken: string;
protected client: Client;
constructor(fields?: DiscordToolParams) {
super();
const { botToken = getEnvironmentVariable("DISCORD_BOT_TOKEN"), client } =
fields ?? {};
if (!botToken) {
throw new Error(
"Environment variable DISCORD_BOT_TOKEN missing, but is required for DiscordGetTextChannelsTool."
);
}
this.client =
client ??
new Client({
intents: [GatewayIntentBits.Guilds],
});
this.botToken = botToken;
}
/** @ignore */
async _call(input: string): Promise<string> {
try {
await this.client.login(this.botToken);
const guild = await this.client.guilds.fetch(input);
const channels = await guild.channels.fetch();
await this.client.destroy();
const results =
channels
.filter((channel) => channel?.type === ChannelType.GuildText)
.map((channel) => ({
id: channel?.id,
name: channel?.name,
createdAt: channel?.createdAt,
})) ?? [];
return JSON.stringify(results);
} catch (err) {
await this.client.destroy();
return "Error getting text channels.";
}
}
}
/**
* A tool for sending messages to a discord channel using a bot.
* It extends the base Tool class and implements the _call method to
* perform the retrieve operation. Requires a bot token and channelId which can be set
* in the environment variables. The _call method takes the message to be
* sent as the input argument.
*/
export class DiscordSendMessagesTool extends Tool {
static lc_name() {
return "DiscordSendMessagesTool";
}
name = "discord-send-messages";
description = `A discord tool useful for sending messages to a discod channel.
Input should be the discord channel message, since we will already have the channel ID.`;
protected botToken: string;
protected channelId: string;
protected client: Client;
constructor(fields?: DiscordSendMessageToolParams) {
super();
const {
botToken = getEnvironmentVariable("DISCORD_BOT_TOKEN"),
channelId = getEnvironmentVariable("DISCORD_CHANNEL_ID"),
client,
} = fields ?? {};
if (!botToken) {
throw new Error(
"Environment variable DISCORD_BOT_TOKEN missing, but is required for DiscordSendMessagesTool."
);
}
if (!channelId) {
throw new Error(
"Environment variable DISCORD_CHANNEL_ID missing, but is required for DiscordSendMessagesTool."
);
}
this.client =
client ??
new Client({
intents: [GatewayIntentBits.Guilds, GatewayIntentBits.GuildMessages],
});
this.botToken = botToken;
this.channelId = channelId;
}
/** @ignore */
async _call(message: string): Promise<string> {
try {
await this.client.login(this.botToken);
const channel = (await this.client.channels.fetch(
this.channelId
)) as TextChannel;
if (!channel) {
throw new Error("Channel not found");
}
if (!(channel.constructor === TextChannel)) {
throw new Error("Channel is not text channel, cannot send message");
}
await channel.send(message);
await this.client.destroy();
return "Message sent successfully.";
} catch (err) {
await this.client.destroy();
return "Error sending message.";
}
}
}
/**
* A tool for searching for messages within a discord channel using a bot.
* It extends the base Tool class and implements the _call method to
* perform the retrieve operation. Requires an bot token which can be set
* in the environment variables, and the discord channel ID of the channel.
* The _call method takes the search term as the input argument.
* The bot must have read permissions to the given channel. It returns the
* message content, author, and time the message was created for each message.
*/
export class DiscordChannelSearchTool extends Tool {
static lc_name() {
return "DiscordChannelSearchTool";
}
name = "discord_channel_search_tool";
description = `A discord toolkit. Useful for searching for messages
within a discord channel. Input should be the search term. The bot
should have read permissions for the channel.`;
protected botToken: string;
protected channelId: string;
protected client: Client;
constructor(fields?: DiscordChannelSearchParams) {
super();
const {
botToken = getEnvironmentVariable("DISCORD_BOT_TOKEN"),
channelId = getEnvironmentVariable("DISCORD_CHANNEL_ID"),
client,
} = fields ?? {};
if (!botToken) {
throw new Error(
"Environment variable DISCORD_BOT_TOKEN missing, but is required for DiscordChannelSearchTool."
);
}
if (!channelId) {
throw new Error(
"Environment variable DISCORD_CHANNEL_ID missing, but is required for DiscordChannelSearchTool."
);
}
this.client =
client ??
new Client({
intents: [GatewayIntentBits.Guilds, GatewayIntentBits.GuildMessages],
});
this.botToken = botToken;
this.channelId = channelId;
}
/** @ignore */
async _call(searchTerm: string): Promise<string> {
try {
await this.client.login(this.botToken);
const channel = (await this.client.channels.fetch(
this.channelId
)) as TextChannel;
if (!channel) {
return "Channel not found";
}
const messages = await channel.messages.fetch();
await this.client.destroy();
const filtered = messages.filter((message) =>
message.content.toLowerCase().includes(searchTerm.toLowerCase())
);
const results =
filtered.map((message) => ({
author: message.author.tag,
content: message.content,
timestamp: message.createdAt,
})) ?? [];
return JSON.stringify(results);
} catch (err) {
await this.client.destroy();
return "Error searching through channel.";
}
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_places.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Tool } from "@langchain/core/tools";
/**
* Interface for parameters required by GooglePlacesAPI class.
*/
export interface GooglePlacesAPIParams {
apiKey?: string;
}
/**
* Tool that queries the Google Places API
*/
export class GooglePlacesAPI extends Tool {
static lc_name() {
return "GooglePlacesAPI";
}
get lc_secrets(): { [key: string]: string } | undefined {
return {
apiKey: "GOOGLE_PLACES_API_KEY",
};
}
name = "google_places";
protected apiKey: string;
description = `A wrapper around Google Places API. Useful for when you need to validate or
discover addresses from ambiguous text. Input should be a search query.`;
constructor(fields?: GooglePlacesAPIParams) {
super(...arguments);
const apiKey =
fields?.apiKey ?? getEnvironmentVariable("GOOGLE_PLACES_API_KEY");
if (apiKey === undefined) {
throw new Error(
`Google Places API key not set. You can set it as "GOOGLE_PLACES_API_KEY" in your environment variables.`
);
}
this.apiKey = apiKey;
}
async _call(input: string) {
const res = await fetch(
`https://places.googleapis.com/v1/places:searchText`,
{
method: "POST",
body: JSON.stringify({
textQuery: input,
languageCode: "en",
}),
headers: {
"X-Goog-Api-Key": this.apiKey,
"X-Goog-FieldMask":
"places.displayName,places.formattedAddress,places.id,places.internationalPhoneNumber,places.websiteUri",
"Content-Type": "application/json",
},
}
);
if (!res.ok) {
let message;
try {
const json = await res.json();
message = json.error.message;
} catch (e) {
message =
"Unable to parse error message: Google did not return a JSON response.";
}
throw new Error(
`Got ${res.status}: ${res.statusText} error from Google Places API: ${message}`
);
}
const json = await res.json();
const results =
json?.places?.map(
(place: {
id?: string;
internationalPhoneNumber?: string;
formattedAddress?: string;
websiteUri?: string;
displayName?: { text?: string };
}) => ({
name: place.displayName?.text,
id: place.id,
address: place.formattedAddress,
phoneNumber: place.internationalPhoneNumber,
website: place.websiteUri,
})
) ?? [];
return JSON.stringify(results);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/stackexchange.ts
|
import { Tool } from "@langchain/core/tools";
export interface StackExchangeAnswer {
items: StackExchangeItem[];
has_more: boolean;
quota_max: number;
quota_remaining: number;
}
export interface StackExchangeItem {
tags: string[];
question_score: number;
is_accepted: boolean;
has_accepted_answer?: boolean;
answer_count?: number;
is_answered: boolean;
question_id: number;
item_type: string;
score: number;
last_activity_date: number;
creation_date: number;
body: string;
excerpt: string;
title: string;
answer_id?: number;
}
type StackExchangeOptions = Record<string, string | number | boolean>;
export interface StackExchangeAPIParams {
/**
* The maximum number of results to return from the search.
* Limiting to 10 to avoid context overload.
* @default 3
*/
maxResult?: number;
/**
* Which part of StackOverflows items to match against. One of 'all', 'title',
* 'body'.
* @default "all"
*/
queryType?: "all" | "title" | "body";
/**
* Additional params to pass to the StackExchange API
*/
options?: StackExchangeOptions;
/**
* Separator between question,answer pairs.
* @default "\n\n"
*/
resultSeparator?: string;
}
/**
* Class for interacting with the StackExchange API
* It extends the base Tool class to perform retrieval.
*/
export class StackExchangeAPI extends Tool {
name = "stackexchange";
description = "Stack Exchange API Implementation";
private pageSize: number;
private maxResult = 3;
private key: string | null;
private accessToken: string | null;
private site = "stackoverflow";
private version = "2.3";
private baseUrl = "https://api.stackexchange.com";
private queryType = "all";
private options?: StackExchangeOptions = {};
private resultSeparator?: string = "\n\n";
constructor(params: StackExchangeAPIParams = {}) {
const { maxResult, queryType = "all", options, resultSeparator } = params;
super();
this.maxResult = maxResult || this.maxResult;
this.pageSize = 100;
this.baseUrl = `${this.baseUrl}/${this.version}/`;
this.queryType = queryType === "all" ? "q" : queryType;
this.options = options || this.options;
this.resultSeparator = resultSeparator || this.resultSeparator;
}
async _call(query: string): Promise<string> {
const params = {
[this.queryType]: query,
site: this.site,
...this.options,
};
const output = await this._fetch<StackExchangeAnswer>(
"search/excerpts",
params
);
if (output.items.length < 1) {
return `No relevant results found for '${query}' on Stack Overflow.`;
}
const questions = output.items
.filter((item) => item.item_type === "question")
.slice(0, this.maxResult);
const answers = output.items.filter((item) => item.item_type === "answer");
const results: string[] = [];
for (const question of questions) {
let res_text = `Question: ${question.title}\n${question.excerpt}`;
const relevant_answers = answers.filter(
(answer) => answer.question_id === question.question_id
);
const accepted_answers = relevant_answers.filter(
(answer) => answer.is_accepted
);
if (relevant_answers.length > 0) {
const top_answer =
accepted_answers.length > 0
? accepted_answers[0]
: relevant_answers[0];
const { excerpt } = top_answer;
res_text += `\nAnswer: ${excerpt}`;
}
results.push(res_text);
}
return results.join(this.resultSeparator);
}
/**
* Call the StackExchange API
* @param endpoint Name of the endpoint from StackExchange API
* @param params Additional parameters passed to the endpoint
* @param page Number of the page to retrieve
* @param filter Filtering properties
*/
private async _fetch<T>(
endpoint: string,
params: StackExchangeOptions = {},
page = 1,
filter = "default"
): Promise<T> {
try {
if (!endpoint) {
throw new Error("No end point provided.");
}
const queryParams = new URLSearchParams({
pagesize: this.pageSize.toString(),
page: page.toString(),
filter,
...params,
});
if (this.key) {
queryParams.append("key", this.key);
}
if (this.accessToken) {
queryParams.append("access_token", this.accessToken);
}
const queryParamsString = queryParams.toString();
const endpointUrl = `${this.baseUrl}${endpoint}?${queryParamsString}`;
return await this._makeRequest(endpointUrl);
} catch (e) {
throw new Error("Error while calling Stack Exchange API");
}
}
/**
* Fetch the result of a specific endpoint
* @param endpointUrl Endpoint to call
*/
private async _makeRequest<T>(endpointUrl: string): Promise<T> {
try {
const response = await fetch(endpointUrl);
if (response.status !== 200) {
throw new Error(`HTTP Error: ${response.statusText}`);
}
return await response.json();
} catch (e) {
throw new Error(`Error while calling Stack Exchange API: ${endpointUrl}`);
}
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/aws_lambda.ts
|
import { DynamicTool, DynamicToolInput } from "./dynamic.js";
/**
* Interface for the configuration of the AWS Lambda function.
*/
interface LambdaConfig {
functionName: string;
region?: string;
accessKeyId?: string;
secretAccessKey?: string;
}
/**
* Interface for the arguments to the LambdaClient constructor.
*/
interface LambdaClientConstructorArgs {
region?: string;
credentials?: {
accessKeyId: string;
secretAccessKey: string;
};
}
/**
* Class for invoking AWS Lambda functions within the LangChain framework.
* Extends the DynamicTool class.
*/
class AWSLambda extends DynamicTool {
get lc_namespace(): string[] {
return [...super.lc_namespace, "aws_lambda"];
}
get lc_secrets(): { [key: string]: string } | undefined {
return {
accessKeyId: "AWS_ACCESS_KEY_ID",
secretAccessKey: "AWS_SECRET_ACCESS_KEY",
};
}
private lambdaConfig: LambdaConfig;
constructor({
name,
description,
...rest
}: LambdaConfig & Omit<DynamicToolInput, "func">) {
super({
name,
description,
func: async (input: string) => this._func(input),
});
this.lambdaConfig = rest;
}
/** @ignore */
async _func(input: string): Promise<string> {
const { Client, Invoker } = await LambdaImports();
const clientConstructorArgs: LambdaClientConstructorArgs = {};
if (this.lambdaConfig.region) {
clientConstructorArgs.region = this.lambdaConfig.region;
}
if (this.lambdaConfig.accessKeyId && this.lambdaConfig.secretAccessKey) {
clientConstructorArgs.credentials = {
accessKeyId: this.lambdaConfig.accessKeyId,
secretAccessKey: this.lambdaConfig.secretAccessKey,
};
}
const lambdaClient = new Client(clientConstructorArgs);
return new Promise((resolve) => {
const payloadUint8Array = new TextEncoder().encode(JSON.stringify(input));
const command = new Invoker({
FunctionName: this.lambdaConfig.functionName,
InvocationType: "RequestResponse",
Payload: payloadUint8Array,
});
lambdaClient
.send(command)
.then((response) => {
const responseData = JSON.parse(
new TextDecoder().decode(response.Payload)
);
resolve(responseData.body ? responseData.body : "request completed.");
})
.catch((error: Error) => {
console.error("Error invoking Lambda function:", error);
resolve("failed to complete request");
});
});
}
}
/**
* Helper function that imports the necessary AWS SDK modules for invoking
* the Lambda function. Returns an object that includes the LambdaClient
* and InvokeCommand classes from the AWS SDK.
*/
async function LambdaImports() {
try {
const { LambdaClient, InvokeCommand } = await import(
"@aws-sdk/client-lambda"
);
return {
Client: LambdaClient as typeof LambdaClient,
Invoker: InvokeCommand as typeof InvokeCommand,
};
} catch (e) {
console.error(e);
throw new Error(
"Failed to load @aws-sdk/client-lambda'. Please install it eg. `yarn add @aws-sdk/client-lambda`."
);
}
}
export { AWSLambda };
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/calculator.ts
|
import { Parser } from "expr-eval";
import { Tool } from "@langchain/core/tools";
/**
* The Calculator class is a tool used to evaluate mathematical
* expressions. It extends the base Tool class.
* @example
* ```typescript
* import { Calculator } from "@langchain/community/tools/calculator";
*
* const calculator = new Calculator();
* const sum = await calculator.invoke("99 + 99");
* console.log("The sum of 99 and 99 is:", sum);
* // The sum of 99 and 99 is: 198
* ```
*/
export class Calculator extends Tool {
static lc_name() {
return "Calculator";
}
get lc_namespace() {
return [...super.lc_namespace, "calculator"];
}
name = "calculator";
/** @ignore */
async _call(input: string) {
try {
return Parser.evaluate(input).toString();
} catch (error) {
return "I don't know how to do that.";
}
}
description = `Useful for getting the result of a math expression. The input to this tool should be a valid mathematical expression that could be executed by a simple calculator.`;
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_routes.ts
|
import { StructuredTool } from "@langchain/core/tools";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { z } from "zod";
/**
* Interfaces for the response from the Google Routes API.
*/
interface Arrival {
arrivalTime: string;
localizedTime: string;
localizedTimezone: string;
arrivalAddress: string;
}
interface Departure {
departureTime: string;
localizedTime: string;
localizedTimezone: string;
departureAddress: string;
}
interface transitDetails {
transitName?: string;
transitNameCode?: string;
transitVehicleType: string;
}
interface travelInstructions {
navigationInstruction: string;
travelMode: string;
}
interface localizedValues {
distance: string;
duration: string;
transitFare?: string;
}
interface TollInfo {
currencyCode: string;
value: string;
}
/**
* Interface for the output of the tool for a transit route.
*/
interface FilteredTransitRoute {
departure: Departure;
arrival: Arrival;
travelInstructions: travelInstructions[];
localizedValues: localizedValues;
transitDetails: transitDetails;
routeLabels: string[];
warnings?: string[];
}
/**
* Interface for the output of the tool for a non-transit route.
* This includes driving, walking, bicycling, and two-wheeler routes.
*/
interface FilteredRoute {
description: string;
distance: string;
duration: string;
routeLabels: string[];
warnings?: string[];
tollInfo?: TollInfo;
}
/**
* Interface for the body of the request to the Google Routes API.
*/
interface Body {
origin: {
address: string;
};
destination: {
address: string;
};
travel_mode: string;
routing_preference?: string;
computeAlternativeRoutes: boolean;
departureTime?: string;
arrivalTime?: string;
transitPreferences?: {
routingPreference: string;
};
extraComputations?: string[];
}
const getTimezoneOffsetInHours = () => {
const offsetInMinutes = new Date().getTimezoneOffset();
const offsetInHours = -offsetInMinutes / 60;
return offsetInHours;
};
/**
* Helper functions to create the response objects for the Google Routes API.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createDeparture(transitDetails: any): Departure {
const { stopDetails, localizedValues } = transitDetails;
return {
departureTime: stopDetails.departureTime,
localizedTime: localizedValues.departureTime.time.text,
localizedTimezone: localizedValues.departureTime.timeZone,
departureAddress: stopDetails.departureStop.name,
};
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createArrival(transitDetails: any): Arrival {
const { stopDetails, localizedValues } = transitDetails;
return {
arrivalTime: stopDetails.arrivalTime,
localizedTime: localizedValues.arrivalTime.time.text,
localizedTimezone: localizedValues.arrivalTime.timeZone,
arrivalAddress: stopDetails.arrivalStop.name,
};
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createTravelInstructions(stepsOverview: any): travelInstructions[] {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
return stepsOverview.multiModalSegments.map((segment: any) => ({
...(segment.navigationInstruction
? {
navigationInstruction: segment.navigationInstruction.instructions,
}
: {}),
travelMode: segment.travelMode,
}));
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createLocalizedValues(route: any): localizedValues {
const { distance, duration, transitFare } = route.localizedValues;
return {
distance: distance.text,
duration: duration.text,
...(transitFare?.text ? { transitFare: transitFare.text } : {}),
};
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createTransitDetails(transitDetails: any): transitDetails {
const { name, nameShort, vehicle } = transitDetails.transitLine;
return {
...(name ? { transitName: name } : {}),
...(nameShort ? { transitNameCode: nameShort } : {}),
transitVehicleType: vehicle.type,
};
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function createRouteLabel(route: any): string[] {
return route.routeLabels;
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
function filterRoutes(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
route: any,
travel_mode: string
): FilteredTransitRoute | FilteredRoute {
if (travel_mode === "TRANSIT") {
const transitStep = route.legs[0].steps.find(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(step: any) => step.transitDetails
);
const filteredRoute: FilteredTransitRoute = {
departure: createDeparture(transitStep.transitDetails),
arrival: createArrival(transitStep.transitDetails),
travelInstructions: createTravelInstructions(route.legs[0].stepsOverview),
localizedValues: createLocalizedValues(route),
transitDetails: createTransitDetails(transitStep.transitDetails),
routeLabels: createRouteLabel(route),
};
if (route.warnings && route.warnings.length > 0) {
filteredRoute.warnings = route.warnings;
}
return filteredRoute;
} else {
const filteredRoute: FilteredRoute = {
description: route.description,
routeLabels: createRouteLabel(route),
...createLocalizedValues(route),
};
if (route.warnings && route.warnings.length > 0) {
filteredRoute.warnings = route.warnings;
}
if (route.travelAdvisory && route.travelAdvisory.tollInfo) {
filteredRoute.tollInfo = {
currencyCode:
route.travelAdvisory.tollInfo.estimatedPrice[0].currencyCode,
value: route.travelAdvisory.tollInfo.estimatedPrice[0].units,
};
}
return filteredRoute;
}
}
/**
* Interface for parameter s required by GoogleRoutesAPI class.
*/
export interface GoogleRoutesAPIParams {
apiKey?: string;
}
/**
* Class for interacting with the Google Routes API
* It extends the StructuredTool class to perform retrieval.
*/
export class GoogleRoutesAPI extends StructuredTool {
static lc_name() {
return "GoogleRoutesAPI";
}
get lc_secrets(): { [key: string]: string } | undefined {
return {
apiKey: "GOOGLE_ROUTES_API_KEY",
};
}
name: string;
description: string;
protected apiKey: string;
schema: z.ZodObject<{
origin: z.ZodString;
destination: z.ZodString;
travel_mode: z.ZodEnum<
["DRIVE", "WALK", "BICYCLE", "TRANSIT", "TWO_WHEELER"]
>;
computeAlternativeRoutes: z.ZodBoolean;
departureTime: z.ZodOptional<z.ZodString>;
arrivalTime: z.ZodOptional<z.ZodString>;
transitPreferences: z.ZodOptional<
z.ZodObject<{
routingPreference: z.ZodEnum<["LESS_WALKING", "FEWER_TRANSFERS"]>;
}>
>;
extraComputations: z.ZodOptional<z.ZodArray<z.ZodEnum<["TOLLS"]>>>;
}>;
constructor(fields?: GoogleRoutesAPIParams) {
super(...arguments);
const apiKey =
fields?.apiKey ?? getEnvironmentVariable("GOOGLE_ROUTES_API_KEY");
if (apiKey === undefined) {
throw new Error(
`Google Routes API key not set. You can set it as "GOOGLE_ROUTES_API_KEY" in your environment variables.`
);
}
this.apiKey = apiKey;
this.name = "google_routes";
this.description = `
This tool retrieves routing info using the Google Routes API for driving, walking, biking, transit, and two-wheeler routes. Get departure/arrival times, travel instructions, transit fare, warnings, alternative routes, tolls prices, and routing preferences like less walking or fewer transfers.
Output:
- "TRANSIT" mode: Departure/arrival details, transit name/code, fare, details, warnings, alternative routes, and expected departure/arrival times.
- Other modes: Route description, distance, duration, warnings, alternative routes, tolls prices, and expected departure/arrival times.
Current time in user's timezone: ${new Date().toLocaleString()}.
`;
this.schema = z.object({
origin: z
.string()
.describe(`Origin address, can be either a place or an address.`),
destination: z
.string()
.describe(`Destination address, can be either a place or an address.`),
travel_mode: z
.enum(["DRIVE", "WALK", "BICYCLE", "TRANSIT", "TWO_WHEELER"])
.describe(`The mode of transport`),
computeAlternativeRoutes: z
.boolean()
.describe(
`Compute alternative routes, set to true if user wants multiple routes, false otherwise.`
),
departureTime: z
.string()
.optional()
.describe(
`Time that the user wants to depart.
There cannot be a departure time if an arrival time is specified.
Expected departure time should be provided as a timestamp in RFC3339 format: YYYY-MM-DDThh:mm:ss+00:00. The date should be in UTC time and the +00:00 represents the UTC offset.
For instance, if the the user's timezone is -5, the offset would be -05:00 meaning YYYY-MM-DDThh:mm:ss-05:00 with YYYY-MM-DDThh:mm:ss being in UTC.
For reference, here is the current time in UTC: ${new Date().toISOString()} and the user's timezone offset is ${getTimezoneOffsetInHours()}.
If the departure time is not specified it should not be included.
`
),
arrivalTime: z
.string()
.optional()
.describe(
`Time that the user wants to arrive.
There cannot be an arrival time if a departure time is specified.
Expected arrival time should be provided as a timestamp in RFC3339 format: YYYY-MM-DDThh:mm:ss+00:00. The date should be in UTC time and the +00:00 represents the UTC offset.
For instance, if the the user's timezone is -5, the offset would be -05:00 meaning YYYY-MM-DDThh:mm:ss-05:00 with YYYY-MM-DDThh:mm:ss being in UTC.
For reference, here is the current time in UTC: ${new Date().toISOString()} and the user's timezone offset is ${getTimezoneOffsetInHours()}.
Reminder that the arrival time must be in the future, if the user asks for a arrival time in the past instead of processing the request, warn them that it is not possible to calculate a route for a past time.
If the user asks for a arrival time in a passed hour today, calculate it for the next day.
If the arrival time is not specified it should not be included. `
),
transitPreferences: z
.object({
routingPreference: z.enum(["LESS_WALKING", "FEWER_TRANSFERS"])
.describe(`Transit routing preference.
By default, it should not be included.`),
})
.optional()
.describe(
`Transit routing preference.
By default, it should not be included.`
),
extraComputations: z
.array(z.enum(["TOLLS"]))
.optional()
.describe(`Calculate tolls for the route.`),
});
}
async _call(input: z.infer<typeof GoogleRoutesAPI.prototype.schema>) {
const {
origin,
destination,
travel_mode,
computeAlternativeRoutes,
departureTime,
arrivalTime,
transitPreferences,
extraComputations,
} = input;
const now = new Date();
if (departureTime && new Date(departureTime) < now) {
return "It is not possible to calculate a route with a past departure time. Warn the user that it is not possible to calculate a route with a past departure time.";
}
if (arrivalTime && new Date(arrivalTime) < now) {
return "It is not possible to calculate a route with a past arrival time. Warn the user that it is not possible to calculate a route with a past arrival time.";
}
if (travel_mode !== "TRANSIT" && arrivalTime) {
return "It is not possible to calculate an arrival time for modes other than transit. Warn the user that it is not possible to calculate an arrival time for the selected mode of transport.";
}
if (travel_mode === "TRANSIT" && extraComputations) {
return "It is not possible to calculate tolls for transit mode. Warn the user that it is not possible to calculate tolls for transit mode.";
}
const body: Body = {
origin: {
address: origin,
},
destination: {
address: destination,
},
travel_mode,
computeAlternativeRoutes: computeAlternativeRoutes ?? false,
departureTime,
arrivalTime,
transitPreferences,
extraComputations: (extraComputations as string[]) ?? [],
};
let fieldMask =
"routes.description,routes.localizedValues,routes.travelAdvisory,routes.legs.steps.transitDetails,routes.routeLabels,routes.warnings";
if (travel_mode === "TRANSIT") {
fieldMask += ",routes.legs.stepsOverview";
}
if (travel_mode === "DRIVE" || travel_mode === "TWO_WHEELER") {
body.routing_preference = "TRAFFIC_AWARE";
}
const res = await fetch(
`https://routes.googleapis.com/directions/v2:computeRoutes`,
{
method: "POST",
body: JSON.stringify(body),
headers: {
"X-Goog-Api-Key": this.apiKey,
"X-Goog-FieldMask": fieldMask,
"Content-Type": "application/json",
},
}
);
if (!res.ok) {
let message;
try {
const json = await res.json();
message = json.error.message;
} catch (e) {
message = `Unable to parse error message: Google did not return a JSON response. Error: ${e}`;
}
throw new Error(
`Got ${res.status}: ${res.statusText} error from Google Routes API: ${message}`
);
}
const json = await res.json();
if (Object.keys(json).length === 0) {
return "Invalid route. The route may be too long or impossible to travel by the selected mode of transport.";
}
const routes: FilteredTransitRoute[] | FilteredRoute[] = json.routes.map(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(route: any) => filterRoutes(route, travel_mode)
);
return JSON.stringify(routes);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/duckduckgo_search.ts
|
import { Tool, ToolParams } from "@langchain/core/tools";
import { search, SearchOptions } from "duck-duck-scrape";
export {
SafeSearchType,
SearchOptions,
SearchTimeType,
} from "duck-duck-scrape";
export interface DuckDuckGoSearchParameters extends ToolParams {
/**
* The search options for the search using the SearchOptions interface
* from the duck-duck-scrape package.
*/
searchOptions?: SearchOptions;
/**
* The maximum number of results to return from the search.
* Limiting to 10 to avoid context overload.
* @default 10
*/
maxResults?: number;
}
const DEFAULT_MAX_RESULTS = 10;
/**
* DuckDuckGo tool integration.
*
* Setup:
* Install `@langchain/community` and `duck-duck-scrape`.
*
* ```bash
* npm install @langchain/community duck-duck-scrape
* ```
*
* ## [Constructor args](https://api.js.langchain.com/classes/_langchain_community.tools_duckduckgo_search.DuckDuckGoSearch.html#constructor)
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";
*
* const tool = new DuckDuckGoSearch({ maxResults: 1 });
* ```
* </details>
*
* <br />
*
* <details>
*
* <summary><strong>Invocation</strong></summary>
*
* ```typescript
* await tool.invoke("what is the current weather in sf?");
*
* // output: [{"title":"San Francisco, CA Current Weather | AccuWeather","link":"https://www.accuweather.com/en/us/san-francisco/94103/current-weather/347629","snippet":"<b>Current</b> <b>weather</b> <b>in</b> San Francisco, CA. Check <b>current</b> conditions in San Francisco, CA with radar, hourly, and more."}]
* ```
* </details>
*
* <br />
*
* <details>
*
* <summary><strong>Invocation with tool call</strong></summary>
*
* ```typescript
* // This is usually generated by a model, but we'll create a tool call directly for demo purposes.
* const modelGeneratedToolCall = {
* args: {
* input: "what is the current weather in sf?",
* },
* id: "tool_call_id",
* name: tool.name,
* type: "tool_call",
* };
* await tool.invoke(modelGeneratedToolCall);
* ```
*
* ```text
* ToolMessage {
* "content": "[{\"title\":\"San Francisco, CA Weather Conditions | Weather Underground\",\"link\":\"https://www.wunderground.com/weather/us/ca/san-francisco\",\"snippet\":\"San Francisco <b>Weather</b> Forecasts. <b>Weather</b> Underground provides local & long-range <b>weather</b> forecasts, weatherreports, maps & tropical <b>weather</b> conditions for the San Francisco area.\"}]",
* "name": "duckduckgo-search",
* "additional_kwargs": {},
* "response_metadata": {},
* "tool_call_id": "tool_call_id"
* }
* ```
* </details>
*/
export class DuckDuckGoSearch extends Tool {
private searchOptions?: SearchOptions;
private maxResults = DEFAULT_MAX_RESULTS;
constructor(params?: DuckDuckGoSearchParameters) {
super(params ?? {});
const { searchOptions, maxResults } = params ?? {};
this.searchOptions = searchOptions;
this.maxResults = maxResults || this.maxResults;
}
static lc_name() {
return "DuckDuckGoSearch";
}
name = "duckduckgo-search";
description =
"A search engine. Useful for when you need to answer questions about current events. Input should be a search query.";
async _call(input: string): Promise<string> {
const { results } = await search(input, this.searchOptions);
return JSON.stringify(
results
.map((result) => ({
title: result.title,
link: result.url,
snippet: result.description,
}))
.slice(0, this.maxResults)
);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/ifttt.ts
|
/** From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
# Creating a webhook
- Go to https://ifttt.com/create
# Configuring the "If This"
- Click on the "If This" button in the IFTTT interface.
