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+ } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "This notebook demonstrates an OpenAPI service-based Retriever-Augmented Generation (RAG) application using a Qdrant vector database with Haystack.\n", + "\n", + "Starting with a user prompt, we fetch the appropriate OpenAPI specification (OAS) from Qdrant, as well as the matching system message. These retrieved text documents are then injected into the Haystack pipeline, facilitating the dynamic invocation of specific OAS services. By merging any OAS service with Large Language Models (LLMs), this cutting-edge method opens a new phase in RAG systems, enabling the generation of deeply contextual responses from structured OAS data services." + ], + "metadata": { + "id": "J8TDTtQwiSn6" + } + }, + { + "cell_type": "markdown", + "source": [ + "Slides: https://tinyurl.com/3b8vvpf8" + ], + "metadata": { + "id": "HxFtmXftV5D0" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Setup\n", + "This notebook demos Haystack 2.x service based RAG, using openapi services.\n", + "\n", + "Let's install necessary libraries and import key modules to build the foundation for the subsequent steps." + ], + "metadata": { + "id": "6zUWNgF-kUeU" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install --no-deps sentence-transformers\n", + "!pip install \"grpcio-tools==1.42\" jsonref openapi3 qdrant-haystack git+https://github.com/deepset-ai/haystack.git@fcd48d662c17893dea8e899023d72885bd721359" + ], + "metadata": { + "id": "bH8Lo7PSumLG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6b1d2f70-5cec-4ec7-e1de-44f3fe968267" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting sentence-transformers\n", + " Downloading sentence_transformers-2.6.1-py3-none-any.whl (163 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/163.3 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collected packages: monotonic, portalocker, lazy-imports, jsonref, hyperframe, hpack, haystack-bm25, h11, grpcio-tools, boilerpy3, backoff, posthog, openapi3, httpcore, h2, httpx, openai, qdrant-client, haystack-ai, qdrant-haystack\n", + "Successfully installed backoff-2.2.1 boilerpy3-1.0.7 grpcio-tools-1.42.0 h11-0.14.0 h2-4.1.0 haystack-ai-2.1.0rc0 haystack-bm25-1.0.2 hpack-4.0.0 httpcore-1.0.4 httpx-0.27.0 hyperframe-6.0.1 jsonref-1.1.0 lazy-imports-0.3.1 monotonic-1.6 openai-1.14.3 openapi3-1.8.2 portalocker-2.8.2 posthog-3.5.0 qdrant-client-1.8.0 qdrant-haystack-3.1.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import getpass\n", + "import os\n", + "import json\n", + "import jsonref\n", + "import requests\n", + "from typing import List, Dict, Any\n", + "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n", + "from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever\n", + "\n", + "from haystack import Pipeline, Document\n", + "from haystack.components.generators.utils import print_streaming_chunk\n", + "from haystack.components.writers import DocumentWriter\n", + "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n", + "from haystack.components.converters import OpenAPIServiceToFunctions, OutputAdapter\n", + "from haystack.components.generators.chat import OpenAIChatGenerator\n", + "from haystack.components.connectors import OpenAPIServiceConnector\n", + "from haystack.components.fetchers import LinkContentFetcher\n", + "from haystack.components.others import Multiplexer\n", + "from haystack.dataclasses import ChatMessage, ByteStream" + ], + "metadata": { + "id": "RhxdVLCb_D8R" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.1 Let's collect the API keys for LLM provider" + ], + "metadata": { + "id": "m3IcPyJnpPMa" + } + }, + { + "cell_type": "code", + "source": [ + "llm_providers = {\"fireworks\": {\"api_base_url\": \"https://api.fireworks.ai/inference/v1\",\n", + " \"text_model\": \"accounts/fireworks/models/mixtral-8x7b-instruct\",\n", + " \"fc_model\": \"accounts/fireworks/models/firefunction-v1\"},\n", + " \"openai\": {\"api_base_url\": \"https://api.openai.com/v1\",\n", + " \"text_model\": \"gpt-4-turbo-preview\",\n", + " \"fc_model\": \"gpt-3.5-turbo\"}\n", + " }\n", + "\n", + "# change this value to another provider defined above - if needed\n", + "selected_provider = \"openai\"\n", + "llm_api_key = getpass.getpass(f\"Enter {selected_provider} api key:\")\n" + ], + "metadata": { + "id": "_9ySL9WqILJT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1530827a-096b-42e6-c4ad-5235ba469b37" + }, + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter openai api key:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.2 And OpenAPI services we'll use:" + ], + "metadata": { + "id": "55-vlrpX_W29" + } + }, + { + "cell_type": "code", + "source": [ + "# https://github.com/settings/tokens\n", + "github_token = getpass.getpass(\"Enter your github token:\")" + ], + "metadata": { + "id": "uqGsQrRYJZ-2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ed6c08e0-e766-4e42-c0ee-4d63bf2605e9" + }, + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter your github token:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Setup QdrantDocumentStore\n", + "\n", + "QdrantDocumentStore is one of many vector DB integrations we offer via Haystack Core Integrations [project](https://github.com/deepset-ai/haystack-core-integrations/)" + ], + "metadata": { + "id": "m-Po2hm0kzKj" + } + }, + { + "cell_type": "code", + "source": [ + "# Note: for the demo we are using Qdrant in local on-disk mode.\n", + "# For production use cases, a Docker container should be preferred.