diff --git a/README.md b/README.md index 9f032c01..98133b70 100644 --- a/README.md +++ b/README.md @@ -88,11 +88,21 @@ You can read more about these in our [Get started with sample projects documenta ### 📊 Dataset & Experiment Examples #### Python Datasets 🐍 + - **[Dataset Experiments](/python/dataset-experiments/)** - Managing test data and running controlled experiments +#### Python Experiments + +- **[Experiments with RAG and tools](/python/experiments/rag-and-tools/)** - Using RAG and tools in an experiment + #### TypeScript Datasets 📜 + - **[Dataset Experiments](/typescript/datasets-experiments/)** - Dataset management and experimentation in TypeScript +#### TypeScript Experiments + +- **[Experiments with RAG and tools](/typescript/experiments/rag-and-tools/)** - Using RAG and tools in an experiment + #### 📚 Additional Resources - [Galileo SDK Documentation](https://v2docs.galileo.ai/sdk-api/overview) - SDK documentation diff --git a/python/experiments/rag-and-tools/.env.example b/python/experiments/rag-and-tools/.env.example new file mode 100644 index 00000000..d3c7de87 --- /dev/null +++ b/python/experiments/rag-and-tools/.env.example @@ -0,0 +1,10 @@ +# Galileo Environment Variables +GALILEO_API_KEY= # Your Galileo API key. +GALILEO_PROJECT= # Your Galileo project name. +GALILEO_LOG_STREAM= # The name of the log stream you want to use for logging. + +# Provide the console url below if you are using a custom deployment, and not using app.galileo.ai +# GALILEO_CONSOLE_URL= # Optional if you are using a hosted version of Galileo + +# OpenAI Environment Variables +OPENAI_API_KEY= # Your OpenAI API key. diff --git a/python/experiments/rag-and-tools/README.md b/python/experiments/rag-and-tools/README.md new file mode 100644 index 00000000..9a755fba --- /dev/null +++ b/python/experiments/rag-and-tools/README.md @@ -0,0 +1,60 @@ +# Experiments with RAG and tools + +This is an example project demonstrating how to use Galileo experiments with applications thar use RAG and tools. + +This code is used in the [Run an experiment against a RAG app](http://v2docs.galileo.ai/how-to-guides/experiments/rag-app/rag-app) how-to guide in the Galileo documentation. + +## Get Started + +To get started with this project, you'll need to have Python 3.9 or later installed. You can then install the required dependencies in a virtual environment: + +```bash +pip install -r requirements.txt +``` + +## Configure environment variables + +You will need to configure environment variables to use this project. Copy the `.env.example` file to `.env`, then update the environment variables in the `.env` file with your OpenAI and Galileo values: + +```ini +# Galileo environment variables +GALILEO_API_KEY= +GALILEO_PROJECT= +GALILEO_LOG_STREAM= + +# OpenAI environment variables +OPENAI_API_KEY= +``` + +## Usage + +This sample contains both an application that generates a fake horoscope using a tool and a mock RAG function, as well as an experiment to test the same code. + +To run the application, run: + +```bash +python app.py +``` + +Traces will be captured and logged to Galileo. + +To run the experiment, run: + +```bash +python experiment.py +``` + +A link to the results of the experiment will be written to the console. + +## Project Structure + +The project structure is as follows: + +```folder +rag-and-tools/ +├── env.example # List of environment variables +├── requirements.txt # Python project requirements +├── app.py # The main python application +├── experiment.py # Code to run the main application as an experiment +└── README.md # Project documentation +``` diff --git a/python/experiments/rag-and-tools/app.py b/python/experiments/rag-and-tools/app.py new file mode 100644 index 00000000..96b06f03 --- /dev/null +++ b/python/experiments/rag-and-tools/app.py @@ -0,0 +1,170 @@ +import json + +from galileo import log, galileo_context + +from galileo.openai import OpenAI + +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv(override=True) + + +# A mock RAG retriever function +@log(span_type="retriever") +def retrieve_horoscope_data(sign): + """ + Mock function to