From a5ef35b77506600b04f29dfe9c8a269c36e86aae Mon Sep 17 00:00:00 2001 From: Laurie Voss Date: Fri, 3 May 2024 15:46:30 -0700 Subject: [PATCH] Initial commit --- .gitignore | 2 + 1_agent/agent.mjs | 64 + 1_agent/package-lock.json | 3395 +++++++++++++++++++++++++++++ 1_agent/package.json | 6 + 2_agentic_rag/agent.mjs | 68 + 2_agentic_rag/package-lock.json | 3395 +++++++++++++++++++++++++++++ 2_agentic_rag/package.json | 6 + 3_rag_and_tools/agent.mjs | 103 + 3_rag_and_tools/package-lock.json | 3395 +++++++++++++++++++++++++++++ 3_rag_and_tools/package.json | 6 + 4_llamaparse/agent.mjs | 102 + 4_llamaparse/package-lock.json | 3395 +++++++++++++++++++++++++++++ 4_llamaparse/package.json | 6 + 5_qdrant/agent.mjs | 111 + 5_qdrant/package-lock.json | 3395 +++++++++++++++++++++++++++++ 5_qdrant/package.json | 6 + data/sf_budget_2023_2024.pdf | Bin 0 -> 10550407 bytes 17 files changed, 17455 insertions(+) create mode 100644 .gitignore create mode 100644 1_agent/agent.mjs create mode 100644 1_agent/package-lock.json create mode 100644 1_agent/package.json create mode 100644 2_agentic_rag/agent.mjs create mode 100644 2_agentic_rag/package-lock.json create mode 100644 2_agentic_rag/package.json create mode 100644 3_rag_and_tools/agent.mjs create mode 100644 3_rag_and_tools/package-lock.json create mode 100644 3_rag_and_tools/package.json create mode 100644 4_llamaparse/agent.mjs create mode 100644 4_llamaparse/package-lock.json create mode 100644 4_llamaparse/package.json create mode 100644 5_qdrant/agent.mjs create mode 100644 5_qdrant/package-lock.json create mode 100644 5_qdrant/package.json create mode 100644 data/sf_budget_2023_2024.pdf diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e8c9f45 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +node_modules +.env diff --git a/1_agent/agent.mjs b/1_agent/agent.mjs new file mode 100644 index 0000000..8170bca --- /dev/null +++ b/1_agent/agent.mjs @@ -0,0 +1,64 @@ +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) diff --git a/1_agent/package-lock.json b/1_agent/package-lock.json new file mode 100644 index 0000000..eb88bc2 --- /dev/null +++ b/1_agent/package-lock.json @@ -0,0 +1,3395 @@ +{ + "name": "1_agent", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } + }, + "node_modules/@anthropic-ai/sdk": { + "version": "0.20.8", + "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.20.8.tgz", + "integrity": "sha512-dTMDrWYIFyoSr9P0b/gT2Nu1scBuEq4LU9SGX901ktP4aQxs2jiSWq6A80pRmVxyjFl3ngFvcOmVVrP0NHNhOg==", + "dependencies": { + "@types/node": "^18.11.18", + "@types/node-fetch": "^2.6.4", + "abort-controller": "^3.0.0", + "agentkeepalive": "^4.2.1", + "form-data-encoder": "1.7.2", + "formdata-node": "^4.3.2", + "node-fetch": "^2.6.7", + "web-streams-polyfill": "^3.2.1" + } + }, + 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b/1_agent/package.json new file mode 100644 index 0000000..1b74fb0 --- /dev/null +++ b/1_agent/package.json @@ -0,0 +1,6 @@ +{ + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } +} diff --git a/2_agentic_rag/agent.mjs b/2_agentic_rag/agent.mjs new file mode 100644 index 0000000..3448769 --- /dev/null +++ b/2_agentic_rag/agent.mjs @@ -0,0 +1,68 @@ +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) diff --git a/2_agentic_rag/package-lock.json b/2_agentic_rag/package-lock.json new