\n", + "# See https://qdrant.tech/documentation/ for more details\n", + "\n", + "document_store = QdrantDocumentStore(\n", + " path=\"./qdrant_1\",\n", + " index=\"Document\",\n", + " embedding_dim=768,\n", + " recreate_index=True\n", + " )" + ], + "metadata": { + "id": "mmVOQAg-C9Zx" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2.1 Add some helper functions we'll use in both indexing and retrieval pipelines" + ], + "metadata": { + "id": "m31frRXzo-W3" + } + }, + { + "cell_type": "code", + "source": [ + "def prepare_fc(openai_functions_schema: Dict[str, Any]) -> Dict[str, Any]:\n", + " return {\n", + " \"tools\": [{\n", + " \"type\": \"function\",\n", + " \"function\": openai_functions_schema\n", + " }],\n", + " \"tool_choice\": {\n", + " \"type\": \"function\",\n", + " \"function\": {\"name\": openai_functions_schema[\"name\"]}\n", + " }\n", + " }\n", + "\n", + "def create_docs(functions, specs, system_messages, service_credentials):\n", + " docs = []\n", + " for function, spec, system_message, service_credential in zip(functions, specs, system_messages, service_credentials):\n", + " d = Document(content=json.dumps(function),\n", + " meta={\"spec\": json.dumps(spec),\n", + " \"system_message\": system_message,\n", + " \"service_credential\": service_credential}) #it's just a demo - relax\n", + " docs.append(d)\n", + " return docs" + ], + "metadata": { + "id": "7U-QKL3nyhX0" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Index OpenAPI specs\n", + "\n", + "Each indexed service Document contains the following descriptors:\n", + "\n", + "* OpenAI function calling JSON\n", + "* OpenAPI specification\n", + "* System message for the service\n", + "* Service credentials" + ], + "metadata": { + "id": "iz57DpxelJKn" + } + }, + { + "cell_type": "code", + "source": [ + "embedding_model = \"sentence-transformers/all-mpnet-base-v2\"\n", + "\n", + "# Note: if you play with the notebook and add more OAS services you are likely to get better service inference\n", + "# results with jina-embeddings-v2-small-en embedding model. jina-embeddings-v2-small-en is an English, monolingual\n", + "# embedding model supporting 8192 sequence length which should be enough for our Document describing OpenAI\n", + "# functions, OAS spec, and sytem message stored.\n", + "\n", + "# embedding_model = \"jina-embeddings-v2-small-en\"\n", + "# os.environ['HF_API_TOKEN'] = getpass.getpass(\"Enter Hugging Face api key:\")" + ], + "metadata": { + "id": "VBRPOeTmZUZO" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "indexing_pipeline = Pipeline()\n", + "indexing_pipeline.add_component(\"fetcher\", LinkContentFetcher())\n", + "indexing_pipeline.add_component(\"mx\", Multiplexer(List[ByteStream]))\n", + "indexing_pipeline.add_component(\"spec_to_functions\", OpenAPIServiceToFunctions())\n", + "indexing_pipeline.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=embedding_model,\n", + " meta_fields_to_embed=[\"spec\", \"system_message\", \"service_credential\"],\n", + " trust_remote_code=True))\n", + "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", + "indexing_pipeline.add_component(\"a1\", OutputAdapter(\"{{functions | create_docs(specs, system_messages, service_credentials)}}\",\n", + " List[Document],\n", + " {\"create_docs\": create_docs}))\n", + "\n", + "indexing_pipeline.add_component(\"a2\", OutputAdapter(\"{{sources | json_objects}}\",\n", + " List[Dict[str, Any]],\n", + " {\"json_objects\": lambda sources: [json.loads(s.to_string()) for s in sources]}))\n", + "\n", + "indexing_pipeline.connect(\"fetcher.streams\", \"mx\")\n", + "indexing_pipeline.connect(\"mx\", \"spec_to_functions.sources\")\n", + "indexing_pipeline.connect(\"mx\", \"a2.sources\")\n", + "indexing_pipeline.connect(\"a2\", \"a1.specs\")\n", + "indexing_pipeline.connect(\"spec_to_functions.functions\", \"a1\")\n", + "indexing_pipeline.connect(\"a1.output\", \"embedder.documents\")\n", + "indexing_pipeline.connect(\"embedder\", \"writer\")" + ], + "metadata": { + "id": "rXrLxTQ8Dl8V", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b7e403e7-6481-418e-c3aa-9f31fe0bf15d" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\n", + "🚅 Components\n", + " - fetcher: LinkContentFetcher\n", + " - mx: Multiplexer\n", + " - spec_to_functions: OpenAPIServiceToFunctions\n", + " - embedder: SentenceTransformersDocumentEmbedder\n", + " - writer: DocumentWriter\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + "🛤️ Connections\n", + " - fetcher.streams -> mx.value (List[ByteStream])\n", + " - mx.value -> spec_to_functions.sources (List[ByteStream])\n", + " - mx.value -> a2.sources (List[ByteStream])\n", + " - spec_to_functions.functions -> a1.functions (List[Dict[str, Any]])\n", + " - embedder.documents -> writer.documents (List[Document])\n", + " - a1.output -> embedder.documents (List[Document])\n", + " - a2.output -> a1.specs (List[Dict[str, Any]])" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# indexing_pipeline.draw(\"/content/indexing_pipeline.png\")" + ], + "metadata": { + "id": "u8_d1RrSlN9n", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 670 + }, + "outputId": "142b0115-dbfd-4b0d-d6f4-5629a4fca803" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:haystack.core.pipeline.draw:Failed to draw the pipeline: could not connect to https://mermaid.ink/img/ (HTTPSConnectionPool(host='mermaid.ink', port=443): Read timed out. (read timeout=10))\n", + "WARNING:haystack.core.pipeline.draw:No pipeline diagram will be saved.