simulate retrieving horoscope data for a given sign. + This is decorated with logging for tracing a retriever span. + """ + horoscopes = { + "Aquarius": [ + "Next Tuesday you will befriend a baby otter.", + "Next Tuesday you will find a dollar on the ground.", + ], + "Taurus": [ + "Next Tuesday you will find a four-leaf clover.", + "Next Tuesday you will have a great conversation with a stranger.", + ], + "Gemini": [ + "Next Tuesday you will learn to juggle.", + "Next Tuesday you will discover a new favorite book.", + ], + } + return horoscopes.get(sign, ["No horoscope available."]) + + +@log(span_type="tool") +def get_horoscope(sign): + """ + Tool function to get a horoscope for a given astrological sign. + """ + return "\n".join(retrieve_horoscope_data(sign)) + + +# Define a list of callable tools for the model +tools = [ + { + "type": "function", + "function": { + "name": "get_horoscope", + "description": "Get today's horoscope for an astrological sign.", + "parameters": { + "type": "object", + "properties": { + "sign": { + "type": "string", + "description": "An astrological sign like Taurus or Aquarius", + }, + }, + "required": ["sign"], + }, + }, + }, +] + +# Map tool names to their implementations +available_tools = {"get_horoscope": get_horoscope} + + +# Create the OpenAI client +client = OpenAI() + + +def call_llm(messages): + """ + Call the LLM with the provided messages and tools. + """ + return client.chat.completions.create( + model="gpt-5", + tools=tools, + messages=messages, + ) + + +def get_users_horoscope(sign: str) -> str: + """ + Get the user's horoscope + """ + # Create a running message history list we will add to over time + message_history = [ + { + "role": "system", + "content": """ + You are a helpful assistant that provides horoscopes. + Provide a flowery response based off any information retrieved. + Include typical horoscope phrases, and characteristics of + the sign in question. + """, + }, + {"role": "user", "content": f"What is my horoscope? I am {sign}."}, + ] + + # Prompt the model with tools defined + response = call_llm(message_history) + + # Add the tool call to the message history + message_history.append( + { + "role": "assistant", + "tool_calls": [ + { + "id": response.choices[0].message.tool_calls[0].id, + "type": "function", + "function": { + "name": response.choices[0].message.tool_calls[0].function.name, + "arguments": response.choices[0].message.tool_calls[0].function.arguments, + }, + } + ], + } + ) + + # Call any tools the model requested + completion_tool_calls = response.choices[0].message.tool_calls + + for call in completion_tool_calls if completion_tool_calls else []: + # Get the tool to call and its arguments + tool_to_call = available_tools[call.function.name] + args = json.loads(call.function.arguments) + + # Call the tool + result = tool_to_call(**args) + + # Add the tool result to the message history + message_history.append( + { + "role": "tool", + "content": result, + "tool_call_id": call.id, + "name": call.function.name, + } + ) + + # Now we call the model again, with the tool results included + response = call_llm(message_history) + + # Return the final response from the model + return response.choices[0].message.content + + +def main(): + """ + Get the user's horoscope + """ + # Start a session and trace + galileo_logger = galileo_context.get_logger_instance() + galileo_logger.start_session("RAG with Tools Example") + galileo_logger.start_trace(input="What is my horoscope? I am Aquarius.", name="Calling LLM with Tool") + + response = get_users_horoscope("Aquarius") + + # Conclude the trace and flush + galileo_logger.conclude(response) + galileo_logger.flush() + + print(response) + + +if __name__ == "__main__": + main() diff --git a/python/experiments/rag-and-tools/experiment.py b/python/experiments/rag-and-tools/experiment.py new file mode 100644 index 00000000..fc64bbc4 --- /dev/null +++ b/python/experiments/rag-and-tools/experiment.py @@ -0,0 +1,42 @@ +import os + +from galileo import GalileoScorers +from galileo.experiments import run_experiment + +from app import