Adding agents, RAG

.gitignore

@@ -1 +1,2 @@
 node_modules
+.env*

10_agents.ts

@@ -0,0 +1,74 @@
+import { config } from 'dotenv';
+config({ path: '.env.local' });
+
+import { createWorkflow, workflowEvent } from "@llama-flow/core";
+import { pipeline } from "node:stream/promises";
+import { Anthropic } from "@llamaindex/anthropic";
+import {
+    agent,
+    AgentStream,
+    tool,
+    Settings,
+} from "llamaindex";
+import { z } from "zod";
+
+////////// create the agent
+
+Settings.llm = new Anthropic({
+    apiKey: process.env.ANTHROPIC_API_KEY,
+    model: "claude-3-7-sonnet-latest",
+});
+
+const sumNumbers = ({ a, b }) => {
+    return `${a + b}`;
+};
+
+const addTool = tool({
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: z.object({
+        a: z.number({
+            description: "First number to sum",
+        }),
+        b: z.number({
+            description: "Second number to sum",
+        }),
+    }),
+    execute: sumNumbers,
+});
+
+const tools = [addTool];
+
+const myAgent = agent({ tools });
+
+////////// define a workflow (optional for such a simple example!)
+
+const startEvent = workflowEvent<string>();
+const stopEvent = workflowEvent<string>();
+
+const workflow = createWorkflow();
+
+// handle the start event
+workflow.handle([startEvent], async (start) => {
+    console.log(`Started the workflow with question: ${start.data}`);
+
+    const response = await myAgent.run(start.data);
+    return stopEvent.with(response.data.result);
+});
+
+////////// run the workflow
+
+// Create a workflow context and send the initial event
+const { stream, sendEvent } = workflow.createContext();
+sendEvent(startEvent.with("What is the sum of 2 and 8?"));
+
+// Process the stream to get the result
+const result = await pipeline(stream, async function (source) {
+    for await (const event of source) {
+        if (stopEvent.include(event)) {
+            return `Result: ${event.data}`;
+        }
+    }
+});
+
+console.log(result)

11_rag.ts

@@ -0,0 +1,104 @@
+import { config } from 'dotenv';
+config({ path: '.env.local' });
+
+import { createWorkflow, workflowEvent } from "@llama-flow/core";
+import { pipeline } from "node:stream/promises";
+import { Anthropic } from "@llamaindex/anthropic";
+import {
+    agent,
+    tool,
+    Settings,
+    QueryEngineTool,
+    VectorStoreIndex,
+} from "llamaindex";
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
+import { z } from "zod";
+
+////////// load data and create a RAG index, and create a query tool
+
+Settings.llm = new Anthropic({
+    apiKey: process.env.ANTHROPIC_API_KEY,
+    model: "claude-3-7-sonnet-latest",
+});
+
+Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: "BAAI/bge-small-en-v1.5",
+    quantized: false,
+});
+
+const reader = new SimpleDirectoryReader();
+const documents = await reader.loadData("./data");
+
+const index = await VectorStoreIndex.fromDocuments(documents);
+
+// You will want a persistent vector store! See https://ts.llamaindex.ai/docs/llamaindex/tutorials/agents/7_qdrant
+
+const retriever = await index.asRetriever();
+
+retriever.similarityTopK = 10;
+
+const queryTool = index.queryTool({
+    metadata: {
+        name: "san_francisco_budget_tool",
+        description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
+    },
+    retriever: retriever,
+})
+
+////////// create the origina number-summing tool
+
+const sumNumbers = ({ a, b }) => {
+    return `${a + b}`;
+};
+
+const addTool = tool({
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: z.object({
+        a: z.number({
+            description: "First number to sum",
+        }),
+        b: z.number({
+            description: "Second number to sum",
+        }),
+    }),
+    execute: sumNumbers,
+});
+
+////////// create the agent with both tools
+
+const tools = [addTool, queryTool];
+const myAgent = agent({ tools });
+
+////////// define a workflow (optional for such a simple example!)
+
+const startEvent = workflowEvent<string>();
+const stopEvent = workflowEvent<string>();
+
+const workflow = createWorkflow();
+
+// handle the start event
+workflow.handle([startEvent], async (start) => {
+    console.log(`Started the workflow with question: ${start.data}`);
+
+    const response = await myAgent.run(start.data);
+    return stopEvent.with(response.data.result);
+});
+
+////////// run the workflow
+
+// Create a workflow context and send the initial event
+const { stream, sendEvent } = workflow.createContext();
+sendEvent(startEvent.with("What is the total budget of San Francisco in 2023-2024?"));
+
+// Process the stream to get the result
+const result = await pipeline(stream, async function (source) {
+    for await (const event of source) {
+        if (stopEvent.include(event)) {
+            return `Result: ${event.data}`;
+        }
+    }
+});
+
+console.log(result)

3_sectioning.ts

@@ -7,15 +7,9 @@ import { pipeline } from "node:stream/promises";
 ////////// define the workflow
 
 const startEvent = workflowEvent<string>();
-const branchAEvent = workflowEvent<string>({
-  type: "branchA"
-});
-const branchBEvent = workflowEvent<string>({
-  type: "branchB"
-});
-const branchCEvent = workflowEvent<string>({
-  type: "branchC"
-});
+const branchAEvent = workflowEvent<string>();
+const branchBEvent = workflowEvent<string>();
+const branchCEvent = workflowEvent<string>();
 const branchCompleteEvent = workflowEvent<string>();
 const allCompleteEvent = workflowEvent<string>();
 const stopEvent = workflowEvent<string>();
@@ -42,7 +36,7 @@ workflow.handle([startEvent], async (start) => {
   );
 
   console.log(`All branches completed`);
-  console.log(results)
+  console.log(results[0].data)
   return allCompleteEvent.with(results.join(", "));
 });
 

