diff --git a/README.md b/README.md index a0f932d..ede0b7e 100644 --- a/README.md +++ b/README.md @@ -68,6 +68,7 @@ For more examples, you may also find our [Blog](https://haystack.deepset.ai/blog | Cohere for Multilingual QA (Haystack 1.x)| Open In Colab| | GPT-4 and Weaviate for Custom Documentation QA (Haystack 1.x)| Open In Colab| | Whisper Transcriber and Weaviate for YouTube video QA (Haystack 1.x)| Open In Colab| +| Trace and Evaluate an Agent with Arize Phoenix | Open In Colab| ## How to Contribute a Cookbook diff --git a/index.toml b/index.toml index 2de2328..d18b1ee 100644 --- a/index.toml +++ b/index.toml @@ -255,3 +255,9 @@ title = "Agentic RAG with Llama 3.2 3B" notebook = "llama32_agentic_rag.ipynb" topics = ["RAG", "Agents", "Web-QA"] new = true + +[[cookbook]] +title = "Trace and Evaluate an Agent with Arize Phoenix" +notebook = "arize_phoenix_tracing_and_evaluating_an_agent.ipynb" +topics = ["Observability", "Evaluation", "Agents"] +new = true diff --git a/notebooks/arize_phoenix_tracing_and_evaluating_an_agent.ipynb b/notebooks/arize_phoenix_tracing_and_evaluating_an_agent.ipynb new file mode 100644 index 0000000..d15a41a --- /dev/null +++ b/notebooks/arize_phoenix_tracing_and_evaluating_an_agent.ipynb @@ -0,0 +1,6209 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "HJsHqaTksDHt" + }, + "source": [ + "# Tutorial: Building a Chat Application with Function Calling\n", + "\n", + "- **Level**: Advanced\n", + "- **Time to complete**: 20 minutes\n", + "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder), [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n", + "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)\n", + "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n", + "\n", + "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PWXXqq_MPn7y" + }, + "source": [ + "## Overview\n", + "\n", + "\n", + "\n", + "📚 Useful Sources:\n", + "* [OpenAIChatGenerator Docs](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n", + "* [OpenAIChatGenerator API Reference](https://docs.haystack.deepset.ai/reference/generator-api#openaichatgenerator)\n", + "* [🧑‍🍳 Cookbook: Function Calling with OpenAIChatGenerator](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/function_calling_with_OpenAIChatGenerator.ipynb)\n", + "\n", + "[OpenAI's function calling](https://platform.openai.com/docs/guides/function-calling) connects large language models to external tools. By providing a `tools` list with functions and their specifications to the OpenAI API calls, you can easily build chat assistants that can answer questions by calling external APIs or extract structured information from text.\n", + "\n", + "In this tutorial, you'll learn how to convert your Haystack pipeline into a function-calling tool and how to implement applications using OpenAI's Chat Completion API through `OpenAIChatGenerator` for agent-like behavior." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K04cnh_IleMV" + }, + "source": [ + "## Setting up the Development Environment\n", + "\n", + "Install Haystack 2.0 and [sentence-transformers](https://pypi.org/project/sentence-transformers/) using pip:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zNyqNVFaPN1A" + }, + "outputs": [], + "source": [ + "%%bash\n", + "\n", + "pip install haystack-ai\n", + "pip install \"sentence-transformers>=3.0.0\"\n", + "pip install arize-phoenix openinference-instrumentation-haystack" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XwlBLN-GwuuU" + }, + "source": [ + "### Enable Telemetry\n", + "\n", + "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/docs/telemetry) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9V0-VuEgwt2u" + }, + "outputs": [], + "source": [ + "from haystack.telemetry import tutorial_running\n", + "\n", + "tutorial_running(40)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2owelT4NpXtw" + }, + "source": [ + "Save your OpenAI API key as an environment variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WM-sVkYonutA" + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "if \"OPENAI_API_KEY\" not in os.environ:\n", + " os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add Observability through Phoenix 🐦‍🔥\n", + "\n", + "Phoenix is an LLM observability and evaluation platform that helps developers monitor, evaluate, and improve the performance of their models. We'll use it to instrument our application and collect data on its behavior, and later evaluate the performance of our chatbot.