mirror of
https://github.com/run-llama/ts-agents.git
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Converting to TypeScript
This commit is contained in:
Laurie Voss
@@ -1,102 +0,0 @@
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import {
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OpenAI,
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FunctionTool,
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OpenAIAgent,
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Settings,
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LlamaParseReader,
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HuggingFaceEmbedding,
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VectorStoreIndex,
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QueryEngineTool
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} from "llamaindex"
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import 'dotenv/config'
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// set LLM and the embedding model
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Settings.llm = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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model: "gpt-4-turbo",
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})
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Settings.embedModel = new HuggingFaceEmbedding({
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modelType: "BAAI/bge-small-en-v1.5",
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quantized: false
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})
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/*
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Set up logging so we can see the work in progress.
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Available events:
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llm-start
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llm-end
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agent-start
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agent-end
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llm-tool-call
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llm-tool-result
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*/
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Settings.callbackManager.on("llm-tool-call", (event) => {
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console.log(event.detail.payload)
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})
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Settings.callbackManager.on("llm-tool-result", (event) => {
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console.log(event.detail.payload)
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})
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// load our data and create a query engine
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const reader = new LlamaParseReader({ resultType: "markdown" });
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const documents = await reader.loadData("../data/sf_budget_2023_2024.pdf");
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const index = await VectorStoreIndex.fromDocuments(documents)
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const retriever = await index.asRetriever()
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retriever.similarityTopK = 10
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const queryEngine = await index.asQueryEngine({
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retriever
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})
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// define a function to sum up numbers
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const sumNumbers = ({ a, b }) => {
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return `${a + b}`;
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}
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// define the query engine as a tool
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const tools = [
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new QueryEngineTool({
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queryEngine: queryEngine,
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metadata: {
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name: "san_francisco_budget_tool",
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description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
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},
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}),
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FunctionTool.from(
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sumNumbers,
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{
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name: "sumNumbers",
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description: "Use this function to sum two numbers",
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parameters: {
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type: "object",
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properties: {
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a: {
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type: "number",
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description: "First number to sum"
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},
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b: {
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type: "number",
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description: "Second number to sum"
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},
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},
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required: ["a", "b"]
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}
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}
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)
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]
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// create the agent
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const agent = new OpenAIAgent({ tools })
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let response = await agent.chat({
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message: "What's the budget of San Francisco for the health service system in 2023-24?",
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})
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console.log(response)
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let response2 = await agent.chat({
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message: "What's the budget of San Francisco for the police department in 2023-24?",
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})
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console.log(response2)
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let response3 = await agent.chat({
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message: "What's the combined budget of San Francisco for the health service system and police department in 2023-24?",
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})
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console.log(response3)
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@@ -0,0 +1,108 @@
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import {
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OpenAI,
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FunctionTool,
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OpenAIAgent,
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Settings,
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LlamaParseReader,
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HuggingFaceEmbedding,
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VectorStoreIndex,
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QueryEngineTool
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} from "llamaindex"
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import 'dotenv/config'
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async function main() {
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// set LLM and the embedding model
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Settings.llm = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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model: "gpt-4o",
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})
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Settings.embedModel = new HuggingFaceEmbedding({
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modelType: "BAAI/bge-small-en-v1.5",
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quantized: false
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})
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/*
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Set up logging so we can see the work in progress.
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Available events:
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llm-start
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llm-end
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agent-start
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agent-end
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llm-tool-call
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llm-tool-result
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*/
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Settings.callbackManager.on("llm-tool-call", (event) => {
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console.log(event.detail.payload)
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})
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Settings.callbackManager.on("llm-tool-result", (event) => {
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console.log(event.detail.payload)
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})
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// load our data and create a query engine
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const reader = new LlamaParseReader({ resultType: "text" });
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const documents = await reader.loadData("../data/sf_budget_2023_2024.pdf");
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const index = await VectorStoreIndex.fromDocuments(documents)
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const retriever = await index.asRetriever()
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retriever.similarityTopK = 10
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const queryEngine = await index.asQueryEngine({
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retriever
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})
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// define a function to sum up numbers
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const sumNumbers = ({ a, b }) => {
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return `${a + b}`;
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}
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// define the query engine as a tool
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const tools = [
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new QueryEngineTool({
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queryEngine: queryEngine,
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metadata: {
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name: "san_francisco_budget_tool",
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description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
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},
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}),
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FunctionTool.from(
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sumNumbers,
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{
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name: "sumNumbers",
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description: "Use this function to sum two numbers",
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parameters: {
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type: "object",
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properties: {
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a: {
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type: "number",
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description: "First number to sum"
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},
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b: {
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type: "number",
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description: "Second number to sum"
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},
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},
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required: ["a", "b"]
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}
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}
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)
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]
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// create the agent
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const agent = new OpenAIAgent({ tools })
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let response = await agent.chat({
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message: "What's the budget of San Francisco for community health in 2023-24?",
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})
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console.log(response)
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let response2 = await agent.chat({
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message: "What's the budget of San Francisco for public protection in 2023-24?",
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})
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console.log(response2)
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let response3 = await agent.chat({
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message: "What's the combined budget of San Francisco for community health and public protection in 2023-24?",
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})
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console.log(response3)
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}
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main().catch(console.error);
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