Ad openpipe/Chat provider with Open-Orca/OpenOrcaxOpenChat-Preview2-13B model (#163)

* Display 4 decimal points in ModelStatsCard

* Add openpipe-chat provider

app/src/components/ChangeModelModal/ModelStatsCard.tsx

@@ -87,7 +87,7 @@ export const ModelStatsCard = ({
               label="Price"
               info={
                 <Text>
-                  ${model.pricePerSecond.toFixed(3)}
+                  ${model.pricePerSecond.toFixed(4)}
                   <Text color="gray.500"> / second</Text>
                 </Text>
               }

app/src/modelProviders/frontendModelProviders.ts

@@ -1,6 +1,7 @@
 import openaiChatCompletionFrontend from "./openai-ChatCompletion/frontend";
 import replicateLlama2Frontend from "./replicate-llama2/frontend";
 import anthropicFrontend from "./anthropic-completion/frontend";
+import openpipeFrontend from "./openpipe-chat/frontend";
 import { type SupportedProvider, type FrontendModelProvider } from "./types";
 
 // Keep attributes here that need to be accessible from the frontend. We can't
@@ -10,6 +11,7 @@ const frontendModelProviders: Record<SupportedProvider, FrontendModelProvider<an
   "openai/ChatCompletion": openaiChatCompletionFrontend,
   "replicate/llama2": replicateLlama2Frontend,
   "anthropic/completion": anthropicFrontend,
+  "openpipe/Chat": openpipeFrontend,
 };
 
 export default frontendModelProviders;

app/src/modelProviders/modelProviders.ts

@@ -1,12 +1,14 @@
 import openaiChatCompletion from "./openai-ChatCompletion";
 import replicateLlama2 from "./replicate-llama2";
 import anthropicCompletion from "./anthropic-completion";
+import openpipeChatCompletion from "./openpipe-chat";
 import { type SupportedProvider, type ModelProvider } from "./types";
 
 const modelProviders: Record<SupportedProvider, ModelProvider<any, any, any>> = {
   "openai/ChatCompletion": openaiChatCompletion,
   "replicate/llama2": replicateLlama2,
   "anthropic/completion": anthropicCompletion,
+  "openpipe/Chat": openpipeChatCompletion,
 };
 
 export default modelProviders;

app/src/modelProviders/openpipe-chat/frontend.ts

@@ -0,0 +1,26 @@
+import { type OpenpipeChatOutput, type SupportedModel } from ".";
+import { type FrontendModelProvider } from "../types";
+import { refinementActions } from "./refinementActions";
+import { templateOpenOrcaPrompt } from "./templatePrompt";
+
+const frontendModelProvider: FrontendModelProvider<SupportedModel, OpenpipeChatOutput> = {
+  name: "OpenAI ChatCompletion",
+
+  models: {
+    "Open-Orca/OpenOrcaxOpenChat-Preview2-13B": {
+      name: "OpenOrca-Platypus2-13B",
+      contextWindow: 4096,
+      pricePerSecond: 0.0003,
+      speed: "medium",
+      provider: "openpipe/Chat",
+      learnMoreUrl: "https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B",
+      templatePrompt: templateOpenOrcaPrompt,
+    },
+  },
+
+  refinementActions,
+
+  normalizeOutput: (output) => ({ type: "text", value: output }),
+};
+
+export default frontendModelProvider;