\n", + "\n", + "To learn more about Phoenix, visit the [website](https://phoenix.arize.com/) or [GitHub repo](https://github.com/arize-ai/phoenix)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Launch the Phoenix App\n", + "\n", + "First we need to launch a local instance of the Phoenix app. Phoenix also provides cloud and self-hosting options you could use instead. If you decide to use one of those hosting options, you can skip the cell below and go straight to [Connecting our application to Phoenix](#connecting-our-application-to-phoenix)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix as px\n", + "px.launch_app()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect our application to Phoenix\n", + "\n", + "Now we need to connect our notebook to the Phoenix app, and instrument our application. Instrumentation is the process of marking specific parts of our application to be tracked by Phoenix. Phoenix luckily provides an automatic way to instrument Haystack." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openinference.instrumentation.haystack import HaystackInstrumentor\n", + "from phoenix.otel import register\n", + "\n", + "# Connect to our Phoenix instance:\n", + "project_name = \"haystack-tutorials-2\"\n", + "tracer_provider = register(project_name=project_name)\n", + "\n", + "# Instrument Haystack:\n", + "HaystackInstrumentor().instrument(tracer_provider=tracer_provider)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, any calls to Haystack pipelines will be traced and logged to Phoenix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating our Agent" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FW91cHchCw_W" + }, + "source": [ + "## Creating a Function Calling Tool from a Haystack Pipeline\n", + "\n", + "To use the function calling of OpenAI, you need to introduce `tools` to your `OpenAIChatGenerator` using its `generation_kwargs` param.\n", + "\n", + "For this example, you'll use a Haystack RAG pipeline as one of your tools. Therefore, you need to index documents to a document store and then build a RAG pipeline on top of it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FWkDXKbeoNqZ" + }, + "source": [ + "### Index Documents with a Pipeline\n", + "\n", + "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) with their embeddings. You will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter).\n", + "\n", + "After adding these components to your pipeline, connect them and run the pipeline.\n", + "\n", + "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 577, + "referenced_widgets": [ + "041743b7cebb47458bfa61e945699655", + "6cb42b06bb31411b8085c18939cebc3f", + "b4f7ac0f0a69402f84cae275f256f3fb", + "9690c99367c644b79b6000862b19ded9", + 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+ "outputs": [], + "source": [ + "from haystack import Pipeline, Document\n", + "from haystack.document_stores.in_memory import InMemoryDocumentStore\n", + "from haystack.components.writers import DocumentWriter\n", + "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n", + "\n", + "documents = [\n", + " Document(content=\"My name is Jean and I live in Paris.\"),\n", + " Document(content=\"My name is Mark and I live in Berlin.\"),\n", + " Document(content=\"My name is Giorgio and I live in Rome.\"),\n", + " Document(content=\"My name is Marta and I live in Madrid.\"),\n", + " Document(content=\"My name is Harry and I live in London.