- Search for "Webhooks" in the search bar.
- Choose the first option for "Receive a web request with a JSON payload."
- Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.
- Click the "Create Trigger" button to save your settings and create your webhook.
# Configuring the "Then That"
- Tap on the "Then That" button in the IFTTT interface.
- Search for the service you want to connect, such as Spotify.
- Choose an action from the service, such as "Add track to a playlist".
- Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".
- Reference the JSON Payload received by the Webhook in your action. For the Spotify
scenario, choose "{{JsonPayload}}" as your search query.
- Tap the "Create Action" button to save your action settings.
- Once you have finished configuring your action, click the "Finish" button to
complete the setup.
- Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions 🎉
# Finishing up
- To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
- Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
*/
import { Tool } from "@langchain/core/tools";
/**
* Represents a tool for creating and managing webhooks with the IFTTT (If
* This Then That) service. The IFTTT service allows users to create
* chains of simple conditional statements, called applets, which are
* triggered based on changes to other web services.
*/
export class IFTTTWebhook extends Tool {
static lc_name() {
return "IFTTTWebhook";
}
private url: string;
name: string;
description: string;
constructor(url: string, name: string, description: string) {
super(...arguments);
this.url = url;
this.name = name;
this.description = description;
}
/** @ignore */
async _call(input: string): Promise<string> {
const headers = { "Content-Type": "application/json" };
const body = JSON.stringify({ this: input });
const response = await fetch(this.url, {
method: "POST",
headers,
body,
});
if (!response.ok) {
throw new Error(`HTTP error ${response.status}`);
}
const result = await response.text();
return result;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/create_draft.ts
|
import { z } from "zod";
import { GmailBaseTool, GmailBaseToolParams } from "./base.js";
import { CREATE_DRAFT_DESCRIPTION } from "./descriptions.js";
export class GmailCreateDraft extends GmailBaseTool {
name = "create_gmail_draft";
schema = z.object({
message: z.string(),
to: z.array(z.string()),
subject: z.string(),
cc: z.array(z.string()).optional(),
bcc: z.array(z.string()).optional(),
});
description = CREATE_DRAFT_DESCRIPTION;
constructor(fields?: GmailBaseToolParams) {
super(fields);
}
private prepareDraftMessage(
message: string,
to: string[],
subject: string,
cc?: string[],
bcc?: string[]
) {
const draftMessage = {
message: {
raw: "",
},
};
const email = [
`To: ${to.join(", ")}`,
`Subject: ${subject}`,
cc ? `Cc: ${cc.join(", ")}` : "",
bcc ? `Bcc: ${bcc.join(", ")}` : "",
"",
message,
].join("\n");
draftMessage.message.raw = Buffer.from(email).toString("base64url");
return draftMessage;
}
async _call(arg: z.output<typeof this.schema>) {
const { message, to, subject, cc, bcc } = arg;
const create_message = this.prepareDraftMessage(
message,
to,
subject,
cc,
bcc
);
const response = await this.gmail.users.drafts.create({
userId: "me",
requestBody: create_message,
});
return `Draft created. Draft Id: ${response.data.id}`;
}
}
export type CreateDraftSchema = {
message: string;
to: string[];
subject: string;
cc?: string[];
bcc?: string[];
};
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/get_message.ts
|
import { z } from "zod";
import { GmailBaseToolParams, GmailBaseTool } from "./base.js";
import { GET_MESSAGE_DESCRIPTION } from "./descriptions.js";
export class GmailGetMessage extends GmailBaseTool {
name = "gmail_get_message";
schema = z.object({
messageId: z.string(),
});
description = GET_MESSAGE_DESCRIPTION;
constructor(fields?: GmailBaseToolParams) {
super(fields);
}
async _call(arg: z.output<typeof this.schema>) {
const { messageId } = arg;
const message = await this.gmail.users.messages.get({
userId: "me",
id: messageId,
});
const { data } = message;
if (!data) {
throw new Error("No data returned from Gmail");
}
const { payload } = data;
if (!payload) {
throw new Error("No payload returned from Gmail");
}
const { headers } = payload;
if (!headers) {
throw new Error("No headers returned from Gmail");
}
const subject = headers.find((header) => header.name === "Subject");
if (!subject) {
throw new Error("No subject returned from Gmail");
}
const body = headers.find((header) => header.name === "Body");
if (!body) {
throw new Error("No body returned from Gmail");
}
const from = headers.find((header) => header.name === "From");
if (!from) {
throw new Error("No from returned from Gmail");
}
const to = headers.find((header) => header.name === "To");
if (!to) {
throw new Error("No to returned from Gmail");
}
const date = headers.find((header) => header.name === "Date");
if (!date) {
throw new Error("No date returned from Gmail");
}
const messageIdHeader = headers.find(
(header) => header.name === "Message-ID"
);
if (!messageIdHeader) {
throw new Error("No message id returned from Gmail");
}
return `Result for the prompt ${messageId} \n${JSON.stringify({
subject: subject.value,
body: body.value,
from: from.value,
to: to.value,
date: date.value,
messageId: messageIdHeader.value,
})}`;
}
}
export type GetMessageSchema = {
messageId: string;
};
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/send_message.ts
|
import { z } from "zod";
import { GmailBaseTool, GmailBaseToolParams } from "./base.js";
import { GET_MESSAGE_DESCRIPTION } from "./descriptions.js";
export class GmailSendMessage extends GmailBaseTool {
name = "gmail_send_message";
schema = z.object({
message: z.string(),
to: z.array(z.string()),
subject: z.string(),
cc: z.array(z.string()).optional(),
bcc: z.array(z.string()).optional(),
});
description = GET_MESSAGE_DESCRIPTION;
constructor(fields?: GmailBaseToolParams) {
super(fields);
}
private createEmailMessage({
message,
to,
subject,
cc,
bcc,
}: z.infer<typeof this.schema>): string {
const emailLines: string[] = [];
// Format the recipient(s)
const formatEmailList = (emails: string | string[]): string =>
Array.isArray(emails) ? emails.join(",") : emails;
emailLines.push(`To: ${formatEmailList(to)}`);
if (cc) emailLines.push(`Cc: ${formatEmailList(cc)}`);
if (bcc) emailLines.push(`Bcc: ${formatEmailList(bcc)}`);
emailLines.push(`Subject: ${subject}`);
emailLines.push("");
emailLines.push(message);
// Convert the email message to base64url string
const email = emailLines.join("\r\n").trim();
// this encode may be an issue
return Buffer.from(email).toString("base64url");
}
async _call({
message,
to,
subject,
cc,
bcc,
}: z.output<typeof this.schema>): Promise<string> {
const rawMessage = this.createEmailMessage({
message,
to,
subject,
cc,
bcc,
});
try {
const response = await this.gmail.users.messages.send({
userId: "me",
requestBody: {
raw: rawMessage,
},
});
return `Message sent. Message Id: ${response.data.id}`;
} catch (error) {
throw new Error(`An error occurred while sending the message: ${error}`);
}
}
}
export type SendMessageSchema = {
message: string;
to: string[];
subject: string;
cc?: string[];
bcc?: string[];
};
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/descriptions.ts
|
export const CREATE_DRAFT_DESCRIPTION = `A tool for creating draft emails in Gmail.
INPUT example:
{
"message": "Hello, this is a test draft",
"to": ["example1@email.com", "example2@email.com"],
"subject": "Test Draft",
"cc": ["cc1@email.com"],
"bcc": ["bcc1@email.com"]
}
OUTPUT:
The output is a confirmation message with the draft ID.
`;
export const GET_MESSAGE_DESCRIPTION = `A tool for retrieving a specific email message from Gmail using its message ID.
INPUT example:
{
"messageId": "unique_message_id_string"
}
OUTPUT:
The output includes detailed information about the retrieved email message. This includes the subject, body, sender (from), recipients (to), date of the email, and the message ID. If any of these details are not available in the email, the tool will throw an error indicating the missing information.
Example Output:
"Result for the prompt unique_message_id_string
{
'subject': 'Email Subject',
'body': 'Email Body Content',
'from': 'sender@email.com',
'to': 'recipient@email.com',
'date': 'Email Date',
'messageId': 'unique_message_id_string'
}"
`;
export const GET_THREAD_DESCRIPTION = `A tool for retrieving an entire email thread from Gmail using the thread ID.
INPUT example:
{
"threadId": "unique_thread_id_string"
}
OUTPUT:
The output includes an array of all the messages in the specified thread. Each message in the array contains detailed information including the subject, body, sender (from), recipients (to), date of the email, and the message ID. If any of these details are not available in a message, the tool will throw an error indicating the missing information.
Example Output:
"Result for the prompt unique_thread_id_string
[
{
'subject': 'Email Subject',
'body': 'Email Body Content',
'from': 'sender@email.com',
'to': 'recipient@email.com',
'date': 'Email Date',
'messageId': 'unique_message_id_string'
},
... (other messages in the thread)
]"
`;
export const SEND_MESSAGE_DESCRIPTION = `A tool for sending an email message using Gmail. It allows users to specify recipients, subject, and the content of the message, along with optional cc and bcc fields.
INPUT example:
{
"message": "Hello, this is a test email",
"to": ["recipient1@email.com", "recipient2@email.com"],
"subject": "Test Email",
"cc": ["cc1@email.com"],
"bcc": ["bcc1@email.com"]
}
OUTPUT:
The output is a confirmation message with the ID of the sent email. If there is an error during the sending process, the tool will throw an error with a description of the problem.
Example Output:
"Message sent. Message Id: unique_message_id_string"
`;
export const SEARCH_DESCRIPTION = `A tool for searching email messages or threads in Gmail using a specific query. It offers the flexibility to choose between messages and threads as the search resource.
INPUT example:
{
"query": "specific search query",
"maxResults": 10, // Optional: number of results to return
"resource": "messages" // Optional: can be "messages" or "threads"
}
OUTPUT:
The output is a JSON list of either email messages or threads, depending on the specified resource, that matches the search query. For 'messages', the output includes details like the message ID, thread ID, snippet, body, subject, and sender of each message. For 'threads', it includes the thread ID, snippet, body, subject, and sender of the first message in each thread. If no data is returned, or if the specified resource is invalid, the tool throws an error with a relevant message.
Example Output for 'messages':
"Result for the query 'specific search query':
[
{
'id': 'message_id',
'threadId': 'thread_id',
'snippet': 'message snippet',
'body': 'message body',
'subject': 'message subject',
'sender': 'sender's email'
},
... (other messages matching the query)
]"
Example Output for 'threads':
"Result for the query 'specific search query':
[
{
'id': 'thread_id',
'snippet': 'thread snippet',
'body': 'first message body',
'subject': 'first message subject',
'sender': 'first message sender'
},
... (other threads matching the query)
]"
`;
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/index.ts
|
export { GmailCreateDraft } from "./create_draft.js";
export { GmailGetMessage } from "./get_message.js";
export { GmailGetThread } from "./get_thread.js";
export { GmailSearch } from "./search.js";
export { GmailSendMessage } from "./send_message.js";
export type { GmailBaseToolParams } from "./base.js";
export type { CreateDraftSchema } from "./create_draft.js";
export type { GetMessageSchema } from "./get_message.js";
export type { GetThreadSchema } from "./get_thread.js";
export type { SearchSchema } from "./search.js";
export type { SendMessageSchema } from "./send_message.js";
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/get_thread.ts
|
import { z } from "zod";
import { GmailBaseTool, GmailBaseToolParams } from "./base.js";
import { GET_THREAD_DESCRIPTION } from "./descriptions.js";
export class GmailGetThread extends GmailBaseTool {
name = "gmail_get_thread";
schema = z.object({
threadId: z.string(),
});
description = GET_THREAD_DESCRIPTION;
constructor(fields?: GmailBaseToolParams) {
super(fields);
}
async _call(arg: z.output<typeof this.schema>) {
const { threadId } = arg;
const thread = await this.gmail.users.threads.get({
userId: "me",
id: threadId,
});
const { data } = thread;
if (!data) {
throw new Error("No data returned from Gmail");
}
const { messages } = data;
if (!messages) {
throw new Error("No messages returned from Gmail");
}
return `Result for the prompt ${threadId} \n${JSON.stringify(
messages.map((message) => {
const { payload } = message;
if (!payload) {
throw new Error("No payload returned from Gmail");
}
const { headers } = payload;
if (!headers) {
throw new Error("No headers returned from Gmail");
}
const subject = headers.find((header) => header.name === "Subject");
if (!subject) {
throw new Error("No subject returned from Gmail");
}
const body = headers.find((header) => header.name === "Body");
if (!body) {
throw new Error("No body returned from Gmail");
}
const from = headers.find((header) => header.name === "From");
if (!from) {
throw new Error("No from returned from Gmail");
}
const to = headers.find((header) => header.name === "To");
if (!to) {
throw new Error("No to returned from Gmail");
}
const date = headers.find((header) => header.name === "Date");
if (!date) {
throw new Error("No date returned from Gmail");
}
const messageIdHeader = headers.find(
(header) => header.name === "Message-ID"
);
if (!messageIdHeader) {
throw new Error("No message id returned from Gmail");
}
return {
subject: subject.value,
body: body.value,
from: from.value,
to: to.value,
date: date.value,
messageId: messageIdHeader.value,
};
})
)}`;
}
}
export type GetThreadSchema = {
threadId: string;
};
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/base.ts
|
import { gmail_v1, google } from "googleapis";
import { z } from "zod";
import { StructuredTool } from "@langchain/core/tools";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
export interface GmailBaseToolParams {
credentials?: {
clientEmail?: string;
privateKey?: string;
keyfile?: string;
subject?: string;
};
scopes?: string[];
}
export abstract class GmailBaseTool extends StructuredTool {
private CredentialsSchema = z
.object({
clientEmail: z
.string()
.min(1)
.default(getEnvironmentVariable("GMAIL_CLIENT_EMAIL") ?? ""),
privateKey: z
.string()
.default(getEnvironmentVariable("GMAIL_PRIVATE_KEY") ?? ""),
keyfile: z
.string()
.default(getEnvironmentVariable("GMAIL_KEYFILE") ?? ""),
subject: z
.string()
.default(getEnvironmentVariable("GMAIL_SUBJECT") ?? ""),
})
.refine(
(credentials) =>
credentials.privateKey !== "" || credentials.keyfile !== "",
{
message:
"Missing GMAIL_PRIVATE_KEY or GMAIL_KEYFILE to interact with Gmail",
}
);
private GmailBaseToolParamsSchema = z
.object({
credentials: this.CredentialsSchema.default({}),
scopes: z.array(z.string()).default(["https://mail.google.com/"]),
})
.default({});
name = "Gmail";
description = "A tool to send and view emails through Gmail";
protected gmail: gmail_v1.Gmail;
constructor(fields?: Partial<GmailBaseToolParams>) {
super(...arguments);
const { credentials, scopes } =
this.GmailBaseToolParamsSchema.parse(fields);
this.gmail = this.getGmail(
scopes,
credentials.clientEmail,
credentials.privateKey,
credentials.keyfile,
credentials.subject
);
}
private getGmail(
scopes: string[],
email: string,
key?: string,
keyfile?: string,
subject?: string
) {
const auth = new google.auth.JWT(email, keyfile, key, scopes, subject);
return google.gmail({ version: "v1", auth });
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/gmail/search.ts
|
import { gmail_v1 } from "googleapis";
import { z } from "zod";
import { GmailBaseTool, GmailBaseToolParams } from "./base.js";
import { SEARCH_DESCRIPTION } from "./descriptions.js";
export class GmailSearch extends GmailBaseTool {
name = "search_gmail";
schema = z.object({
query: z.string(),
maxResults: z.number().optional(),
resource: z.enum(["messages", "threads"]).optional(),
});
description = SEARCH_DESCRIPTION;
constructor(fields?: GmailBaseToolParams) {
super(fields);
}
async _call(arg: z.output<typeof this.schema>) {
const { query, maxResults = 10, resource = "messages" } = arg;
const response = await this.gmail.users.messages.list({
userId: "me",
q: query,
maxResults,
});
const { data } = response;
if (!data) {
throw new Error("No data returned from Gmail");
}
const { messages } = data;
if (!messages) {
throw new Error("No messages returned from Gmail");
}
if (resource === "messages") {
const parsedMessages = await this.parseMessages(messages);
return `Result for the query ${query}:\n${JSON.stringify(
parsedMessages
)}`;
} else if (resource === "threads") {
const parsedThreads = await this.parseThreads(messages);
return `Result for the query ${query}:\n${JSON.stringify(parsedThreads)}`;
}
throw new Error(`Invalid resource: ${resource}`);
}
async parseMessages(
messages: gmail_v1.Schema$Message[]
): Promise<gmail_v1.Schema$Message[]> {
const parsedMessages = await Promise.all(
messages.map(async (message) => {
const messageData = await this.gmail.users.messages.get({
userId: "me",
format: "raw",
id: message.id ?? "",
});
const headers = messageData.data.payload?.headers || [];
const subject = headers.find((header) => header.name === "Subject");
const sender = headers.find((header) => header.name === "From");
let body = "";
if (messageData.data.payload?.parts) {
body = messageData.data.payload.parts
.map((part) => part.body?.data ?? "")
.join("");
} else if (messageData.data.payload?.body?.data) {
body = messageData.data.payload.body.data;
}
return {
id: message.id,
threadId: message.threadId,
snippet: message.snippet,
body,
subject,
sender,
};
})
);
return parsedMessages;
}
async parseThreads(
threads: gmail_v1.Schema$Thread[]
): Promise<gmail_v1.Schema$Thread[]> {
const parsedThreads = await Promise.all(
threads.map(async (thread) => {
const threadData = await this.gmail.users.threads.get({
userId: "me",
format: "raw",
id: thread.id ?? "",
});
const headers = threadData.data.messages?.[0]?.payload?.headers || [];
const subject = headers.find((header) => header.name === "Subject");
const sender = headers.find((header) => header.name === "From");
let body = "";
if (threadData.data.messages?.[0]?.payload?.parts) {
body = threadData.data.messages[0].payload.parts
.map((part) => part.body?.data ?? "")
.join("");
} else if (threadData.data.messages?.[0]?.payload?.body?.data) {
body = threadData.data.messages[0].payload.body.data;
}
return {
id: thread.id,
snippet: thread.snippet,
body,
subject,
sender,
};
})
);
return parsedThreads;
}
}
export type SearchSchema = {
query: string;
maxResults?: number;
resource?: "messages" | "threads";
};
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/view.ts
|
import { CallbackManagerForToolRun } from "@langchain/core/callbacks/manager";
import { GoogleCalendarBase, GoogleCalendarAgentParams } from "./base.js";
import { VIEW_TOOL_DESCRIPTION } from "./descriptions.js";
import { runViewEvents } from "./commands/run-view-events.js";
/**
* @example
* ```typescript
* const googleCalendarViewTool = new GoogleCalendarViewTool({
* credentials: {
* clientEmail: process.env.GOOGLE_CALENDAR_CLIENT_EMAIL,
* privateKey: process.env.GOOGLE_CALENDAR_PRIVATE_KEY,
* calendarId: process.env.GOOGLE_CALENDAR_CALENDAR_ID,
* },
* scopes: [
* "https:
* "https:
* ],
* model: new ChatOpenAI({}),
* });
* const viewInput = `What meetings do I have this week?`;
* const viewResult = await googleCalendarViewTool.invoke({ input: viewInput });
* console.log("View Result", viewResult);
* ```
*/
export class GoogleCalendarViewTool extends GoogleCalendarBase {
name = "google_calendar_view";
description = VIEW_TOOL_DESCRIPTION;
constructor(fields: GoogleCalendarAgentParams) {
super(fields);
}
async _call(query: string, runManager?: CallbackManagerForToolRun) {
const auth = await this.getAuth();
const model = this.getModel();
return runViewEvents(
query,
{
auth,
model,
calendarId: this.calendarId,
},
runManager
);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/create.ts
|
import { CallbackManagerForToolRun } from "@langchain/core/callbacks/manager";
import { GoogleCalendarBase, GoogleCalendarAgentParams } from "./base.js";
import { runCreateEvent } from "./commands/run-create-events.js";
import { CREATE_TOOL_DESCRIPTION } from "./descriptions.js";
/**
* @example
* ```typescript
* const googleCalendarCreateTool = new GoogleCalendarCreateTool({
* credentials: {
* clientEmail: process.env.GOOGLE_CALENDAR_CLIENT_EMAIL,
* privateKey: process.env.GOOGLE_CALENDAR_PRIVATE_KEY,
* calendarId: process.env.GOOGLE_CALENDAR_CALENDAR_ID,
* },
* scopes: [
* "https:
* "https:
* ],
* model: new ChatOpenAI({}),
* });
* const createInput = `Create a meeting with John Doe next Friday at 4pm - adding to the agenda of it the result of 99 + 99`;
* const createResult = await googleCalendarCreateTool.invoke({
* input: createInput,
* });
* console.log("Create Result", createResult);
* ```
*/
export class GoogleCalendarCreateTool extends GoogleCalendarBase {
name = "google_calendar_create";
description = CREATE_TOOL_DESCRIPTION;
constructor(fields: GoogleCalendarAgentParams) {
super(fields);
}
async _call(query: string, runManager?: CallbackManagerForToolRun) {
const auth = await this.getAuth();
const model = this.getModel();
return runCreateEvent(
query,
{
auth,
model,
calendarId: this.calendarId,
},
runManager
);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/descriptions.ts
|
export const CREATE_TOOL_DESCRIPTION = `A tool for creating Google Calendar events and meetings.
INPUT example:
"action": "google_calendar_create",
"action_input": "create a new meeting with John Doe tomorrow at 4pm"
OUTPUT:
Output is a confirmation of a created event.
`;
export const VIEW_TOOL_DESCRIPTION = `A tool for retrieving Google Calendar events and meetings.
INPUT examples:
"action": "google_calendar_view",
"action_input": "display meetings for today"
"action": "google_calendar_view",
"action_input": "show events for tomorrow"
"action": "google_calendar_view",
"action_input": "display meetings for tomorrow between 4pm and 8pm"
OUTPUT:
- title, start time, end time, attendees, description (if available)
`;
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/index.ts
|
export { GoogleCalendarCreateTool } from "./create.js";
export { GoogleCalendarViewTool } from "./view.js";
export type { GoogleCalendarAgentParams } from "./base.js";
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/base.ts
|
import { google } from "googleapis";
import { Tool } from "@langchain/core/tools";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { BaseLLM } from "@langchain/core/language_models/llms";
export interface GoogleCalendarAgentParams {
credentials?: {
clientEmail?: string;
privateKey?: string;
calendarId?: string;
};
scopes?: string[];
model?: BaseLLM;
}
export class GoogleCalendarBase extends Tool {
name = "Google Calendar";
description =
"A tool to lookup Google Calendar events and create events in Google Calendar";
protected clientEmail: string;
protected privateKey: string;
protected calendarId: string;
protected scopes: string[];
protected llm: BaseLLM;
constructor(
fields: GoogleCalendarAgentParams = {
credentials: {
clientEmail: getEnvironmentVariable("GOOGLE_CALENDAR_CLIENT_EMAIL"),
privateKey: getEnvironmentVariable("GOOGLE_CALENDAR_PRIVATE_KEY"),
calendarId: getEnvironmentVariable("GOOGLE_CALENDAR_CALENDAR_ID"),
},
scopes: [
"https://www.googleapis.com/auth/calendar",
"https://www.googleapis.com/auth/calendar.events",
],
}
) {
super(...arguments);
if (!fields.model) {
throw new Error("Missing llm instance to interact with Google Calendar");
}
if (!fields.credentials) {
throw new Error("Missing credentials to authenticate to Google Calendar");
}
if (!fields.credentials.clientEmail) {
throw new Error(
"Missing GOOGLE_CALENDAR_CLIENT_EMAIL to interact with Google Calendar"
);
}
if (!fields.credentials.privateKey) {
throw new Error(
"Missing GOOGLE_CALENDAR_PRIVATE_KEY to interact with Google Calendar"
);
}
if (!fields.credentials.calendarId) {
throw new Error(
"Missing GOOGLE_CALENDAR_CALENDAR_ID to interact with Google Calendar"
);
}
this.clientEmail = fields.credentials.clientEmail;
this.privateKey = fields.credentials.privateKey;
this.calendarId = fields.credentials.calendarId;
this.scopes = fields.scopes || [];
this.llm = fields.model;
}
getModel() {
return this.llm;
}
async getAuth() {
const auth = new google.auth.JWT(
this.clientEmail,
undefined,
this.privateKey,
this.scopes
);
return auth;
}
async _call(input: string) {
return input;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/prompts/view-events-prompt.ts
|
export const VIEW_EVENTS_PROMPT = `
Date format: YYYY-MM-DDThh:mm:ss+00:00
Based on this event description: 'View my events on Thursday',
output a json of the following parameters:
Today's datetime on UTC time 2023-05-02T10:00:00+00:00, it's Tuesday and timezone
of the user is -5, take into account the timezone of the user and today's date.
If the user is searching for events with a specific title, person or location, put it into the search_query parameter.
1. time_min
2. time_max
3. user_timezone
4. max_results
5. search_query
event_summary:
{{
"time_min": "2023-05-04T00:00:00-05:00",
"time_max": "2023-05-04T23:59:59-05:00",
"user_timezone": "America/New_York",
"max_results": 10,
"search_query": ""
}}
Date format: YYYY-MM-DDThh:mm:ss+00:00
Based on this event description: '{query}', output a json of the
following parameters:
Today's datetime on UTC time {date}, today it's {dayName} and timezone of the user {u_timezone},
take into account the timezone of the user and today's date.
If the user is searching for events with a specific title, person or location, put it into the search_query parameter.
1. time_min
2. time_max
3. user_timezone
4. max_results
5. search_query
event_summary:
`;
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/prompts/create-event-prompt.ts
|
export const CREATE_EVENT_PROMPT = `
Date format: YYYY-MM-DDThh:mm:ss+00:00
Based on this event description: "Joey birthday tomorrow at 7 pm",
output a json of the following parameters:
Today's datetime on UTC time 2023-05-02T10:00:00+00:00, it's Tuesday and timezone
of the user is -5, take into account the timezone of the user and today's date.
1. event_summary
2. event_start_time
3. event_end_time
4. event_location
5. event_description
6. user_timezone
event_summary:
{{
"event_summary": "Joey birthday",
"event_start_time": "2023-05-03T19:00:00-05:00",
"event_end_time": "2023-05-03T20:00:00-05:00",
"event_location": "",
"event_description": "",
"user_timezone": "America/New_York"
}}
Date format: YYYY-MM-DDThh:mm:ss+00:00
Based on this event description: "Create a meeting for 5 pm on Saturday with Joey",
output a json of the following parameters:
Today's datetime on UTC time 2023-05-04T10:00:00+00:00, it's Thursday and timezone
of the user is -5, take into account the timezone of the user and today's date.
1. event_summary
2. event_start_time
3. event_end_time
4. event_location
5. event_description
6. user_timezone
event_summary:
{{
"event_summary": "Meeting with Joey",
"event_start_time": "2023-05-06T17:00:00-05:00",
"event_end_time": "2023-05-06T18:00:00-05:00",
"event_location": "",
"event_description": "",
"user_timezone": "America/New_York"
}}
Date format: YYYY-MM-DDThh:mm:ss+00:00
Based on this event description: "{query}", output a json of the
following parameters:
Today's datetime on UTC time {date}, it's {dayName} and timezone of the user {u_timezone},
take into account the timezone of the user and today's date.