app/src/modelProviders/openpipe-chat/getCompletion.ts

@@ -0,0 +1,104 @@
+/* eslint-disable @typescript-eslint/no-unsafe-call */
+import { isArray, isString } from "lodash-es";
+import OpenAI, { APIError } from "openai";
+
+import { type CompletionResponse } from "../types";
+import { type OpenpipeChatInput, type OpenpipeChatOutput } from ".";
+import frontendModelProvider from "./frontend";
+
+const modelEndpoints: Record<OpenpipeChatInput["model"], string> = {
+  "Open-Orca/OpenOrcaxOpenChat-Preview2-13B": "https://5ef82gjxk8kdys-8000.proxy.runpod.net/v1",
+};
+
+export async function getCompletion(
+  input: OpenpipeChatInput,
+  onStream: ((partialOutput: OpenpipeChatOutput) => void) | null,
+): Promise<CompletionResponse<OpenpipeChatOutput>> {
+  const { model, messages, ...rest } = input;
+
+  const templatedPrompt = frontendModelProvider.models[model].templatePrompt?.(messages);
+
+  if (!templatedPrompt) {
+    return {
+      type: "error",
+      message: "Failed to generate prompt",
+      autoRetry: false,
+    };
+  }
+
+  const openai = new OpenAI({
+    baseURL: modelEndpoints[model],
+  });
+  const start = Date.now();
+  let finalCompletion: OpenpipeChatOutput = "";
+
+  try {
+    if (onStream) {
+      const resp = await openai.completions.create(
+        { model, prompt: templatedPrompt, ...rest, stream: true },
+        {
+          maxRetries: 0,
+        },
+      );
+
+      for await (const part of resp) {
+        finalCompletion += part.choices[0]?.text;
+        onStream(finalCompletion);
+      }
+      if (!finalCompletion) {
+        return {
+          type: "error",
+          message: "Streaming failed to return a completion",
+          autoRetry: false,
+        };
+      }
+    } else {
+      const resp = await openai.completions.create(
+        { model, prompt: templatedPrompt, ...rest, stream: false },
+        {
+          maxRetries: 0,
+        },
+      );
+      finalCompletion = resp.choices[0]?.text || "";
+      if (!finalCompletion) {
+        return {
+          type: "error",
+          message: "Failed to return a completion",
+          autoRetry: false,
+        };
+      }
+    }
+    const timeToComplete = Date.now() - start;
+
+    return {
+      type: "success",
+      statusCode: 200,
+      value: finalCompletion,
+      timeToComplete,
+    };
+  } catch (error: unknown) {
+    if (error instanceof APIError) {
+      // The types from the sdk are wrong
+      const rawMessage = error.message as string | string[];
+      // If the message is not a string, stringify it
+      const message = isString(rawMessage)
+        ? rawMessage
+        : isArray(rawMessage)
+        ? rawMessage.map((m) => m.toString()).join("\n")
+        : (rawMessage as any).toString();
+      return {
+        type: "error",
+        message,
+        autoRetry: error.status === 429 || error.status === 503,
+        statusCode: error.status,
+      };
+    } else {
+      console.error(error);
+      return {
+        type: "error",
+        message: (error as Error).message,
+        autoRetry: true,
+      };
+    }
+  }
+}

app/src/modelProviders/openpipe-chat/index.ts

@@ -0,0 +1,50 @@
+import { type JSONSchema4 } from "json-schema";
+import { type ModelProvider } from "../types";
+import inputSchema from "./input.schema.json";
+import { getCompletion } from "./getCompletion";
+import frontendModelProvider from "./frontend";
+
+const supportedModels = ["Open-Orca/OpenOrcaxOpenChat-Preview2-13B"] as const;
+
+export type SupportedModel = (typeof supportedModels)[number];
+
+export type OpenpipeChatInput = {
+  model: SupportedModel;
+  messages: {
+    role: "system" | "user" | "assistant";
+    content: string;
+  }[];
+  temperature?: number;
+  top_p?: number;
+  stop?: string[] | string;
+  max_tokens?: number;
+  presence_penalty?: number;
+  frequency_penalty?: number;
+};
+
+export type OpenpipeChatOutput = string;
+
+export type OpenpipeChatModelProvider = ModelProvider<
+  SupportedModel,
+  OpenpipeChatInput,
+  OpenpipeChatOutput
+>;
+
+const modelProvider: OpenpipeChatModelProvider = {
+  getModel: (input) => {
+    if (supportedModels.includes(input.model as SupportedModel))
+      return input.model as SupportedModel;
+
+    return null;
+  },
+  inputSchema: inputSchema as JSONSchema4,
+  canStream: true,
+  getCompletion,
+  getUsage: (input, output) => {
+    // TODO: Implement this
+    return null;
+  },
+  ...frontendModelProvider,
+};
+
+export default modelProvider;