\"),\n", + "]\n", + "\n", + "document_store = InMemoryDocumentStore()\n", + "\n", + "indexing_pipeline = Pipeline()\n", + "indexing_pipeline.add_component(\n", + " instance=SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n", + ")\n", + "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n", + "\n", + "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n", + "\n", + "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dwC6fWQy-7pI" + }, + "source": [ + "### Build a RAG Pipeline\n", + "\n", + "Build a basic retrieval augmented generative pipeline with [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) and [OpenAIGenerator](https://docs.haystack.deepset.ai/docs/openaigenerator).\n", + "\n", + "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "23JuUuql7PZ8", + "outputId": "b33cbdf5-93d3-46c2-d618-ed9bb76c2d4c" + }, + "outputs": [], + "source": [ + "from haystack.components.embedders import SentenceTransformersTextEmbedder\n", + "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n", + "from haystack.components.builders import PromptBuilder\n", + "from haystack.components.generators import OpenAIGenerator\n", + "\n", + "template = \"\"\"\n", + "Answer the questions based on the given context.\n", + "\n", + "Context:\n", + "{% for document in documents %}\n", + " {{ document.content }}\n", + "{% endfor %}\n", + "Question: {{ question }}\n", + "Answer:\n", + "\"\"\"\n", + "rag_pipe = Pipeline()\n", + "rag_pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n", + "rag_pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n", + "rag_pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n", + "rag_pipe.add_component(\"llm\", OpenAIGenerator(model=\"gpt-3.5-turbo\"))\n", + "\n", + "rag_pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n", + "rag_pipe.connect(\"retriever\", \"prompt_builder.documents\")\n", + "rag_pipe.connect(\"prompt_builder\", \"llm\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3vjtLi5A-XAi" + }, + "source": [ + "### Run the Pipeline\n", + "Test this pipeline with a query and see if it works as expected before you start using it as a function calling tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 171, + "referenced_widgets": [ + "6f4a57e2a1ea45bd98c7b93dd44f7059", + "5b4bd1a310064a7a9c7a59df8207aa99", + "1bad78781754402cb69bd8a184a4837d", + "82f9052fbc3649feb3265a3fd923f8ac", + "8468f0f89a26485ebc51ffdd6e69746d", + "15bce8c8eaac422d8cf65d60df2b467e", + "470dca09340f481fb381f18aac4da630", + "c9c5e00085874c76b587031841b906ba", + "86df742e769c492fa274fecf84e38f33", + "2bb05097cf4e4f18afbc2a1c32047ddb", + "7879458eca5a461ab42a02d5fcd4d7fa" + ] + }, + "id": "h4bXXC2Z7eaj", + "outputId": "c069ff2a-7399-4605-f3cc-998c1ebe1504" + }, + "outputs": [], + "source": [ + "query = \"Where does Mark live?\"\n", + "rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xeQf1QT0_OA0" + }, + "source": [ + "### Convert the Haystack Pipeline into a Tool\n", + "\n", + "Wrap the `rag_pipe.run` call with a function called `rag_pipeline_func`. This `rag_pipeline_func` function will accept a `query` and return the response coming from the LLM of the RAG pipeline you built before. You will then introduce this function as a tool to your `OpenAIChatGenerator`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hCUFdKV5_Z7k" + }, + "outputs": [], + "source": [ + "def rag_pipeline_func(query: str):\n", + " result = rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})\n", + "\n", + " return {\"reply\": result[\"llm\"][\"replies\"][0]}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zJt-mzb4oHxj" + }, + "source": [ + "## Creating Your `tools` List\n", + "\n", + "In addition to the `rag_pipeline_func` tool, create a new tool called `get_current_weather` to be used to get weather information of cities. For demonstration purposes, you can use hardcoded data in this function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XaQgnmPoJD13" + }, + "outputs": [], + "source": [ + "WEATHER_INFO = {\n", + " \"Berlin\": {\"weather\": \"mostly sunny\", \"temperature\": 7, \"unit\": \"celsius\"},\n", + " \"Paris\": {\"weather\": \"mostly cloudy\", \"temperature\": 8, \"unit\": \"celsius\"},\n", + " \"Rome\": {\"weather\": \"sunny\", \"temperature\": 14, \"unit\": \"celsius\"},\n", + " \"Madrid\": {\"weather\": \"sunny\", \"temperature\": 