1. event_summary
2. event_start_time
3. event_end_time
4. event_location
5. event_description
6. user_timezone
event_summary:
`;
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/prompts/index.ts
|
export { CREATE_EVENT_PROMPT } from "./create-event-prompt.js";
export { VIEW_EVENTS_PROMPT } from "./view-events-prompt.js";
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/utils/get-timezone-offset-in-hours.ts
|
const getTimezoneOffsetInHours = () => {
const offsetInMinutes = new Date().getTimezoneOffset();
const offsetInHours = -offsetInMinutes / 60;
return offsetInHours;
};
export { getTimezoneOffsetInHours };
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/commands/run-create-events.ts
|
import { google, calendar_v3 } from "googleapis";
import type { JWT, GaxiosResponse } from "googleapis-common";
import { PromptTemplate } from "@langchain/core/prompts";
import { CallbackManagerForToolRun } from "@langchain/core/callbacks/manager";
import { BaseLLM } from "@langchain/core/language_models/llms";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { CREATE_EVENT_PROMPT } from "../prompts/index.js";
import { getTimezoneOffsetInHours } from "../utils/get-timezone-offset-in-hours.js";
type CreateEventParams = {
eventSummary: string;
eventStartTime: string;
eventEndTime: string;
userTimezone: string;
eventLocation?: string;
eventDescription?: string;
};
const createEvent = async (
{
eventSummary,
eventStartTime,
eventEndTime,
userTimezone,
eventLocation = "",
eventDescription = "",
}: CreateEventParams,
calendarId: string,
auth: JWT
) => {
const calendar = google.calendar("v3");
const event = {
summary: eventSummary,
location: eventLocation,
description: eventDescription,
start: {
dateTime: eventStartTime,
timeZone: userTimezone,
},
end: {
dateTime: eventEndTime,
timeZone: userTimezone,
},
};
try {
const createdEvent = await calendar.events.insert({
auth,
calendarId,
requestBody: event,
});
return createdEvent;
} catch (error) {
return {
error: `An error occurred: ${error}`,
};
}
};
type RunCreateEventParams = {
calendarId: string;
auth: JWT;
model: BaseLLM;
};
const runCreateEvent = async (
query: string,
{ calendarId, auth, model }: RunCreateEventParams,
runManager?: CallbackManagerForToolRun
) => {
const prompt = new PromptTemplate({
template: CREATE_EVENT_PROMPT,
inputVariables: ["date", "query", "u_timezone", "dayName"],
});
const createEventChain = prompt.pipe(model).pipe(new StringOutputParser());
const date = new Date().toISOString();
const u_timezone = getTimezoneOffsetInHours();
const dayName = new Date().toLocaleString("en-us", { weekday: "long" });
const output = await createEventChain.invoke(
{
query,
date,
u_timezone,
dayName,
},
runManager?.getChild()
);
const loaded = JSON.parse(output);
const [
eventSummary,
eventStartTime,
eventEndTime,
eventLocation,
eventDescription,
userTimezone,
] = Object.values(loaded);
const event = await createEvent(
{
eventSummary,
eventStartTime,
eventEndTime,
userTimezone,
eventLocation,
eventDescription,
} as CreateEventParams,
calendarId,
auth
);
if (!(event as { error: string }).error) {
return `Event created successfully, details: event ${
(event as GaxiosResponse<calendar_v3.Schema$Event>).data.htmlLink
}`;
}
return `An error occurred creating the event: ${
(event as { error: string }).error
}`;
};
export { runCreateEvent };
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/google_calendar/commands/run-view-events.ts
|
import { calendar_v3 } from "googleapis";
import type { JWT } from "googleapis-common";
import { PromptTemplate } from "@langchain/core/prompts";
import { BaseLLM } from "@langchain/core/language_models/llms";
import { CallbackManagerForToolRun } from "@langchain/core/callbacks/manager";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { VIEW_EVENTS_PROMPT } from "../prompts/index.js";
import { getTimezoneOffsetInHours } from "../utils/get-timezone-offset-in-hours.js";
type RunViewEventParams = {
calendarId: string;
auth: JWT;
model: BaseLLM;
};
const runViewEvents = async (
query: string,
{ model, auth, calendarId }: RunViewEventParams,
runManager?: CallbackManagerForToolRun
) => {
const calendar = new calendar_v3.Calendar({});
const prompt = new PromptTemplate({
template: VIEW_EVENTS_PROMPT,
inputVariables: ["date", "query", "u_timezone", "dayName"],
});
const viewEventsChain = prompt.pipe(model).pipe(new StringOutputParser());
const date = new Date().toISOString();
const u_timezone = getTimezoneOffsetInHours();
const dayName = new Date().toLocaleString("en-us", { weekday: "long" });
const output = await viewEventsChain.invoke(
{
query,
date,
u_timezone,
dayName,
},
runManager?.getChild()
);
const loaded = JSON.parse(output);
try {
const response = await calendar.events.list({
auth,
calendarId,
...loaded,
});
const curatedItems =
response.data && response.data.items
? response.data.items.map(
({
status,
summary,
description,
start,
end,
}: // eslint-disable-next-line @typescript-eslint/no-explicit-any
any) => ({
status,
summary,
description,
start,
end,
})
)
: [];
return `Result for the prompt "${query}": \n${JSON.stringify(
curatedItems,
null,
2
)}`;
} catch (error) {
return `An error occurred: ${error}`;
}
};
export { runViewEvents };
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/google_custom_search.int.test.ts
|
import { test } from "@jest/globals";
import { GoogleCustomSearch } from "../google_custom_search.js";
test.skip("GoogleCustomSearchTool", async () => {
const tool = new GoogleCustomSearch();
// @eslint-disable-next-line/@typescript-eslint/ban-ts-comment
// @ts-expect-error unused var
const result = await tool.invoke("What is Langchain?");
// console.log({ result });
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/google_calendar.test.ts
|
import { jest, expect, describe } from "@jest/globals";
import { LLM } from "@langchain/core/language_models/llms";
import {
GoogleCalendarCreateTool,
GoogleCalendarViewTool,
} from "../google_calendar/index.js";
jest.mock("googleapis", () => ({
google: {
auth: {
JWT: jest.fn().mockImplementation(() => ({})),
},
},
}));
jest.mock("@langchain/core/utils/env", () => ({
getEnvironmentVariable: () => "key",
}));
// jest.mock("../google_calendar/commands/run-create-events.js", () => ({
// runCreateEvent: jest.fn(),
// }));
// jest.mock("../google_calendar/commands/run-view-events.js", () => ({
// runViewEvents: jest.fn(),
// }));
class FakeLLM extends LLM {
_llmType() {
return "fake";
}
async _call(prompt: string): Promise<string> {
return prompt;
}
}
describe("GoogleCalendarCreateTool", () => {
it("should be setup with correct parameters", async () => {
const params = {
credentials: {
clientEmail: "test@email.com",
privateKey: "privateKey",
calendarId: "calendarId",
},
model: new FakeLLM({}),
};
const instance = new GoogleCalendarCreateTool(params);
expect(instance.name).toBe("google_calendar_create");
});
it("should throw an error if missing credentials", async () => {
const params = {
credentials: {},
model: new FakeLLM({}),
};
expect(() => new GoogleCalendarCreateTool(params)).toThrow(
"Missing GOOGLE_CALENDAR_CLIENT_EMAIL to interact with Google Calendar"
);
});
it("should throw an error if missing model", async () => {
const params = {
credentials: {
clientEmail: "test",
},
};
expect(() => new GoogleCalendarCreateTool(params)).toThrow(
"Missing llm instance to interact with Google Calendar"
);
});
});
describe("GoogleCalendarViewTool", () => {
it("should be setup with correct parameters", async () => {
const params = {
credentials: {
clientEmail: "test@email.com",
privateKey: "privateKey",
calendarId: "calendarId",
},
model: new FakeLLM({}),
};
const instance = new GoogleCalendarViewTool(params);
expect(instance.name).toBe("google_calendar_view");
});
it("should throw an error if missing credentials", async () => {
const params = {
credentials: {},
model: new FakeLLM({}),
};
expect(() => new GoogleCalendarViewTool(params)).toThrow(
"Missing GOOGLE_CALENDAR_CLIENT_EMAIL to interact with Google Calendar"
);
});
it("should throw an error if missing model", async () => {
const params = {
credentials: {
clientEmail: "test",
},
};
expect(() => new GoogleCalendarViewTool(params)).toThrow(
"Missing llm instance to interact with Google Calendar"
);
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/discord.int.test.ts
|
import { test } from "@jest/globals";
import {
DiscordGetMessagesTool,
DiscordChannelSearchTool,
DiscordSendMessagesTool,
DiscordGetGuildsTool,
DiscordGetTextChannelsTool,
} from "../discord.js";
test.skip("DiscordGetMessagesTool", async () => {
const tool = new DiscordGetMessagesTool();
const result = await tool.invoke("1153400523718938780");
// console.log(result);
expect(result).toBeTruthy();
expect(result).not.toEqual("Channel not found.");
expect(result).not.toEqual("Error getting messages.");
});
test.skip("DiscordGetGuildsTool", async () => {
const tool = new DiscordGetGuildsTool();
const result = await tool.invoke("");
// console.log(result);
expect(result).toBeTruthy();
expect(result).not.toEqual("Error getting guilds.");
});
test.skip("DiscordChannelSearchTool", async () => {
const tool = new DiscordChannelSearchTool();
const result = await tool.invoke("Test");
// console.log(result);
expect(result).toBeTruthy();
expect(result).not.toEqual("Error searching through channel.");
});
test.skip("DiscordGetTextChannelsTool", async () => {
const tool = new DiscordGetTextChannelsTool();
const result = await tool.invoke("1153400523718938775");
// console.log(result);
expect(result).toBeTruthy();
expect(result).not.toEqual("Error getting text channels.");
});
test.skip("DiscordSendMessagesTool", async () => {
const tool = new DiscordSendMessagesTool();
const result = await tool.invoke("test message from new code");
// console.log(result);
expect(result).toEqual("Message sent successfully.");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/wolframalpha.test.ts
|
import { jest, afterEach, beforeEach, describe, expect } from "@jest/globals";
import { WolframAlphaTool } from "../wolframalpha.js";
const MOCK_APP_ID = "[MOCK_APP_ID]";
const QUERY_1 = "What is 2 + 2?";
const MOCK_ANSWER = "[MOCK_ANSWER]";
describe("wolfram alpha test suite", () => {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
let fetchMock: any;
beforeEach(() => {
fetchMock = jest.spyOn(global, "fetch").mockImplementation(
async () =>
({
text: () => Promise.resolve(MOCK_ANSWER),
} as Response)
);
});
afterEach(() => {
fetchMock.mockRestore();
});
test("test query parameters passed correctly", async () => {
const wolframAlpha = new WolframAlphaTool({
appid: MOCK_APP_ID,
});
await wolframAlpha._call(QUERY_1);
const [url] = fetchMock.mock.calls[0];
const parsedUrl = new URL(url);
const params = new URLSearchParams(parsedUrl.search);
expect(fetchMock).toBeCalledTimes(1);
expect(params.get("appid")).toBe(MOCK_APP_ID);
expect(params.get("input")).toBe(QUERY_1);
});
test("test answer retrieved", async () => {
const wolframAlpha = new WolframAlphaTool({
appid: MOCK_APP_ID,
});
const answer = await wolframAlpha._call(QUERY_1);
expect(answer).toBe(MOCK_ANSWER);
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/aiplugin.int.test.ts
|
import { test, expect } from "@jest/globals";
import { AIPluginTool } from "../aiplugin.js";
test("AIPluginTool", async () => {
const tool = await AIPluginTool.fromPluginUrl(
"https://www.klarna.com/.well-known/ai-plugin.json"
);
expect(await tool.invoke({})).toMatchInlineSnapshot(`
"Usage Guide: Assistant uses the Klarna plugin to get relevant product suggestions for any shopping or product discovery purpose. Assistant will reply with the following 3 paragraphs 1) Search Results 2) Product Comparison of the Search Results 3) Followup Questions. The first paragraph contains a list of the products with their attributes listed clearly and concisely as bullet points under the product, together with a link to the product and an explanation. Links will always be returned and should be shown to the user. The second paragraph compares the results returned in a summary sentence starting with "In summary". Assistant comparisons consider only the most important features of the products that will help them fit the users request, and each product mention is brief, short and concise. In the third paragraph assistant always asks helpful follow-up questions and end with a question mark. When assistant is asking a follow-up question, it uses it's product expertise to provide information pertaining to the subject of the user's request that may guide them in their search for the right product.
OpenAPI Spec in JSON or YAML format:
{"openapi":"3.0.1","info":{"version":"v0","title":"Open AI Klarna product Api"},"servers":[{"url":"https://www.klarna.com/us/shopping"}],"tags":[{"name":"open-ai-product-endpoint","description":"Open AI Product Endpoint. Query for products."}],"paths":{"/public/openai/v0/products":{"get":{"tags":["open-ai-product-endpoint"],"summary":"API for fetching Klarna product information","operationId":"productsUsingGET","parameters":[{"name":"countryCode","in":"query","description":"ISO 3166 country code with 2 characters based on the user location. Currently, only US, GB, DE, SE and DK are supported.","required":true,"schema":{"type":"string"}},{"name":"q","in":"query","description":"A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. If the user speaks another language than English, translate their request into English (example: translate fia med knuff to ludo board game)!","required":true,"schema":{"type":"string"}},{"name":"size","in":"query","description":"number of products returned","required":false,"schema":{"type":"integer"}},{"name":"min_price","in":"query","description":"(Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.","required":false,"schema":{"type":"integer"}},{"name":"max_price","in":"query","description":"(Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.","required":false,"schema":{"type":"integer"}}],"responses":{"200":{"description":"Products found","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductResponse"}}}},"503":{"description":"one or more services are unavailable"}},"deprecated":false}}},"components":{"schemas":{"Product":{"type":"object","properties":{"attributes":{"type":"array","items":{"type":"string"}},"name":{"type":"string"},"price":{"type":"string"},"url":{"type":"string"}},"title":"Product"},"ProductResponse":{"type":"object","properties":{"products":{"type":"array","items":{"$ref":"#/components/schemas/Product"}}},"title":"ProductResponse"}}}}"
`);
expect(await tool.invoke({})).toMatch(/Usage Guide/);
expect(await tool.invoke("")).toMatch(/OpenAPI Spec/);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/searchapi.test.ts
|
import { test, expect } from "@jest/globals";
import { SearchApi } from "../searchapi.js";
describe("SearchApi test suite", () => {
class SearchApiUrlTester extends SearchApi {
testThisUrl(): string {
return this.buildUrl("Query");
}
}
test("Test default url", async () => {
const searchApi = new SearchApiUrlTester("ApiKey", {
hl: "en",
gl: "us",
});
expect(searchApi.testThisUrl()).toEqual(
"https://www.searchapi.io/api/v1/search?engine=google&api_key=ApiKey&hl=en&gl=us&q=Query"
);
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/brave_search.int.test.ts
|
import { test } from "@jest/globals";
import { BraveSearch } from "../brave_search.js";
test.skip("BraveSearchTool", async () => {
const tool = new BraveSearch();
// @eslint-disable-next-line/@typescript-eslint/ban-ts-comment
// @ts-expect-error unused var
const result = await tool.invoke("What is Langchain?");
// console.log({ result });
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/google_places.int.test.ts
|
import { expect, describe } from "@jest/globals";
import { GooglePlacesAPI } from "../google_places.js";
describe("GooglePlacesAPI", () => {
test("should be setup with correct parameters", async () => {
const instance = new GooglePlacesAPI();
expect(instance.name).toBe("google_places");
});
test("GooglePlacesAPI returns expected result for valid query", async () => {
const tool = new GooglePlacesAPI();
const result = await tool.invoke("EatonCenter");
expect(result).toContain("220 Yonge St");
expect(result).toContain("CF Toronto Eaton Centre");
});
test("GooglePlacesAPI returns '' for query on an non-existent place", async () => {
const tool = new GooglePlacesAPI();
const result = await tool.invoke("ihfwehnwfi");
expect(result).toContain("");
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/serpapi.test.ts
|
import { test, expect } from "@jest/globals";
import { SerpAPI } from "../serpapi.js";
describe("serp api test suite", () => {
class SerpApiUrlTester extends SerpAPI {
testThisUrl(): string {
return this.buildUrl("search", this.params, this.baseUrl);
}
}
test("Test default url", async () => {
const serpApi = new SerpApiUrlTester(
"Not a real key but constructor error if not set",
{
hl: "en",
gl: "us",
}
);
expect(serpApi.testThisUrl()).toEqual(
"https://serpapi.com/search?hl=en&gl=us"
);
});
test("Test override url", async () => {
const serpApiProxied = new SerpApiUrlTester(
"Not a real key but constructor error if not set",
{
gl: "us",
},
"https://totallyProxied.com"
);
expect(
serpApiProxied.testThisUrl() === "https://totallyProxied.com/search?gl=us"
);
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/wikipedia.int.test.ts
|
import { test, expect } from "@jest/globals";
import { WikipediaQueryRun } from "../wikipedia_query_run.js";
test.skip("WikipediaQueryRunTool returns a string for valid query", async () => {
const tool = new WikipediaQueryRun();
const result = await tool.invoke("Langchain");
expect(typeof result).toBe("string");
});
test.skip("WikipediaQueryRunTool returns non-empty string for valid query", async () => {
const tool = new WikipediaQueryRun();
const result = await tool.invoke("Langchain");
// console.log(result);
expect(result).not.toBe("");
});
test.skip("WikipediaQueryRunTool returns 'No good Wikipedia Search Result was found' for bad query", async () => {
const tool = new WikipediaQueryRun();
const result = await tool.invoke("kjdsfklfjskladjflkdsajflkadsjf");
// console.log(result);
expect(result).toBe("No good Wikipedia Search Result was found");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/gmail.test.ts
|
import { jest, expect, describe } from "@jest/globals";
import { GmailGetMessage } from "../gmail/index.js";
jest.mock("googleapis", () => ({
google: {
auth: {
JWT: jest.fn().mockImplementation(() => ({})),
},
},
}));
describe("GmailBaseTool using GmailGetMessage", () => {
it("should be setup with correct parameters", async () => {
const params = {
credentials: {
clientEmail: "test@email.com",
privateKey: "privateKey",
},
scopes: ["gmail_scope1"],
};
const instance = new GmailGetMessage(params);
expect(instance.name).toBe("gmail_get_message");
});
it("should throw an error if both privateKey and keyfile are missing", async () => {
const params = {
credentials: {},
scopes: ["gmail_scope1"],
};
expect(() => new GmailGetMessage(params)).toThrow();
});
it("should throw error with only client_email", async () => {
const params = {
credentials: {
clientEmail: "client_email",
},
};
expect(() => new GmailGetMessage(params)).toThrow();
});
it("should throw error with only private_key", async () => {
const params = {
credentials: {
privateKey: "privateKey",
},
};
expect(() => new GmailGetMessage(params)).toThrow();
});
it("should throw error with only keyfile", async () => {
const params = {
credentials: {
keyfile: "keyfile",
},
};
expect(() => new GmailGetMessage(params)).toThrow();
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/google_routes.int.test.ts
|
import { describe, expect } from "@jest/globals";
import { GoogleRoutesAPI } from "../google_routes.js";
describe.skip("GooglePlacesAPI", () => {
test("should be setup with correct parameters", async () => {
const instance = new GoogleRoutesAPI();
expect(instance.name).toBe("google_routes");
});
test("GooglePlacesAPI returns expected result for valid query", async () => {
const tool = new GoogleRoutesAPI();
const result = await tool.invoke({
origin: "Big Ben, London, UK",
destination: "Buckingham Palace, London, UK",
travel_mode: "WALK",
computeAlternativeRoutes: false,
});
expect(result).toContain("Birdcage Walk");
});
test("GoogleRoutesAPI returns 'Invalid route. The route may be too long or impossible to travel by the selected mode of transport.' for route on an non-existent place or one that is too far", async () => {
const tool = new GoogleRoutesAPI();
const result = await tool.invoke({
origin: "Sao Paulo, Brazil",
destination: "New York, USA",
travel_mode: "DRIVE",
computeAlternativeRoutes: false,
});
expect(result).toContain(
"Invalid route. The route may be too long or impossible to travel by the selected mode of transport."
);
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/aws_lambda.test.ts
|
import { test, jest, expect } from "@jest/globals";
import LambdaClient from "@aws-sdk/client-lambda";
import { AWSLambda } from "../aws_lambda.js";
jest.mock("@aws-sdk/client-lambda", () => ({
LambdaClient: jest.fn().mockImplementation(() => ({
send: jest.fn().mockImplementation(() =>
Promise.resolve({
Payload: new TextEncoder().encode(
JSON.stringify({ body: "email sent." })
),
})
),
})),
InvokeCommand: jest.fn().mockImplementation(() => ({})),
}));
test("AWSLambda invokes the correct lambda function and returns the response.body contents", async () => {
if (!LambdaClient) {
// this is to avoid a linting error. S3Client is mocked above.
}
const lambda = new AWSLambda({
name: "email-sender",
description:
"Sends an email with the specified content to holtkam2@gmail.com",
region: "us-east-1",
accessKeyId: "abc123",
secretAccessKey: "xyz456/1T+PzUZ2fd",
functionName: "testFunction1",
});
const result = await lambda.invoke("Hello world! This is an email.");
expect(result).toBe("email sent.");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/tests/stackexchange.int.test.ts
|
import { test, expect } from "@jest/globals";
import { StackExchangeAPI } from "../stackexchange.js";
test("StackAPITool returns a string for valid query", async () => {
const tool = new StackExchangeAPI();
const result = await tool.invoke("zsh: command not found: python");
expect(typeof result).not.toBe("hello");
});
test("StackAPITool returns non-empty string for valid query", async () => {
const tool = new StackExchangeAPI({
queryType: "title",
});
const result = await tool.invoke("zsh: command not found: python");
expect(result).toContain("zsh: command not found: python");
});
test("StackAPITool returns 'No relevant results found for 'sjefbsmnazdkhbazkbdoaencopebfoubaef' on Stack Overflow for bad query", async () => {
const tool = new StackExchangeAPI();
const result = await tool.invoke("sjefbsmnazdkhbazkbdoaencopebfoubaef");
expect(result).toBe(
"No relevant results found for 'sjefbsmnazdkhbazkbdoaencopebfoubaef' on Stack Overflow."
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools
|
lc_public_repos/langchainjs/libs/langchain-community/src/tools/fixtures/wordoftheday.html
|
<!DOCTYPE html>
<html lang="en">
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[endif]--><head>
<script type="text/javascript" src="https://ats.rlcdn.com/ats.js"></script>
<script
type="text/javascript"
async=""
src="https://d9.flashtalking.com/d9core"
></script>
<script
type="text/javascript"
async=""
src="https://collector.brandmetrics.com/c.js?siteid=f9816ecc-b51b-4747-bc3e-1ea86a0677a2&toploc=www.merriam-webster.com&rnd=3183516"
></script>
<script
async=""
src="//cdn.confiant-integrations.net/gptprebidnative/202210130953/wrap.js"
></script>
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type="text/javascript"
src="https://cdn.brandmetrics.com/tag/aa466d868b2742ffa2cc31bb6341dc12/cafemedia.js"
></script>
<script
type="text/javascript"
src="https://cdn.confiant-integrations.net/mOinGM9MTu5v-Lto835XLhlrSPY/gpt_and_prebid/config.js"
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<script
type="text/javascript"
src="https://sb.scorecardresearch.com/beacon.js"
></script>
<script
async=""
src="https://www.clarity.ms/eus-c/s/0.7.6/clarity.js"
></script>
<script
type="text/javascript"
async=""
src="https://www.google-analytics.com/analytics.js"
></script>
<script
type="text/javascript"
async=""
src="https://www.googletagmanager.com/gtag/js?id=G-M7RZHNRRPK&l=dataLayer&cx=c"
></script>
<script
type="text/javascript"
async=""
src="https://www.googletagmanager.com/gtag/js?id=UA-296234-25&l=dataLayer&cx=c"
></script>
<script
async=""
src="https://www.clarity.ms/tag/fq2f5zdaqe?ref=gtm2"
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<script src="https://ads.adthrive.com/builds/customizations/61575e8e934c48ea554b3caa.js"></script>
<script src="https://ads.adthrive.com/builds/core/16dfc7b/es2018/js/adthrive.min.js?deployment=2023-04-10-5:16dfc7b:con&experiments=rubiconFloors,recencyFrequency,ttdSync&siteid=61575e8e934c48ea554b3caa"></script>
<script src="https://ads.adthrive.com/builds/core/16dfc7b/vendor/prebid/es2018/prebid.min.js"></script>
<script src="https://securepubads.g.doubleclick.net/tag/js/gpt.js"></script>
<script src="https://c.amazon-adsystem.com/aax2/apstag.js"></script>
<script
async=""
src="https://www.googletagmanager.com/gtm.js?id=GTM-WW4KHXF"
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<script
async=""
src="https://ads.adthrive.com/sites/61575e8e934c48ea554b3caa/ads.min.js?referrer=https%3A%2F%2Fwww.merriam-webster.com%2Fword-of-the-day&threshold=26"
></script>
<script>
(function (w, d) {
w.adthrive = w.adthrive || {};
w.adthrive.cmd = w.adthrive.cmd || [];
w.adthrive.plugin = "adthrive-ads-1.0.40-manual";
w.adthrive.host = "ads.adthrive.com";
w.adthrive.threshold = Math.floor(Math.random() * 100 + 1);
var s = d.createElement("script");
s.async = true;
s.referrerpolicy = "no-referrer-when-downgrade";
s.src =
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w.adthrive.host +
"/sites/61575e8e934c48ea554b3caa/ads.min.js?referrer=" +
w.encodeURIComponent(w.location.href) +
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w.adthrive.threshold;
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Word of the Day: Foible | Merriam-Webster</title>
<meta
name="description"
content="Build your vocabulary: get a new word every day from Merriam-Webster dictionary. Learn the meaning, history, and fun facts about Foible. Also available as podcast, newsletter, and on the finest social networks."
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<link
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content="https://www.merriam-webster.com/word-of-the-day/foible-2023-04-10"
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<meta
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content="Many word lovers agree that the pen is mightier than the sword. But be they honed in wit or form, even the sharpest tools in the shed have their flaws. That’s where foible comes in handy. Borrowed"
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content="https://www.merriam-webster.com/word-of-the-day/foible-2023-04-10"
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<meta
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content="Many word lovers agree that the pen is mightier than the sword. But be they honed in wit or form, even the sharpest tools in the shed have their flaws. That’s where foible comes in handy. Borrowed"
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class="loggedout-links d-flex justify-content-evenly ps-4 pe-4 p-lg-0 align-items-center mt-1 mt-lg-0"
>
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class="btn btn-gradient text-white btn-md active text-decoration-none mr-1 mr-lg-3 me-1 me-lg-3 d-lg-none d-xl-none d-xxl-inline"
rel="nofollow"
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role="button"
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>
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<h1 class="d-inline">Word of the Day</h1>
<span> : April 10, 2023</span>
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<h2 class="word-header-txt">foible</h2>
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<span class="main-attr">noun</span>
<!--<span class="word-syllables">fru <span class="dot"></span> gal</span>-->
<span class="word-syllables">FOY-bul</span>
<!--<span class="pr">\FOY-bul\</span>-->
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<h2>What It Means</h2>
<p>
<em>Foibles</em> are minor flaws or shortcomings in
character or behavior. In fencing,
<em>foible</em> refers to the part of a sword's blade
between the middle and point, which is considered the
weakest part.
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// He was amused daily by the <em>foibles</em> of his
eccentric neighbor.
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<span class="wotd-example-label">foible</span> in
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"Films about important historical moments are often
marked by a heavy solemnity, a sometimes suffocating
respectfulness that can make one forget that these
events involved real people, human beings with
passions and <em>foibles</em>." — Michael Ordoña,
<em>The Los Angeles Times</em>, 20 Jan. 2023
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23.4921V22.1017L28.4104 21.9924L28.4043 22.0167L28.3921 21.9924C27.9246 22.2049 27.2324 22.3931 26.6859 22.466L26.6677 22.7574C27.026 22.7817 27.3781 22.8425 27.6817 22.9942L27.6696 27.2871L26.6313 27.4632L26.607 27.7971H26.6313H26.7102L29.6733 27.8214L29.6976 27.4814L28.6411 27.2567L28.6654 24.3483C28.6654 24.1479 29.2119 22.806 29.8433 22.806C29.983 22.806 30.1834 22.9092 30.3048 23.0003L30.6205 23.0307L30.8452 22.2353ZM45.6545 34.8162L45.4845 35.0894V33.6201L45.187 33.5108L45.1809 33.535L45.1688 33.5108C44.677 33.7354 43.9544 33.9358 43.3776 34.0087L43.3533 34.3183C43.7297 34.3426 44.1001 34.4033 44.4159 34.5673L44.4037 39.0787L43.3108 39.2669L43.2926 39.6191L46.5107 39.6434L46.535 39.2912L45.4299 39.0544L45.4481 35.9942C45.4481 35.7877 46.0188 34.373 46.6928 34.373C46.8385 34.373 47.0511 34.4823 47.1786 34.5794L47.5065 34.6098L47.7433 33.7718C47.5307 33.5897 47.0268 33.5593 46.7718 33.5593C46.116 33.5472 45.9217 34.3122 45.6545 34.8162ZM22.302 39.0726C22.2656 39.0726 22.2353 39.0726 22.2049 39.0726C20.2983 38.9572 20.0372 37.5182 20.0312 35.8788L23.3828 35.9031C23.7472 35.9031 23.8929 35.8545 23.8929 35.3748C23.8929 34.3062 22.976 33.5593 21.707 33.5593C20.2012 33.5654 18.9929 34.6887 18.9868 36.6864C18.9929 38.7204 20.189 39.8498 21.707 39.8498C22.3324 39.8498 22.7635 39.7648 22.8607 39.7102L24.324 38.1922L23.9172 38.1558C23.4375 38.6597 22.976 39.0726 22.302 39.0726ZM21.5248 33.9419C22.4417 33.9419 22.8364 34.5976 22.8364 35.4477C22.8364 35.4963 22.8364 35.5448 22.8364 35.5934L20.0615 35.5691C20.1283 34.7555 20.6626 33.9419 21.5248 33.9419ZM43.3776 38.1193C42.904 38.6172 42.4425 39.0301 41.7685 39.0301C41.7321 39.0301 41.7017 39.0301 41.6653 39.0301C39.7587 38.9147 39.4976 37.4757 39.4916 35.8363L42.8432 35.8606C43.2076 35.8606 43.3533 35.812 43.3533 35.3323C43.3533 34.2637 42.4364 33.5168 41.1674 33.5168C39.6616 33.5229 38.4472 34.6462 38.4472 36.6439C38.4472 38.684 39.6494 39.8073 41.1674 39.8073C41.7867 39.8073 42.2239 39.7223 42.3211 39.6677L43.7844 38.1497L43.3776 38.1193ZM40.9913 33.8994C41.9082 33.8994 42.3028 34.5551 42.3028 35.4052C42.3028 35.4538 42.3028 35.5023 42.2968 35.5509L39.5219 35.5266C39.5948 34.7191 40.1352 33.8994 40.9913 33.8994ZM48.9091 24.5729V25.3926H51.1374V24.5729H48.9091Z"
fill="#004990"
></path>
</svg>
</a>
<div class="text-uppercase mt-2">
<a
class="fw-bold text-uppercase text-deocration-none wgt-header"
style="letter-spacing: 1px"
href="/games"
>
Test Your Vocabulary
</a>
</div>
<hr class="m-2 w-100" />
<h4 class="jc-wgt-title-type1 fs-6 m-0 p-0">
<span
class="text-capitalize small"
style="letter-spacing: 1px"
>
<p class="lead m-0">
What Did You Just Call Me?