file mode 100644 index 0000000..50eac00 --- /dev/null +++ b/2_agentic_rag/package-lock.json @@ -0,0 +1,3395 @@ +{ + "name": "2_agentic_rag", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } + }, + "node_modules/@anthropic-ai/sdk": { + "version": "0.20.8", + "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.20.8.tgz", + "integrity": "sha512-dTMDrWYIFyoSr9P0b/gT2Nu1scBuEq4LU9SGX901ktP4aQxs2jiSWq6A80pRmVxyjFl3ngFvcOmVVrP0NHNhOg==", + "dependencies": { + "@types/node": "^18.11.18", + "@types/node-fetch": "^2.6.4", + "abort-controller": "^3.0.0", + "agentkeepalive": "^4.2.1", + "form-data-encoder": "1.7.2", + "formdata-node": "^4.3.2", + "node-fetch": "^2.6.7", + "web-streams-polyfill": "^3.2.1" + } + }, + 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b/2_agentic_rag/package.json new file mode 100644 index 0000000..1b74fb0 --- /dev/null +++ b/2_agentic_rag/package.json @@ -0,0 +1,6 @@ +{ + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } +} diff --git a/3_rag_and_tools/agent.mjs b/3_rag_and_tools/agent.mjs new file mode 100644 index 0000000..ca49367 --- /dev/null +++ b/3_rag_and_tools/agent.mjs @@ -0,0 +1,103 @@ +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) + diff --git a/3_rag_and_tools/package-lock.json b/3_rag_and_tools/package-lock.json new file mode 100644 index 0000000..8f87df2 --- /dev/null +++ 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b/3_rag_and_tools/package.json new file mode 100644 index 0000000..1b74fb0 --- /dev/null +++ b/3_rag_and_tools/package.json @@ -0,0 +1,6 @@ +{ + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } +} diff --git a/4_llamaparse/agent.mjs b/4_llamaparse/agent.mjs new file mode 100644 index 0000000..487ee2d --- /dev/null +++ b/4_llamaparse/agent.mjs @@ -0,0 +1,102 @@ +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) diff --git a/4_llamaparse/package-lock.json b/4_llamaparse/package-lock.json new file mode 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b/4_llamaparse/package.json new file mode 100644 index 0000000..1b74fb0 --- /dev/null +++ b/4_llamaparse/package.json @@ -0,0 +1,6 @@ +{ + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } +} diff --git a/5_qdrant/agent.mjs b/5_qdrant/agent.mjs new file mode 100644 index 0000000..ad760e3 --- /dev/null +++ b/5_qdrant/agent.mjs @@ -0,0 +1,111 @@ +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) diff --git a/5_qdrant/package-lock.json b/5_qdrant/package-lock.json new file mode 100644 index 0000000..9341127 --- /dev/null +++ b/5_qdrant/package-lock.json @@ -0,0 +1,3395 @@ +{ + "name": "4_llamaparse", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "dotenv": "^16.4.5", + "llamaindex": "^0.3.1" + } + }, + "node_modules/@anthropic-ai/sdk": { + "version": "0.20.8", + "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.20.8.tgz", + "integrity": "sha512-dTMDrWYIFyoSr9P0b/gT2Nu1scBuEq4LU9SGX901ktP4aQxs2jiSWq6A80pRmVxyjFl3ngFvcOmVVrP0NHNhOg==", + "dependencies": { + "@types/node": "^18.11.18", + "@types/node-fetch": "^2.6.4", + "abort-controller": "^3.0.0", + "agentkeepalive": "^4.2.1", + "form-data-encoder": "1.7.2", + "formdata-node": "^4.3.2", + "node-fetch": "^2.6.7", + "web-streams-polyfill": "^3.2.1" + } + }, + "node_modules/@anthropic-ai/sdk/node_modules/@types/node": { + 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