get_users_horoscope + + +def main(): + """ + Run the horoscope experiment + """ + # Define a dataset of astrological signs to use + # in the experiment + dataset = [ + {"input": "Aquarius"}, + {"input": "Taurus"}, + {"input": "Gemini"}, + {"input": "Leo"}, + ] + + # Run the experiment + results = run_experiment( + "horoscope-experiment-2", + dataset=dataset, + function=get_users_horoscope, + metrics=[ + GalileoScorers.tool_error_rate, + GalileoScorers.tool_selection_quality, + GalileoScorers.chunk_attribution_utilization, + GalileoScorers.context_adherence, + ], + project=os.environ["GALILEO_PROJECT"], + ) + + # Print a link to the experiment results + print("Experiment Results:") + print(results["link"]) + + +if __name__ == "__main__": + main() diff --git a/python/experiments/rag-and-tools/requirements.txt b/python/experiments/rag-and-tools/requirements.txt new file mode 100644 index 00000000..511a3cf0 --- /dev/null +++ b/python/experiments/rag-and-tools/requirements.txt @@ -0,0 +1,2 @@ +galileo[openai] +python-dotenv diff --git a/typescript/experiments/rag-and-tools/.env.example b/typescript/experiments/rag-and-tools/.env.example new file mode 100644 index 00000000..d3c7de87 --- /dev/null +++ b/typescript/experiments/rag-and-tools/.env.example @@ -0,0 +1,10 @@ +# Galileo Environment Variables +GALILEO_API_KEY= # Your Galileo API key. +GALILEO_PROJECT= # Your Galileo project name. +GALILEO_LOG_STREAM= # The name of the log stream you want to use for logging. + +# Provide the console url below if you are using a custom deployment, and not using app.galileo.ai +# GALILEO_CONSOLE_URL= # Optional if you are using a hosted version of Galileo + +# OpenAI Environment Variables +OPENAI_API_KEY= # Your OpenAI API key. diff --git a/typescript/experiments/rag-and-tools/README.md b/typescript/experiments/rag-and-tools/README.md new file mode 100644 index 00000000..408ed331 --- /dev/null +++ b/typescript/experiments/rag-and-tools/README.md @@ -0,0 +1,61 @@ +# Experiments with RAG and tools + +This is an example project demonstrating how to use Galileo experiments with applications thar use RAG and tools. + +This code is used in the [Run an experiment against a RAG app](http://v2docs.galileo.ai/how-to-guides/experiments/rag-app/rag-app) how-to guide in the Galileo documentation. + +## Get Started + +Install the dependencies: + +```bash +npm i +``` + +## Configure environment variables + +You will need to configure environment variables to use this project. Copy the `.env.example` file to `.env`, then update the environment variables in the `.env` file with your OpenAI and Galileo values: + +```ini +# Galileo environment variables +GALILEO_API_KEY= +GALILEO_PROJECT= +GALILEO_LOG_STREAM= + +# OpenAI environment variables +OPENAI_API_KEY= +``` + +## Usage + +This sample contains both an application that generates a fake horoscope using a tool and a mock RAG function, as well as an experiment to test the same code. + +To run the application, run: + +```bash +npx tsx app.ts +``` + +Traces will be captured and logged to Galileo. + +To run the experiment, run: + +```bash +npx tsx experiment.ts +``` + +A link to the results of the experiment will be written to the console. + +## Project Structure + +The project structure is as follows: + +```folder +rag-and-tools/ +├── env.example # List of environment variables +├── package.json # NPM project requirements +├── package-lock.json # NPM project requirements +├── app.ts # The main python application +├── experiment.ts # Code to run the main application as an experiment +└── README.md # Project documentation +``` diff --git a/typescript/experiments/rag-and-tools/app.ts b/typescript/experiments/rag-and-tools/app.ts new file mode 100644 index 00000000..94b6effe --- /dev/null +++ b/typescript/experiments/rag-and-tools/app.ts @@ -0,0 +1,155 @@ +import dotenv from "dotenv"; +import { OpenAI } from "openai"; +import { getLogger, wrapOpenAI } from "galileo"; +import { sleep } from "openai/core.mjs"; + +// Load environment variables from .env +dotenv.config(); + +// A mock RAG retriever function +async function retrieveHoroscopeData(sign: string): Promise { + const horoscopes: Record = { + Aquarius: [ + "Next Tuesday you will befriend a baby otter.", + "Next Tuesday you will find a dollar on the ground.", + ], + Taurus: [ + "Next Tuesday you will find a four-leaf clover.", + "Next Tuesday you will have a great conversation with a stranger.", + ], + Gemini: [ + "Next Tuesday you will learn to juggle.", + "Next Tuesday you will discover a new favorite book.", + ], + }; + const response = horoscopes[sign] ?? ["No horoscope available."]; + + await sleep(100); // Simulate RAG latency + + return response; +} + +// Tool function to get today's horoscope for a given astrological sign +async function getHoroscope({ sign }: { sign: string }): Promise { + const response = (await retrieveHoroscopeData(sign)).join("\n"); + + await sleep(100); // Simulate tool latency + + return response; +} + +// Define a list of callable tools for the model +const tools: any[] = [ + { + type: "function", + function: { + name: "get_horoscope", + description: "Get today's horoscope for an astrological sign.", + parameters: { + type: "object", + properties: { + sign: { + type: "string", + description: "An astrological sign like Taurus or Aquarius", + }, + }, + required: ["sign"], + }, + }, + }, +]; + +// Map tool names to their implementations +const availableTools: Record any> = { + get_horoscope: getHoroscope, +}; + +// Create the OpenAI client +const openai = wrapOpenAI(new OpenAI()); + +// Call the LLM with the provided messages and tools +async function callLlm(messages: any[]) { + const response = await openai.chat.completions.create({ + model: "gpt-5", + tools, + messages, + }); + + return response +} + +// Get the user's horoscope given a sign +export async function getUsersHoroscope(sign: string): Promise { + // Create a running message history list we will add to over time + const messageHistory: any[] = [ + { + role: "system", + content: `You are a helpful assistant that provides horoscopes. +Provide a flowery response based off any information retrieved. +Include typical horoscope phrases, and characteristics of +the sign in question. +`, + }, + { role: "user", content: `What is my horoscope? I am ${sign}.` }, + ]; + + // Prompt the model with tools defined + let response = await callLlm(messageHistory); + + // Add the tool call to the message history (if any) + const completionToolCalls = response.choices[0].message.tool_calls ?? []; + if (completionToolCalls.length > 0) { + messageHistory.push({ + role: "assistant", + tool_calls: completionToolCalls.map((tc: any) => ({ + id: tc.id, + type: "function", + function: { + name: tc.function.name, + arguments: tc.function.arguments, + }, + })), + }); + + // Call any tools the model requested + for (const call of completionToolCalls) { + const toolToCall = availableTools[call.function.name]; + const args = JSON.parse(call.function.arguments ?? "{}"); + const result = await toolToCall(args); + messageHistory.push({ + role: "tool", + content: String(result), + tool_call_id: call.id, + name: call.function.name, + }); + } + + // Now we call the model again, with the tool results included + response = await callLlm(messageHistory); + } + + return response.choices[0].message.content ?? ""; +} + +async function main() { + // Get the user's horoscope + + // Start a session and trace + const galileoLogger = getLogger(); + await galileoLogger.startSession({ name: "RAG with Tools Example" }); + galileoLogger.startTrace({ input: "What is my horoscope? I am Aquarius.", name: "Calling LLM with Tool" }); + + const response = await getUsersHoroscope("Aquarius"); + + // Conclude the trace and flush + galileoLogger.conclude({ output: response }); + await galileoLogger.flush(); + + console.log(response); +} + +(async () => { + if (require.main === module) { + await main(); + } +})(); \ No newline at end of file diff --git a/typescript/experiments/rag-and-tools/experiment.ts b/typescript/experiments/rag-and-tools/experiment.ts new file mode 100644 index 00000000..d7c727a8 --- /dev/null +++ b/typescript/experiments/rag-and-tools/experiment.ts @@ -0,0 +1,31 @@ +import dotenv from "dotenv"; +import { GalileoScorers, runExperiment } from "galileo"; + +import { getUsersHoroscope } from "./app.ts"; + +// Load environment variables from .env +dotenv.config(); + +(async () => { + // Run an experiment to evaluate horoscope retrieval + + const dataset = [{ input: "Aquarius" }, { input: "Taurus" }, { input: "Gemini" }, { input: "Leo" }]; + + const experiment = await runExperiment({ + name: "horoscope-experiment", + dataset, + function: async (row: { value: string }) => { + return await getUsersHoroscope(row.value) + }, + metrics: [ + GalileoScorers.ToolErrorRate, + GalileoScorers.ToolSelectionQuality, + GalileoScorers.ChunkAttributionUtilization, + GalileoScorers.ContextAdherence, + ], + projectName: process.env.GALILEO_PROJECT, + }); + + console.log("Experiment Results:"); + console.log(experiment); +})(); \ No newline at end of file diff --git a/typescript/experiments/rag-and-tools/package-lock.json b/typescript/experiments/rag-and-tools/package-lock.json new file mode 100644 index 00000000..4e8703d6 --- /dev/null +++ b/typescript/experiments/rag-and-tools/package-lock.json @@ -0,0 +1,1544 @@ +{ + "name": "rag-and-tools", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "dotenv": "^17.2.3", + "galileo": "^1.29.0", + "tsx": "^4.20.6" + } + }, + "node_modules/@cfworker/json-schema": { + 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b/typescript/experiments/rag-and-tools/package.json new file mode 100644 index 00000000..ab7b9b8b --- /dev/null +++ b/typescript/experiments/rag-and-tools/package.json @@ -0,0 +1,7 @@ +{ + "dependencies": { + "dotenv": "^17.2.3", + "galileo": "^1.29.0", + "tsx": "^4.20.6" + } +} diff --git a/typescript/galileo-1.4.0.tgz b/typescript/galileo-1.4.0.tgz deleted file mode 100644 index 5101971b..00000000 Binary files a/typescript/galileo-1.4.0.tgz and /dev/null differ diff --git a/typescript/galileo-sdk-documentation.md b/typescript/galileo-sdk-documentation.md deleted file mode 100644 index d182ab93..00000000 --- a/typescript/galileo-sdk-documentation.md +++ /dev/null @@ -1,611 +0,0 @@ -# Galileo SDK Documentation - -## Introduction - -Galileo is an observability platform for LLM applications. The Galileo SDK provides tools to monitor, debug, and improve AI systems by tracking inputs, outputs, and performance metrics. This documentation covers the installation, setup, and usage of the Galileo SDK for TypeScript/JavaScript applications. - -## Table of Contents - -1. [Installation](#installation) -2. [Getting Started](#getting-started) -3. [Core Concepts](#core-concepts) -4. [API Reference](#api-reference) - - [Initialization](#initialization) - - [Logging](#logging) - - [OpenAI Integration](#openai-integration) - - [Workflow Management](#workflow-management) - - [Utility Functions](#utility-functions) -5. [Use Cases](#use-cases) - - [Simple Chatbot](#simple-chatbot) - - [RAG Applications](#rag-applications) - - [Agent Systems](#agent-systems) -6. [Advanced Features](#advanced-features) -7. [Troubleshooting](#troubleshooting) - -## Installation - -### Standard Installation - -```bash -npm install galileo -``` - -### Installation without Optional Dependencies - -```bash -npm install galileo --no-optional -``` - -## Getting Started - -### Basic Setup - -1. Import the necessary functions from the Galileo package: - -```typescript -import { init, flush, wrapOpenAI } from 'galileo'; -import { OpenAI } from 'openai'; -``` - -2. Initialize Galileo with your project and log stream names: - -```typescript -init({ - projectName: 'my-project', - logStreamName: 'development' -}); -``` - -3. Wrap your OpenAI client for automatic logging: - -```typescript -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); -``` - -4. Use the wrapped client as you would normally: - -```typescript -const response = await openai.chat.completions.create({ - model: "gpt-4o", - messages: [{ content: "Hello, how can I help you today?", role: "user" }], -}); -``` - -5. Flush logs before your application exits: - -```typescript -await flush(); -``` - -### Environment Variables - -You can configure