5_orchestrator.ts

@@ -0,0 +1,106 @@
+import { config } from 'dotenv';
+config({ path: '.env.local' });
+
+import { createWorkflow, workflowEvent, getContext } from "@llama-flow/core";
+import { until } from "@llama-flow/core/stream/until";
+import { collect } from "@llama-flow/core/stream/consumer";
+import { filter } from "@llama-flow/core/stream/filter";
+import { pipeline } from "node:stream/promises";
+import { Anthropic } from "@llamaindex/anthropic";
+
+////////// define the workflow
+
+const startEvent = workflowEvent<string>();
+const subquestionEvent = workflowEvent<string>();
+const questionAnsweredEvent = workflowEvent<string>();
+const synthesizeEvent = workflowEvent<string>();
+const stopEvent = workflowEvent<string>();
+
+const workflow = createWorkflow();
+
+// initialize the LLM
+const llm = new Anthropic({
+  apiKey: process.env.ANTHROPIC_API_KEY,
+});
+
+// handle the start event
+workflow.handle([startEvent], async (start) => {
+
+  // take a complicated question and split it up
+  let prompt = `We have been ask a complicated question: <question>${start.data}</question>.
+    Split it up into a few different questions that are easier to answer.
+    Return the questions with one question per line.
+    Do not include any other text, preamble or explanation in your response.
+  `
+  let result = await llm.complete({prompt:prompt})
+
+  // split up the result into an array of questions
+  let questions = result.text.split("\n").map(q => q.trim()).filter(q => q !== "");
+  console.log(`Questions: ${questions}`);
+
+  // emit the questions
+  const { sendEvent, stream } = getContext();
+  for (let question of questions) {
+    sendEvent(subquestionEvent.with(question));
+  }
+
+  // get all the answers to the questions
+  let condition = 0;
+  const results = await collect(
+    until(
+      filter(stream, (ev) => questionAnsweredEvent.include(ev)),
+      () => {
+        condition++;
+        return condition === questions.length;
+      },
+    ),
+  );
+
+  // synthesize the answers into a final answer
+  let answers = results.map(r => r.data)
+  console.log(answers)
+  return synthesizeEvent.with({
+    answers: answers.join("\n"),
+    question: start.data
+  });
+});
+
+// handle each sub-question
+workflow.handle([subquestionEvent], async (subquestion) => {
+  console.log(`Answering sub-question: ${subquestion.data}`);
+  
+  let prompt = `Answer the question: <question>${subquestion.data}</question>.
+    Return the answer as a short answer.
+  `
+  let result = await llm.complete({prompt:prompt})
+  console.log(`Answer: ${result.text}`);
+  return questionAnsweredEvent.with(result.text)
+});
+
+// handle the collected results
+workflow.handle([synthesizeEvent], async (synthesize) => {
+
+  let prompt = `You were given the complicated question ${synthesize.data.question}. 
+    We split it into multiple simpler questions. 
+    The answers to the questions are: <answers>${synthesize.data.answers}</answers>.`
+  console.log(`Synthesizing answer: ${prompt}`);
+  let result = await llm.complete({prompt:prompt})  
+  return stopEvent.with(result.text);
+});
+
+////////// run the workflow
+
+// Create a workflow context and send the initial event
+const { stream, sendEvent } = workflow.createContext();
+sendEvent(startEvent.with("How has US tariff policy changed over the lifetime of the United States? Go back as far as you can."));
+
+// Process the stream to get the result
+const result = await pipeline(stream, async function (source) {
+  for await (const event of source) {
+    if (stopEvent.include(event)) {
+      return `Result: ${event.data}`;
+    }
+  }
+});
+
+console.log(result)

6_browser/README.md

@@ -1,36 +0,0 @@
-This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-# or
-bun dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

6_looping.ts

@@ -1,7 +1,4 @@
 import { createWorkflow, workflowEvent, getContext } from "@llama-flow/core";
-import { until } from "@llama-flow/core/stream/until";
-import { collect } from "@llama-flow/core/stream/consumer";
-import { filter } from "@llama-flow/core/stream/filter";
 import { pipeline } from "node:stream/promises";
 
 ////////// define the workflow

7_browser/README.md

@@ -0,0 +1,13 @@
+This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
+
+## Getting Started
+
+First, run the development server:
+
+```bash
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+This is a completely vanilla Next.js app. It's just a simple page that displays a list of events from a workflow.

7_server/README.md

@@ -1,36 +0,0 @@
-This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
-
-## Getting Started
-
-First, run the development server:
-
-```bash
-npm run dev
-# or
-yarn dev
-# or
-pnpm dev
-# or
-bun dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
-
-This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

8_server/README.md

@@ -0,0 +1,23 @@
+This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
+
+## Getting Started
+
+Install dependencies:
+
+```bash
+npm install
+```
+
+Set an ANTHROPIC_API_KEY environment variable or put it in `.env.local`:
+
+```bash
+export ANTHROPIC_API_KEY=<your-anthropic-api-key>
+```
+
+Then run the development server:
+
+```bash
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.

9_storing.md

@@ -0,0 +1 @@
+See 8_server/src/api/run-workflow/route.ts

package.json

@@ -2,7 +2,12 @@
   "type": "module",
   "dependencies": {
     "@llama-flow/core": "^0.3.4",
+    "@llamaindex/anthropic": "^0.3.3",
+    "@llamaindex/huggingface": "^0.1.6",
+    "@llamaindex/readers": "^3.0.2",
+    "dotenv": "^16.5.0",
     "llamaindex": "^0.10.2",
-    "tsx": "^4.19.3"
+    "tsx": "^4.19.3",
+    "zod": "^3.24.3"
   }
 }