hf-begin

Author: tripolskypetr

demo/hf-inference/.env.example

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+HF_API_KEY=

demo/hf-inference/.gitignore

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+# dependencies (bun install)
+node_modules
+
+# output
+out
+dist
+*.tgz
+
+# code coverage
+coverage
+*.lcov
+
+# logs
+logs
+_.log
+report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json
+
+# dotenv environment variable files
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# caches
+.eslintcache
+.cache
+*.tsbuildinfo
+
+# IntelliJ based IDEs
+.idea
+
+# Finder (MacOS) folder config
+.DS_Store

demo/hf-inference/bun.lock

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+console.log("Hello via Bun!");

demo/hf-inference/index.ts

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+{
+  "name": "hf-inference",
+  "module": "index.ts",
+  "type": "module",
+  "private": true,
+  "scripts": {
+    "start": "bun ./src/index.ts",
+    "build:docs": "bun ./src/docs.mjs"
+  },
+  "devDependencies": {
+    "@types/bun": "latest"
+  },
+  "peerDependencies": {
+    "typescript": "^5"
+  },
+  "dependencies": {
+    "@huggingface/inference": "^4.7.1",
+    "agent-swarm-kit": "^1.1.141",
+    "functools-kit": "^1.0.89",
+    "plantuml": "^0.0.2"
+  }
+}

demo/hf-inference/package.json

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+import { dumpDocs } from "agent-swarm-kit";
+import plantuml from "plantuml";
+
+import "./logic/index";
+
+await dumpDocs("demo/langchain-stream", './docs/chat', plantuml);
+
+process.kill(process.pid);

demo/hf-inference/src/docs.mjs

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+import "./logic";
+
+import { randomString } from "functools-kit";
+import { getAgentName, listenEvent, session } from "agent-swarm-kit";
+import readline from "readline";
+import { SwarmName } from "./logic/enum/SwarmName";
+
+const rl = readline.createInterface({
+  input: process.stdin,
+  output: process.stdout,
+});
+
+const CLIENT_ID = randomString();
+
+const { complete } = session(CLIENT_ID, SwarmName.RootSwarm);
+
+const askQuestion = () => {
+  rl.question("pharma-bot => ", async (input) => {
+    if (input === "exit") {
+      rl.close();
+      return;
+    }
+    console.time("Timing");
+    const data = await complete(input);
+    process.stdout.write("\n");
+    console.timeEnd("Timing");
+
+    process.stdout.write(`[${await getAgentName(CLIENT_ID)}]: ${data}`);
+
+    askQuestion();
+  });
+};
+
+askQuestion();
+
+listenEvent(CLIENT_ID, "llm-new-token", (token: string) => {
+  process.stdout.write(token);
+});
+
+rl.on("close", () => {
+  process.exit(0);
+});

demo/hf-inference/src/index.ts

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+import { addAgent } from "agent-swarm-kit";
+import { AgentName } from "../enum/AgentName";
+import { CompletionName } from "../enum/CompletionName";
+import { str } from "functools-kit";
+import { ToolName } from "../enum/ToolName";
+
+addAgent({
+  docDescription: "This agent functions as a pharmaceutical seller within the langchain-stream project, providing real-time consultations on pharma products using the CohereCompletion, which streams pending tokens via LangChain alongside Ollama and LMStudio completions, and it employs the AddToCartTool only when necessary to facilitate purchases.",
+  agentName: AgentName.TriageAgent,
+  completion: CompletionName.HfCompletion,
+  prompt: str.newline(
+    "You are the pharma seller agent.",
+    "Provide me the consultation about the pharma product"
+  ),
+  system: [
+    `To add the pharma product to the cart call the next tool: ${ToolName.AddToCartTool}`,
+    "Call the tools only when nessesary, if not required, just speek with users",
+  ],
+  tools: [ToolName.AddToCartTool],
+  dependsOn: [],
+});

demo/hf-inference/src/logic/agent/triage.agent.ts

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+import {
+  addCompletion,
+  type ICompletionArgs,
+  type IModelMessage,
+} from "agent-swarm-kit";
+import { CompletionName } from "../enum/CompletionName";
+import { InferenceClient } from "@huggingface/inference";
+
+const client = new InferenceClient(process.env.HF_API_KEY);
+
+addCompletion({
+  completionName: CompletionName.HfCompletion,
+  getCompletion: async ({
+    agentName,
+    messages: rawMessages,
+    mode,
+    tools: rawTools,
+  }: ICompletionArgs): Promise<IModelMessage> => {
+    const messages = rawMessages.map(
+      ({ role, content, tool_calls, tool_call_id }) => {
+        if (role === "tool") {
+          return {
+            role: "tool" as const,
+            content,
+            tool_call_id: tool_call_id!,
+          };
+        }
+        if (role === "assistant" && tool_calls) {
+          return {
+            role: "assistant" as const,
+            content,
+            tool_calls: tool_calls.map((tc) => ({
+              id: tc.id,
+              type: tc.type,
+              function: {
+                name: tc.function.name,
+                arguments:
+                  typeof tc.function.arguments === "string"
+                    ? tc.function.arguments
+                    : JSON.stringify(tc.function.arguments),
+              },
+            })),
+          };
+        }
+        return {
+          role: role as "user" | "assistant" | "system",
+          content,
+        };
+      }
+    );
+
+    const tools = rawTools?.map(({ function: f }) => ({
+      type: "function" as const,
+      function: {
+        name: f.name,
+        description: f.description,
+        parameters: f.parameters,
+      },
+    }));
+
+    const completion = await client.chatCompletion({
+      model: "openai/gpt-oss-120b",
+      messages,
+      ...(tools && { tools }),
+    });
+
+    const choice = completion.choices[0];
+    const text = choice.message.content || "";
+    const tool_calls = choice.message.tool_calls || [];
+
+    const result = {
+      content: text,
+      mode,
+      agentName: agentName!,
+      role: "assistant" as const,
+      tool_calls: tool_calls.map(({ id, type, function: f }) => ({
+        id: id!,
+        type: type as "function",
+        function: {
+          name: f.name,
+          arguments:
+            typeof f.arguments === "string"
+              ? JSON.parse(f.arguments)
+              : f.arguments,
+        },
+      })),
+    };
+
+    return result;
+  },
+});

demo/hf-inference/src/logic/completion/hf.completion.ts

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+export enum AgentName {
+    TriageAgent = "triage_agent",
+}