</p>
</span>
</h4>
</div>
</div>
<div class="wgt-games-side-items">
<div class="wgts-quiz-with-answers">
<ul class="wgts-quiz-question m-0 p-0 mb-2">
<li class="wgts-quiz-image">
<div class="thumbnail">
<span
class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="brown chihuahua sitting on the floor with squinting eyes looking at the camera"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-side-widget/brown%20chihuahua%20sitting%20on%20the%20floor%20with%20squinting%20eyes%20looking%20at%20the%20camera-9700-0a5a68cddc529ae67b13eb5595f1fe3f@1x.jpg"
data-caption=""
data-credit="getty images"
data-type="quiz"
data-dim="quiz-global-side-widget"
data-ret-pref="brown chihuahua sitting on the floor with squinting eyes looking at the camera-9700-0a5a68cddc529ae67b13eb5595f1fe3f"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-side-widget/brown%20chihuahua%20sitting%20on%20the%20floor%20with%20squinting%20eyes%20looking%20at%20the%20camera-9700-0a5a68cddc529ae67b13eb5595f1fe3f@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-global-side-widget/brown%20chihuahua%20sitting%20on%20the%20floor%20with%20squinting%20eyes%20looking%20at%20the%20camera-9700-0a5a68cddc529ae67b13eb5595f1fe3f@2x.jpg 2x"
pair-id="9702"
image-id="9700"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</div>
</li>
<li class="lead">
Before we went to her house, Hannah told
us her aunt was a
<em>flibbertigibbet</em>.
</li>
</ul>
<div class="content m-0 p-0 mb-4">
<ul class="quiz-answer-list m-0 p-0">
<li>
<a
class="quiz-answer"
href="/games/insult-compliment?q=3818&a=14182"
>Insulting</a
>
<a
class="quiz-answer"
href="/games/insult-compliment?q=3818&a=14183"
>Complimentary</a
>
</li>
</ul>
</div>
</div>
<div class="wgts-quiz">
<div class="thumbnail">
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="Name That Thing"
data-src="https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/80x60@1x.jpg"
data-dim="80x60"
data-srcset="https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/80x60@1x.jpg 1x, https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/80x60@2x.jpg 2x"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</div>
<div class="content">
<p class="blurb p-0 m-0">
You know what it looks like… but what is
it called?
</p>
<a
class="to-quiz"
href="/games/name-that-thing"
>TAKE THE QUIZ</a
>
</div>
</div>
<div class="wgts-quiz last">
<div class="thumbnail">
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="Solve today's spelling word game by finding as many words as you can with using just 7 letters. Longer words score more points."
data-src="https://merriam-webster.com/assets/mw/static/images/games/iframe/blossom-word-game/485x364@1x.jpg"
data-srcset="https://merriam-webster.com/assets/mw/static/images/games/iframe/blossom-word-game/485x364@1x.jpg 1x, https://merriam-webster.com/assets/mw/static/images/games/iframe/blossom-word-game/485x364@2x.jpg 2x"
data-dim="485x364"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</div>
<div class="content">
<p class="blurb p-0 m-0">
<!--
-->Can you make 12 words with 7 letters?<!--
-->
</p>
<a
class="to-quiz"
href="/games/blossom-word-game"
>PLAY</a
>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<hr class="blue-divide thin-divide desktop-only" />
<!--2-nd part of word-->
<div class="did-you-know-wrapper">
<h2>Did You Know?</h2>
<p>
Many word lovers agree that the pen is mightier than the
sword. But be they
<a
href="https://www.merriam-webster.com/dictionary/hone#h1"
>honed</a
>
in wit or form, even the sharpest tools in the shed have
their flaws. That’s where <em>foible</em> comes in
handy. Borrowed from French in the 1600s, the word
originally referred to the weakest part of a fencing
sword, that part being the portion between the middle
and the pointed tip. The English <em>foible</em> soon
came to be applied not only to weaknesses in blades but
also to minor failings in character. The French source
of <em>foible</em> is also at a remove from the fencing
arena; the French <em>foible</em> means "weak," and it
comes from the same Old French term, <em>feble</em>,
that gave us
<em
><a
href="https://www.merriam-webster.com/dictionary/feeble"
>feeble</a
></em
>.
</p>
<span
class="scrollDepth"
data-eventname="wotd-did-you-know"
></span>
</div>
<hr class="blue-divide thin-divide no-float" />
<div class="game-recirc-widget">
<h2>Test Your Vocabulary with M-W Quizzes</h2>
<div class="chalenges-bar">
<button
class="slick-prev slick-arrow"
aria-label="Previous"
type="button"
style=""
>
Previous
</button>
<div
class="challenges-slider bar-items slick-initialized slick-slider"
>
<div class="slick-list draggable">
<div
class="slick-track"
style="
opacity: 1;
width: 30000px;
transform: translate3d(-852px, 0px, 0px);
"
>
<div
class="item slick-slide slick-cloned"
style=""
data-slick-index="-3"
id=""
aria-hidden="true"
tabindex="-1"
>
<a
class="img-link"
href="/games/famous-novels-first-lines?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="book-magic-glow"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb@1x.jpg"
data-caption="book with a magical glow"
data-credit=""
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb@2x.jpg 2x"
pair-id="9964"
image-id="9962"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
tabindex="-1"
>Famous Novels, First Lines Quiz</a
>
</p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-cloned"
style=""
data-slick-index="-2"
id=""
aria-hidden="true"
tabindex="-1"
>
<a
class="img-link"
href="/games/weather-words?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="colorful-umbrella-among-black-umbrellas"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da@1x.jpg"
data-caption="Colorful umbrella in a sea of black umbrellas"
data-credit="Getty"
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da@2x.jpg 2x"
pair-id="9949"
image-id="9947"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/weather-words?src=wotdl"
tabindex="-1"
>Weather Words</a
>
</p>
<a
href="/games/weather-words?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-cloned"
style=""
data-slick-index="-1"
id=""
aria-hidden="true"
tabindex="-1"
>
<a
class="img-link"
href="/games/animals-3?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="serval"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/serval-9917-20e9bee53bdc9afa944a1a815d531c07@1x.jpg"
data-caption="serval"
data-credit="Getty"
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="serval-9917-20e9bee53bdc9afa944a1a815d531c07"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/serval-9917-20e9bee53bdc9afa944a1a815d531c07@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/serval-9917-20e9bee53bdc9afa944a1a815d531c07@2x.jpg 2x"
pair-id="9919"
image-id="9917"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/animals-3?src=wotdl"
tabindex="-1"
>Name That Animal: Volume 3</a
>
</p>
<a
href="/games/animals-3?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-current slick-active"
style=""
data-slick-index="0"
aria-hidden="false"
tabindex="0"
>
<a
class="img-link"
href="/games/name-that-hat?src=wotdl"
tabindex="0"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="back-of-head-mortarboard"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/back-of-head-mortarboard-10064-4e5e855e2ec66d5e2e40cf7aad826e68@1x.jpg"
data-caption="mortarboard"
data-credit="Getty"
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="back-of-head-mortarboard-10064-4e5e855e2ec66d5e2e40cf7aad826e68"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/back-of-head-mortarboard-10064-4e5e855e2ec66d5e2e40cf7aad826e68@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/back-of-head-mortarboard-10064-4e5e855e2ec66d5e2e40cf7aad826e68@2x.jpg 2x"
pair-id="10066"
image-id="10064"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/name-that-hat?src=wotdl"
tabindex="0"
>Name That Hat!</a
>
</p>
<a
href="/games/name-that-hat?src=wotdl"
class="arrow-link"
tabindex="0"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-active"
style=""
data-slick-index="1"
aria-hidden="false"
tabindex="0"
>
<a
class="img-link"
href="/games/ntt-flowers-quiz?src=wotdl"
tabindex="0"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="name that thing flower edition"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/name%20that%20thing%20flower%20edition-3542-c2569c2183097e7ecf1abe632526ed37@1x.jpg"
data-caption=""
data-credit=""
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="name that thing flower edition-3542-c2569c2183097e7ecf1abe632526ed37"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/name%20that%20thing%20flower%20edition-3542-c2569c2183097e7ecf1abe632526ed37@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/name%20that%20thing%20flower%20edition-3542-c2569c2183097e7ecf1abe632526ed37@2x.jpg 2x"
pair-id="3545"
image-id="3542"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/ntt-flowers-quiz?src=wotdl"
tabindex="0"
>Name That Flower</a
>
</p>
<a
href="/games/ntt-flowers-quiz?src=wotdl"
class="arrow-link"
tabindex="0"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-active"
style=""
data-slick-index="2"
aria-hidden="false"
tabindex="0"
>
<a
class="img-link"
href="/games/feeling-lucky?src=wotdl"
tabindex="0"
>
<span class="lazyload-container ratio-4-3"
><img
data-sizes="auto"
alt="womans hand holding a green four leaf clover"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/womans%20hand%20holding%20a%20green%20four%20leaf%20clover-9991-238ff35c2de30a43a767d786fe20d2ef@1x.jpg"
data-caption=""
data-credit="Getty Images"
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="womans hand holding a green four leaf clover-9991-238ff35c2de30a43a767d786fe20d2ef"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/womans%20hand%20holding%20a%20green%20four%20leaf%20clover-9991-238ff35c2de30a43a767d786fe20d2ef@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/womans%20hand%20holding%20a%20green%20four%20leaf%20clover-9991-238ff35c2de30a43a767d786fe20d2ef@2x.jpg 2x"
pair-id="9993"
image-id="9991"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
/></span>
</a>
<p>
<a
href="/games/feeling-lucky?src=wotdl"
tabindex="0"
>Are You Feeling Lucky?</a
>
</p>
<a
href="/games/feeling-lucky?src=wotdl"
class="arrow-link"
tabindex="0"
>Play Now</a
>
</div>
<div
class="item slick-slide"
style=""
data-slick-index="3"
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tabindex="-1"
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class="img-link"
href="/games/famous-novels-first-lines?src=wotdl"
tabindex="-1"
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is_retina="true"
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data-caption="book with a magical glow"
data-credit=""
data-type="quiz"
data-dim="quiz-games-landing-weekly-sm"
data-ret-pref="book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb@2x.jpg 2x"
pair-id="9964"
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src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
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</a>
<p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
tabindex="-1"
>Famous Novels, First Lines Quiz</a
>
</p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
class="arrow-link"
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</div>
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href="/games/weather-words?src=wotdl"
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data-caption="Colorful umbrella in a sea of black umbrellas"
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pair-id="9949"
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</a>
<p>
<a
href="/games/weather-words?src=wotdl"
tabindex="-1"
>Weather Words</a
>
</p>
<a
href="/games/weather-words?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
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href="/games/animals-3?src=wotdl"
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data-caption="serval"
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data-type="quiz"
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data-ret-x="1"
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pair-id="9919"
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</a>
<p>
<a
href="/games/animals-3?src=wotdl"
tabindex="-1"
>Name That Animal: Volume 3</a
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</p>
<a
href="/games/animals-3?src=wotdl"
class="arrow-link"
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class="item slick-slide slick-cloned"
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href="/games/name-that-hat?src=wotdl"
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</p>
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class="arrow-link"
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data-caption=""
data-credit=""
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<a
href="/games/ntt-flowers-quiz?src=wotdl"
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</p>
<a
href="/games/ntt-flowers-quiz?src=wotdl"
class="arrow-link"
tabindex="-1"
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id=""
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class="img-link"
href="/games/feeling-lucky?src=wotdl"
tabindex="-1"
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<span class="lazyload-container ratio-4-3"
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data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/womans%20hand%20holding%20a%20green%20four%20leaf%20clover-9991-238ff35c2de30a43a767d786fe20d2ef@1x.jpg"
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<a
href="/games/feeling-lucky?src=wotdl"
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</p>
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href="/games/feeling-lucky?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
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class="item slick-slide slick-cloned"
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id=""
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tabindex="-1"
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class="img-link"
href="/games/famous-novels-first-lines?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
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data-sizes="auto"
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data-caption="book with a magical glow"
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data-ret-pref="book-magic-glow-9962-96dfef56a128e8f332bfd32e04ce87eb"
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pair-id="9964"
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<p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
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</p>
<a
href="/games/famous-novels-first-lines?src=wotdl"
class="arrow-link"
tabindex="-1"
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>
</div>
<div
class="item slick-slide slick-cloned"
style=""
data-slick-index="10"
id=""
aria-hidden="true"
tabindex="-1"
>
<a
class="img-link"
href="/games/weather-words?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
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data-sizes="auto"
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data-caption="Colorful umbrella in a sea of black umbrellas"
data-credit="Getty"
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data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/colorful-umbrella-among-black-umbrellas-9947-dfecb4285a7aa027f48bb3d49356b1da@2x.jpg 2x"
pair-id="9949"
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class="lazyload"
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</a>
<p>
<a
href="/games/weather-words?src=wotdl"
tabindex="-1"
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</p>
<a
href="/games/weather-words?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
>
</div>
<div
class="item slick-slide slick-cloned"
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data-slick-index="11"
id=""
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tabindex="-1"
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<a
class="img-link"
href="/games/animals-3?src=wotdl"
tabindex="-1"
>
<span class="lazyload-container ratio-4-3"
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data-sizes="auto"
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data-caption="serval"
data-credit="Getty"
data-type="quiz"
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data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/serval-9917-20e9bee53bdc9afa944a1a815d531c07@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-games-landing-weekly-sm/serval-9917-20e9bee53bdc9afa944a1a815d531c07@2x.jpg 2x"
pair-id="9919"
image-id="9917"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw=="
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</a>
<p>
<a
href="/games/animals-3?src=wotdl"
tabindex="-1"
>Name That Animal: Volume 3</a
>
</p>
<a
href="/games/animals-3?src=wotdl"
class="arrow-link"
tabindex="-1"
>Play Now</a
>
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<div class="wod-activity-container">
<h2>Test Your Vocabulary</h2>
<p>
Unscramble the letters to create a word that refers to a
particular kind of fencing sword: BRASE.
</p>
<a href="http://bit.ly/3JZNjCF" class="view-answer-link"
>VIEW THE ANSWER</a
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<h2>More Words of the Day</h2>
</div>
<ul class="more-wod-items">
<li>
<h4>Apr 09</h4>
<h2>
<a
href="/word-of-the-day/auspicious-2023-04-09"
rel="nofollow"
>auspicious</a
>
</h2>
</li>
<li>
<h4>Apr 08</h4>
<h2>
<a
href="/word-of-the-day/circumscribe-2023-04-08"
rel="nofollow"
>circumscribe</a
>
</h2>
</li>
<li>
<h4>Apr 07</h4>
<h2>
<a
href="/word-of-the-day/equivocal-2023-04-07"
rel="nofollow"
>equivocal</a
>
</h2>
</li>
<li>
<h4>Apr 06</h4>
<h2>
<a
href="/word-of-the-day/seder-2023-04-06"
rel="nofollow"
>seder</a
>
</h2>
</li>
<li>
<h4>Apr 05</h4>
<h2>
<a
href="/word-of-the-day/gerrymander-2023-04-05"
rel="nofollow"
>gerrymander</a
>
</h2>
</li>
<li>
<h4>Apr 04</h4>
<h2>
<a
href="/word-of-the-day/belated-2023-04-04"
rel="nofollow"
>belated</a
>
</h2>
</li>
</ul>
<div class="cta-container">
<a href="/word-of-the-day/calendar"
>SEE ALL WORDS OF THE DAY</a
>
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Can you solve 4 words at once?
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<span class="items__header">Words at Play</span>
<ul>
<li>
<a
class="item-card"
href="/words-at-play/12-longest-unusually-long-english-words"
>
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="top 10 unusually long and interesting words vol 2 embourgeoisement"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/top%2010%20unusually%20long%20and%20interesting%20words%20vol%202%20embourgeoisement-446-d03855602d5bf5706e68429129b89019@1x.jpg"
data-caption=""
data-credit=""
data-type="article"
data-dim="art-global-footer-recirc"
data-ret-pref="top 10 unusually long and interesting words vol 2 embourgeoisement-446-d03855602d5bf5706e68429129b89019"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/top%2010%20unusually%20long%20and%20interesting%20words%20vol%202%20embourgeoisement-446-d03855602d5bf5706e68429129b89019@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/top%2010%20unusually%20long%20and%20interesting%20words%20vol%202%20embourgeoisement-446-d03855602d5bf5706e68429129b89019@2x.jpg 2x"
pair-id="446"
image-id="446"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text"
>13 Unusually Long English Words</span
>
<p class="mell-gr235">
<span>Pulchritudinous and many more</span>
</p>
</div>
</a>
</li>
<li>
<a
class="item-card"
href="/words-at-play/mums-the-letter-when-letters-dont-say-a-thing"
>
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="image536372841"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image536372841-4232-ace70d7a79d9aedc117d0b5c8daa40e3@1x.jpg"
data-caption=""
data-credit=""
data-type="article"
data-dim="art-global-footer-recirc"
data-ret-pref="image536372841-4232-ace70d7a79d9aedc117d0b5c8daa40e3"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image536372841-4232-ace70d7a79d9aedc117d0b5c8daa40e3@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image536372841-4232-ace70d7a79d9aedc117d0b5c8daa40e3@2x.jpg 2x"
pair-id="4234"
image-id="4232"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text"
>Every Letter Is Silent, Sometimes</span
>
<p class="mell-gr235">
<span>When each letter can be seen but not heard</span>
</p>
</div>
</a>
</li>
<li>
<a
class="item-card"
href="/words-at-play/8-nicer-ways-to-say-stupid"
>
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="image576109549"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image576109549-4449-ca3d24dc53afcdd597ea3c01af71edf4@1x.jpg"
data-caption=""
data-credit=""
data-type="article"
data-dim="art-global-footer-recirc"
data-ret-pref="image576109549-4449-ca3d24dc53afcdd597ea3c01af71edf4"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image576109549-4449-ca3d24dc53afcdd597ea3c01af71edf4@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/image576109549-4449-ca3d24dc53afcdd597ea3c01af71edf4@2x.jpg 2x"
pair-id="4451"
image-id="4449"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text"
>'Dunderhead' and Other ‘Nicer’ Ways to Say Stupid</span
>
<p class="mell-gr235">
<span>As illustrated by some very smart pups</span>
</p>
</div>
</a>
</li>
<li>
<a class="item-card" href="/time-traveler/">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="merriam webster time traveler"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/merriam-webster-time-traveler-5771-630b7456c73849b51efad8bc5a55aae9@1x.jpg"
data-caption=""
data-credit=""
data-type="article"
data-dim="art-global-footer-recirc"
data-ret-pref="merriam-webster-time-traveler-5771-630b7456c73849b51efad8bc5a55aae9"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/merriam-webster-time-traveler-5771-630b7456c73849b51efad8bc5a55aae9@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/article/art-global-footer-recirc/merriam-webster-time-traveler-5771-630b7456c73849b51efad8bc5a55aae9@2x.jpg 2x"
pair-id="5773"
image-id="5771"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text"
>When Were Words First Used?</span
>
<p class="mell-gr235">
<span>Look up any year to find out</span>
</p>
</div>
</a>
</li>
</ul>
</div>
<div class="additional-items additional-items-widget">
<span class="items__header">Ask the Editors</span>
<ul>
<li>
<a class="item-card" href="/video/weird-plurals">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="video moose goose weird plurals"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-moose-goose-weird-plurals-1138@1x.jpg"
data-caption=""
data-credit=""
data-type="video"
data-dim="vid-global-footer-recirc"
data-ret-pref="video-moose-goose-weird-plurals-1138"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-moose-goose-weird-plurals-1138@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-moose-goose-weird-plurals-1138@2x.jpg 2x"
pair-id="1140"
image-id="1138"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Weird Plurals</span>
<p class="mell-gr235">
<span
>One goose, two geese. One moose, two... moose.
Wh...</span
>
</p>
</div>
</a>
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<li>
<a class="item-card" href="/video/irregardless">
<div class="item-content">
<span
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><img
data-sizes="auto"
alt="video irregardless grammar peeve blend of the synonyms irrespective and regardless"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-irregardless-grammar-peeve-blend-of-the-synonyms-irrespective-and-regardless-1144@1x.jpg"
data-caption=""
data-credit=""
data-type="video"
data-dim="vid-global-footer-recirc"
data-ret-pref="video-irregardless-grammar-peeve-blend-of-the-synonyms-irrespective-and-regardless-1144"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-irregardless-grammar-peeve-blend-of-the-synonyms-irrespective-and-regardless-1144@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-irregardless-grammar-peeve-blend-of-the-synonyms-irrespective-and-regardless-1144@2x.jpg 2x"
pair-id="1146"
image-id="1144"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Irregardless</span>
<p class="mell-gr235">
<span
>It is in fact a real word (but that doesn't mean
...</span
>
</p>
</div>
</a>
</li>
<li>
<a class="item-card" href="/video/bring-vs-take">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="bring vs take video"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/bring-vs-take-video-1587@1x.jpg"
data-caption=""
data-credit=""
data-type="video"
data-dim="vid-global-footer-recirc"
data-ret-pref="bring-vs-take-video-1587"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/bring-vs-take-video-1587@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/bring-vs-take-video-1587@2x.jpg 2x"
pair-id="1595"
image-id="1587"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Bring vs. Take</span>
<p class="mell-gr235">
<span
>Both words imply motion, but the difference may
b...</span
>
</p>
</div>
</a>
</li>
<li>
<a class="item-card" href="/video/defenestration">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="video defenesetration"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-defenesetration-1163@1x.jpg"
data-caption=""
data-credit=""
data-type="video"
data-dim="vid-global-footer-recirc"
data-ret-pref="video-defenesetration-1163"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-defenesetration-1163@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/video/vid-global-footer-recirc/video-defenesetration-1163@2x.jpg 2x"
pair-id="1165"
image-id="1163"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Defenestration</span>
<p class="mell-gr235">
<span
>The fascinating story behind many people's
favori...</span
>
</p>
</div>
</a>
</li>
</ul>
</div>
<div class="additional-items additional-items-widget">
<span class="items__header">Word Games</span>
<ul>
<li>
<a class="item-card" href="/games/name-that-hat">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="back-of-head-mortarboard"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/back-of-head-mortarboard-10064-e628383268d81f98a3a47ca0f9d93478@1x.jpg"
data-caption="mortarboard"
data-credit="Getty"
data-type="quiz"
data-dim="quiz-global-footer-recirc"
data-ret-pref="back-of-head-mortarboard-10064-e628383268d81f98a3a47ca0f9d93478"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/back-of-head-mortarboard-10064-e628383268d81f98a3a47ca0f9d93478@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/back-of-head-mortarboard-10064-e628383268d81f98a3a47ca0f9d93478@2x.jpg 2x"
pair-id="10066"
image-id="10064"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Name That Hat!</span>
<p class="mell-gr235">
<span>Time to put on your thinking cap.</span>
</p>
<p class="quiz-link">Take the quiz</p>
</div>
</a>
</li>
<li>
<a class="item-card" href="/games/ntt-flowers-quiz">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="name that thing flower edition"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/name%20that%20thing%20flower%20edition-3542-d832e19a67f24cf3b20a4d856bb7ced6@1x.jpg"
data-caption=""
data-credit=""
data-type="quiz"
data-dim="quiz-global-footer-recirc"
data-ret-pref="name that thing flower edition-3542-d832e19a67f24cf3b20a4d856bb7ced6"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/name%20that%20thing%20flower%20edition-3542-d832e19a67f24cf3b20a4d856bb7ced6@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/name%20that%20thing%20flower%20edition-3542-d832e19a67f24cf3b20a4d856bb7ced6@2x.jpg 2x"
pair-id="3545"
image-id="3542"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Name That Flower</span>
<p class="mell-gr235">
<span
>Can you tell the difference between a lilac and
a...</span
>
</p>
<p class="quiz-link">Take the quiz</p>
</div>
</a>
</li>
<li>
<a class="item-card" href="/games/name-that-thing">
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="Name That Thing"
data-src="https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/261x174@1x.jpg"
data-dim="261x174"
data-srcset="https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/261x174@1x.jpg 1x, https://merriam-webster.com/assets/mw/static/images/old-quizzes/name-that-thing/261x174@2x.jpg 2x"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Name That Thing</span>
<p class="mell-gr235">
<span
>You know what it looks like… but what is it
cal...</span
>
</p>
<p class="quiz-link">Take the quiz</p>
</div>
</a>
</li>
<li>
<a
class="item-card"
href="/word-games/scripps-spelling-bee-quiz"
>
<div class="item-content">
<span
><span class="lazyload-container ratio-3-2"
><img
data-sizes="auto"
alt="winning words from the national spelling bee logo"
is_retina="true"
data-src="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/winning-words-from-the-national-spelling-bee-logo-3736-9bdcdf51c9576d8b4572b774b3ba9e75@1x.jpg"
data-caption=""
data-credit=""
data-type="quiz"
data-dim="quiz-global-footer-recirc"
data-ret-pref="winning-words-from-the-national-spelling-bee-logo-3736-9bdcdf51c9576d8b4572b774b3ba9e75"
data-ret-x="1"
data-srcset="https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/winning-words-from-the-national-spelling-bee-logo-3736-9bdcdf51c9576d8b4572b774b3ba9e75@1x.jpg 1x, https://merriam-webster.com/assets/mw/images/quiz/quiz-global-footer-recirc/winning-words-from-the-national-spelling-bee-logo-3736-9bdcdf51c9576d8b4572b774b3ba9e75@2x.jpg 2x"
pair-id="3739"
image-id="3736"
class="lazyload"
src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" /></span
></span>
<span class="item-title-text">Spelling Bee Quiz</span>
<p class="mell-gr235">
<span
>Can you outdo past winners of the National
Spelli...</span
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0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/hf.ts
|
import { HfInference, HfInferenceEndpoint } from "@huggingface/inference";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the HuggingFaceInferenceEmbeddings class.
*/
export interface HuggingFaceInferenceEmbeddingsParams extends EmbeddingsParams {
apiKey?: string;
model?: string;
endpointUrl?: string;
}
/**
* Class that extends the Embeddings class and provides methods for
* generating embeddings using Hugging Face models through the
* HuggingFaceInference API.
*/
export class HuggingFaceInferenceEmbeddings
extends Embeddings
implements HuggingFaceInferenceEmbeddingsParams
{
apiKey?: string;
model: string;
endpointUrl?: string;
client: HfInference | HfInferenceEndpoint;
constructor(fields?: HuggingFaceInferenceEmbeddingsParams) {
super(fields ?? {});
this.model = fields?.model ?? "BAAI/bge-base-en-v1.5";
this.apiKey =
fields?.apiKey ?? getEnvironmentVariable("HUGGINGFACEHUB_API_KEY");
this.endpointUrl = fields?.endpointUrl;
this.client = this.endpointUrl
? new HfInference(this.apiKey).endpoint(this.endpointUrl)
: new HfInference(this.apiKey);
}
async _embed(texts: string[]): Promise<number[][]> {
// replace newlines, which can negatively affect performance.
const clean = texts.map((text) => text.replace(/\n/g, " "));
return this.caller.call(() =>
this.client.featureExtraction({
model: this.model,
inputs: clean,
})
) as Promise<number[][]>;
}
/**
* Method that takes a document as input and returns a promise that
* resolves to an embedding for the document. It calls the _embed method
* with the document as the input and returns the first embedding in the
* resulting array.
* @param document Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
embedQuery(document: string): Promise<number[]> {
return this._embed([document]).then((embeddings) => embeddings[0]);
}
/**
* Method that takes an array of documents as input and returns a promise
* that resolves to a 2D array of embeddings for each document. It calls
* the _embed method with the documents as the input.
* @param documents Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
embedDocuments(documents: string[]): Promise<number[][]> {
return this._embed(documents);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/baidu_qianfan.ts
|
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
/** @deprecated Install and import from @langchain/baidu-qianfan instead. */
export interface BaiduQianfanEmbeddingsParams extends EmbeddingsParams {
/** Model name to use */
modelName: "embedding-v1" | "bge_large_zh" | "bge_large_en" | "tao-8k";
/**
* Timeout to use when making requests to BaiduQianfan.
*/
timeout?: number;
/**
* The maximum number of characters allowed for embedding in a single request varies by model:
* - Embedding-V1 model: up to 1000 characters
* - bge-large-zh model: up to 2000 characters
* - bge-large-en model: up to 2000 characters
* - tao-8k model: up to 28000 characters
*
* Note: These limits are model-specific and should be adhered to for optimal performance.
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text.
*/
stripNewLines?: boolean;
}
interface EmbeddingCreateParams {
input: string[];
}
interface EmbeddingResponse {
data: { object: "embedding"; index: number; embedding: number[] }[];
usage: {
prompt_tokens: number;
total_tokens: number;
};
id: string;
}
interface EmbeddingErrorResponse {
error_code: number | string;
error_msg: string;
}
export class BaiduQianfanEmbeddings
extends Embeddings
implements BaiduQianfanEmbeddingsParams
{
modelName: BaiduQianfanEmbeddingsParams["modelName"] = "embedding-v1";
batchSize = 16;
stripNewLines = true;
baiduApiKey: string;
baiduSecretKey: string;
accessToken: string;
constructor(
fields?: Partial<BaiduQianfanEmbeddingsParams> & {
verbose?: boolean;
baiduApiKey?: string;
baiduSecretKey?: string;
}
) {
const fieldsWithDefaults = { maxConcurrency: 2, ...fields };
super(fieldsWithDefaults);
const baiduApiKey =
fieldsWithDefaults?.baiduApiKey ??
getEnvironmentVariable("BAIDU_API_KEY");
const baiduSecretKey =
fieldsWithDefaults?.baiduSecretKey ??
getEnvironmentVariable("BAIDU_SECRET_KEY");
if (!baiduApiKey) {
throw new Error("Baidu API key not found");
}
if (!baiduSecretKey) {
throw new Error("Baidu Secret key not found");
}
this.baiduApiKey = baiduApiKey;
this.baiduSecretKey = baiduSecretKey;
this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
if (this.modelName === "tao-8k") {
if (fieldsWithDefaults?.batchSize && fieldsWithDefaults.batchSize !== 1) {
throw new Error(
"tao-8k model supports only a batchSize of 1. Please adjust your batchSize accordingly"
);
}
this.batchSize = 1;
} else {
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
}
this.stripNewLines =
fieldsWithDefaults?.stripNewLines ?? this.stripNewLines;
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the BaiduQianFan API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
const batchRequests = batches.map((batch) => {
const params = this.getParams(batch);
return this.embeddingWithRetry(params);
});
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const batchResponse = batchResponses[i] || [];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j]);
}
}
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param text Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const params = this.getParams([
this.stripNewLines ? text.replace(/\n/g, " ") : text,
]);
const embeddings = (await this.embeddingWithRetry(params)) || [[]];
return embeddings[0];
}
/**
* Method to generate an embedding params.
* @param texts Array of documents to generate embeddings for.
* @returns an embedding params.
