Add LLMs module using grafana-llm-app

package.json

@@ -26,19 +26,20 @@
   "license": "Apache-2.0",
   "peerDependencies": {
     "@emotion/css": "11.1.3",
-    "@grafana/data": "^9.2.0",
-    "@grafana/runtime": "^9.2.0",
-    "@grafana/ui": "^9.2.0",
+    "@grafana/data": "^10.0.0",
+    "@grafana/runtime": "^10.0.0",
+    "@grafana/ui": "^10.0.0",
     "react": "17.0.2",
     "react-dom": "17.0.2",
-    "react-select": "^5.2.1"
+    "react-select": "^5.2.1",
+    "rxjs": "7.8.0"
   },
   "devDependencies": {
-    "@grafana/data": "^9.2.0",
-    "@grafana/eslint-config": "^5.1.0",
-    "@grafana/runtime": "^9.2.0",
-    "@grafana/tsconfig": "^1.2.0-rc1",
-    "@grafana/ui": "^9.2.0",
+    "@grafana/data": "^10.0.0",
+    "@grafana/eslint-config": "^6.0.0",
+    "@grafana/runtime": "^10.0.0",
+    "@grafana/tsconfig": "^1.3.0-rc1",
+    "@grafana/ui": "^10.0.0",
     "@rollup/plugin-node-resolve": "15.0.1",
     "@swc/core": "^1.3.56",
     "@swc/jest": "^0.2.26",

src/index.ts

@@ -1,3 +1,4 @@
+export * as llms from './llms';
 export * from './sql-editor';
 export * from './QueryEditor';
 export * from './ConfigEditor';

src/llms/index.ts

@@ -0,0 +1 @@
+export * as openai from './openai';