Galileo using environment variables: - -``` -# Galileo Environment Variables -GALILEO_API_KEY=your-galileo-api-key # Your Galileo API key. -GALILEO_PROJECT=your-galileo-project-name # Your Galileo project name. -GALILEO_LOG_STREAM=your-galileo-log-stream # The name of the log stream you want to use for logging. - -# Provide the console url below if you are using a custom deployment, and not using app.galileo.ai -# GALILEO_CONSOLE_URL=your-galileo-console-url # Optional if you are using a hosted version of Galileo -``` - -## Core Concepts - -### Traces and Spans - -Galileo uses a tracing model similar to distributed tracing systems: - -- **Trace**: Represents a complete user interaction or workflow -- **Span**: Represents a single operation within a trace (e.g., an LLM call, a retrieval operation, or a tool execution) - -### Span Types - -Galileo supports several span types: - -- **LLM**: For language model interactions -- **Retriever**: For document retrieval operations in RAG systems -- **Tool**: For function calls and tool usage -- **Workflow**: For grouping related spans - -## API Reference - -### Initialization - -#### `init(options)` - -Initializes the Galileo client with project and log stream information. - -```typescript -init({ - projectName: 'my-project', - logStreamName: 'development' -}); -``` - -Parameters: -- `options.projectName` (optional): The name of your project -- `options.logStreamName` (optional): The name of your log stream - -If not provided, these values will be read from environment variables `GALILEO_PROJECT` and `GALILEO_LOG_STREAM`. - -#### `flush()` - -Uploads all captured traces to the Galileo platform. - -```typescript -await flush(); -``` - -### Logging - -#### `log(options, fn)` - -Wraps a function to log its execution as a span in Galileo. - -```typescript -const myLoggedFunction = log( - { spanType: 'tool', name: 'my-function' }, - async (param1, param2) => { - // Function implementation - return result; - } -); -``` - -Parameters: -- `options.spanType`: The type of span ('llm', 'retriever', 'tool', or 'workflow') -- `options.name`: A name for the span -- `options.params`: Additional parameters to log -- `fn`: The function to wrap - -### OpenAI Integration - -#### `wrapOpenAI(openAIClient, logger?)` - -Wraps an OpenAI client to automatically log all chat completions. - -```typescript -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); -``` - -Parameters: -- `openAIClient`: The OpenAI client instance to wrap -- `logger` (optional): A custom GalileoLogger instance - -### Workflow Management - -#### GalileoLogger Class - -The core class for managing traces and spans. - -```typescript -import { GalileoLogger } from 'galileo'; - -const logger = new GalileoLogger({ - projectName: 'my-project', - logStreamName: 'development' -}); -``` - -##### Methods - -###### `startTrace(input, output?, name?, createdAt?, durationNs?, userMetadata?, tags?)` - -Starts a new trace. - -```typescript -logger.startTrace('User query', undefined, 'user-interaction'); -``` - -###### `addLlmSpan(options)` - -Adds an LLM span to the current trace. - -```typescript -logger.addLlmSpan({ - input: messages, - output: response, - model: 'gpt-4o', - name: 'completion-generation' -}); -``` - -###### `addRetrieverSpan(input, output, name?, durationNs?, createdAt?, userMetadata?, tags?, statusCode?)` - -Adds a retriever span to the current trace. - -```typescript -logger.addRetrieverSpan( - 'search query', - retrievedDocuments, - 'document-retrieval' -); -``` - -###### `addToolSpan(input, output?, name?, durationNs?, createdAt?, userMetadata?, tags?, statusCode?, toolCallId?)` - -Adds a tool span to the current trace. - -```typescript -logger.addToolSpan( - JSON.stringify(toolInput), - JSON.stringify(toolOutput), - 'calculator-tool' -); -``` - -###### `addWorkflowSpan(input, output?, name?, durationNs?, createdAt?, userMetadata?, tags?)