*/
private getParams(
texts: EmbeddingCreateParams["input"]
): EmbeddingCreateParams {
return {
input: texts,
};
}
/**
* Private method to make a request to the BaiduAI API to generate
* embeddings. Handles the retry logic and returns the response from the
* API.
* @param request Request to send to the BaiduAI API.
* @returns Promise that resolves to the response from the API.
*/
private async embeddingWithRetry(body: EmbeddingCreateParams) {
if (!this.accessToken) {
this.accessToken = await this.getAccessToken();
}
return fetch(
`https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/${this.modelName}?access_token=${this.accessToken}`,
{
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify(body),
}
).then(async (response) => {
const embeddingData: EmbeddingResponse | EmbeddingErrorResponse =
await response.json();
if ("error_code" in embeddingData && embeddingData.error_code) {
throw new Error(
`${embeddingData.error_code}: ${embeddingData.error_msg}`
);
}
return (embeddingData as EmbeddingResponse).data.map(
({ embedding }) => embedding
);
});
}
/**
* Method that retrieves the access token for making requests to the Baidu
* API.
* @returns The access token for making requests to the Baidu API.
*/
private async getAccessToken() {
const url = `https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=${this.baiduApiKey}&client_secret=${this.baiduSecretKey}`;
const response = await fetch(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Accept: "application/json",
},
});
if (!response.ok) {
const text = await response.text();
const error = new Error(
`Baidu get access token failed with status code ${response.status}, response: ${text}`
);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(error as any).response = response;
throw error;
}
const json = await response.json();
return json.access_token;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/llama_cpp.ts
|
/* eslint-disable import/no-extraneous-dependencies */
import { LlamaModel, LlamaContext, getLlama } from "node-llama-cpp";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import {
LlamaBaseCppInputs,
createLlamaModel,
createLlamaContext,
} from "../utils/llama_cpp.js";
/**
* Note that the modelPath is the only required parameter. For testing you
* can set this in the environment variable `LLAMA_PATH`.
*/
export interface LlamaCppEmbeddingsParams
extends LlamaBaseCppInputs,
EmbeddingsParams {}
/**
* @example
* ```typescript
* // Initialize LlamaCppEmbeddings with the path to the model file
* const embeddings = await LlamaCppEmbeddings.initialize({
* modelPath: llamaPath,
* });
*
* // Embed a query string using the Llama embeddings
* const res = embeddings.embedQuery("Hello Llama!");
*
* // Output the resulting embeddings
* console.log(res);
*
* ```
*/
export class LlamaCppEmbeddings extends Embeddings {
_model: LlamaModel;
_context: LlamaContext;
public constructor(inputs: LlamaCppEmbeddingsParams) {
super(inputs);
const _inputs = inputs;
_inputs.embedding = true;
}
/**
* Initializes the llama_cpp model for usage in the embeddings wrapper.
* @param inputs - the inputs passed onto the model.
* @returns A Promise that resolves to the LlamaCppEmbeddings type class.
*/
public static async initialize(
inputs: LlamaBaseCppInputs
): Promise<LlamaCppEmbeddings> {
const instance = new LlamaCppEmbeddings(inputs);
const llama = await getLlama();
instance._model = await createLlamaModel(inputs, llama);
instance._context = await createLlamaContext(instance._model, inputs);
return instance;
}
/**
* Generates embeddings for an array of texts.
* @param texts - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const tokensArray = [];
for (const text of texts) {
const encodings = await this.caller.call(
() =>
new Promise((resolve) => {
resolve(this._model.tokenize(text));
})
);
tokensArray.push(encodings);
}
const embeddings: number[][] = [];
for (const tokens of tokensArray) {
const embedArray: number[] = [];
for (let i = 0; i < tokens.length; i += 1) {
const nToken: number = +tokens[i];
embedArray.push(nToken);
}
embeddings.push(embedArray);
}
return embeddings;
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
const tokens: number[] = [];
const encodings = await this.caller.call(
() =>
new Promise((resolve) => {
resolve(this._model.tokenize(text));
})
);
for (let i = 0; i < encodings.length; i += 1) {
const token: number = +encodings[i];
tokens.push(token);
}
return tokens;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/bedrock.ts
|
import {
BedrockRuntimeClient,
InvokeModelCommand,
} from "@aws-sdk/client-bedrock-runtime";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import type { CredentialType } from "../utils/bedrock/index.js";
/**
* @deprecated The BedrockEmbeddings integration has been moved to the `@langchain/aws` package. Import from `@langchain/aws` instead.
*
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the BedrockEmbeddings class.
*/
export interface BedrockEmbeddingsParams extends EmbeddingsParams {
/**
* Model Name to use. Defaults to `amazon.titan-embed-text-v1` if not provided
*
*/
model?: string;
/**
* A client provided by the user that allows them to customze any
* SDK configuration options.
*/
client?: BedrockRuntimeClient;
region?: string;
credentials?: CredentialType;
}
/**
* @deprecated The BedrockEmbeddings integration has been moved to the `@langchain/aws` package. Import from `@langchain/aws` instead.
*
* Class that extends the Embeddings class and provides methods for
* generating embeddings using the Bedrock API.
* @example
* ```typescript
* const embeddings = new BedrockEmbeddings({
* region: "your-aws-region",
* credentials: {
* accessKeyId: "your-access-key-id",
* secretAccessKey: "your-secret-access-key",
* },
* model: "amazon.titan-embed-text-v1",
* });
*
* // Embed a query and log the result
* const res = await embeddings.embedQuery(
* "What would be a good company name for a company that makes colorful socks?"
* );
* console.log({ res });
* ```
*/
export class BedrockEmbeddings
extends Embeddings
implements BedrockEmbeddingsParams
{
model: string;
client: BedrockRuntimeClient;
batchSize = 512;
constructor(fields?: BedrockEmbeddingsParams) {
super(fields ?? {});
this.model = fields?.model ?? "amazon.titan-embed-text-v1";
this.client =
fields?.client ??
new BedrockRuntimeClient({
region: fields?.region,
credentials: fields?.credentials,
});
}
/**
* Protected method to make a request to the Bedrock API to generate
* embeddings. Handles the retry logic and returns the response from the
* API.
* @param request Request to send to the Bedrock API.
* @returns Promise that resolves to the response from the API.
*/
protected async _embedText(text: string): Promise<number[]> {
return this.caller.call(async () => {
try {
// replace newlines, which can negatively affect performance.
const cleanedText = text.replace(/\n/g, " ");
const res = await this.client.send(
new InvokeModelCommand({
modelId: this.model,
body: JSON.stringify({
inputText: cleanedText,
}),
contentType: "application/json",
accept: "application/json",
})
);
const body = new TextDecoder().decode(res.body);
return JSON.parse(body).embedding;
} catch (e) {
console.error({
error: e,
});
// eslint-disable-next-line no-instanceof/no-instanceof
if (e instanceof Error) {
throw new Error(
`An error occurred while embedding documents with Bedrock: ${e.message}`
);
}
throw new Error(
"An error occurred while embedding documents with Bedrock"
);
}
});
}
/**
* Method that takes a document as input and returns a promise that
* resolves to an embedding for the document. It calls the _embedText
* method with the document as the input.
* @param document Document for which to generate an embedding.
* @returns Promise that resolves to an embedding for the input document.
*/
embedQuery(document: string): Promise<number[]> {
return this.caller.callWithOptions(
{},
this._embedText.bind(this),
document
);
}
/**
* Method to generate embeddings for an array of texts. Calls _embedText
* method which batches and handles retry logic when calling the AWS Bedrock API.
* @param documents Array of texts for which to generate embeddings.
* @returns Promise that resolves to a 2D array of embeddings for each input document.
*/
async embedDocuments(documents: string[]): Promise<number[][]> {
return Promise.all(documents.map((document) => this._embedText(document)));
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/jina.ts
|
import { existsSync, readFileSync } from "fs";
import { parse } from "url";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
/**
* The default Jina API URL for embedding requests.
*/
const JINA_API_URL = "https://api.jina.ai/v1/embeddings";
/**
* Check if a URL is a local file.
* @param url - The URL to check.
* @returns True if the URL is a local file, False otherwise.
*/
function isLocal(url: string): boolean {
const urlParsed = parse(url);
if (urlParsed.protocol === null || urlParsed.protocol === "file:") {
return existsSync(urlParsed.pathname || "");
}
return false;
}
/**
* Get the bytes string of a file.
* @param filePath - The path to the file.
* @returns The bytes string of the file.
*/
function getBytesStr(filePath: string): string {
const imageFile = readFileSync(filePath);
return Buffer.from(imageFile).toString("base64");
}
/**
* Input parameters for the Jina embeddings
*/
export interface JinaEmbeddingsParams extends EmbeddingsParams {
/**
* The API key to use for authentication.
* If not provided, it will be read from the `JINA_API_KEY` environment variable.
*/
apiKey?: string;
/**
* The model ID to use for generating embeddings.
* Default: `jina-embeddings-v2-base-en`
*/
model?: string;
}
/**
* Response from the Jina embeddings API.
*/
export interface JinaEmbeddingsResponse {
/**
* The embeddings generated for the input texts.
*/
data: { index: number; embedding: number[] }[];
/**
* The detail of the response e.g usage, model used etc.
*/
detail?: string;
}
/**
* A class for generating embeddings using the Jina API.
* @example
* ```typescript
* // Embed a query using the JinaEmbeddings class
* const model = new JinaEmbeddings();
* const res = await model.embedQuery(
* "What would be a good name for a semantic search engine ?",
* );
* console.log({ res });
* ```
*/
export class JinaEmbeddings extends Embeddings implements JinaEmbeddingsParams {
apiKey: string;
model: string;
/**
* Constructor for the JinaEmbeddings class.
* @param fields - An optional object with properties to configure the instance.
*/
constructor(fields?: Partial<JinaEmbeddingsParams> & { verbose?: boolean }) {
const fieldsWithDefaults = {
model: "jina-embeddings-v2-base-en",
...fields,
};
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiKey ||
getEnvironmentVariable("JINA_API_KEY") ||
getEnvironmentVariable("JINA_AUTH_TOKEN");
if (!apiKey) {
throw new Error("Jina API key not found");
}
this.model = fieldsWithDefaults?.model ?? this.model;
this.apiKey = apiKey;
}
/**
* Generates embeddings for an array of inputs.
* @param input - An array of strings or objects to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
// eslint-disable-next-line @typescript-eslint/no-explicit-any
private async _embed(input: any): Promise<number[][]> {
const response = await fetch(JINA_API_URL, {
method: "POST",
headers: {
Authorization: `Bearer ${this.apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({ input, model: this.model }),
});
const json = (await response.json()) as JinaEmbeddingsResponse;
if (!json.data) {
throw new Error(json.detail || "Unknown error from Jina API");
}
const sortedEmbeddings = json.data.sort((a, b) => a.index - b.index);
return sortedEmbeddings.map((item) => item.embedding);
}
/**
* Generates embeddings for an array of texts.
* @param texts - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
return this._embed(texts);
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
const embeddings = await this._embed([text]);
return embeddings[0];
}
/**
* Generates embeddings for an array of image URIs.
* @param uris - An array of image URIs to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedImages(uris: string[]): Promise<number[][]> {
const input = uris.map((uri) => (isLocal(uri) ? getBytesStr(uri) : uri));
return this._embed(input);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/gradient_ai.ts
|
import { Gradient } from "@gradientai/nodejs-sdk";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* Interface for GradientEmbeddings parameters. Extends EmbeddingsParams and
* defines additional parameters specific to the GradientEmbeddings class.
*/
export interface GradientEmbeddingsParams extends EmbeddingsParams {
/**
* Gradient AI Access Token.
* Provide Access Token if you do not wish to automatically pull from env.
*/
gradientAccessKey?: string;
/**
* Gradient Workspace Id.
* Provide workspace id if you do not wish to automatically pull from env.
*/
workspaceId?: string;
}
/**
* Class for generating embeddings using the Gradient AI's API. Extends the
* Embeddings class and implements GradientEmbeddingsParams and
*/
export class GradientEmbeddings
extends Embeddings
implements GradientEmbeddingsParams
{
gradientAccessKey?: string;
workspaceId?: string;
batchSize = 128;
// eslint-disable-next-line @typescript-eslint/no-explicit-any
model: any;
constructor(fields: GradientEmbeddingsParams) {
super(fields);
this.gradientAccessKey =
fields?.gradientAccessKey ??
getEnvironmentVariable("GRADIENT_ACCESS_TOKEN");
this.workspaceId =
fields?.workspaceId ?? getEnvironmentVariable("GRADIENT_WORKSPACE_ID");
if (!this.gradientAccessKey) {
throw new Error("Missing Gradient AI Access Token");
}
if (!this.workspaceId) {
throw new Error("Missing Gradient AI Workspace ID");
}
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the Gradient API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
await this.setModel();
const mappedTexts = texts.map((text) => ({ input: text }));
const batches = chunkArray(mappedTexts, this.batchSize);
const batchRequests = batches.map((batch) =>
this.caller.call(async () =>
this.model.generateEmbeddings({
inputs: batch,
})
)
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { embeddings: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j].embedding);
}
}
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embedDocuments method with the document as the input.
* @param text Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const data = await this.embedDocuments([text]);
return data[0];
}
/**
* Method to set the model to use for generating embeddings.
* @sets the class' `model` value to that of the retrieved Embeddings Model.
*/
async setModel() {
if (this.model) return;
const gradient = new Gradient({
accessToken: this.gradientAccessKey,
workspaceId: this.workspaceId,
});
this.model = await gradient.getEmbeddingsModel({
slug: "bge-large",
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/alibaba_tongyi.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
export interface AlibabaTongyiEmbeddingsParams extends EmbeddingsParams {
/** Model name to use */
modelName: "text-embedding-v2";
/**
* Timeout to use when making requests to AlibabaTongyi.
*/
timeout?: number;
/**
* The maximum number of documents to embed in a single request. This is
* limited by the AlibabaTongyi API to a maximum of 2048.
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text.
*/
stripNewLines?: boolean;
parameters?: {
/**
* 取值：query 或者 document，默认值为 document
* 说明：文本转换为向量后可以应用于检索、聚类、分类等下游任务，
* 对检索这类非对称任务为了达到更好的检索效果建议区分查询文本（query）和
* 底库文本（document）类型, 聚类、分类等对称任务可以不用特殊指定，
* 采用系统默认值"document"即可
*/
text_type?: "query" | "document";
};
}
interface EmbeddingCreateParams {
model: AlibabaTongyiEmbeddingsParams["modelName"];
input: {
texts: string[];
};
parameters?: AlibabaTongyiEmbeddingsParams["parameters"];
}
interface EmbeddingResponse {
output: {
embeddings: { text_index: number; embedding: number[] }[];
};
usage: {
total_tokens: number;
};
request_id: string;
}
interface EmbeddingErrorResponse {
code: string;
message: string;
request_id: string;
}
export class AlibabaTongyiEmbeddings
extends Embeddings
implements AlibabaTongyiEmbeddingsParams
{
modelName: AlibabaTongyiEmbeddingsParams["modelName"] = "text-embedding-v2";
batchSize = 24;
stripNewLines = true;
apiKey: string;
parameters: EmbeddingCreateParams["parameters"];
constructor(
fields?: Partial<AlibabaTongyiEmbeddingsParams> & {
verbose?: boolean;
apiKey?: string;
}
) {
const fieldsWithDefaults = { maxConcurrency: 2, ...fields };
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiKey ?? getEnvironmentVariable("ALIBABA_API_KEY");
if (!apiKey) throw new Error("AlibabaAI API key not found");
this.apiKey = apiKey;
this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.stripNewLines =
fieldsWithDefaults?.stripNewLines ?? this.stripNewLines;
this.parameters = {
text_type: fieldsWithDefaults?.parameters?.text_type ?? "document",
};
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the AlibabaTongyi API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
const batchRequests = batches.map((batch) => {
const params = this.getParams(batch);
return this.embeddingWithRetry(params);
});
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const batchResponse = batchResponses[i] || [];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j]);
}
}
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param text Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const params = this.getParams([
this.stripNewLines ? text.replace(/\n/g, " ") : text,
]);
const embeddings = (await this.embeddingWithRetry(params)) || [[]];
return embeddings[0];
}
/**
* Method to generate an embedding params.
* @param texts Array of documents to generate embeddings for.
* @returns an embedding params.
*/
private getParams(
texts: EmbeddingCreateParams["input"]["texts"]
): EmbeddingCreateParams {
return {
model: this.modelName,
input: {
texts,
},
parameters: this.parameters,
};
}
/**
* Private method to make a request to the OpenAI API to generate
* embeddings. Handles the retry logic and returns the response from the
* API.
* @param request Request to send to the OpenAI API.
* @returns Promise that resolves to the response from the API.
*/
private async embeddingWithRetry(body: EmbeddingCreateParams) {
return fetch(
"https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding",
{
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
},
body: JSON.stringify(body),
}
).then(async (response) => {
const embeddingData: EmbeddingResponse | EmbeddingErrorResponse =
await response.json();
if ("code" in embeddingData && embeddingData.code) {
throw new Error(`${embeddingData.code}: ${embeddingData.message}`);
}
return (embeddingData as EmbeddingResponse).output.embeddings.map(
({ embedding }) => embedding
);
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/togetherai.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* Interface for TogetherAIEmbeddingsParams parameters. Extends EmbeddingsParams and
* defines additional parameters specific to the TogetherAIEmbeddings class.
*/
export interface TogetherAIEmbeddingsParams extends EmbeddingsParams {
/**
* The API key to use for the TogetherAI API.
* @default {process.env.TOGETHER_AI_API_KEY}
*/
apiKey?: string;
/**
* Model name to use
* Alias for `model`
* @default {"togethercomputer/m2-bert-80M-8k-retrieval"}
*/
modelName?: string;
/**
* Model name to use
* @default {"togethercomputer/m2-bert-80M-8k-retrieval"}
*/
model?: string;
/**
* Timeout to use when making requests to TogetherAI.
* @default {undefined}
*/
timeout?: number;
/**
* The maximum number of documents to embed in a single request.
* @default {512}
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text. May not be suitable
* for all use cases.
* @default {false}
*/
stripNewLines?: boolean;
}
/** @ignore */
interface TogetherAIEmbeddingsResult {
object: string;
data: Array<{
object: "embedding";
embedding: number[];
index: number;
}>;
model: string;
request_id: string;
}
/**
* Class for generating embeddings using the TogetherAI API. Extends the
* Embeddings class and implements TogetherAIEmbeddingsParams.
* @example
* ```typescript
* const embeddings = new TogetherAIEmbeddings({
* apiKey: process.env.TOGETHER_AI_API_KEY, // Default value
* model: "togethercomputer/m2-bert-80M-8k-retrieval", // Default value
* });
* const res = await embeddings.embedQuery(
* "What would be a good company name a company that makes colorful socks?"
* );
* ```
*/
export class TogetherAIEmbeddings
extends Embeddings
implements TogetherAIEmbeddingsParams
{
modelName = "togethercomputer/m2-bert-80M-8k-retrieval";
model = "togethercomputer/m2-bert-80M-8k-retrieval";
apiKey: string;
batchSize = 512;
stripNewLines = false;
timeout?: number;
private embeddingsAPIUrl = "https://api.together.xyz/api/v1/embeddings";
constructor(fields?: Partial<TogetherAIEmbeddingsParams>) {
super(fields ?? {});
const apiKey =
fields?.apiKey ?? getEnvironmentVariable("TOGETHER_AI_API_KEY");
if (!apiKey) {
throw new Error("TOGETHER_AI_API_KEY not found.");
}
this.apiKey = apiKey;
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
this.timeout = fields?.timeout;
this.batchSize = fields?.batchSize ?? this.batchSize;
this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
}
private constructHeaders() {
return {
accept: "application/json",
"content-type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
};
}
private constructBody(input: string) {
const body = {
model: this?.model,
input,
};
return body;
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the TogetherAI API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
let batchResponses: TogetherAIEmbeddingsResult[] = [];
for await (const batch of batches) {
const batchRequests = batch.map((item) => this.embeddingWithRetry(item));
const response = await Promise.all(batchRequests);
batchResponses = batchResponses.concat(response);
}
const embeddings: number[][] = batchResponses.map(
(response) => response.data[0].embedding
);
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param {string} text Document to generate an embedding for.
* @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const { data } = await this.embeddingWithRetry(
this.stripNewLines ? text.replace(/\n/g, " ") : text
);
return data[0].embedding;
}
/**
* Private method to make a request to the TogetherAI API to generate
* embeddings. Handles the retry logic and returns the response from the
* API.
* @param {string} input The input text to embed.
* @returns Promise that resolves to the response from the API.
* @TODO Figure out return type and statically type it.
*/
private async embeddingWithRetry(
input: string
): Promise<TogetherAIEmbeddingsResult> {
const body = JSON.stringify(this.constructBody(input));
const headers = this.constructHeaders();
return this.caller.call(async () => {
const fetchResponse = await fetch(this.embeddingsAPIUrl, {
method: "POST",
headers,
body,
});
if (fetchResponse.status === 200) {
return fetchResponse.json();
}
throw new Error(
`Error getting prompt completion from Together AI. ${JSON.stringify(
await fetchResponse.json(),
null,
2
)}`
);
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/cloudflare_workersai.ts
|
import { Ai } from "@cloudflare/ai";
import { Fetcher } from "@cloudflare/workers-types";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
type AiTextEmbeddingsInput = {
text: string | string[];
};
type AiTextEmbeddingsOutput = {
shape: number[];
data: number[][];
};
/** @deprecated Install and import from "@langchain/cloudflare" instead. */
export interface CloudflareWorkersAIEmbeddingsParams extends EmbeddingsParams {
/** Binding */
binding: Fetcher;
/**
* Model name to use
* Alias for `model`
*/
modelName?: string;
/** Model name to use */
model?: string;
/**
* The maximum number of documents to embed in a single request.
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text. This is recommended by
* OpenAI, but may not be suitable for all use cases.
*/
stripNewLines?: boolean;
}
/** @deprecated Install and import from "@langchain/cloudflare" instead. */
export class CloudflareWorkersAIEmbeddings extends Embeddings {
modelName = "@cf/baai/bge-base-en-v1.5";
model = "@cf/baai/bge-base-en-v1.5";
batchSize = 50;
stripNewLines = true;
ai: Ai;
constructor(fields: CloudflareWorkersAIEmbeddingsParams) {
super(fields);
if (!fields.binding) {
throw new Error(
"Must supply a Workers AI binding, eg { binding: env.AI }"
);
}
this.ai = new Ai(fields.binding);
this.modelName = fields?.model ?? fields.modelName ?? this.model;
this.model = this.modelName;
this.stripNewLines = fields.stripNewLines ?? this.stripNewLines;
}
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
const batchRequests = batches.map((batch) => this.runEmbedding(batch));
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batchResponse = batchResponses[i];
for (let j = 0; j < batchResponse.length; j += 1) {
embeddings.push(batchResponse[j]);
}
}
return embeddings;
}
async embedQuery(text: string): Promise<number[]> {
const data = await this.runEmbedding([
this.stripNewLines ? text.replace(/\n/g, " ") : text,
]);
return data[0];
}
private async runEmbedding(texts: string[]) {
return this.caller.call(async () => {
const response: AiTextEmbeddingsOutput = await this.ai.run(this.model, {
text: texts,
} as AiTextEmbeddingsInput);
return response.data;
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/deepinfra.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* The default model name to use for generating embeddings.
*/
const DEFAULT_MODEL_NAME = "sentence-transformers/clip-ViT-B-32";
/**
* The default batch size to use for generating embeddings.
* This is limited by the DeepInfra API to a maximum of 1024.
*/
const DEFAULT_BATCH_SIZE = 1024;
/**
* Environment variable name for the DeepInfra API token.
*/
const API_TOKEN_ENV_VAR = "DEEPINFRA_API_TOKEN";
export interface DeepInfraEmbeddingsRequest {
inputs: string[];
normalize?: boolean;
image?: string;
webhook?: string;
}
/**
* Input parameters for the DeepInfra embeddings
*/
export interface DeepInfraEmbeddingsParams extends EmbeddingsParams {
/**
* The API token to use for authentication.
* If not provided, it will be read from the `DEEPINFRA_API_TOKEN` environment variable.
*/
apiToken?: string;
/**
* The model ID to use for generating completions.
* Default: `sentence-transformers/clip-ViT-B-32`
*/
modelName?: string;
/**
* The maximum number of texts to embed in a single request. This is
* limited by the DeepInfra API to a maximum of 1024.
*/
batchSize?: number;
}
/**
* Response from the DeepInfra embeddings API.
*/
export interface DeepInfraEmbeddingsResponse {
/**
* The embeddings generated for the input texts.
*/
embeddings: number[][];
/**
* The number of tokens in the input texts.
*/
input_tokens: number;
/**
* The status of the inference.
*/
request_id?: string;
}
/**
* A class for generating embeddings using the DeepInfra API.
* @example
* ```typescript
* // Embed a query using the DeepInfraEmbeddings class
* const model = new DeepInfraEmbeddings();
* const res = await model.embedQuery(
* "What would be a good company name for a company that makes colorful socks?",
* );
* console.log({ res });
* ```
*/
export class DeepInfraEmbeddings
extends Embeddings
implements DeepInfraEmbeddingsParams
{
apiToken: string;
batchSize: number;
modelName: string;
/**
* Constructor for the DeepInfraEmbeddings class.
* @param fields - An optional object with properties to configure the instance.
*/
constructor(
fields?: Partial<DeepInfraEmbeddingsParams> & {
verbose?: boolean;
}
) {
const fieldsWithDefaults = {
modelName: DEFAULT_MODEL_NAME,
batchSize: DEFAULT_BATCH_SIZE,
...fields,
};
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiToken || getEnvironmentVariable(API_TOKEN_ENV_VAR);
if (!apiKey) {
throw new Error("DeepInfra API token not found");
}
this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.apiToken = apiKey;
}
/**
* Generates embeddings for an array of texts.
* @param inputs - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(inputs: string[]): Promise<number[][]> {
const batches = chunkArray(inputs, this.batchSize);
const batchRequests = batches.map((batch: string[]) =>
this.embeddingWithRetry({
inputs: batch,
})
);
const batchResponses = await Promise.all(batchRequests);
const out: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { embeddings } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
out.push(embeddings[j]);
}
}
return out;
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
const { embeddings } = await this.embeddingWithRetry({
inputs: [text],
});
return embeddings[0];
}
/**
* Generates embeddings with retry capabilities.
* @param request - An object containing the request parameters for generating embeddings.
* @returns A Promise that resolves to the API response.
*/
private async embeddingWithRetry(
request: DeepInfraEmbeddingsRequest
): Promise<DeepInfraEmbeddingsResponse> {
const response = await this.caller.call(() =>
fetch(`https://api.deepinfra.com/v1/inference/${this.modelName}`, {
method: "POST",
headers: {
Authorization: `Bearer ${this.apiToken}`,
"Content-Type": "application/json",
},
body: JSON.stringify(request),
}).then((res) => res.json())
);
return response as DeepInfraEmbeddingsResponse;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/minimax.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import { ConfigurationParameters } from "../chat_models/minimax.js";
/**
* Interface for MinimaxEmbeddings parameters. Extends EmbeddingsParams and
* defines additional parameters specific to the MinimaxEmbeddings class.
*/
export interface MinimaxEmbeddingsParams extends EmbeddingsParams {
/**
* Model name to use
* Alias for `model`
*/
modelName: string;
/** Model name to use */
model: string;
/**
* API key to use when making requests. Defaults to the value of
* `MINIMAX_GROUP_ID` environment variable.
*/
minimaxGroupId?: string;
/**
* Secret key to use when making requests. Defaults to the value of
* `MINIMAX_API_KEY` environment variable.
* Alias for `apiKey`
*/
minimaxApiKey?: string;
/**
* Secret key to use when making requests. Defaults to the value of
* `MINIMAX_API_KEY` environment variable.
*/
apiKey?: string;
/**
* The maximum number of documents to embed in a single request. This is
* limited by the Minimax API to a maximum of 4096.
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text. This is recommended by
* Minimax, but may not be suitable for all use cases.
*/
stripNewLines?: boolean;
/**
* The target use-case after generating the vector.
* When using embeddings, the vector of the target content is first generated through the db and stored in the vector database,
* and then the vector of the retrieval text is generated through the query.
* Note: For the parameters of the partial algorithm, we adopted a separate algorithm plan for query and db.
* Therefore, for a paragraph of text, if it is to be used as a retrieval text, it should use the db,
* and if it is used as a retrieval text, it should use the query.
*/
type?: "db" | "query";
}
export interface CreateMinimaxEmbeddingRequest {
/**
* @type {string}
* @memberof CreateMinimaxEmbeddingRequest
*/
model: string;
/**
* Text to generate vector expectation
* @type {CreateEmbeddingRequestInput}
* @memberof CreateMinimaxEmbeddingRequest
*/
texts: string[];
/**
* The target use-case after generating the vector. When using embeddings,
* first generate the vector of the target content through the db and store it in the vector database,
* and then generate the vector of the retrieval text through the query.
* Note: For the parameter of the algorithm, we use the algorithm scheme of query and db separation,
* so a text, if it is to be retrieved as a text, should use the db,
* if it is used as a retrieval text, should use the query.
