Initial commit

.gitignore

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+node_modules
+.env

1_agent/agent.mjs

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+import { 
+    OpenAI, 
+    FunctionTool, 
+    ReActAgent,
+    Settings
+} from "llamaindex"
+import 'dotenv/config'
+
+Settings.llm = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4-turbo",
+})
+/*
+  Set up logging so we can see the work in progress.
+  Available events:
+  llm-start
+  llm-end
+  agent-start
+  agent-end
+  llm-tool-call
+  llm-tool-result
+*/
+Settings.callbackManager.on("llm-tool-call", (event) => {
+    console.log(event.detail.payload)
+})
+Settings.callbackManager.on("llm-tool-result", (event) => {
+    console.log(event.detail.payload)
+})
+
+const sumNumbers = ({a, b}) => {
+    return `${a + b}`;
+}
+
+const tools = [
+    FunctionTool.from(
+        sumNumbers,
+        {
+            name: "sumNumbers",
+            description: "Use this function to sum two numbers",
+            parameters: {
+                type: "object",
+                properties: {
+                    a: {
+                        type: "number",
+                        description: "First number to sum"
+                    },
+                    b: {
+                        type: "number",
+                        description: "Second number to sum"
+                    },
+                },
+                required: ["a", "b"]
+            }
+        }
+    )
+]
+
+const agent = new ReActAgent({tools})
+
+let response = await agent.chat({
+    message: "Add 101 and 303",
+})
+
+console.log(response)

1_agent/package.json

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+{
+  "dependencies": {
+    "dotenv": "^16.4.5",
+    "llamaindex": "^0.3.1"
+  }
+}

2_agentic_rag/agent.mjs

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+import { 
+    OpenAI, 
+    FunctionTool, 
+    OpenAIAgent, 
+    Settings, 
+    SimpleDirectoryReader,
+    HuggingFaceEmbedding,
+    VectorStoreIndex,
+    QueryEngineTool
+} from "llamaindex"
+import 'dotenv/config'
+
+// set LLM and the embedding model
+Settings.llm = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4-turbo",
+})
+Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: "BAAI/bge-small-en-v1.5",
+    quantized: false
+})
+/*
+  Set up logging so we can see the work in progress.
+  Available events:
+  llm-start
+  llm-end
+  agent-start
+  agent-end
+  llm-tool-call
+  llm-tool-result
+*/
+Settings.callbackManager.on("llm-tool-call", (event) => {
+    console.log(event.detail.payload)
+})
+Settings.callbackManager.on("llm-tool-result", (event) => {
+    console.log(event.detail.payload)
+})
+
+// load our data and create a query engine
+const reader = new SimpleDirectoryReader()
+const documents = await reader.loadData("../data")
+const index = await VectorStoreIndex.fromDocuments(documents)
+const retriever = await index.asRetriever()
+retriever.similarityTopK = 10
+const queryEngine = await index.asQueryEngine({
+    retriever
+})
+
+
+// define the query engine as a tool
+const tools = [
+    new QueryEngineTool({
+        queryEngine: queryEngine,
+        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.`,
+        },
+      }),
+]
+
+// create the agent
+const agent = new OpenAIAgent({tools})
+
+let response = await agent.chat({
+    message: "What's the budget of San Francisco in 2023-2024?",
+})
+
+console.log(response)

2_agentic_rag/package.json

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+{
+  "dependencies": {
+    "dotenv": "^16.4.5",
+    "llamaindex": "^0.3.1"
+  }
+}