10, \"unit\": \"celsius\"},\n", + " \"London\": {\"weather\": \"cloudy\", \"temperature\": 9, \"unit\": \"celsius\"},\n", + "}\n", + "\n", + "\n", + "def get_current_weather(location: str):\n", + " if location in WEATHER_INFO:\n", + " return WEATHER_INFO[location]\n", + "\n", + " # fallback data\n", + " else:\n", + " return {\"weather\": \"sunny\", \"temperature\": 21.8, \"unit\": \"fahrenheit\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M6Pfmf7YSwsL" + }, + "source": [ + "Now, add function specifications for `rag_pipeline_func` and `get_current_weather` to your `tools` list by following [OpenAI's tool schema](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools). Provide detailed descriptions about `rag_pipeline_func` and `query` so that OpenAI can generate the adaquate arguments for this tool." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rfYYSytSVtPm" + }, + "outputs": [], + "source": [ + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"rag_pipeline_func\",\n", + " \"description\": \"Get information about where people live\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The query to use in the search. Infer this from the user's message. It should be a question or a statement\",\n", + " }\n", + " },\n", + " \"required\": [\"query\"],\n", + " },\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"get_current_weather\",\n", + " \"description\": \"Get the current weather\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"location\": {\"type\": \"string\", \"description\": \"The city and state, e.g. San Francisco, CA\"}\n", + " },\n", + " \"required\": [\"location\"],\n", + " },\n", + " },\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bkRPp3JKpZgf" + }, + "source": [ + "## Running OpenAIChatGenerator with Tools\n", + "\n", + "To use the function calling feature, you need to pass the list of tools in the `run()` method of OpenAIChatGenerator as `generation_kwargs`.\n", + "\n", + "Instruct the model to use provided tools with a system message and then provide a query that requires a function call as a user message:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OEScMyqctzFN" + }, + "outputs": [], + "source": [ + "from haystack.dataclasses import ChatMessage\n", + "from haystack.components.generators.chat import OpenAIChatGenerator\n", + "from haystack.components.generators.utils import print_streaming_chunk\n", + "\n", + "messages = [\n", + " ChatMessage.from_system(\n", + " \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n", + " ),\n", + " ChatMessage.from_user(\"Can you tell me where Mark lives?\"),\n", + "]\n", + "\n", + "agent = Pipeline()\n", + "agent.add_component(\"chat_generator\", OpenAIChatGenerator(model=\"gpt-3.5-turbo\", streaming_callback=print_streaming_chunk))\n", + "response = agent._run_component(\"chat_generator\", {\"messages\":messages, \"generation_kwargs\":{\"tools\": tools}})\n", + "response" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zYwb-SqaGL3O" + }, + "source": [ + "As a response, you'll get a `ChatMessage` with information about the tool name and arguments in JSON format:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "{'replies': [\n", + " ChatMessage(\n", + " content='[{\"index\": 0, \"id\": \"call_3VnT0XQH0ye41g3Ip5CRz4ri\", \"function\": {\"arguments\": \"{\\\\\"query\\\\\":\\\\\"Where does Mark live?\\\\\"}\", \"name\": \"rag_pipeline_func\"}, \"type\": \"function\"}]', role=, \n", + " name=None, \n", + " meta={'model': 'gpt-3.5-turbo-0125', 'index': 0, 'finish_reason': 'tool_calls', 'usage': {}}\n", + " )\n", + " ]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_ulFfHcnGfsw" + }, + "source": [ + "You can then parse the message content string into JSON and call the corresponding function with the provided arguments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 101, + "referenced_widgets": [ + "910f6e07a2b843158e74a297f4934f97", + "67e6a7663e2c4ed2bcc6720b3f0d2304", + "6003f08f432242f7abd500f0c8288bde", + "633c9d1b9b884dc7b3344706ac59cfe1", + "bbae8eacb99e44c187fba6130185b391", + "20a67d4e977944ea946ed1c1c379f12d", + "3d91c901b1e64eb292581d79d6aa8377", + "ca4662baf35d4b9bb11757bc6c777341", + "88b71d22e27a4df4aa0dc09cb55f69d2", + "6e7f64f64b174118b512641642ce58d8", + "3e4b40347d844c51a62d90a7f20d25d8" + ] + }, + "id": "JkU5S2t_Id_f", + "outputId": "1b2776ec-a53f-4cd6-ebd8-cca1eb3f7a46" + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "## Parse function calling information\n", + "function_call = json.loads(response[\"replies\"][0].content)[0]\n", + "function_name = function_call[\"function\"][\"name\"]\n", + "function_args = json.loads(function_call[\"function\"][\"arguments\"])\n", + "print(\"Function Name:\", function_name)\n", + "print(\"Function Arguments:\", function_args)\n", + "\n", + "## Find the correspoding function and call it with the given arguments\n", + "available_functions = {\"rag_pipeline_func\": rag_pipeline_func, \"get_current_weather\": get_current_weather}\n", + "function_to_call = available_functions[function_name]\n", + "function_response = function_to_call(**function_args)\n", + "print(\"Function Response:\", function_response)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5UkjGYEfwPzS" + }, + "source": [ + "## Building the Chat Application\n", + "\n", + "As you notice above, OpenAI Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code. That's why, to build an end-to-end chat application, you need to check if the OpenAI response is a `tool_calls` for every message. If so, you need to call the corresponding function with the provided arguments and send the function response back to OpenAI. Otherwise, append both user and messages to the `messages` list to have a regular conversation with the model. \n", + "\n", + "To build a nice UI for your application, you can use [Gradio](https://www.gradio.app/) that comes with a chat interface. Install `gradio`, run the code cell below and use the input box to interact with the chat application that has access to two tools you've created above. \n", + "\n", + "Example queries you can try:\n", + "* \"***What is the capital of Sweden?***\": A basic query without any function calls\n", + "* \"***Can you tell me where Giorgio lives?***\": A basic query with one function call\n", + "* \"***What's the weather like in Berlin?***\", \"***Is it sunny there?***\": To see the messages are being recorded and sent\n", + "* \"***What's the weather like where Jean lives?***\": To force two function calls\n", + "* \"***What's the weather like today?***\": To force OpenAI to ask more clarification\n", + "\n", + "> Keep in mind that OpenAI models can sometimes hallucinate answers or tools and might not work as expected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 153, + "referenced_widgets": [ + "235721f8c1c14ce3885fba24638bc27f", + "512686ee6df7438ca249d4d3cf71339f", + "bfa94cb046944ec28573b99b251e5b63", + "3d1550f31d82429b9f502ff8a029feb3", + "8013ed5cc4b44f3a811a9b84f4f31117", + "b964f88add2b434a93470693ef46ee3b", + "bffb93bc214042e2a123aca51795784a", + "384f9461abe640f0bb963dfc377d8a0e", + "ba23159c36d442098ea77b188ac6f23f", + "6ce7c086bd9348708f3ec399011af452", + "c96c3d7d4aba49d29d30f69e6114a78c" + ] + }, + "id": "sK_JeKZLhXcy", + "outputId": "b0c8c776-4151-44a0-9acd-b12f53119af8" + }, + "outputs": [], + "source": [ + "import json\n", + "from typing import List\n", + "from haystack.dataclasses import ChatMessage\n", + "from haystack.components.generators.chat import OpenAIChatGenerator\n", + "from haystack import Pipeline, component\n", + "\n", + "chat_generator = OpenAIChatGenerator(model=\"gpt-3.5-turbo\")\n", + "response = None\n", + "messages = [\n", + " ChatMessage.from_system(\n", + " \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n", + " )\n", + "]\n", + "\n", + "@component\n", + "class ToolHandler():\n", + " @component.output_types(final_response=ChatMessage)\n", + " def run(self, messages: List[ChatMessage]):\n", + " # return {\"final_response\": ChatMessage.from_user(\"test\")}\n", + " response = messages[-1]\n", + " # if OpenAI response is a tool call\n", + " if