* @type {string}
* @memberof CreateMinimaxEmbeddingRequest
*/
type: "db" | "query";
}
/**
* Class for generating embeddings using the Minimax API. Extends the
* Embeddings class and implements MinimaxEmbeddingsParams
* @example
* ```typescript
* const embeddings = new MinimaxEmbeddings();
*
* // Embed a single query
* const queryEmbedding = await embeddings.embedQuery("Hello world");
* console.log(queryEmbedding);
*
* // Embed multiple documents
* const documentsEmbedding = await embeddings.embedDocuments([
* "Hello world",
* "Bye bye",
* ]);
* console.log(documentsEmbedding);
* ```
*/
export class MinimaxEmbeddings
extends Embeddings
implements MinimaxEmbeddingsParams
{
modelName = "embo-01";
model = "embo-01";
batchSize = 512;
stripNewLines = true;
minimaxGroupId?: string;
minimaxApiKey?: string;
apiKey?: string;
type: "db" | "query" = "db";
apiUrl: string;
basePath?: string = "https://api.minimax.chat/v1";
headers?: Record<string, string>;
constructor(
fields?: Partial<MinimaxEmbeddingsParams> & {
configuration?: ConfigurationParameters;
}
) {
const fieldsWithDefaults = { maxConcurrency: 2, ...fields };
super(fieldsWithDefaults);
this.minimaxGroupId =
fields?.minimaxGroupId ?? getEnvironmentVariable("MINIMAX_GROUP_ID");
if (!this.minimaxGroupId) {
throw new Error("Minimax GroupID not found");
}
this.minimaxApiKey =
fields?.apiKey ??
fields?.minimaxApiKey ??
getEnvironmentVariable("MINIMAX_API_KEY");
this.apiKey = this.minimaxApiKey;
if (!this.apiKey) {
throw new Error("Minimax ApiKey not found");
}
this.modelName =
fieldsWithDefaults?.model ?? fieldsWithDefaults?.modelName ?? this.model;
this.model = this.modelName;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.type = fieldsWithDefaults?.type ?? this.type;
this.stripNewLines =
fieldsWithDefaults?.stripNewLines ?? this.stripNewLines;
this.basePath = fields?.configuration?.basePath ?? this.basePath;
this.apiUrl = `${this.basePath}/embeddings`;
this.headers = fields?.configuration?.headers ?? this.headers;
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the Minimax API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
const batchRequests = batches.map((batch) =>
this.embeddingWithRetry({
model: this.model,
texts: batch,
type: this.type,
})
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { vectors: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j]);
}
}
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param text Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const { vectors } = await this.embeddingWithRetry({
model: this.model,
texts: [this.stripNewLines ? text.replace(/\n/g, " ") : text],
type: this.type,
});
return vectors[0];
}
/**
* Private method to make a request to the Minimax API to generate
* embeddings. Handles the retry logic and returns the response from the
* API.
* @param request Request to send to the Minimax API.
* @returns Promise that resolves to the response from the API.
*/
private async embeddingWithRetry(request: CreateMinimaxEmbeddingRequest) {
const makeCompletionRequest = async () => {
const url = `${this.apiUrl}?GroupId=${this.minimaxGroupId}`;
const response = await fetch(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
...this.headers,
},
body: JSON.stringify(request),
});
const json = await response.json();
return json;
};
return this.caller.call(makeCompletionRequest);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/ollama.ts
|
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { OllamaInput, OllamaRequestParams } from "../utils/ollama.js";
type CamelCasedRequestOptions = Omit<
OllamaInput,
"baseUrl" | "model" | "format" | "headers"
>;
/**
* Interface for OllamaEmbeddings parameters. Extends EmbeddingsParams and
* defines additional parameters specific to the OllamaEmbeddings class.
*/
interface OllamaEmbeddingsParams extends EmbeddingsParams {
/** The Ollama model to use, e.g: "llama2:13b" */
model?: string;
/** Base URL of the Ollama server, defaults to "http://localhost:11434" */
baseUrl?: string;
/** Extra headers to include in the Ollama API request */
headers?: Record<string, string>;
/** Defaults to "5m" */
keepAlive?: string;
/** Advanced Ollama API request parameters in camelCase, see
* https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
* for details of the available parameters.
*/
requestOptions?: CamelCasedRequestOptions;
}
/**
* @deprecated OllamaEmbeddings have been moved to the `@langchain/ollama` package. Install it with `npm install @langchain/ollama`.
*/
export class OllamaEmbeddings extends Embeddings {
model = "llama2";
baseUrl = "http://localhost:11434";
headers?: Record<string, string>;
keepAlive = "5m";
requestOptions?: OllamaRequestParams["options"];
constructor(params?: OllamaEmbeddingsParams) {
super({ maxConcurrency: 1, ...params });
if (params?.model) {
this.model = params.model;
}
if (params?.baseUrl) {
this.baseUrl = params.baseUrl;
}
if (params?.headers) {
this.headers = params.headers;
}
if (params?.keepAlive) {
this.keepAlive = params.keepAlive;
}
if (params?.requestOptions) {
this.requestOptions = this._convertOptions(params.requestOptions);
}
}
/** convert camelCased Ollama request options like "useMMap" to
* the snake_cased equivalent which the ollama API actually uses.
* Used only for consistency with the llms/Ollama and chatModels/Ollama classes
*/
_convertOptions(requestOptions: CamelCasedRequestOptions) {
const snakeCasedOptions: Record<string, unknown> = {};
const mapping: Record<keyof CamelCasedRequestOptions, string> = {
embeddingOnly: "embedding_only",
f16KV: "f16_kv",
frequencyPenalty: "frequency_penalty",
keepAlive: "keep_alive",
logitsAll: "logits_all",
lowVram: "low_vram",
mainGpu: "main_gpu",
mirostat: "mirostat",
mirostatEta: "mirostat_eta",
mirostatTau: "mirostat_tau",
numBatch: "num_batch",
numCtx: "num_ctx",
numGpu: "num_gpu",
numGqa: "num_gqa",
numKeep: "num_keep",
numPredict: "num_predict",
numThread: "num_thread",
penalizeNewline: "penalize_newline",
presencePenalty: "presence_penalty",
repeatLastN: "repeat_last_n",
repeatPenalty: "repeat_penalty",
ropeFrequencyBase: "rope_frequency_base",
ropeFrequencyScale: "rope_frequency_scale",
temperature: "temperature",
stop: "stop",
tfsZ: "tfs_z",
topK: "top_k",
topP: "top_p",
typicalP: "typical_p",
useMLock: "use_mlock",
useMMap: "use_mmap",
vocabOnly: "vocab_only",
};
for (const [key, value] of Object.entries(requestOptions)) {
const snakeCasedOption = mapping[key as keyof CamelCasedRequestOptions];
if (snakeCasedOption) {
snakeCasedOptions[snakeCasedOption] = value;
} else {
// Just pass unknown options through
snakeCasedOptions[key] = value;
}
}
return snakeCasedOptions;
}
async _request(prompt: string): Promise<number[]> {
const { model, baseUrl, keepAlive, requestOptions } = this;
let formattedBaseUrl = baseUrl;
if (formattedBaseUrl.startsWith("http://localhost:")) {
// Node 18 has issues with resolving "localhost"
// See https://github.com/node-fetch/node-fetch/issues/1624
formattedBaseUrl = formattedBaseUrl.replace(
"http://localhost:",
"http://127.0.0.1:"
);
}
const response = await fetch(`${formattedBaseUrl}/api/embeddings`, {
method: "POST",
headers: {
"Content-Type": "application/json",
...this.headers,
},
body: JSON.stringify({
prompt,
model,
keep_alive: keepAlive,
options: requestOptions,
}),
});
if (!response.ok) {
throw new Error(
`Request to Ollama server failed: ${response.status} ${response.statusText}`
);
}
const json = await response.json();
return json.embedding;
}
async _embed(texts: string[]): Promise<number[][]> {
const embeddings: number[][] = await Promise.all(
texts.map((text) => this.caller.call(() => this._request(text)))
);
return embeddings;
}
async embedDocuments(documents: string[]) {
return this._embed(documents);
}
async embedQuery(document: string) {
return (await this.embedDocuments([document]))[0];
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/fireworks.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the FireworksEmbeddings class.
*/
export interface FireworksEmbeddingsParams extends EmbeddingsParams {
/**
* @deprecated Use `model` instead.
*/
modelName: string;
model: string;
/**
* The maximum number of documents to embed in a single request. This is
* limited by the Fireworks AI API to a maximum of 8.
*/
batchSize?: number;
}
/**
* Interface for the request body to generate embeddings.
*/
export interface CreateFireworksEmbeddingRequest {
/**
* @type {string}
* @memberof CreateFireworksEmbeddingRequest
*/
model: string;
/**
* Text to generate vector expectation
* @type {CreateEmbeddingRequestInput}
* @memberof CreateFireworksEmbeddingRequest
*/
input: string | string[];
}
/**
* A class for generating embeddings using the Fireworks AI API.
*/
export class FireworksEmbeddings
extends Embeddings
implements FireworksEmbeddingsParams
{
/**
* @deprecated Use `model` instead.
*/
modelName = "nomic-ai/nomic-embed-text-v1.5";
model = "nomic-ai/nomic-embed-text-v1.5";
batchSize = 8;
private apiKey: string;
basePath?: string = "https://api.fireworks.ai/inference/v1";
apiUrl: string;
headers?: Record<string, string>;
/**
* Constructor for the FireworksEmbeddings class.
* @param fields - An optional object with properties to configure the instance.
*/
constructor(
fields?: Partial<FireworksEmbeddingsParams> & {
verbose?: boolean;
apiKey?: string;
}
) {
const fieldsWithDefaults = { ...fields };
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiKey || getEnvironmentVariable("FIREWORKS_API_KEY");
if (!apiKey) {
throw new Error("Fireworks AI API key not found");
}
this.model = fieldsWithDefaults?.model ?? this.model;
this.modelName = this.model;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.apiKey = apiKey;
this.apiUrl = `${this.basePath}/embeddings`;
}
/**
* Generates embeddings for an array of texts.
* @param texts - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(texts, this.batchSize);
const batchRequests = batches.map((batch) =>
this.embeddingWithRetry({
model: this.model,
input: batch,
})
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { data: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j].embedding);
}
}
return embeddings;
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
const { data } = await this.embeddingWithRetry({
model: this.model,
input: text,
});
return data[0].embedding;
}
/**
* Makes a request to the Fireworks AI API to generate embeddings for an array of texts.
* @param request - An object with properties to configure the request.
* @returns A Promise that resolves to the response from the Fireworks AI API.
*/
private async embeddingWithRetry(request: CreateFireworksEmbeddingRequest) {
const makeCompletionRequest = async () => {
const url = `${this.apiUrl}`;
const response = await fetch(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
...this.headers,
},
body: JSON.stringify(request),
});
if (!response.ok) {
const { error: message } = await response.json();
const error = new Error(
`Error ${response.status}: ${message ?? "Unspecified error"}`
);
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(error as any).response = response;
throw error;
}
const json = await response.json();
return json;
};
return this.caller.call(makeCompletionRequest);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/voyage.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the VoyageEmbeddings class.
*/
export interface VoyageEmbeddingsParams extends EmbeddingsParams {
modelName: string;
/**
* The maximum number of documents to embed in a single request. This is
* limited by the Voyage AI API to a maximum of 8.
*/
batchSize?: number;
/**
* Input type for the embeddings request.
*/
inputType?: string;
}
/**
* Interface for the request body to generate embeddings.
*/
export interface CreateVoyageEmbeddingRequest {
/**
* @type {string}
* @memberof CreateVoyageEmbeddingRequest
*/
model: string;
/**
* Text to generate vector expectation
* @type {CreateEmbeddingRequestInput}
* @memberof CreateVoyageEmbeddingRequest
*/
input: string | string[];
/**
* Input type for the embeddings request.
*/
input_type?: string;
}
/**
* A class for generating embeddings using the Voyage AI API.
*/
export class VoyageEmbeddings
extends Embeddings
implements VoyageEmbeddingsParams
{
modelName = "voyage-01";
batchSize = 8;
private apiKey: string;
basePath?: string = "https://api.voyageai.com/v1";
apiUrl: string;
headers?: Record<string, string>;
inputType?: string;
/**
* Constructor for the VoyageEmbeddings class.
* @param fields - An optional object with properties to configure the instance.
*/
constructor(
fields?: Partial<VoyageEmbeddingsParams> & {
verbose?: boolean;
apiKey?: string;
inputType?: string; // Make inputType optional
}
) {
const fieldsWithDefaults = { ...fields };
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiKey || getEnvironmentVariable("VOYAGEAI_API_KEY");
if (!apiKey) {
throw new Error("Voyage AI API key not found");
}
this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.apiKey = apiKey;
this.apiUrl = `${this.basePath}/embeddings`;
this.inputType = fieldsWithDefaults?.inputType;
}
/**
* Generates embeddings for an array of texts.
* @param texts - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(texts, this.batchSize);
const batchRequests = batches.map((batch) =>
this.embeddingWithRetry({
model: this.modelName,
input: batch,
input_type: this.inputType,
})
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { data: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j].embedding);
}
}
return embeddings;
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
const { data } = await this.embeddingWithRetry({
model: this.modelName,
input: text,
input_type: this.inputType,
});
return data[0].embedding;
}
/**
* Makes a request to the Voyage AI API to generate embeddings for an array of texts.
* @param request - An object with properties to configure the request.
* @returns A Promise that resolves to the response from the Voyage AI API.
*/
private async embeddingWithRetry(request: CreateVoyageEmbeddingRequest) {
const makeCompletionRequest = async () => {
const url = `${this.apiUrl}`;
const response = await fetch(url, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${this.apiKey}`,
...this.headers,
},
body: JSON.stringify(request),
});
const json = await response.json();
return json;
};
return this.caller.call(makeCompletionRequest);
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/ibm.ts
|
import { Embeddings } from "@langchain/core/embeddings";
import {
EmbeddingParameters,
TextEmbeddingsParams,
} from "@ibm-cloud/watsonx-ai/dist/watsonx-ai-ml/vml_v1.js";
import { WatsonXAI } from "@ibm-cloud/watsonx-ai";
import { AsyncCaller } from "@langchain/core/utils/async_caller";
import { WatsonxAuth, WatsonxParams } from "../types/ibm.js";
import { authenticateAndSetInstance } from "../utils/ibm.js";
export interface WatsonxEmbeddingsParams
extends Pick<TextEmbeddingsParams, "headers"> {
truncateInputTokens?: number;
}
export interface WatsonxInputEmbeddings
extends Omit<WatsonxParams, "idOrName"> {
truncateInputTokens?: number;
}
export class WatsonxEmbeddings
extends Embeddings
implements WatsonxEmbeddingsParams, WatsonxParams
{
model: string;
serviceUrl: string;
version: string;
spaceId?: string;
projectId?: string;
truncateInputTokens?: number;
maxRetries?: number;
maxConcurrency?: number;
private service: WatsonXAI;
constructor(fields: WatsonxInputEmbeddings & WatsonxAuth) {
const superProps = { maxConcurrency: 2, ...fields };
super(superProps);
this.model = fields.model;
this.version = fields.version;
this.serviceUrl = fields.serviceUrl;
this.truncateInputTokens = fields.truncateInputTokens;
this.maxConcurrency = fields.maxConcurrency;
this.maxRetries = fields.maxRetries ?? 0;
if (fields.projectId && fields.spaceId)
throw new Error("Maximum 1 id type can be specified per instance");
else if (!fields.projectId && !fields.spaceId)
throw new Error(
"No id specified! At least id of 1 type has to be specified"
);
this.projectId = fields?.projectId;
this.spaceId = fields?.spaceId;
this.serviceUrl = fields?.serviceUrl;
const {
watsonxAIApikey,
watsonxAIAuthType,
watsonxAIBearerToken,
watsonxAIUsername,
watsonxAIPassword,
watsonxAIUrl,
version,
serviceUrl,
} = fields;
const auth = authenticateAndSetInstance({
watsonxAIApikey,
watsonxAIAuthType,
watsonxAIBearerToken,
watsonxAIUsername,
watsonxAIPassword,
watsonxAIUrl,
version,
serviceUrl,
});
if (auth) this.service = auth;
else throw new Error("You have not provided one type of authentication");
}
scopeId() {
if (this.projectId)
return { projectId: this.projectId, modelId: this.model };
else return { spaceId: this.spaceId, modelId: this.model };
}
invocationParams(): EmbeddingParameters {
return {
truncate_input_tokens: this.truncateInputTokens,
};
}
async listModels() {
const listModelParams = {
filters: "function_embedding",
};
const caller = new AsyncCaller({
maxConcurrency: this.maxConcurrency,
maxRetries: this.maxRetries,
});
const listModels = await caller.call(() =>
this.service.listFoundationModelSpecs(listModelParams)
);
return listModels.result.resources?.map((item) => item.model_id);
}
private async embedSingleText(inputs: string[]) {
const textEmbeddingParams: TextEmbeddingsParams = {
inputs,
...this.scopeId(),
parameters: this.invocationParams(),
};
const caller = new AsyncCaller({
maxConcurrency: this.maxConcurrency,
maxRetries: this.maxRetries,
});
const embeddings = await caller.call(() =>
this.service.embedText(textEmbeddingParams)
);
return embeddings.result.results.map((item) => item.embedding);
}
async embedDocuments(documents: string[]): Promise<number[][]> {
const data = await this.embedSingleText(documents);
return data;
}
async embedQuery(document: string): Promise<number[]> {
const data = await this.embedSingleText([document]);
return data[0];
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/premai.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
import Prem from "@premai/prem-sdk";
/**
* Interface for PremEmbeddings parameters. Extends EmbeddingsParams and
* defines additional parameters specific to the PremEmbeddings class.
*/
export interface PremEmbeddingsParams extends EmbeddingsParams {
/**
* The Prem API key to use for requests.
* @default process.env.PREM_API_KEY
*/
apiKey?: string;
baseUrl?: string;
/**
* The ID of the project to use.
*/
project_id?: number | string;
/**
* The model to generate the embeddings.
*/
model: string;
encoding_format?: ("float" | "base64") & string;
batchSize?: number;
}
/**
* Class for generating embeddings using the Prem AI's API. Extends the
* Embeddings class and implements PremEmbeddingsParams and
*/
export class PremEmbeddings extends Embeddings implements PremEmbeddingsParams {
client: Prem;
batchSize = 128;
apiKey?: string;
project_id: number;
model: string;
encoding_format?: ("float" | "base64") & string;
constructor(fields: PremEmbeddingsParams) {
super(fields);
const apiKey = fields?.apiKey || getEnvironmentVariable("PREM_API_KEY");
if (!apiKey) {
throw new Error(
`Prem API key not found. Please set the PREM_API_KEY environment variable or provide the key into "apiKey"`
);
}
const projectId =
fields?.project_id ??
parseInt(getEnvironmentVariable("PREM_PROJECT_ID") ?? "-1", 10);
if (!projectId || projectId === -1 || typeof projectId !== "number") {
throw new Error(
`Prem project ID not found. Please set the PREM_PROJECT_ID environment variable or provide the key into "project_id"`
);
}
this.client = new Prem({
apiKey,
});
this.project_id = projectId;
this.model = fields.model ?? this.model;
this.encoding_format = fields.encoding_format ?? this.encoding_format;
}
/**
* Method to generate embeddings for an array of documents. Splits the
* documents into batches and makes requests to the Prem API to generate
* embeddings.
* @param texts Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each document.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
const mappedTexts = texts.map((text) => text);
const batches = chunkArray(mappedTexts, this.batchSize);
const batchRequests = batches.map((batch) =>
this.caller.call(async () =>
this.client.embeddings.create({
input: batch,
model: this.model,
encoding_format: this.encoding_format,
project_id: this.project_id,
})
)
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { data: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse[j].embedding);
}
}
return embeddings;
}
/**
* Method to generate an embedding for a single document. Calls the
* embedDocuments method with the document as the input.
* @param text Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const data = await this.embedDocuments([text]);
return data[0];
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tensorflow.ts
|
import { load } from "@tensorflow-models/universal-sentence-encoder";
import * as tf from "@tensorflow/tfjs-core";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the TensorFlowEmbeddings class.
*/
export interface TensorFlowEmbeddingsParams extends EmbeddingsParams {}
/**
* Class that extends the Embeddings class and provides methods for
* generating embeddings using the Universal Sentence Encoder model from
* TensorFlow.js.
* @example
* ```typescript
* const embeddings = new TensorFlowEmbeddings();
* const store = new MemoryVectorStore(embeddings);
*
* const documents = [
* "A document",
* "Some other piece of text",
* "One more",
* "And another",
* ];
*
* await store.addDocuments(
* documents.map((pageContent) => new Document({ pageContent }))
* );
* ```
*/
export class TensorFlowEmbeddings extends Embeddings {
constructor(fields?: TensorFlowEmbeddingsParams) {
super(fields ?? {});
try {
tf.backend();
} catch (e) {
throw new Error("No TensorFlow backend found, see instructions at ...");
}
}
_cached: ReturnType<typeof load>;
/**
* Private method that loads the Universal Sentence Encoder model if it
* hasn't been loaded already. It returns a promise that resolves to the
* loaded model.
* @returns Promise that resolves to the loaded Universal Sentence Encoder model.
*/
private async load() {
if (this._cached === undefined) {
this._cached = load();
}
return this._cached;
}
private _embed(texts: string[]) {
return this.caller.call(async () => {
const model = await this.load();
return model.embed(texts);
});
}
/**
* Method that takes a document as input and returns a promise that
* resolves to an embedding for the document. It calls the _embed method
* with the document as the input and processes the result to return a
* single embedding.
* @param document Document to generate an embedding for.
* @returns Promise that resolves to an embedding for the input document.
*/
embedQuery(document: string): Promise<number[]> {
return this._embed([document])
.then((embeddings) => embeddings.array())
.then((embeddings) => embeddings[0]);
}
/**
* Method that takes an array of documents as input and returns a promise
* that resolves to a 2D array of embeddings for each document. It calls
* the _embed method with the documents as the input and processes the
* result to return the embeddings.
* @param documents Array of documents to generate embeddings for.
* @returns Promise that resolves to a 2D array of embeddings for each input document.
*/
embedDocuments(documents: string[]): Promise<number[][]> {
return this._embed(documents).then((embeddings) => embeddings.array());
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/cohere.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the CohereEmbeddings class.
* @deprecated Use `CohereEmbeddingsParams` from `@langchain/cohere` instead.
*/
export interface CohereEmbeddingsParams extends EmbeddingsParams {
modelName: string;
/**
* The maximum number of documents to embed in a single request. This is
* limited by the Cohere API to a maximum of 96.
*/
batchSize?: number;
}
/**
* A class for generating embeddings using the Cohere API.
* @example
* ```typescript
* // Embed a query using the CohereEmbeddings class
* const model = new ChatOpenAI();
* const res = await model.embedQuery(
* "What would be a good company name for a company that makes colorful socks?",
* );
* console.log({ res });
* ```
* @deprecated Use `CohereEmbeddings` from `@langchain/cohere` instead.
*/
export class CohereEmbeddings
extends Embeddings
implements CohereEmbeddingsParams
{
modelName = "small";
batchSize = 48;
private apiKey: string;
private client: typeof import("cohere-ai");
/**
* Constructor for the CohereEmbeddings class.
* @param fields - An optional object with properties to configure the instance.
*/
constructor(
fields?: Partial<CohereEmbeddingsParams> & {
verbose?: boolean;
apiKey?: string;
}
) {
const fieldsWithDefaults = { maxConcurrency: 2, ...fields };
super(fieldsWithDefaults);
const apiKey =
fieldsWithDefaults?.apiKey || getEnvironmentVariable("COHERE_API_KEY");
if (!apiKey) {
throw new Error("Cohere API key not found");
}
this.modelName = fieldsWithDefaults?.modelName ?? this.modelName;
this.batchSize = fieldsWithDefaults?.batchSize ?? this.batchSize;
this.apiKey = apiKey;
}
/**
* Generates embeddings for an array of texts.
* @param texts - An array of strings to generate embeddings for.
* @returns A Promise that resolves to an array of embeddings.
*/
async embedDocuments(texts: string[]): Promise<number[][]> {
await this.maybeInitClient();
const batches = chunkArray(texts, this.batchSize);
const batchRequests = batches.map((batch) =>
this.embeddingWithRetry({
model: this.modelName,
texts: batch,
})
);
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batch = batches[i];
const { body: batchResponse } = batchResponses[i];
for (let j = 0; j < batch.length; j += 1) {
embeddings.push(batchResponse.embeddings[j]);
}
}
return embeddings;
}
/**
* Generates an embedding for a single text.
* @param text - A string to generate an embedding for.
* @returns A Promise that resolves to an array of numbers representing the embedding.
*/
async embedQuery(text: string): Promise<number[]> {
await this.maybeInitClient();
const { body } = await this.embeddingWithRetry({
model: this.modelName,
texts: [text],
});
return body.embeddings[0];
}
/**
* Generates embeddings with retry capabilities.
* @param request - An object containing the request parameters for generating embeddings.
* @returns A Promise that resolves to the API response.
*/
private async embeddingWithRetry(
request: Parameters<typeof this.client.embed>[0]
) {
await this.maybeInitClient();
return this.caller.call(this.client.embed.bind(this.client), request);
}
/**
* Initializes the Cohere client if it hasn't been initialized already.
*/
private async maybeInitClient() {
if (!this.client) {
const { cohere } = await CohereEmbeddings.imports();
this.client = cohere;
this.client.init(this.apiKey);
}
}
/** @ignore */
static async imports(): Promise<{
cohere: typeof import("cohere-ai");
}> {
try {
const { default: cohere } = await import("cohere-ai");
return { cohere };
} catch (e) {
throw new Error(
"Please install cohere-ai as a dependency with, e.g. `yarn add cohere-ai`"
);
}
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/hf_transformers.ts
|
import type {
PretrainedOptions,
FeatureExtractionPipelineOptions,
FeatureExtractionPipeline,
} from "@xenova/transformers";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { chunkArray } from "@langchain/core/utils/chunk_array";
export interface HuggingFaceTransformersEmbeddingsParams
extends EmbeddingsParams {
/**
* Model name to use
* Alias for `model`
*/
modelName: string;
/** Model name to use */
model: string;
/**
* Timeout to use when making requests to OpenAI.
*/
timeout?: number;
/**
* The maximum number of documents to embed in a single request.
*/
batchSize?: number;
/**
* Whether to strip new lines from the input text. This is recommended by
* OpenAI, but may not be suitable for all use cases.
*/
stripNewLines?: boolean;
/**
* Optional parameters for the pretrained model.
*/
pretrainedOptions?: PretrainedOptions;
/**
* Optional parameters for the pipeline.
*/
pipelineOptions?: FeatureExtractionPipelineOptions;
}
/**
* @example
* ```typescript
* const model = new HuggingFaceTransformersEmbeddings({
* model: "Xenova/all-MiniLM-L6-v2",
* });
*
* // Embed a single query
* const res = await model.embedQuery(
* "What would be a good company name for a company that makes colorful socks?"
* );
* console.log({ res });
*
* // Embed multiple documents
* const documentRes = await model.embedDocuments(["Hello world", "Bye bye"]);
* console.log({ documentRes });
* ```
*/
export class HuggingFaceTransformersEmbeddings
extends Embeddings
implements HuggingFaceTransformersEmbeddingsParams
{
modelName = "Xenova/all-MiniLM-L6-v2";
model = "Xenova/all-MiniLM-L6-v2";
batchSize = 512;
stripNewLines = true;
timeout?: number;
pretrainedOptions?: PretrainedOptions;
pipelineOptions?: FeatureExtractionPipelineOptions;
private pipelinePromise: Promise<FeatureExtractionPipeline>;
constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
super(fields ?? {});
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
this.timeout = fields?.timeout;
this.pretrainedOptions = fields?.pretrainedOptions ?? {};
this.pipelineOptions = {
pooling: "mean",
normalize: true,
...fields?.pipelineOptions,
};
}
async embedDocuments(texts: string[]): Promise<number[][]> {
const batches = chunkArray(
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
this.batchSize
);
const batchRequests = batches.map((batch) => this.runEmbedding(batch));
const batchResponses = await Promise.all(batchRequests);
const embeddings: number[][] = [];
for (let i = 0; i < batchResponses.length; i += 1) {
const batchResponse = batchResponses[i];
for (let j = 0; j < batchResponse.length; j += 1) {
embeddings.push(batchResponse[j]);
}
}
return embeddings;
}
async embedQuery(text: string): Promise<number[]> {
const data = await this.runEmbedding([
this.stripNewLines ? text.replace(/\n/g, " ") : text,
]);
return data[0];
}
private async runEmbedding(texts: string[]) {
const pipe = await (this.pipelinePromise ??= (
await import("@xenova/transformers")
).pipeline("feature-extraction", this.model, this.pretrainedOptions));
return this.caller.call(async () => {
const output = await pipe(texts, this.pipelineOptions);
return output.tolist();
});
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/zhipuai.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { encodeApiKey } from "../utils/zhipuai.js";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the ZhipuAIEmbeddingsParams class.
*/
export interface ZhipuAIEmbeddingsParams extends EmbeddingsParams {
/**
* Model Name to use
*/
modelName?: "embedding-2";
/**
* ZhipuAI API key to use
*/
apiKey?: string;
/**
* Whether to strip new lines from the input text.
*/
stripNewLines?: boolean;
}
interface EmbeddingData {
embedding: number[];
index: number;
object: string;
}
interface TokenUsage {
completion_tokens: number;
prompt_tokens: number;
total_tokens: number;
}
export interface ZhipuAIEmbeddingsResult {
model: string;
data: EmbeddingData[];
object: string;
usage: TokenUsage;
}
export class ZhipuAIEmbeddings
extends Embeddings
implements ZhipuAIEmbeddingsParams
{
modelName: ZhipuAIEmbeddingsParams["modelName"] = "embedding-2";
apiKey?: string;
stripNewLines = true;
private embeddingsAPIURL = "https://open.bigmodel.cn/api/paas/v4/embeddings";
constructor(fields?: ZhipuAIEmbeddingsParams) {
super(fields ?? {});
this.modelName = fields?.modelName ?? this.modelName;
this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
this.apiKey = fields?.apiKey ?? getEnvironmentVariable("ZHIPUAI_API_KEY");
if (!this.apiKey) {
throw new Error("ZhipuAI API key not found");
}
}
/**
* Private method to make a request to the TogetherAI API to generate
* embeddings. Handles the retry logic and returns the response from the API.
* @param {string} input The input text to embed.
* @returns Promise that resolves to the response from the API.
* @TODO Figure out return type and statically type it.
*/
private async embeddingWithRetry(
input: string
): Promise<ZhipuAIEmbeddingsResult> {
const text = this.stripNewLines ? input.replace(/\n/g, " ") : input;
const body = JSON.stringify({ input: text, model: this.modelName });
const headers = {
Accept: "application/json",
"Content-Type": "application/json",
Authorization: encodeApiKey(this.apiKey),
};
return this.caller.call(async () => {
const fetchResponse = await fetch(this.embeddingsAPIURL, {
method: "POST",
headers,
body,
});
if (fetchResponse.status === 200) {
return fetchResponse.json();
}
throw new Error(
`Error getting embeddings from ZhipuAI. ${JSON.stringify(
await fetchResponse.json(),
null,
2
)}`
);
});
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param {string} text Document to generate an embedding for.