3_rag_and_tools/agent.mjs

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+import {
+    OpenAI,
+    FunctionTool,
+    OpenAIAgent,
+    Settings,
+    SimpleDirectoryReader,
+    HuggingFaceEmbedding,
+    VectorStoreIndex,
+    QueryEngineTool
+} from "llamaindex"
+import 'dotenv/config'
+
+// set LLM and the embedding model
+Settings.llm = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4-turbo",
+})
+Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: "BAAI/bge-small-en-v1.5",
+    quantized: false
+})
+/*
+  Set up logging so we can see the work in progress.
+  Available events:
+  llm-start
+  llm-end
+  agent-start
+  agent-end
+  llm-tool-call
+  llm-tool-result
+*/
+Settings.callbackManager.on("llm-tool-call", (event) => {
+    console.log(event.detail.payload)
+})
+Settings.callbackManager.on("llm-tool-result", (event) => {
+    console.log(event.detail.payload)
+})
+
+// load our data and create a query engine
+const reader = new SimpleDirectoryReader()
+const documents = await reader.loadData("../data")
+const index = await VectorStoreIndex.fromDocuments(documents)
+const retriever = await index.asRetriever()
+retriever.similarityTopK = 10
+const queryEngine = await index.asQueryEngine({
+    retriever
+})
+
+// define a function to sum up numbers
+const sumNumbers = ({ a, b }) => {
+    return `${a + b}`;
+}
+
+// define the query engine as a tool
+const tools = [
+    new QueryEngineTool({
+        queryEngine: queryEngine,
+        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.`,
+        },
+    }),
+    FunctionTool.from(
+        sumNumbers,
+        {
+            name: "sumNumbers",
+            description: "Use this function to sum two numbers",
+            parameters: {
+                type: "object",
+                properties: {
+                    a: {
+                        type: "number",
+                        description: "First number to sum"
+                    },
+                    b: {
+                        type: "number",
+                        description: "Second number to sum"
+                    },
+                },
+                required: ["a", "b"]
+            }
+        }
+    )
+]
+
+// create the agent
+const agent = new OpenAIAgent({ tools })
+
+let response = await agent.chat({
+    message: "What's the budget of San Francisco for community health in 2023-24?",
+})
+console.log(response)
+
+let response2 = await agent.chat({
+    message: "What's the budget of San Francisco for public protection in 2023-24?",
+})
+console.log(response2)
+
+let response3 = await agent.chat({
+    message: "What's the combined budget of San Francisco for community health and public protection in 2023-24?",
+})
+console.log(response3)
+

3_rag_and_tools/package.json

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+{
+  "dependencies": {
+    "dotenv": "^16.4.5",
+    "llamaindex": "^0.3.1"
+  }
+}

4_llamaparse/agent.mjs

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+import {
+    OpenAI,
+    FunctionTool,
+    OpenAIAgent,
+    Settings,
+    LlamaParseReader,
+    HuggingFaceEmbedding,
+    VectorStoreIndex,
+    QueryEngineTool
+} from "llamaindex"
+import 'dotenv/config'
+
+// set LLM and the embedding model
+Settings.llm = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4-turbo",
+})
+Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: "BAAI/bge-small-en-v1.5",
+    quantized: false
+})
+/*
+  Set up logging so we can see the work in progress.
+  Available events:
+  llm-start
+  llm-end
+  agent-start
+  agent-end
+  llm-tool-call
+  llm-tool-result
+*/
+Settings.callbackManager.on("llm-tool-call", (event) => {
+    console.log(event.detail.payload)
+})
+Settings.callbackManager.on("llm-tool-result", (event) => {
+    console.log(event.detail.payload)
+})
+
+// load our data and create a query engine
+const reader = new LlamaParseReader({ resultType: "markdown" });
+const documents = await reader.loadData("../data/sf_budget_2023_2024.pdf");
+const index = await VectorStoreIndex.fromDocuments(documents)
+const retriever = await index.asRetriever()
+retriever.similarityTopK = 10
+const queryEngine = await index.asQueryEngine({
+    retriever
+})
+
+// define a function to sum up numbers
+const sumNumbers = ({ a, b }) => {
+    return `${a + b}`;
+}
+
+// define the query engine as a tool
+const tools = [
+    new QueryEngineTool({
+        queryEngine: queryEngine,
+        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.`,
+        },
+    }),
+    FunctionTool.from(
+        sumNumbers,
+        {
+            name: "sumNumbers",
+            description: "Use this function to sum two numbers",
+            parameters: {
+                type: "object",
+                properties: {
+                    a: {
+                        type: "number",
+                        description: "First number to sum"
+                    },
+                    b: {
+                        type: "number",
+                        description: "Second number to sum"
+                    },
+                },
+                required: ["a", "b"]
+            }
+        }
+    )
+]
+
+// create the agent
+const agent = new OpenAIAgent({ tools })
+
+let response = await agent.chat({
+    message: "What's the budget of San Francisco for the health service system in 2023-24?",
+})
+console.log(response)
+
+let response2 = await agent.chat({
+    message: "What's the budget of San Francisco for the police department in 2023-24?",
+})
+console.log(response2)
+
+let response3 = await agent.chat({
+    message: "What's the combined budget of San Francisco for the health service system and police department in 2023-24?",
+})
+console.log(response3)