response and response.meta and response.meta[\"finish_reason\"] == \"tool_calls\":\n", + " function_calls = json.loads(response.content)\n", + " print(response.content)\n", + " for function_call in function_calls:\n", + " ## Parse function calling information\n", + " function_name = function_call[\"function\"][\"name\"]\n", + " function_args = json.loads(function_call[\"function\"][\"arguments\"])\n", + "\n", + " ## Find the correspoding function and call it with the given arguments\n", + " function_to_call = available_functions[function_name]\n", + " function_response = function_to_call(**function_args)\n", + "\n", + " ## Append function response to the messages list using `ChatMessage.from_function`\n", + " messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\n", + " return self.run(messages)\n", + " \n", + " # Regular Conversation\n", + " else:\n", + " return {\"final_response\": response}\n", + "\n", + "def chatbot_with_fc(message, history):\n", + " messages.append(ChatMessage.from_user(message))\n", + " agent = Pipeline()\n", + " agent.add_component(\"chat_generator\", OpenAIChatGenerator(model=\"gpt-3.5-turbo\"))\n", + " agent.add_component(\"tool_handler\", ToolHandler())\n", + " agent.connect(\"chat_generator\", \"tool_handler\")\n", + " \n", + " response = agent.run({\"messages\": messages, \"generation_kwargs\": {\"tools\": tools}})\n", + " \n", + " if response['tool_handler'][\"final_response\"] is None:\n", + " return \"no response\"\n", + " else:\n", + " return response['tool_handler'][\"final_response\"].content\n", + "\n", + "## Uncomment the line below to launch the chat app with UI\n", + "# demo.launch()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chatbot_with_fc(\"Can you tell me where Giorgio lives?\", [])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluate our Agent\n", + "\n", + "We can now evaluate any part of our agent. We'll focus on two areas in this example:\n", + "* **RAG Pipeline**: Evaluate the performance of the RAG pipeline.\n", + "* **Function Calling**: Evaluate the performance of the function calling tool.\n", + "\n", + "We'll use Phoenix for each of these evaluations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RAG Pipeline Relevancy Evaluation\n", + "\n", + "All evaluations in Phoenix follow the same process:\n", + "1. Export data from your Phoenix project.\n", + "2. Run some form of evaluation, either using Phoenix or not.\n", + "3. Import the results into Phoenix\n", + "\n", + "In this example, we'll evaluate the relevancy of the documents retrieved by the RAG pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "import phoenix as px\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.session.evaluation import get_retrieved_documents\n", + "\n", + "client = px.Client()\n", + "\n", + "retrieved_documents_df = get_retrieved_documents(client, project_name=project_name)\n", + "retrieved_documents_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spans_df = client.get_spans_dataframe(project_name=project_name)\n", + "\n", + "def get_input_from_trace_id(trace_id):\n", + " inputs = spans_df.loc[spans_df[\"context.trace_id\"] == trace_id, \"attributes.llm.input_messages\"].values\n", + " for i in inputs:\n", + " if i is not None:\n", + " return i\n", + " return None\n", + "\n", + "retrieved_documents_df[\"input\"] = retrieved_documents_df[\"context.trace_id\"].map(get_input_from_trace_id)\n", + "retrieved_documents_df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.evals import OpenAIModel, RelevanceEvaluator, run_evals\n", + "\n", + "relevance_evaluator = RelevanceEvaluator(OpenAIModel(model=\"gpt-4o-mini\"))\n", + "\n", + "retrieved_documents_relevance_df = run_evals(\n", + " evaluators=[relevance_evaluator],\n", + " dataframe=retrieved_documents_df,\n", + " provide_explanation=True,\n", + " concurrency=20,\n", + ")[0]\n", + "retrieved_documents_relevance_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.trace import DocumentEvaluations\n", + "\n", + "px.Client().log_evaluations(\n", + " DocumentEvaluations(dataframe=retrieved_documents_relevance_df, eval_name=\"Relevance\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Function Calling Evaluation\n", + "\n", + "Next we can evaluate how good our agent is at calling the right function for a given query.