src/llms/openai.ts

@@ -0,0 +1,242 @@
+/**
+ * OpenAI API client.
+ *
+ * This module contains functions used to make requests to the OpenAI API via
+ * the Grafana LLM app plugin. That plugin must be installed, enabled and configured
+ * in order for these functions to work.
+ *
+ * The {@link enabled} function can be used to check if the plugin is enabled and configured.
+ */
+
+import { isLiveChannelMessageEvent, LiveChannelAddress, LiveChannelMessageEvent, LiveChannelScope } from "@grafana/data";
+import { getBackendSrv, getGrafanaLiveSrv } from "@grafana/runtime";
+
+import { Observable } from "rxjs";
+import { filter, map, takeWhile } from "rxjs/operators";
+
+const LLM_PLUGIN_ID = 'grafana-llm-app';
+const LLM_PLUGIN_ROUTE = `/api/plugins/${LLM_PLUGIN_ID}`;
+
+/**
+ * The role of a message's author.
+ */
+export type Role = 'system' | 'user' | 'assistant' | 'function';
+
+/**
+ * A message in a conversation.
+ */
+export interface Message {
+  /**
+   * The role of the message's author.
+   */
+  role: Role;
+
+  /**
+   * The contents of the message. content is required for all messages, and may be null for assistant messages with function calls.
+   */
+  content: string;
+
+  /**
+   * The name of the author of this message.
+   *
+   * This is required if role is 'function', and it should be the name of the function whose response is in the content.
+   *
+   * May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
+   */
+  name?: string;
+
+  /**
+   * The name and arguments of a function that should be called, as generated by the model.
+   */
+  function_call?: Object;
+}
+
+/**
+ * A function the model may generate JSON inputs for.
+ */
+export interface Function {
+  /**
+   * The name of the function to be called.
+   *
+   * Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
+   */
+  name: string;
+  /**
+   * A description of what the function does, used by the model to choose when and how to call the function.
+   */
+  description?: string;
+  /*
+   * The parameters the functions accepts, described as a JSON Schema object. See the OpenAI guide for examples, and the JSON Schema reference for documentation about the format.
+   *
+   * To describe a function that accepts no parameters, provide the value {"type": "object", "properties": {}}.
+   */
+  parameters: Object;
+}
+
+export interface ChatCompletionsRequest {
+  /**
+   * ID of the model to use.
+   *
+   * See the model endpoint compatibility table for details on which models work with the Chat Completions API.
+   */
+  model: string;
+  /**
+   * A list of messages comprising the conversation so far.
+   */
+  messages: Message[];
+  /**
+   * A list of functions the model may generate JSON inputs for.
+   */
+  functions?: Function[];
+  /**
+   * Controls how the model responds to function calls.
+   *
+   * "none" means the model does not call a function, and responds to the end-user.
+   * "auto" means the model can pick between an end-user or calling a function.
+   * Specifying a particular function via {"name": "my_function"} forces the model to call that function.
+   *
+   * "none" is the default when no functions are present. "auto" is the default if functions are present.
+   */
+  function_call?: 'none' | 'auto' | { name: string };
+  /**
+   * What sampling temperature to use, between 0 and 2.
+   * Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
+   *
+   * We generally recommend altering this or top_p but not both.
+   */
+  temperature?: number;
+  /**
+   * An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
+   * So 0.1 means only the tokens comprising the top 10% probability mass are considered.
+   *
+   * We generally recommend altering this or temperature but not both.
+   */
+  top_p?: number;
+  /**
+   * How many chat completion choices to generate for each input message.
+   */
+  n?: number;
+  /**
+   * Up to 4 sequences where the API will stop generating further tokens.
+   */
+  stop?: string | string[];
+  /**
+   * The maximum number of tokens to generate in the chat completion.
+   *
+   * The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.
+   */
+  max_tokens?: number;
+  /**
+   * Number between -2.0 and 2.0.
+   *
+   * Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
+   */
+  presence_penalty?: number;
+  /**
+   * Number between -2.0 and 2.0.
+   *
+   * Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
+   */
+  frequency_penalty?: number;
+  /**
+   * Modify the likelihood of specified tokens appearing in the completion.
+   *
+   * Accepts a json object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
+   * Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,
+   * but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban
+   * or exclusive selection of the relevant token.
+   */
+  logit_bias?: { [key: string]: number };
+  /**
+   * A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
+   */
+  user?: string;
+}
+
+interface Choice {
+  message: Message;
+}
+
+interface ChatCompletionsResponse<T = Choice> {
+  choices: T[];
+}
+
+interface ContentMessage {
+  content: string;
+}
+
+interface RoleMessage {
+  role: string;
+}
+
+interface DoneMessage {
+  done: boolean;
+}
+
+type ChatCompletionsDelta = ContentMessage | RoleMessage | DoneMessage;
+
+interface ChatCompletionsChunk {
+  delta: ChatCompletionsDelta;
+}
+
+const isContentMessage = (message: any): message is ContentMessage => {
+  return message.content !== undefined;
+}
+
+const isDoneMessage = (message: any): message is DoneMessage => {
+  return message.done !== undefined;
+}
+
+/**
+ * Make a request to OpenAI's chat-completions API via the Grafana LLM plugin proxy.
+ */
+export async function chatCompletions(request: ChatCompletionsRequest): Promise<string> {
+  const response = await getBackendSrv().post<ChatCompletionsResponse>('/api/plugins/grafana-llm-app/resources/openai/v1/chat/completions', request, {
+    headers: { 'Content-Type': 'application/json' }
+  });
+  return response.choices[0].message.content;
+}
+
+/**
+ * Make a streaming request to OpenAI's chat-completions API via the Grafana LLM plugin proxy.
+ *
+ * A stream of tokens will be returned as an `Observable<string>`. Use rxjs' `scan` if you want
+ * to produce a new stream containing the concatenated tokens so far.
+ *
+ * @example <caption>Example of accumulating tokens in a stream.</caption>
+ * const stream = streamChatCompletions({ model: 'gpt-3.5-turbo', messages: [
+ *   { role: 'system', content: 'You are a great bot.' },
+ *   { role: 'user', content: 'Hello, bot.' },
+ * ]}).pipe(scan((acc, delta) => acc + delta, ''));
+ */
+export function streamChatCompletions(request: ChatCompletionsRequest): Observable<string> {
+  const channel: LiveChannelAddress = {
+    scope: LiveChannelScope.Plugin,
+    namespace: LLM_PLUGIN_ID,
+    path: `/openai/v1/chat/completions`,
+    data: request,
+  };
+  const messages = getGrafanaLiveSrv()
+    .getStream(channel)
+    .pipe(filter((event) => isLiveChannelMessageEvent(event))) as Observable<LiveChannelMessageEvent<ChatCompletionsResponse<ChatCompletionsChunk>>>
+  return messages.pipe(
+    takeWhile((event) => !isDoneMessage(event.message.choices[0].delta)),
+    map((event) => event.message.choices[0].delta),
+    filter((delta) => isContentMessage(delta)),
+    map((delta) => (delta as ContentMessage).content),
+  );
+}
+
+/**
+ * Check if the OpenAI API is enabled via the LLM plugin.
+ */
+export const enabled = async () => {
+  try {
+    const settings = await getBackendSrv().get(`${LLM_PLUGIN_ROUTE}/settings`, undefined, undefined, {
+      showSuccessAlert: false, showErrorAlert: false,
+    });
+    return settings.enabled && (settings?.secureJsonFields?.openAIKey ?? false);
+  } catch (_e) {
+    return false;
+  }
+}