4_llamaparse/package.json

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+{
+  "dependencies": {
+    "dotenv": "^16.4.5",
+    "llamaindex": "^0.3.1"
+  }
+}

5_qdrant/agent.mjs

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+import {
+    OpenAI,
+    FunctionTool,
+    OpenAIAgent,
+    Settings,
+    LlamaParseReader,
+    HuggingFaceEmbedding,
+    VectorStoreIndex,
+    QueryEngineTool,
+    QdrantVectorStore
+} from "llamaindex"
+import 'dotenv/config'
+
+// set LLM and the embedding model
+Settings.llm = new OpenAI({
+    apiKey: process.env.OPENAI_API_KEY,
+    model: "gpt-4-turbo",
+})
+Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: "BAAI/bge-small-en-v1.5",
+    quantized: false
+})
+/*
+  Set up logging so we can see the work in progress.
+  Available events:
+  llm-start
+  llm-end
+  agent-start
+  agent-end
+  llm-tool-call
+  llm-tool-result
+*/
+Settings.callbackManager.on("llm-tool-call", (event) => {
+    console.log(event.detail.payload)
+})
+Settings.callbackManager.on("llm-tool-result", (event) => {
+    console.log(event.detail.payload)
+})
+
+// initialize qdrant vector store
+const vectorStore = new QdrantVectorStore({
+    url: "http://localhost:6333",
+});
+
+// load our data and create a query engine
+const reader = new LlamaParseReader({ resultType: "markdown" });
+const documents = await reader.loadData("../data/sf_budget_2023_2024.pdf");
+const index = await VectorStoreIndex.fromDocuments(
+    documents,
+    {vectorStore}
+)
+const retriever = await index.asRetriever()
+retriever.similarityTopK = 10
+const queryEngine = await index.asQueryEngine({
+    retriever
+})
+
+// define a function to sum up numbers
+const sumNumbers = ({ a, b }) => {
+    return `${a + b}`;
+}
+
+// define the query engine as a tool
+const tools = [
+    new QueryEngineTool({
+        queryEngine: queryEngine,
+        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.`,
+        },
+    }),
+    FunctionTool.from(
+        sumNumbers,
+        {
+            name: "sumNumbers",
+            description: "Use this function to sum two numbers",
+            parameters: {
+                type: "object",
+                properties: {
+                    a: {
+                        type: "number",
+                        description: "First number to sum"
+                    },
+                    b: {
+                        type: "number",
+                        description: "Second number to sum"
+                    },
+                },
+                required: ["a", "b"]
+            }
+        }
+    )
+]
+
+// create the agent
+const agent = new OpenAIAgent({ tools })
+
+let response = await agent.chat({
+    message: "What's the budget of San Francisco for the health service system in 2023-24?",
+})
+console.log(response)
+
+let response2 = await agent.chat({
+    message: "What's the budget of San Francisco for the police department in 2023-24?",
+})
+console.log(response2)
+
+let response3 = await agent.chat({
+    message: "What's the combined budget of San Francisco for the health service system and police department in 2023-24?",
+})
+console.log(response3)

5_qdrant/package.json

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+{
+  "dependencies": {
+    "dotenv": "^16.4.5",
+    "llamaindex": "^0.3.1"
+  }
+}