* @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const { data } = await this.embeddingWithRetry(text);
return data[0].embedding;
}
/**
* Method that takes an array of documents as input and returns a promise
* that resolves to a 2D array of embeddings for each document. It calls
* the embedQuery method for each document in the array.
* @param documents Array of documents for which to generate embeddings.
* @returns Promise that resolves to a 2D array of embeddings for each input document.
*/
embedDocuments(documents: string[]): Promise<number[][]> {
return Promise.all(documents.map((doc) => this.embedQuery(doc)));
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/bedrock.int.test.ts
|
/* eslint-disable no-process-env */
/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { expect, test } from "@jest/globals";
import { BedrockRuntimeClient } from "@aws-sdk/client-bedrock-runtime";
import { HNSWLib } from "../../vectorstores/hnswlib.js";
import { BedrockEmbeddings } from "../bedrock.js";
const getClient = () => {
if (
!process.env.BEDROCK_AWS_REGION ||
!process.env.BEDROCK_AWS_ACCESS_KEY_ID ||
!process.env.BEDROCK_AWS_SECRET_ACCESS_KEY
) {
throw new Error("Missing environment variables for AWS");
}
const client = new BedrockRuntimeClient({
region: process.env.BEDROCK_AWS_REGION,
credentials: {
accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
},
});
return client;
};
test("Test BedrockEmbeddings.embedQuery", async () => {
const client = getClient();
const embeddings = new BedrockEmbeddings({
maxRetries: 1,
client,
});
const res = await embeddings.embedQuery("Hello world");
// console.log(res);
expect(typeof res[0]).toBe("number");
});
test("Test BedrockEmbeddings.embedDocuments with passed region and credentials", async () => {
const client = getClient();
const embeddings = new BedrockEmbeddings({
maxRetries: 1,
client,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"we need",
"at least",
"six documents",
"to test pagination",
]);
// console.log(res);
expect(res).toHaveLength(6);
res.forEach((r) => {
expect(typeof r[0]).toBe("number");
});
});
test("Test end to end with HNSWLib", async () => {
const client = getClient();
const vectorStore = await HNSWLib.fromTexts(
["Hello world", "Bye bye", "hello nice world"],
[{ id: 2 }, { id: 1 }, { id: 3 }],
new BedrockEmbeddings({
maxRetries: 1,
client,
})
);
expect(vectorStore.index?.getCurrentCount()).toBe(3);
const resultOne = await vectorStore.similaritySearch("hello world", 1);
const resultOneMetadatas = resultOne.map(({ metadata }) => metadata);
expect(resultOneMetadatas).toEqual([{ id: 2 }]);
const resultTwo = await vectorStore.similaritySearch("hello world", 2);
const resultTwoMetadatas = resultTwo.map(({ metadata }) => metadata);
expect(resultTwoMetadatas).toEqual([{ id: 2 }, { id: 3 }]);
const resultThree = await vectorStore.similaritySearch("hello world", 3);
const resultThreeMetadatas = resultThree.map(({ metadata }) => metadata);
expect(resultThreeMetadatas).toEqual([{ id: 2 }, { id: 3 }, { id: 1 }]);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/ollama.int.test.ts
|
import { test, expect } from "@jest/globals";
import { OllamaEmbeddings } from "../ollama.js";
test.skip("Test OllamaEmbeddings.embedQuery", async () => {
const embeddings = new OllamaEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test OllamaEmbeddings.embedDocuments", async () => {
const embeddings = new OllamaEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/hf_transformers.int.test.ts
|
import { test, expect } from "@jest/globals";
import { HuggingFaceTransformersEmbeddings } from "../hf_transformers.js";
import { HNSWLib } from "../../vectorstores/hnswlib.js";
test("HuggingFaceTransformersEmbeddings", async () => {
const embeddings = new HuggingFaceTransformersEmbeddings();
const texts = [
"Hello world!",
"Hello bad world!",
"Hello nice world!",
"Hello good world!",
"1 + 1 = 2",
"1 + 1 = 3",
];
const queryEmbedding = await embeddings.embedQuery(texts[0]);
expect(queryEmbedding).toHaveLength(384);
expect(typeof queryEmbedding[0]).toBe("number");
const store = await HNSWLib.fromTexts(texts, {}, embeddings);
expect(await store.similaritySearch(texts[4], 2)).toMatchInlineSnapshot(`
[
Document {
"metadata": {},
"pageContent": "1 + 1 = 2",
},
Document {
"metadata": {},
"pageContent": "1 + 1 = 3",
},
]
`);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/llama_cpp.int.test.ts
|
/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { test, expect } from "@jest/globals";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { LlamaCppEmbeddings } from "../llama_cpp.js";
const llamaPath = getEnvironmentVariable("LLAMA_PATH")!;
test.skip("Test LlamaCppEmbeddings.embedQuery", async () => {
const embeddings = await LlamaCppEmbeddings.initialize({
modelPath: llamaPath,
});
const res = await embeddings.embedQuery("Hello Llama");
expect(typeof res[0]).toBe("number");
});
test.skip("Test LlamaCppEmbeddings.embedDocuments", async () => {
const embeddings = await LlamaCppEmbeddings.initialize({
modelPath: llamaPath,
});
const res = await embeddings.embedDocuments(["Hello Llama", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test LlamaCppEmbeddings concurrency", async () => {
const embeddings = await LlamaCppEmbeddings.initialize({
modelPath: llamaPath,
batchSize: 1,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello Llama",
"Bye bye",
"Hello Panda",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/minimax.int.test.ts
|
import { test, expect } from "@jest/globals";
import { MinimaxEmbeddings } from "../minimax.js";
test.skip("Test MinimaxEmbeddings.embedQuery", async () => {
const embeddings = new MinimaxEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test MinimaxEmbeddings.embedDocuments", async () => {
const embeddings = new MinimaxEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test MinimaxEmbeddings concurrency", async () => {
const embeddings = new MinimaxEmbeddings({
batchSize: 1,
maxConcurrency: 2,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/hf.int.test.ts
|
import { test, expect } from "@jest/globals";
import { HuggingFaceInferenceEmbeddings } from "../hf.js";
import { HNSWLib } from "../../vectorstores/hnswlib.js";
test("HuggingFaceInferenceEmbeddings", async () => {
const embeddings = new HuggingFaceInferenceEmbeddings();
const texts = [
"Hello world!",
"Hello bad world!",
"Hello nice world!",
"Hello good world!",
"1 + 1 = 2",
"1 + 1 = 3",
];
const queryEmbedding = await embeddings.embedQuery(texts[0]);
expect(queryEmbedding).toHaveLength(768);
expect(typeof queryEmbedding[0]).toBe("number");
const store = await HNSWLib.fromTexts(texts, {}, embeddings);
expect(await store.similaritySearch(texts[4], 2)).toMatchInlineSnapshot(`
[
Document {
"metadata": {},
"pageContent": "1 + 1 = 2",
},
Document {
"metadata": {},
"pageContent": "1 + 1 = 3",
},
]
`);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/ibm.test.ts
|
/* eslint-disable no-process-env */
/* eslint-disable @typescript-eslint/no-explicit-any */
import { testProperties } from "../../llms/tests/ibm.test.js";
import { WatsonxEmbeddings, WatsonxInputEmbeddings } from "../ibm.js";
const fakeAuthProp = {
watsonxAIAuthType: "iam",
watsonxAIApikey: "fake_key",
};
describe("Embeddings unit tests", () => {
describe("Positive tests", () => {
test("Basic properties", () => {
const testProps = {
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID || "testString",
};
const instance = new WatsonxEmbeddings({ ...testProps, ...fakeAuthProp });
testProperties(instance, testProps);
});
test("Basic properties", () => {
const testProps: WatsonxInputEmbeddings = {
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID || "testString",
truncateInputTokens: 10,
maxConcurrency: 2,
maxRetries: 2,
};
const instance = new WatsonxEmbeddings({ ...testProps, ...fakeAuthProp });
testProperties(instance, testProps);
});
});
describe("Negative tests", () => {
test("Missing id", async () => {
const testProps = {
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
};
expect(
() =>
new WatsonxEmbeddings({
...testProps,
...fakeAuthProp,
})
).toThrowError();
});
test("Missing other props", async () => {
// @ts-expect-error Intentionally passing wrong value
const testPropsProjectId: WatsonxInputLLM = {
projectId: process.env.WATSONX_AI_PROJECT_ID || "testString",
};
expect(
() =>
new WatsonxEmbeddings({
...testPropsProjectId,
})
).toThrowError();
// @ts-expect-error //Intentionally passing wrong value
const testPropsServiceUrl: WatsonxInputLLM = {
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
};
expect(
() =>
new WatsonxEmbeddings({
...testPropsServiceUrl,
})
).toThrowError();
const testPropsVersion = {
version: "2024-05-31",
};
expect(
() =>
new WatsonxEmbeddings({
// @ts-expect-error Intentionally passing wrong props
testPropsVersion,
})
).toThrowError();
});
test("Passing more than one id", async () => {
const testProps = {
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID || "testString",
spaceId: process.env.WATSONX_AI_PROJECT_ID || "testString",
};
expect(
() =>
new WatsonxEmbeddings({
...testProps,
...fakeAuthProp,
})
).toThrowError();
});
test("Invalid properties", () => {
const testProps = {
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID || "testString",
};
const notExTestProps = {
notExisting: 12,
notExObj: {
notExProp: 12,
},
};
const instance = new WatsonxEmbeddings({
...testProps,
...notExTestProps,
...fakeAuthProp,
});
testProperties(instance, testProps, notExTestProps);
});
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/cohere.int.test.ts
|
import { test, expect } from "@jest/globals";
import { CohereEmbeddings } from "../cohere.js";
test("Test CohereEmbeddings.embedQuery", async () => {
const embeddings = new CohereEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test("Test CohereEmbeddings.embedDocuments", async () => {
const embeddings = new CohereEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test("Test CohereEmbeddings concurrency", async () => {
const embeddings = new CohereEmbeddings({
batchSize: 1,
maxConcurrency: 2,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/jina.int.test.ts
|
import { test, expect } from "@jest/globals";
import { JinaEmbeddings } from "../jina.js";
test("Test JinaEmbeddings.embedQuery", async () => {
const embeddings = new JinaEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test("Test JinaEmbeddings.embedDocuments", async () => {
const embeddings = new JinaEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test("Test JinaEmbeddings concurrency", async () => {
const embeddings = new JinaEmbeddings();
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"we need",
"at least",
"six documents",
"to test concurrency",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
test("Test JinaEmbeddings.embedImages", async () => {
const embeddings = new JinaEmbeddings();
const res = await embeddings.embedImages([
"https://avatars.githubusercontent.com/u/126733545?v=4",
]);
expect(typeof res[0][0]).toBe("number");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/deepinfra.int.test.ts
|
import { test, expect } from "@jest/globals";
import { DeepInfraEmbeddings } from "../deepinfra.js";
test("Test DeepInfraEmbeddings.embedQuery", async () => {
const embeddings = new DeepInfraEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test("Test DeepInfraEmbeddings.embedDocuments", async () => {
const embeddings = new DeepInfraEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test("Test DeepInfraEmbeddings concurrency", async () => {
const embeddings = new DeepInfraEmbeddings({
batchSize: 1,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"we need",
"at least",
"six documents",
"to test concurrency",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/premai.int.test.ts
|
import { describe, test, expect } from "@jest/globals";
import { PremEmbeddings } from "../premai.js";
describe("EmbeddingsPrem", () => {
test.skip("Test embedQuery", async () => {
const client = new PremEmbeddings({ model: "@cf/baai/bge-small-en-v1.5" });
const res = await client.embedQuery("Hello world");
// console.log(res);
expect(typeof res[0]).toBe("number");
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/fireworks.int.test.ts
|
import { test, expect } from "@jest/globals";
import { FireworksEmbeddings } from "../fireworks.js";
test.skip("Test FireworksEmbeddings.embedQuery", async () => {
const embeddings = new FireworksEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test FireworksEmbeddings.embedDocuments", async () => {
const embeddings = new FireworksEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test FireworksEmbeddings concurrency", async () => {
const embeddings = new FireworksEmbeddings({
batchSize: 1,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/togetherai.int.test.ts
|
import { test, expect } from "@jest/globals";
import { TogetherAIEmbeddings } from "../togetherai.js";
test.skip("Test TogetherAIEmbeddings.embedQuery", async () => {
const embeddings = new TogetherAIEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
expect(res.length).toBe(768);
});
test.skip("Test TogetherAIEmbeddings.embedDocuments", async () => {
const embeddings = new TogetherAIEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
expect(res[0].length).toBe(768);
expect(res[1].length).toBe(768);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/tensorflow.int.test.ts
|
import { test, expect } from "@jest/globals";
import "@tensorflow/tfjs-backend-cpu";
import { TensorFlowEmbeddings } from "../tensorflow.js";
import { HNSWLib } from "../../vectorstores/hnswlib.js";
test("TensorflowEmbeddings", async () => {
const embeddings = new TensorFlowEmbeddings();
const texts = [
"Hello world!",
"Hello bad world!",
"Hello nice world!",
"Hello good world!",
"1 + 1 = 2",
"1 + 1 = 3",
];
const queryEmbedding = await embeddings.embedQuery(texts[0]);
expect(queryEmbedding).toHaveLength(512);
expect(typeof queryEmbedding[0]).toBe("number");
const store = await HNSWLib.fromTexts(texts, {}, embeddings);
expect(await store.similaritySearch(texts[4], 2)).toMatchInlineSnapshot(`
[
Document {
"metadata": {},
"pageContent": "1 + 1 = 2",
},
Document {
"metadata": {},
"pageContent": "1 + 1 = 3",
},
]
`);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/voyage.int.test.ts
|
import { test, expect } from "@jest/globals";
import { VoyageEmbeddings } from "../voyage.js";
test.skip("Test VoyageEmbeddings.embedQuery with input_type", async () => {
const embeddings = new VoyageEmbeddings({ inputType: "document" });
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test VoyageEmbeddings.embedDocuments with input_type", async () => {
const embeddings = new VoyageEmbeddings({ inputType: "document" });
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test VoyageEmbeddings concurrency with input_type", async () => {
const embeddings = new VoyageEmbeddings({
batchSize: 1,
maxConcurrency: 2,
inputType: "document",
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
test.skip("Test VoyageEmbeddings without input_type", async () => {
const embeddings = new VoyageEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/baidu_qianfan.int.test.ts
|
import { test, expect } from "@jest/globals";
import { BaiduQianfanEmbeddings } from "../baidu_qianfan.js";
test.skip("Test BaiduQianfanEmbeddings.embedQuery", async () => {
const embeddings = new BaiduQianfanEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test BaiduQianfanEmbeddings.embedDocuments", async () => {
const embeddings = new BaiduQianfanEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test BaiduQianfanEmbeddings concurrency", async () => {
const embeddings = new BaiduQianfanEmbeddings({
batchSize: 1,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/tencent_hunyuan.test.ts
|
import { test, expect } from "@jest/globals";
import { TencentHunyuanEmbeddings } from "../tencent_hunyuan/index.js";
test.skip("Test TencentHunyuanEmbeddings.embedQuery", async () => {
const embeddings = new TencentHunyuanEmbeddings();
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test.skip("Test TencentHunyuanEmbeddings.embedDocuments", async () => {
const embeddings = new TencentHunyuanEmbeddings();
const res = await embeddings.embedDocuments(["Hello world", "Bye bye"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test.skip("Test TencentHunyuanEmbeddings concurrency", async () => {
const embeddings = new TencentHunyuanEmbeddings();
const res = await embeddings.embedDocuments([
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
"Hello world",
"Bye bye",
]);
expect(res).toHaveLength(6);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tests/ibm.int.test.ts
|
/* eslint-disable no-process-env */
import { test } from "@jest/globals";
import { WatsonxEmbeddings } from "../ibm.js";
describe("Test embeddings", () => {
test("embedQuery method", async () => {
const embeddings = new WatsonxEmbeddings({
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID,
});
const res = await embeddings.embedQuery("Hello world");
expect(typeof res[0]).toBe("number");
});
test("embedDocuments", async () => {
const embeddings = new WatsonxEmbeddings({
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID,
});
const res = await embeddings.embedDocuments(["Hello world", "Bye world"]);
expect(res).toHaveLength(2);
expect(typeof res[0][0]).toBe("number");
expect(typeof res[1][0]).toBe("number");
});
test("Concurrency", async () => {
const embeddings = new WatsonxEmbeddings({
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID,
maxConcurrency: 4,
});
const res = await embeddings.embedDocuments([
"Hello world",
"Bye world",
"Hello world",
"Bye world",
"Hello world",
"Bye world",
"Hello world",
"Bye world",
]);
expect(res).toHaveLength(8);
expect(res.find((embedding) => typeof embedding[0] !== "number")).toBe(
undefined
);
});
test("List models", async () => {
const embeddings = new WatsonxEmbeddings({
model: "ibm/slate-125m-english-rtrvr",
version: "2024-05-31",
serviceUrl: process.env.WATSONX_AI_SERVICE_URL as string,
projectId: process.env.WATSONX_AI_PROJECT_ID,
maxConcurrency: 4,
});
const res = await embeddings.listModels();
expect(res?.length).toBeGreaterThan(0);
if (res) expect(typeof res[0]).toBe("string");
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tencent_hunyuan/web.ts
|
import { sign } from "../../utils/tencent_hunyuan/web.js";
import {
TencentHunyuanEmbeddings as BaseTencentHunyuanEmbeddings,
TencentHunyuanEmbeddingsParams,
} from "./base.js";
/**
* Class for generating embeddings using the Tencent Hunyuan API.
*/
export class TencentHunyuanEmbeddings extends BaseTencentHunyuanEmbeddings {
constructor(fields?: TencentHunyuanEmbeddingsParams) {
super({ ...fields, sign } ?? { sign });
}
}
export { type TencentHunyuanEmbeddingsParams } from "./base.js";
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tencent_hunyuan/index.ts
|
import { sign } from "../../utils/tencent_hunyuan/index.js";
import {
TencentHunyuanEmbeddings as BaseTencentHunyuanEmbeddings,
TencentHunyuanEmbeddingsParams,
} from "./base.js";
/**
* Class for generating embeddings using the Tencent Hunyuan API.
*/
export class TencentHunyuanEmbeddings extends BaseTencentHunyuanEmbeddings {
constructor(fields?: TencentHunyuanEmbeddingsParams) {
super({ ...fields, sign } ?? { sign });
}
}
export { type TencentHunyuanEmbeddingsParams } from "./base.js";
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings
|
lc_public_repos/langchainjs/libs/langchain-community/src/embeddings/tencent_hunyuan/base.ts
|
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
import { sign } from "../../utils/tencent_hunyuan/common.js";
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the TencentHunyuanEmbeddingsParams class.
*/
export interface TencentHunyuanEmbeddingsParams extends EmbeddingsParams {
/**
* Tencent Cloud API Host.
* @default "hunyuan.tencentcloudapi.com"
*/
host?: string;
/**
* SecretID to use when making requests, can be obtained from https://console.cloud.tencent.com/cam/capi.
* Defaults to the value of `TENCENT_SECRET_ID` environment variable.
*/
tencentSecretId?: string;
/**
* Secret key to use when making requests, can be obtained from https://console.cloud.tencent.com/cam/capi.
* Defaults to the value of `TENCENT_SECRET_KEY` environment variable.
*/
tencentSecretKey?: string;
}
/**
* Interface that extends EmbeddingsParams and defines additional
* parameters specific to the TencentHunyuanEmbeddingsParams class.
*/
interface TencentHunyuanEmbeddingsParamsWithSign
extends TencentHunyuanEmbeddingsParams {
/**
* Tencent Cloud API v3 sign method.
*/
sign: sign;
}
/**
* Interface representing the embedding data.
* See https://cloud.tencent.com/document/api/1729/101838#EmbeddingData.
*/
interface EmbeddingData {
Embedding: number[];
Index: number;
Object: string;
}
/**
* Interface representing the usage of tokens in text embedding.
* See https://cloud.tencent.com/document/api/1729/101838#EmbeddingUsage.
*/
interface EmbeddingUsage {
TotalTokens: number;
PromptTokens: number;
CompletionTokens?: number;
}
/**
* Interface representing a error response from Tencent Hunyuan embedding.
* See https://cloud.tencent.com/document/product/1729/102832
*/
interface Error {
Code: string;
Message: string;
}
/**
* Interface representing a response from Tencent Hunyuan embedding.
* See https://cloud.tencent.com/document/product/1729/102832
*/
interface GetEmbeddingResponse {
Response: {
Error?: Error;
Data: EmbeddingData[];
Usage: EmbeddingUsage;
RequestId: string;
};
}
/**
* Class for generating embeddings using the Tencent Hunyuan API.
*/
export class TencentHunyuanEmbeddings
extends Embeddings
implements TencentHunyuanEmbeddingsParams
{
tencentSecretId?: string;
tencentSecretKey?: string;
host = "hunyuan.tencentcloudapi.com";
sign: sign;
constructor(fields?: TencentHunyuanEmbeddingsParamsWithSign) {
super(fields ?? {});
this.tencentSecretId =
fields?.tencentSecretId ?? getEnvironmentVariable("TENCENT_SECRET_ID");
if (!this.tencentSecretId) {
throw new Error("Tencent SecretID not found");
}
this.tencentSecretKey =
fields?.tencentSecretKey ?? getEnvironmentVariable("TENCENT_SECRET_KEY");
if (!this.tencentSecretKey) {
throw new Error("Tencent SecretKey not found");
}
this.host = fields?.host ?? this.host;
if (!fields?.sign) {
throw new Error("Sign method undefined");
}
this.sign = fields?.sign;
}
/**
* Private method to make a request to the TogetherAI API to generate
* embeddings. Handles the retry logic and returns the response from the API.
* @param {string} input The input text to embed.
* @returns Promise that resolves to the response from the API.
* @TODO Figure out return type and statically type it.
*/
private async embeddingWithRetry(
input: string
): Promise<GetEmbeddingResponse> {
const request = { Input: input };
const timestamp = Math.trunc(Date.now() / 1000);
const headers = {
"Content-Type": "application/json",
"X-TC-Action": "GetEmbedding",
"X-TC-Version": "2023-09-01",
"X-TC-Timestamp": timestamp.toString(),
Authorization: "",
};
headers.Authorization = this.sign(
this.host,
request,
timestamp,
this.tencentSecretId ?? "",
this.tencentSecretKey ?? "",
headers
);
return this.caller.call(async () => {
const response = await fetch(`https://${this.host}`, {
headers,
method: "POST",
body: JSON.stringify(request),
});
if (response.ok) {
return response.json();
}
throw new Error(
`Error getting embeddings from Tencent Hunyuan. ${JSON.stringify(
await response.json(),
null,
2
)}`
);
});
}
/**
* Method to generate an embedding for a single document. Calls the
* embeddingWithRetry method with the document as the input.
* @param {string} text Document to generate an embedding for.
* @returns {Promise<number[]>} Promise that resolves to an embedding for the document.
*/
async embedQuery(text: string): Promise<number[]> {
const { Response } = await this.embeddingWithRetry(text);
if (Response?.Error?.Message) {
throw new Error(`[${Response.RequestId}] ${Response.Error.Message}`);
}
return Response.Data[0].Embedding;
}
/**
* Method that takes an array of documents as input and returns a promise
* that resolves to a 2D array of embeddings for each document. It calls
* the embedQuery method for each document in the array.
* @param documents Array of documents for which to generate embeddings.
* @returns Promise that resolves to a 2D array of embeddings for each input document.
*/
embedDocuments(documents: string[]): Promise<number[][]> {
return Promise.all(documents.map((doc) => this.embedQuery(doc)));
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/googlemakersuitehub.ts
|
import type { protos } from "@google-ai/generativelanguage";
import type { google } from "@google-ai/generativelanguage/build/protos/protos.js";
import { GoogleAuth, GoogleAuthOptions } from "google-auth-library";
import type { BaseLanguageModel } from "@langchain/core/language_models/base";
import { Runnable } from "@langchain/core/runnables";
import { PromptTemplate } from "@langchain/core/prompts";
import {
AsyncCaller,
AsyncCallerCallOptions,
} from "@langchain/core/utils/async_caller";
import { ChatGooglePaLM } from "./chat_models.js";
import { GooglePaLM } from "./llms.js";
import {
GoogleAbstractedClientOpsMethod,
GoogleResponse,
GoogleVertexAIConnectionParams,
} from "../../../types/googlevertexai-types.js";
import { GoogleConnection } from "../../../utils/googlevertexai-connection.js";
/**
* Configuration that allows us to load or pull a prompt that has been created
* by the Google MakerSuite site and saved in Google Drive.
*
* There is a simple in-memory cache of these prompts that is refreshed
* after the cacheTimeout specified in the configuration.
*/
export interface MakerSuiteHubConfig {
/**
* How long, in milliseconds, before a prompt is assumed stale and should
* be refreshed from the copy in Google Drive.
*/
cacheTimeout: number;
caller?: AsyncCaller;
}
type MakerSuitePromptType = "text" | "data" | "chat";
export interface MakerSuitePromptVariable {
variableId: string;
displayName: string;
}
export interface MakerSuiteRunSettings {
temperature?: number;
model: string;
candidateCount?: number;
topP?: number;
topK?: number;
maxOutputTokens: number;
safetySettings?: protos.google.ai.generativelanguage.v1beta2.ISafetySetting[];
}
export interface MakerSuiteTextPromptData {
textPrompt: {
value?: string;
variables?: MakerSuitePromptVariable[];
};
runSettings?: MakerSuiteRunSettings;
testExamples?: unknown;
}
export interface MakerSuiteDataPromptColumn {
columnId: string;
displayName: string;
isInput?: boolean;
}
export interface MakerSuiteDataPromptRow {
rowId: string;
columnBindings: Record<string, string>;
}
export interface MakerSuiteDataPromptData {
dataPrompt: {
preamble: string;
columns: MakerSuiteDataPromptColumn[];
rows: MakerSuiteDataPromptRow[];
rowsUsed: string[];
};
runSettings?: MakerSuiteRunSettings;
testExamples?: unknown;
}
export interface MakerSuiteChatExchange {
request?: string;
response?: string;
source: string;
id: string;
}
export interface MakerSuiteChatPromptData {
multiturnPrompt: {
preamble: string;
primingExchanges: MakerSuiteChatExchange[];
sessions: {
sessionExchanges: MakerSuiteChatExchange[];
}[];
};
runSettings?: MakerSuiteRunSettings;
}
/**
* These are the possible formats that the JSON generated by MakerSuite
* and saved in Google Drive can be.
*/
export type MakerSuitePromptData =
| MakerSuiteTextPromptData
| MakerSuiteDataPromptData
| MakerSuiteChatPromptData;
/**
* A class that represents the Prompt that has been created by MakerSuite
* and loaded from Google Drive. It exposes methods to turn this prompt
* into a Template, a Model, and into a chain consisting of these two elements.
* In general, this class should be created by the MakerSuiteHub class and
* not instantiated manually.
*/
export class MakerSuitePrompt {
promptType: MakerSuitePromptType;
promptData: MakerSuitePromptData;
constructor(promptData: MakerSuitePromptData) {
this.promptData = promptData;
this._determinePromptType();
}
_determinePromptType() {
if (Object.hasOwn(this.promptData, "textPrompt")) {
this.promptType = "text";
} else if (Object.hasOwn(this.promptData, "dataPrompt")) {
this.promptType = "data";
} else if (Object.hasOwn(this.promptData, "multiturnPrompt")) {
this.promptType = "chat";
} else {
const error = new Error("Unable to identify prompt type.");
// eslint-disable-next-line @typescript-eslint/no-explicit-any
(error as any).promptData = this.promptData;
throw error;
}
}
_promptValueText(): string {
return (
(this.promptData as MakerSuiteTextPromptData)?.textPrompt?.value ?? ""
);
}
_promptValueData(): string {
const promptData: MakerSuiteDataPromptData = this
.promptData as MakerSuiteDataPromptData;
const dataPrompt = promptData?.dataPrompt;
let prompt = `${dataPrompt?.preamble}\n` || "";
dataPrompt?.rows.forEach((row) => {
// Add the data for each row, as long as it is listed as used
if (dataPrompt?.rowsUsed.includes(row.rowId)) {
// Add each input column
dataPrompt?.columns.forEach((column) => {
if (column.isInput) {
prompt += `${column.displayName} ${
row.columnBindings[column.columnId]
}\n`;
}
});
// Add each output column
dataPrompt?.columns.forEach((column) => {
if (!column.isInput) {
prompt += `${column.displayName} ${
row.columnBindings[column.columnId]
}\n`;
}
});
}
});
// Add the input column prompts
dataPrompt?.columns.forEach((column) => {
if (column.isInput) {
prompt += `${column.displayName} {${column.displayName.replace(
":",
""
)}}\n`;
}
});
// Add just the first output column
const firstOutput = dataPrompt?.columns.find((column) => !column.isInput);
prompt += `${firstOutput?.displayName} `;
return prompt;
}
_promptValueChat(): string {
return (
(this.promptData as MakerSuiteChatPromptData)?.multiturnPrompt
?.preamble ?? ""
);
}
_promptValue(): string {
switch (this.promptType) {
case "text":
return this._promptValueText();
case "data":
return this._promptValueData();
case "chat":
return this._promptValueChat();
default:
throw new Error(`Invalid promptType: ${this.promptType}`);
}
}
/**
* Create a template from the prompt, including any "test input" specified
* in MakerSuite as a template parameter.
*/
toTemplate(): PromptTemplate {
const value = this._promptValue();
const promptString = value.replaceAll(/{{.*:(.+):.*}}/g, "{$1}");
return PromptTemplate.fromTemplate(promptString);
}
_modelName(): string {
let ret = this.promptData?.runSettings?.model;
if (!ret) {
ret =
this.promptType === "chat"
? "models/chat-bison-001"
: "models/text-bison-001";
}
return ret;
}
_examples(): google.ai.generativelanguage.v1beta2.IExample[] {
const promptData: MakerSuiteChatPromptData = this
.promptData as MakerSuiteChatPromptData;
const ret: google.ai.generativelanguage.v1beta2.IExample[] =
promptData?.multiturnPrompt?.primingExchanges
.map((exchange) => {
const example: google.ai.generativelanguage.v1beta2.IExample = {};
if (exchange?.request) {
example.input = {
content: exchange.request,
};
}
if (exchange?.response) {
example.output = {
content: exchange.response,
};
}
return example;
})
.filter((value) => Object.keys(value).length);
return ret;
}
/**
* Create a model from the prompt with all the parameters (model name,
* temperature, etc) set as they were in MakerSuite.