app/src/modelProviders/openpipe-chat/input.schema.json

@@ -0,0 +1,88 @@
+{
+  "type": "object",
+  "properties": {
+    "model": {
+      "description": "ID of the model to use.",
+      "example": "Open-Orca/OpenOrcaxOpenChat-Preview2-13B",
+      "type": "string",
+      "enum": ["Open-Orca/OpenOrcaxOpenChat-Preview2-13B"]
+    },
+    "messages": {
+      "description": "A list of messages comprising the conversation so far.",
+      "type": "array",
+      "minItems": 1,
+      "items": {
+        "type": "object",
+        "properties": {
+          "role": {
+            "type": "string",
+            "enum": ["system", "user", "assistant"],
+            "description": "The role of the messages author. One of `system`, `user`, or `assistant`."
+          },
+          "content": {
+            "type": "string",
+            "description": "The contents of the message. `content` is required for all messages."
+          }
+        },
+        "required": ["role", "content"]
+      }
+    },
+    "temperature": {
+      "type": "number",
+      "minimum": 0,
+      "maximum": 2,
+      "default": 1,
+      "example": 1,
+      "nullable": true,
+      "description": "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.\n\nWe generally recommend altering this or `top_p` but not both.\n"
+    },
+    "top_p": {
+      "type": "number",
+      "minimum": 0,
+      "maximum": 1,
+      "default": 1,
+      "example": 1,
+      "nullable": true,
+      "description": "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.\n\nWe generally recommend altering this or `temperature` but not both.\n"
+    },
+    "stop": {
+      "description": "Up to 4 sequences where the API will stop generating further tokens.\n",
+      "default": null,
+      "oneOf": [
+        {
+          "type": "string",
+          "nullable": true
+        },
+        {
+          "type": "array",
+          "minItems": 1,
+          "maxItems": 4,
+          "items": {
+            "type": "string"
+          }
+        }
+      ]
+    },
+    "max_tokens": {
+      "description": "The maximum number of [tokens](/tokenizer) to generate in the chat completion.\n\nThe total length of input tokens and generated tokens is limited by the model's context length. [Example Python code](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb) for counting tokens.\n",
+      "type": "integer"
+    },
+    "presence_penalty": {
+      "type": "number",
+      "default": 0,
+      "minimum": -2,
+      "maximum": 2,
+      "nullable": true,
+      "description": "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.\n\n[See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)\n"
+    },
+    "frequency_penalty": {
+      "type": "number",
+      "default": 0,
+      "minimum": -2,
+      "maximum": 2,
+      "nullable": true,
+      "description": "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.\n\n[See more information about frequency and presence penalties.](/docs/api-reference/parameter-details)\n"
+    }
+  },
+  "required": ["model", "messages"]
+}

app/src/modelProviders/openpipe-chat/refinementActions.ts

@@ -0,0 +1,3 @@
+import { type RefinementAction } from "../types";
+
+export const refinementActions: Record<string, RefinementAction> = {};

app/src/modelProviders/openpipe-chat/templatePrompt.ts

@@ -0,0 +1,24 @@
+import { type OpenpipeChatInput } from ".";
+
+export const templateOpenOrcaPrompt = (messages: OpenpipeChatInput["messages"]) => {
+  const splitter = "<|end_of_turn|>"; // end of turn splitter
+
+  const formattedMessages = messages.map((message) => {
+    if (message.role === "system" || message.role === "user") {
+      return "User: " + message.content;
+    } else {
+      return "Assistant: " + message.content;
+    }
+  });
+
+  let prompt = formattedMessages.join(splitter);
+
+  // Ensure that the prompt ends with an assistant message
+  const lastUserIndex = prompt.lastIndexOf("User:");
+  const lastAssistantIndex = prompt.lastIndexOf("Assistant:");
+  if (lastUserIndex > lastAssistantIndex) {
+    prompt += splitter + "Assistant:";
+  }
+
+  return prompt;
+};

app/src/modelProviders/types.ts

@@ -2,11 +2,13 @@ import { type JSONSchema4 } from "json-schema";
 import { type IconType } from "react-icons";
 import { type JsonValue } from "type-fest";
 import { z } from "zod";
+import { type OpenpipeChatInput } from "./openpipe-chat";
 
 export const ZodSupportedProvider = z.union([
   z.literal("openai/ChatCompletion"),
   z.literal("replicate/llama2"),
   z.literal("anthropic/completion"),
+  z.literal("openpipe/Chat"),
 ]);
 
 export type SupportedProvider = z.infer<typeof ZodSupportedProvider>;
@@ -22,6 +24,7 @@ export type Model = {
   description?: string;
   learnMoreUrl?: string;
   apiDocsUrl?: string;
+  templatePrompt?: (initialPrompt: OpenpipeChatInput["messages"]) => string;
 };
 
 export type ProviderModel = { provider: z.infer<typeof ZodSupportedProvider>; model: string };