` - -Adds a workflow span to the current trace. - -```typescript -logger.addWorkflowSpan( - 'workflow input', - undefined, - 'user-workflow' -); -``` - -###### `conclude(options)` - -Concludes the current span or trace. - -```typescript -logger.conclude({ - output: 'Final result', - durationNs: performance.now() - startTime -}); -``` - -### Utility Functions - -#### Project Management - -```typescript -import { getProjects, createProject, getProject } from 'galileo'; - -// Get all projects -const projects = await getProjects(); - -// Create a new project -const newProject = await createProject('My New Project'); - -// Get a specific project -const project = await getProject('My Project'); -``` - -#### Log Stream Management - -```typescript -import { getLogStreams, createLogStream, getLogStream } from 'galileo'; - -// Get all log streams for a project -const logStreams = await getLogStreams('My Project'); - -// Create a new log stream -const newLogStream = await createLogStream('My Project', 'production'); - -// Get a specific log stream -const logStream = await getLogStream('My Project', 'development'); -``` - -#### Dataset Management - -```typescript -import { getDatasets, createDataset, getDatasetContent, getDataset } from 'galileo'; - -// Get all datasets -const datasets = await getDatasets(); - -// Create a new dataset -const newDataset = await createDataset('My Dataset'); - -// Get dataset content -const content = await getDatasetContent('My Dataset'); - -// Get a specific dataset -const dataset = await getDataset('My Dataset'); -``` - -## Use Cases - -### Simple Chatbot - -```typescript -import { init, flush, wrapOpenAI } from 'galileo'; -import { OpenAI } from 'openai'; -import dotenv from 'dotenv'; - -dotenv.config(); - -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -async function run() { - // Initialize Galileo - init({ - projectName: 'chatbot-project', - logStreamName: 'development' - }); - - // Make an OpenAI request (automatically logged) - const result = await openai.chat.completions.create({ - model: "gpt-4o", - messages: [{ content: "Say hello world!", role: "user" }], - }); - - console.log(result.choices[0].message.content); - - // Flush logs before exiting - await flush(); -} - -run().catch(console.error); -``` - -### RAG Applications - -```typescript -import { log, init, flush, wrapOpenAI } from 'galileo'; -import { OpenAI } from 'openai'; - -// Initialize Galileo -init({ - projectName: 'rag-project', - logStreamName: 'development' -}); - -const openai = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -// Create a logged retriever function -const retrieveDocuments = log( - { spanType: 'retriever' }, - async (query) => { - // Implement your retrieval logic here - return documents; - } -); - -// Main RAG function -async function rag(query) { - // Retrieve documents (logged as a retriever span) - const documents = await retrieveDocuments(query); - - // Format documents for the prompt - const formattedDocs = documents.map((doc, i) => - `Document ${i+1}: ${doc.text}` - ).join('\n\n'); - - // Create the prompt - const prompt = ` - Answer the following question based on the context provided: - - Question: ${query} - - Context: - ${formattedDocs} - `; - - // Generate response (automatically logged as an LLM span) - const response = await openai.chat.completions.create({ - model: "gpt-4o", - messages: [ - { role: "system", content: "You are a helpful assistant." }, - { role: "user", content: prompt } - ], - }); - - return response.choices[0].message.content; -} - -// Example usage -async function main() { - const answer = await rag("What is Galileo?"); - console.log(answer); - - // Flush logs before exiting - await flush(); -} - -main().catch(console.error); -``` - -### Agent Systems - -```typescript -import { log, init, flush, wrapOpenAI } from 'galileo'; -import { OpenAI } from 'openai'; - -// Initialize Galileo -init({ - projectName: 'agent-project', - logStreamName: 'development' -}); - -const client = wrapOpenAI(new OpenAI({ apiKey: process.env.OPENAI_API_KEY })); - -// Define tools with logging -const calculator = log( - { spanType: 'tool', name: 'calculator' }, - async (expression) => { - try { - const result = Function(`'use strict'; return (${expression})`)(); - return `The result of ${expression} is ${result}`; - } catch (error) { - return `Error calculating ${expression}: ${error.message}`; - } - } -); - -// Main processing function with logging -const processQuery = log( - { spanType: 'workflow' }, - async (query) => { - // Initialize conversation - const messages = [ - { role: "system", content: "You are a helpful assistant that can use tools." }, - { role: "user", content: query } - ]; - - // Define tools for the model - const tools = [ - { - type: "function", - function: { - name: "calculator", - description: "Calculate the result of a mathematical expression", - parameters: { - type: "object", - properties: { - expression: { - type: "string", - description: "The mathematical expression to evaluate" - } - }, - required: ["expression"] - } - } - } - ]; - - // Agent loop - while (true) { - // Get next action from LLM - const response = await client.chat.completions.create({ - model: "gpt-4o", - messages: messages, - tools: tools, - }); - - const assistantMessage = response.choices[0].message; - messages.push(assistantMessage); - - // Check if LLM is done - if (!assistantMessage.tool_calls || assistantMessage.tool_calls.length === 0) { - return assistantMessage.content; - } - - // Process tool calls - for (const toolCall of assistantMessage.tool_calls) { - const functionName = toolCall.function.name; - const functionArgs = JSON.parse(toolCall.function.arguments); - - // Execute the appropriate tool - let result; - if (functionName === "calculator") { - result = await calculator(functionArgs.expression); - } else { - result = `Unknown tool: ${functionName}`; - } - - // Add tool result to conversation - messages.push({ - tool_call_id: toolCall.id, - role: "tool", - name: functionName, - content: result - }); - } - } - } -); - -// Example usage -async function main() { - const result = await processQuery("What is 123 * 456?"); - console.log(result); - - // Flush logs before exiting - await flush(); -} - -main().catch(console.error); -``` - -## Advanced Features - -### Custom Metadata - -You can add custom metadata to spans for additional context: - -```typescript -logger.addLlmSpan({ - input: messages, - output: response, - model: 'gpt-4o', - metadata: { - user_id: 'user-123', - session_id: 'session-456', - feature: 'product-search' - } -}); -``` - -### Tagging - -Add tags to spans for easier filtering and analysis: - -```typescript -logger.addToolSpan( - toolInput, - toolOutput, - 'search-tool', - undefined, - undefined, - undefined, - ['production', 'search-feature', 'v2'] -); -``` - -### Performance Metrics - -Track performance metrics for spans: - -```typescript -const startTime = performance.now(); -// ... perform operation -const endTime = performance.now(); - -logger.addLlmSpan({ - input: messages, - output: response, - model: 'gpt-4o', - durationNs: (endTime - startTime) * 1000000, // Convert ms to ns - numInputTokens: inputTokenCount, - numOutputTokens: outputTokenCount, - totalTokens: totalTokenCount -}); -``` - -## Troubleshooting - -### Common Issues - -1. **Missing API Key** - - Ensure that the `GALILEO_API_KEY` environment variable is set or that you're providing the API key directly. - -2. **Logs Not Appearing** - - Make sure you're calling `flush()` before your application exits to ensure all logs are sent to the Galileo platform. - -3. **Incorrect Project or Log Stream Names** - - Verify that the project and log stream names you're using exist in your Galileo account. - -### Debugging - -Enable debug logging by setting the `GALILEO_DEBUG` environment variable: - -``` -GALILEO_DEBUG=true -``` - -This will output additional information about the SDK's operations to help diagnose issues. - -## Conclusion - -The Galileo SDK provides comprehensive tools for monitoring and debugging LLM applications. By integrating Galileo into your application, you can gain valuable insights into your AI system's performance, identify issues, and improve the quality of your AI-powered features. - -For more information, visit the [Galileo website](https://www.rungalileo.io/) or contact support at support@rungalileo.io. \ No newline at end of file