*/
toModel(): BaseLanguageModel {
const modelName = this._modelName();
const modelSettings = {
modelName,
...this.promptData?.runSettings,
};
if (this.promptType === "chat") {
const examples = this._examples();
return new ChatGooglePaLM({
examples,
...modelSettings,
});
} else {
return new GooglePaLM(modelSettings);
}
}
/**
* Create a RunnableSequence based on the template and model that can
* be created from this prompt. The template will have parameters available
* based on the "test input" that was set in MakerSuite, and the model
* will have the parameters (model name, temperature, etc) from those in
* MakerSuite.
*/
toChain() {
return this.toTemplate().pipe(this.toModel() as Runnable);
}
}
interface DriveFileReadParams
extends GoogleVertexAIConnectionParams<GoogleAuthOptions> {
fileId: string;
}
interface DriveCallOptions extends AsyncCallerCallOptions {
// Empty, I think
}
interface DriveFileMakerSuiteResponse extends GoogleResponse {
data: MakerSuitePromptData;
}
export class DriveFileReadConnection
extends GoogleConnection<DriveCallOptions, DriveFileMakerSuiteResponse>
implements DriveFileReadParams
{
endpoint: string;
apiVersion: string;
fileId: string;
constructor(fields: DriveFileReadParams, caller: AsyncCaller) {
super(
caller,
new GoogleAuth({
scopes: "https://www.googleapis.com/auth/drive.readonly",
...fields.authOptions,
})
);
this.endpoint = fields.endpoint ?? "www.googleapis.com";
this.apiVersion = fields.apiVersion ?? "v3";
this.fileId = fields.fileId;
}
async buildUrl(): Promise<string> {
return `https://${this.endpoint}/drive/${this.apiVersion}/files/${this.fileId}?alt=media`;
}
buildMethod(): GoogleAbstractedClientOpsMethod {
return "GET";
}
async request(
options?: DriveCallOptions
): Promise<DriveFileMakerSuiteResponse> {
return this._request(undefined, options ?? {});
}
}
export interface CacheEntry {
updated: number;
prompt: MakerSuitePrompt;
}
/**
* A class allowing access to MakerSuite prompts that have been saved in
* Google Drive.
* MakerSuite prompts are pulled based on their Google Drive ID (which is available
* from Google Drive or as part of the URL when the prompt is open in MakerSuite).
* There is a basic cache that will store the prompt in memory for a time specified
* in milliseconds. This defaults to 0, indicating the prompt should always be
* pulled from Google Drive.
*/
export class MakerSuiteHub {
cache: Record<string, CacheEntry> = {};
cacheTimeout: number;
caller: AsyncCaller;
constructor(config?: MakerSuiteHubConfig) {
this.cacheTimeout = config?.cacheTimeout ?? 0;
this.caller = config?.caller ?? new AsyncCaller({});
}
/**
* Is the current cache entry valid, or does it need to be refreshed.
* It will need to be refreshed under any of the following conditions:
* - It does not currently exist in the cache
* - The cacheTimeout is 0
* - The cache was last updated longer ago than the cacheTimeout allows
* @param entry - The cache entry, including when this prompt was last refreshed
*/
isValid(entry: CacheEntry): boolean {
// If we don't have a record, it can't be valid
// And if the cache timeout is 0, we will always refresh, so the cache is invalid
if (!entry || this.cacheTimeout === 0) {
return false;
}
const now = Date.now();
const expiration = entry.updated + this.cacheTimeout;
return expiration > now;
}
/**
* Get a MakerSuitePrompt that is specified by the Google Drive ID.
* This will always be loaded from Google Drive.
* @param id
* @return A MakerSuitePrompt which can be used to create a template, model, or chain.
*/
async forcePull(id: string): Promise<MakerSuitePrompt> {
const fields: DriveFileReadParams = {
fileId: id,
};
const connection = new DriveFileReadConnection(fields, this.caller);
const result = await connection.request();
const ret = new MakerSuitePrompt(result.data);
this.cache[id] = {
prompt: ret,
updated: Date.now(),
};
return ret;
}
/**
* Get a MakerSuitePrompt that is specified by the Google Drive ID. This may be
* loaded from Google Drive or, if there is a valid copy in the cache, the cached
* copy will be returned.
* @param id
* @return A MakerSuitePrompt which can be used to create a template, model, or chain.
*/
async pull(id: string): Promise<MakerSuitePrompt> {
const entry = this.cache[id];
const ret = this.isValid(entry) ? entry.prompt : await this.forcePull(id);
return ret;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/llms.ts
|
import { TextServiceClient, protos } from "@google-ai/generativelanguage";
import { GoogleAuth } from "google-auth-library";
import { type BaseLLMParams, LLM } from "@langchain/core/language_models/llms";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
/**
* @deprecated - Deprecated by Google. Will be removed in 0.3.0
*
* Input for Text generation for Google Palm
*/
export interface GooglePaLMTextInput extends BaseLLMParams {
/**
* Model Name to use
*
* Alias for `model`
*
* Note: The format must follow the pattern - `models/{model}`
*/
modelName?: string;
/**
* Model Name to use
*
* Note: The format must follow the pattern - `models/{model}`
*/
model?: string;
/**
* Controls the randomness of the output.
*
* Values can range from [0.0,1.0], inclusive. A value closer to 1.0
* will produce responses that are more varied and creative, while
* a value closer to 0.0 will typically result in more straightforward
* responses from the model.
*
* Note: The default value varies by model
*/
temperature?: number;
/**
* Maximum number of tokens to generate in the completion.
*/
maxOutputTokens?: number;
/**
* Top-p changes how the model selects tokens for output.
*
* Tokens are selected from most probable to least until the sum
* of their probabilities equals the top-p value.
*
* For example, if tokens A, B, and C have a probability of
* .3, .2, and .1 and the top-p value is .5, then the model will
* select either A or B as the next token (using temperature).
*
* Note: The default value varies by model
*/
topP?: number;
/**
* Top-k changes how the model selects tokens for output.
*
* A top-k of 1 means the selected token is the most probable among
* all tokens in the model’s vocabulary (also called greedy decoding),
* while a top-k of 3 means that the next token is selected from
* among the 3 most probable tokens (using temperature).
*
* Note: The default value varies by model
*/
topK?: number;
/**
* The set of character sequences (up to 5) that will stop output generation.
* If specified, the API will stop at the first appearance of a stop
* sequence.
*
* Note: The stop sequence will not be included as part of the response.
*/
stopSequences?: string[];
/**
* A list of unique `SafetySetting` instances for blocking unsafe content. The API will block
* any prompts and responses that fail to meet the thresholds set by these settings. If there
* is no `SafetySetting` for a given `SafetyCategory` provided in the list, the API will use
* the default safety setting for that category.
*/
safetySettings?: protos.google.ai.generativelanguage.v1beta2.ISafetySetting[];
/**
* Google Palm API key to use
*/
apiKey?: string;
}
/**
* @deprecated - Deprecated by Google. Will be removed in 0.3.0
*
* Google Palm 2 Language Model Wrapper to generate texts
*/
export class GooglePaLM extends LLM implements GooglePaLMTextInput {
lc_serializable = true;
get lc_secrets(): { [key: string]: string } | undefined {
return {
apiKey: "GOOGLE_PALM_API_KEY",
};
}
modelName = "models/text-bison-001";
model = "models/text-bison-001";
temperature?: number; // default value chosen based on model
maxOutputTokens?: number; // defaults to 64
topP?: number; // default value chosen based on model
topK?: number; // default value chosen based on model
stopSequences: string[] = [];
safetySettings?: protos.google.ai.generativelanguage.v1beta2.ISafetySetting[]; // default safety setting for that category
apiKey?: string;
private client: TextServiceClient;
constructor(fields?: GooglePaLMTextInput) {
super(fields ?? {});
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
this.temperature = fields?.temperature ?? this.temperature;
if (this.temperature && (this.temperature < 0 || this.temperature > 1)) {
throw new Error("`temperature` must be in the range of [0.0,1.0]");
}
this.maxOutputTokens = fields?.maxOutputTokens ?? this.maxOutputTokens;
if (this.maxOutputTokens && this.maxOutputTokens < 0) {
throw new Error("`maxOutputTokens` must be a positive integer");
}
this.topP = fields?.topP ?? this.topP;
if (this.topP && this.topP < 0) {
throw new Error("`topP` must be a positive integer");
}
if (this.topP && this.topP > 1) {
throw new Error("Google PaLM `topP` must in the range of [0,1]");
}
this.topK = fields?.topK ?? this.topK;
if (this.topK && this.topK < 0) {
throw new Error("`topK` must be a positive integer");
}
this.stopSequences = fields?.stopSequences ?? this.stopSequences;
this.safetySettings = fields?.safetySettings ?? this.safetySettings;
if (this.safetySettings && this.safetySettings.length > 0) {
const safetySettingsSet = new Set(
this.safetySettings.map((s) => s.category)
);
if (safetySettingsSet.size !== this.safetySettings.length) {
throw new Error(
"The categories in `safetySettings` array must be unique"
);
}
}
this.apiKey =
fields?.apiKey ?? getEnvironmentVariable("GOOGLE_PALM_API_KEY");
if (!this.apiKey) {
throw new Error(
"Please set an API key for Google Palm 2 in the environment variable GOOGLE_PALM_API_KEY or in the `apiKey` field of the GooglePalm constructor"
);
}
this.client = new TextServiceClient({
authClient: new GoogleAuth().fromAPIKey(this.apiKey),
});
}
_llmType(): string {
return "googlepalm";
}
async _call(
prompt: string,
options: this["ParsedCallOptions"]
): Promise<string> {
const res = await this.caller.callWithOptions(
{ signal: options.signal },
this._generateText.bind(this),
prompt
);
return res ?? "";
}
protected async _generateText(
prompt: string
): Promise<string | null | undefined> {
const res = await this.client.generateText({
model: this.model,
temperature: this.temperature,
candidateCount: 1,
topK: this.topK,
topP: this.topP,
maxOutputTokens: this.maxOutputTokens,
stopSequences: this.stopSequences,
safetySettings: this.safetySettings,
prompt: {
text: prompt,
},
});
return res[0].candidates && res[0].candidates.length > 0
? res[0].candidates[0].output
: undefined;
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/chat_models.ts
|
import { DiscussServiceClient } from "@google-ai/generativelanguage";
import type { protos } from "@google-ai/generativelanguage";
import { GoogleAuth } from "google-auth-library";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import {
AIMessage,
BaseMessage,
ChatMessage,
isBaseMessage,
} from "@langchain/core/messages";
import { ChatResult } from "@langchain/core/outputs";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import {
BaseChatModel,
type BaseChatModelParams,
} from "@langchain/core/language_models/chat_models";
export type BaseMessageExamplePair = {
input: BaseMessage;
output: BaseMessage;
};
/**
* @deprecated - Deprecated by Google. Will be removed in 0.3.0
*
* An interface defining the input to the ChatGooglePaLM class.
*/
export interface GooglePaLMChatInput extends BaseChatModelParams {
/**
* Model Name to use
*
* Note: The format must follow the pattern - `models/{model}`
* Alias for `model`
*/
modelName?: string;
/**
* Model Name to use
*
* Note: The format must follow the pattern - `models/{model}`
*/
model?: string;
/**
* Controls the randomness of the output.
*
* Values can range from [0.0,1.0], inclusive. A value closer to 1.0
* will produce responses that are more varied and creative, while
* a value closer to 0.0 will typically result in less surprising
* responses from the model.
*
* Note: The default value varies by model
*/
temperature?: number;
/**
* Top-p changes how the model selects tokens for output.
*
* Tokens are selected from most probable to least until the sum
* of their probabilities equals the top-p value.
*
* For example, if tokens A, B, and C have a probability of
* .3, .2, and .1 and the top-p value is .5, then the model will
* select either A or B as the next token (using temperature).
*
* Note: The default value varies by model
*/
topP?: number;
/**
* Top-k changes how the model selects tokens for output.
*
* A top-k of 1 means the selected token is the most probable among
* all tokens in the model’s vocabulary (also called greedy decoding),
* while a top-k of 3 means that the next token is selected from
* among the 3 most probable tokens (using temperature).
*
* Note: The default value varies by model
*/
topK?: number;
examples?:
| protos.google.ai.generativelanguage.v1beta2.IExample[]
| BaseMessageExamplePair[];
/**
* Google Palm API key to use
*/
apiKey?: string;
}
function getMessageAuthor(message: BaseMessage) {
const type = message._getType();
if (ChatMessage.isInstance(message)) {
return message.role;
}
return message.name ?? type;
}
/**
* @deprecated - Deprecated by Google. Will be removed in 0.3.0
*
* A class that wraps the Google Palm chat model.
*
* @example
* ```typescript
* const model = new ChatGooglePaLM({
* apiKey: "<YOUR API KEY>",
* temperature: 0.7,
* model: "models/chat-bison-001",
* topK: 40,
* topP: 1,
* examples: [
* {
* input: new HumanMessage("What is your favorite sock color?"),
* output: new AIMessage("My favorite sock color be arrrr-ange!"),
* },
* ],
* });
* const questions = [
* new SystemMessage(
* "You are a funny assistant that answers in pirate language."
* ),
* new HumanMessage("What is your favorite food?"),
* ];
* const res = await model.invoke(questions);
* console.log({ res });
* ```
*/
export class ChatGooglePaLM
extends BaseChatModel
implements GooglePaLMChatInput
{
static lc_name() {
return "ChatGooglePaLM";
}
lc_serializable = true;
get lc_secrets(): { [key: string]: string } | undefined {
return {
apiKey: "GOOGLE_PALM_API_KEY",
};
}
modelName = "models/chat-bison-001";
model = "models/chat-bison-001";
temperature?: number; // default value chosen based on model
topP?: number; // default value chosen based on model
topK?: number; // default value chosen based on model
examples: protos.google.ai.generativelanguage.v1beta2.IExample[] = [];
apiKey?: string;
private client: DiscussServiceClient;
constructor(fields?: GooglePaLMChatInput) {
super(fields ?? {});
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
this.temperature = fields?.temperature ?? this.temperature;
if (this.temperature && (this.temperature < 0 || this.temperature > 1)) {
throw new Error("`temperature` must be in the range of [0.0,1.0]");
}
this.topP = fields?.topP ?? this.topP;
if (this.topP && this.topP < 0) {
throw new Error("`topP` must be a positive integer");
}
this.topK = fields?.topK ?? this.topK;
if (this.topK && this.topK < 0) {
throw new Error("`topK` must be a positive integer");
}
this.examples =
fields?.examples?.map((example) => {
if (
(isBaseMessage(example.input) &&
typeof example.input.content !== "string") ||
(isBaseMessage(example.output) &&
typeof example.output.content !== "string")
) {
throw new Error(
"GooglePaLM example messages may only have string content."
);
}
return {
input: {
...example.input,
content: example.input?.content as string,
},
output: {
...example.output,
content: example.output?.content as string,
},
};
}) ?? this.examples;
this.apiKey =
fields?.apiKey ?? getEnvironmentVariable("GOOGLE_PALM_API_KEY");
if (!this.apiKey) {
throw new Error(
"Please set an API key for Google Palm 2 in the environment variable GOOGLE_PALM_API_KEY or in the `apiKey` field of the GooglePalm constructor"
);
}
this.client = new DiscussServiceClient({
authClient: new GoogleAuth().fromAPIKey(this.apiKey),
});
}
_combineLLMOutput() {
return [];
}
_llmType() {
return "googlepalm";
}
async _generate(
messages: BaseMessage[],
options: this["ParsedCallOptions"],
runManager?: CallbackManagerForLLMRun
): Promise<ChatResult> {
const palmMessages = await this.caller.callWithOptions(
{ signal: options.signal },
this._generateMessage.bind(this),
this._mapBaseMessagesToPalmMessages(messages),
this._getPalmContextInstruction(messages),
this.examples
);
const chatResult = this._mapPalmMessagesToChatResult(palmMessages);
// Google Palm doesn't provide streaming as of now. But to support streaming handlers
// we call the handler with entire response text
void runManager?.handleLLMNewToken(
chatResult.generations.length > 0 ? chatResult.generations[0].text : ""
);
return chatResult;
}
protected async _generateMessage(
messages: protos.google.ai.generativelanguage.v1beta2.IMessage[],
context?: string,
examples?: protos.google.ai.generativelanguage.v1beta2.IExample[]
): Promise<protos.google.ai.generativelanguage.v1beta2.IGenerateMessageResponse> {
const [palmMessages] = await this.client.generateMessage({
candidateCount: 1,
model: this.model,
temperature: this.temperature,
topK: this.topK,
topP: this.topP,
prompt: {
context,
examples,
messages,
},
});
return palmMessages;
}
protected _getPalmContextInstruction(
messages: BaseMessage[]
): string | undefined {
// get the first message and checks if it's a system 'system' messages
const systemMessage =
messages.length > 0 && getMessageAuthor(messages[0]) === "system"
? messages[0]
: undefined;
if (
systemMessage?.content !== undefined &&
typeof systemMessage.content !== "string"
) {
throw new Error("Non-string system message content is not supported.");
}
return systemMessage?.content;
}
protected _mapBaseMessagesToPalmMessages(
messages: BaseMessage[]
): protos.google.ai.generativelanguage.v1beta2.IMessage[] {
// remove all 'system' messages
const nonSystemMessages = messages.filter(
(m) => getMessageAuthor(m) !== "system"
);
// requires alternate human & ai messages. Throw error if two messages are consecutive
nonSystemMessages.forEach((msg, index) => {
if (index < 1) return;
if (
getMessageAuthor(msg) === getMessageAuthor(nonSystemMessages[index - 1])
) {
throw new Error(
`Google PaLM requires alternate messages between authors`
);
}
});
return nonSystemMessages.map((m) => {
if (typeof m.content !== "string") {
throw new Error(
"ChatGooglePaLM does not support non-string message content."
);
}
return {
author: getMessageAuthor(m),
content: m.content,
citationMetadata: {
citationSources: m.additional_kwargs.citationSources as
| protos.google.ai.generativelanguage.v1beta2.ICitationSource[]
| undefined,
},
};
});
}
protected _mapPalmMessagesToChatResult(
msgRes: protos.google.ai.generativelanguage.v1beta2.IGenerateMessageResponse
): ChatResult {
if (
msgRes.candidates &&
msgRes.candidates.length > 0 &&
msgRes.candidates[0]
) {
const message = msgRes.candidates[0];
return {
generations: [
{
text: message.content ?? "",
message: new AIMessage({
content: message.content ?? "",
name: message.author === null ? undefined : message.author,
additional_kwargs: {
citationSources: message.citationMetadata?.citationSources,
filters: msgRes.filters, // content filters applied
},
}),
},
],
};
}
// if rejected or error, return empty generations with reason in filters
return {
generations: [],
llmOutput: {
filters: msgRes.filters,
},
};
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests/googlemakersuitehub.int.test.ts
|
// noinspection DuplicatedCode
import fs from "fs";
import { fileURLToPath } from "node:url";
import * as path from "path";
import { describe, test } from "@jest/globals";
import { HumanMessage } from "@langchain/core/messages";
import { AsyncCaller } from "@langchain/core/utils/async_caller";
import { ChatGooglePaLM } from "../chat_models.js";
import { GooglePaLM } from "../llms.js";
import {
DriveFileReadConnection,
MakerSuiteHub,
MakerSuitePrompt,
} from "../googlemakersuitehub.js";
describe.skip("Google Maker Suite Hub Integration", () => {
describe("Prompt", () => {
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const chatFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/chatPrompt.json`,
"utf8"
)
);
const dataFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/dataPrompt.json`,
"utf8"
)
);
const textFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/textPrompt.json`,
"utf8"
)
);
test("text chain", async () => {
const prompt = new MakerSuitePrompt(textFile);
const chain = prompt.toChain();
const result = await chain.invoke({
product: "shoes",
});
// console.log("text chain result", result);
expect(result).toBeTruthy();
});
test("data chain", async () => {
const prompt = new MakerSuitePrompt(dataFile);
const chain = prompt.toChain();
const result = await chain.invoke({
description: "shoes",
});
// console.log("data chain result", result);
expect(result).toBeTruthy();
});
test("chat model", async () => {
const prompt = new MakerSuitePrompt(chatFile);
const model = prompt.toModel() as ChatGooglePaLM;
const message = new HumanMessage("Hello!");
const result = await model.invoke([message]);
expect(result).toBeTruthy();
// console.log({ result });
});
});
describe("Drive", () => {
test("file get media", async () => {
const fileId = "1IAWobj3BYvbj5X3JOAKaoXTcNJlZLdpK";
const caller = new AsyncCaller({});
const connection = new DriveFileReadConnection({ fileId }, caller);
// console.log("connection client", connection?.client);
// @eslint-disable-next-line/@typescript-eslint/ban-ts-comment
// @ts-expect-error unused var
const result = await connection.request();
// console.log(result);
});
});
describe("Hub", () => {
const hub = new MakerSuiteHub();
test("text model", async () => {
const prompt = await hub.pull("1gxLasQIeQdwR4wxtV_nb93b_g9f0GaMm");
const model = prompt.toModel() as GooglePaLM;
const result = await model.invoke(
"What would be a good name for a company that makes socks"
);
// console.log("text chain result", result);
expect(result).toBeTruthy();
});
test("text chain", async () => {
const prompt = await hub.pull("1gxLasQIeQdwR4wxtV_nb93b_g9f0GaMm");
const result = await prompt.toChain().invoke({ product: "socks" });
// console.log("text chain result", result);
expect(result).toBeTruthy();
});
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests/googlemakersuitehub.test.ts
|
import fs from "fs";
import { fileURLToPath } from "node:url";
import * as path from "path";
import { describe, expect, test } from "@jest/globals";
import { ChatGooglePaLM } from "../chat_models.js";
import { MakerSuiteHub, MakerSuitePrompt } from "../googlemakersuitehub.js";
describe("Google Maker Suite Hub", () => {
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const chatFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/chatPrompt.json`,
"utf8"
)
);
const dataFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/dataPrompt.json`,
"utf8"
)
);
const textFile = JSON.parse(
fs.readFileSync(
`${__dirname}/googlemakersuite-files/textPrompt.json`,
"utf8"
)
);
// We don't need a real key
// eslint-disable-next-line no-process-env
process.env.GOOGLE_PALM_API_KEY = "test";
describe("Prompt", () => {
test("text type", () => {
const prompt = new MakerSuitePrompt(textFile);
expect(prompt.promptType).toEqual("text");
});
test("text template", () => {
const prompt = new MakerSuitePrompt(textFile);
const template = prompt.toTemplate();
expect(template.template).toEqual(
"What would be a good name for a company that makes {product}?"
);
});
test("text model", () => {
const prompt = new MakerSuitePrompt(textFile);
const model = prompt.toModel();
// console.log(model.lc_namespace);
expect(model.lc_namespace).toEqual(["langchain", "llms", "googlepalm"]);
});
test("data type", () => {
const prompt = new MakerSuitePrompt(dataFile);
expect(prompt.promptType).toEqual("data");
});
test("data template", () => {
const prompt = new MakerSuitePrompt(dataFile);
const template = prompt.toTemplate();
// console.log("data template", template.template);
expect(template.template).toEqual(
"Given a product description, you should return a name for that product that includes something about rainbows.\n" +
"description: socks\n" +
"product: spectrum socks\n" +
"description: hair ties\n" +
"product: rainbows^2\n" +
"description: {description}\n" +
"product: "
);
});
test("data model", () => {
const prompt = new MakerSuitePrompt(dataFile);
const model = prompt.toModel();
expect(model.lc_namespace).toEqual(["langchain", "llms", "googlepalm"]);
});
test("chat type", () => {
const prompt = new MakerSuitePrompt(chatFile);
expect(prompt.promptType).toEqual("chat");
});
test("chat model", () => {
const prompt = new MakerSuitePrompt(chatFile);
const model = prompt.toModel();
expect(model.lc_namespace).toEqual([
"langchain",
"chat_models",
"googlepalm",
]);
expect((model as ChatGooglePaLM).examples).toEqual([
{
input: { content: "What time is it?" },
output: { content: "2023-09-16T02:03:04-0500" },
},
]);
});
});
describe("MakerSuiteHub", () => {
test("isValid no entry", () => {
const nonexistentId = "nonexistent";
const hub = new MakerSuiteHub({ cacheTimeout: 1000 });
const entry = hub.cache[nonexistentId];
const isValid = hub.isValid(entry);
expect(isValid).toEqual(false);
});
test("isValid timeout 0", () => {
// This should never be valid because the cache timeout will be 0
const fakeId = "fake";
const hub = new MakerSuiteHub({ cacheTimeout: 0 });
const entry = {
updated: Date.now(),
prompt: new MakerSuitePrompt({
textPrompt: {
value: "test",
},
}),
};
hub.cache[fakeId] = entry;
const isValid = hub.isValid(entry);
expect(isValid).toEqual(false);
});
test("isValid valid", () => {
const fakeId = "fake";
const hub = new MakerSuiteHub({ cacheTimeout: 60000 });
const entry = {
updated: Date.now(),
prompt: new MakerSuitePrompt({
textPrompt: {
value: "test",
},
}),
};
hub.cache[fakeId] = entry;
const isValid = hub.isValid(entry);
expect(isValid).toEqual(true);
});
test("isValid timeout", () => {
const fakeId = "fake";
const hub = new MakerSuiteHub({ cacheTimeout: 60000 });
const entry = {
updated: Date.now() - 100000,
prompt: new MakerSuitePrompt({
textPrompt: {
value: "test",
},
}),
};
hub.cache[fakeId] = entry;
const isValid = hub.isValid(entry);
expect(isValid).toEqual(false);
});
});
});
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests/googlemakersuite-files/chatPrompt.json
|
{
"runSettings": {
"temperature": 0.25,
"model": "models/chat-bison-001",
"candidateCount": 1,
"topP": 0.95,
"topK": 40,
"maxOutputTokens": 1024
},
"multiturnPrompt": {
"preamble": "You are responsible for returning date and time information based on the user\u0027s input. All your date and time responses should be as an ISO8601 formatted response that include the year, month, day, hour, minute, second, and time zone offset. You should only return this time information and nothing else.\n",
"primingExchanges": [
{
"request": "What time is it?",
"response": "2023-09-16T02:03:04-0500",
"source": "MODEL_EDITED",
"id": "4B26EE6A-6171-4626-80B6-207491D106BF"
},
{
"source": "USER",
"id": "36D72C06-423E-4E00-BA27-DEC44CBA5A13"
}
],
"sessions": [
{
"sessionExchanges": [
{
"request": "What time is it?",
"response": "The time is 2023-09-16T02:04:05-0500.",
"source": "MODEL",
"id": "9E4D6C9A-81D2-4EE9-BE8B-96C84F1FDA64"
},
{
"request": "What time was it one week ago?",
"response": "The time one week ago was 2023-09-09T02:04:05-0500.",
"source": "MODEL",
"id": "EA76CED1-F626-46F3-8CCA-4C9C821B2003"
}
]
}
]
}
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests/googlemakersuite-files/textPrompt.json
|
{
"textPrompt": {
"value": "What would be a good name for a company that makes {{30E275F8-0B60-4E71-843D-9865F4D4EFD4:product:}}?",
"variables": [
{
"variableId": "30E275F8-0B60-4E71-843D-9865F4D4EFD4",
"displayName": "product"
}
]
},
"runSettings": {
"temperature": 0.7,
"model": "models/text-bison-001",
"candidateCount": 1,
"topP": 0.95,
"topK": 40,
"maxOutputTokens": 1024,
"safetySettings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"threshold": "BLOCK_LOW_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"threshold": "BLOCK_LOW_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
}
]
},
"testExamples": [
{
"inputBindings": {
"30E275F8-0B60-4E71-843D-9865F4D4EFD4": ""
},
"modelResponses": [{}]
}
]
}
|
0
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests
|
lc_public_repos/langchainjs/libs/langchain-community/src/experimental/hubs/makersuite/tests/googlemakersuite-files/dataPrompt.json
|
{
"dataPrompt": {
"preamble": "Given a product description, you should return a name for that product that includes something about rainbows.",
"columns": [
{
"columnId": "CFA438B7-313B-4A50-AF74-3ACBC9216A4B",
"displayName": "description:",
"isInput": true
},
{
"columnId": "7D38B937-6636-4AAC-9DFD-B4A60D98C011",
"displayName": "product:"
}
],
"rows": [
{
"columnBindings": {
"CFA438B7-313B-4A50-AF74-3ACBC9216A4B": "socks",
"7D38B937-6636-4AAC-9DFD-B4A60D98C011": "spectrum socks"
},
"rowId": "D412F0BE-5C81-401D-917C-758A6F14EEC4"
},
{
"columnBindings": {
"CFA438B7-313B-4A50-AF74-3ACBC9216A4B": "hair ties",
"7D38B937-6636-4AAC-9DFD-B4A60D98C011": "rainbows^2"
},
"rowId": "C77A45B4-74B4-41F3-AD8E-06F0C1D9D820"
}
],
"rowsUsed": [
"D412F0BE-5C81-401D-917C-758A6F14EEC4",
"C77A45B4-74B4-41F3-AD8E-06F0C1D9D820"
]
},
"runSettings": {
"temperature": 0.7,
"model": "models/text-bison-001",
"candidateCount": 1,
"topP": 0.95,
"topK": 40,
"maxOutputTokens": 1024,
"safetySettings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"threshold": "BLOCK_LOW_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"threshold": "BLOCK_LOW_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
}
]
},
"testExamples": [
{
"inputBindings": {
"CFA438B7-313B-4A50-AF74-3ACBC9216A4B": "condoms"
},
"modelResponses": [
{
"response": "rainbow rhapsody",
"safetyRatings": [
{
"category": "HARM_CATEGORY_DEROGATORY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_TOXICITY",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_VIOLENCE",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_SEXUAL",
"probability": "LOW"
},
{
"category": "HARM_CATEGORY_MEDICAL",
"probability": "NEGLIGIBLE"
},
{
"category": "HARM_CATEGORY_DANGEROUS",
"probability": "NEGLIGIBLE"
}
]
}
]
}
]
}
|
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