\n", + "\n", + "We'll use the same process as above:\n", + "1. Export data from your Phoenix project.\n", + "2. Run some form of evaluation, either using Phoenix or not.\n", + "3. Import the results into Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "client = px.Client()\n", + "\n", + "def extract_function_from_output_message(output_message):\n", + " if output_message is not None:\n", + " message_string = str(output_message)\n", + " if \"current_weather\" in message_string:\n", + " return \"get_current_weather\"\n", + " elif \"rag_pipeline_func\" in message_string:\n", + " return \"rag_pipeline_func\"\n", + " return None\n", + "\n", + "functions_df = client.get_spans_dataframe(\"'func' in output.value\", project_name=project_name)\n", + "functions_df = functions_df[[\"attributes.llm.output_messages\", \"attributes.llm.input_messages\"]]\n", + "functions_df[\"chosen_function\"] = functions_df[\"attributes.llm.output_messages\"].apply(extract_function_from_output_message)\n", + "functions_df = functions_df.dropna(subset=[\"chosen_function\"])\n", + "functions_df = functions_df.rename(columns={\"attributes.llm.input_messages\": \"query\"})\n", + "\n", + "functions_df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm_as_a_judge_prompt = \"\"\"\n", + "You are an expert evaluator. Your task is to assess the accuracy of function calls made by an AI agent based on a given query. Below is the list of available functions and their descriptions:\n", + "\n", + "{tools_string}\n", + "\n", + "Given the query and the function call made by the agent, evaluate if the chosen function is the most appropriate one. \n", + "Label the chosen function as `correct` if it's the most appropriate one, and `incorrect` if it's not. Additionally, provide an explanation for your evaluation.\n", + "\n", + "Query: {query}\n", + "Chosen Function: {chosen_function}\n", + "\n", + "****\n", + "Example response:\n", + "Label: correct\n", + "Explanation: The `rag_pipeline_func` is appropriate for answering questions based on provided documents, which is suitable for the given query.\n", + "\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.evals import (\n", + " OpenAIModel,\n", + " llm_classify\n", + ")\n", + "\n", + "eval_model = OpenAIModel(model=\"gpt-4o-mini\")\n", + "\n", + "tools_string = \"\\n\".join([f\"{tool['function']['name']}: {tool['function']['description']}\" for tool in tools])\n", + "\n", + "llm_as_a_judge_prompt = llm_as_a_judge_prompt.replace(\"{tools_string}\", tools_string)\n", + "\n", + "function_calling_df = llm_classify(\n", + " dataframe=functions_df,\n", + " template=llm_as_a_judge_prompt,\n", + " rails=[\"correct\", \"incorrect\"],\n", + " model=eval_model,\n", + " provide_explanation=True,\n", + " concurrency=20,\n", + ")\n", + "function_calling_df[\"score\"] = function_calling_df[\"label\"].apply(lambda x: 1 if x == \"correct\" else 0)\n", + "function_calling_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.trace import SpanEvaluations\n", + "\n", + "px.Client().log_evaluations(\n", + " SpanEvaluations(dataframe=function_calling_df, eval_name=\"Function Calling\"),\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rgGKjm7cpv1_" + }, + "source": [ + "## What's next\n", + "\n", + "🎉 Congratulations! You've learned how to build chat applications that demonstrate agent-like behavior using OpenAI function calling and Haystack Pipelines.\n", + "\n", + "If you liked this tutorial, there's more to learn about Haystack 2.0:\n", + "- [Serializing LLM Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n", + "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n", + "\n", + "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n", + "\n", + "Thanks for reading!" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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