\n" + ] + }, + { + "output_type": "error", + "ename": "PipelineDrawingError", + "evalue": "There was an issue with https://mermaid.ink/, see the stacktrace for details.", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTimeoutError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 537\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 706\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1302\u001b[0m self.__class__)\n\u001b[0;32m-> 1303\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m retries = retries.increment(\n\u001b[0m\u001b[1;32m 846\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_e\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_pool\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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470\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 471\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/util/util.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0;31m# Make the request on the HTTPConnection object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m response = self._make_request(\n\u001b[0m\u001b[1;32m 792\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 540\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_raise_timeout\u001b[0;34m(self, err, url, timeout_value)\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketTimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m raise ReadTimeoutError(\n\u001b[0m\u001b[1;32m 372\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"Read timed out. (read timeout={timeout_value})\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mReadTimeoutError\u001b[0m: HTTPSConnectionPool(host='mermaid.ink', port=443): Read timed out. (read timeout=10)", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mReadTimeout\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/haystack/core/pipeline/draw.py\u001b[0m in \u001b[0;36m_to_mermaid_image\u001b[0;34m(graph)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m>=\u001b[0m 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verify, cert, json)\u001b[0m\n\u001b[1;32m 588\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0mr\u001b[0m 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\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 533\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_InvalidHeader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mReadTimeout\u001b[0m: HTTPSConnectionPool(host='mermaid.ink', port=443): Read timed out. (read timeout=10)", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mPipelineDrawingError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mindexing_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/indexing_pipeline.png\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/haystack/core/pipeline/pipeline.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;31m# Before drawing we edit a bit the graph, to avoid modifying the original that is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;31m# used for running the pipeline we copy it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m \u001b[0mimage_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_mermaid_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 553\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/haystack/core/pipeline/draw.py\u001b[0m in \u001b[0;36m_to_mermaid_image\u001b[0;34m(graph)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Exact URL requested: {url}\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No pipeline diagram will be saved.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m raise PipelineDrawingError(\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;34m\"There was an issue with https://mermaid.ink/, see the stacktrace for details.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m ) from exc\n", + "\u001b[0;31mPipelineDrawingError\u001b[0m: There was an issue with https://mermaid.ink/, see the stacktrace for details." + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 We'll use three OAS services: GitHub, SerperDev, TinyURL\n", + "\n", + "\n", + "\n", + "* GitHub - compare branches operation\n", + "* SerperDev - a search engine bridge\n", + "* TinyURL - URL shortener \n", + "\n" + ], + "metadata": { + "id": "Wi6T5IMVZYOW" + } + }, + { + "cell_type": "code", + "source": [ + "services = {\"open_api_specs\": [\"https://bit.ly/github_compare\"],\n", + " \"system_messages\":[\"https://bit.ly/pr_auto_system\"]}" + ], + "metadata": { + "id": "TPSaQHgKSGvG" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = indexing_pipeline.run(data={\"fetcher\": {\"urls\": services[\"open_api_specs\"]},\n", + " \"a1\":{\"service_credentials\": [github_token],\n", + " \"system_messages\": [requests.get(url).text for url in services[\"system_messages\"]]\n", + " }\n", + " })\n", + "print(f\"\\nWrote {result['writer']['documents_written']} documents\")" + ], + "metadata": { + "id": "F7rKAns3Do6Q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582, + 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SentenceTransformersTextEmbedder\n", + " - retriever: QdrantEmbeddingRetriever\n", + " - mx: Multiplexer\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + " - a3: OutputAdapter\n", + " - a4: OutputAdapter\n", + " - a5: OutputAdapter\n", + " - fn_llm: OpenAIChatGenerator\n", + " - openapi_container: OpenAPIServiceConnector\n", + " - llm: OpenAIChatGenerator\n", + "🛤️ Connections\n", + " - embedder.embedding -> retriever.query_embedding (List[float])\n", + " - retriever.documents -> mx.value (List[Document])\n", + " - mx.value -> a1.documents (List[Document])\n", + " - mx.value -> a2.documents (List[Document])\n", + " - mx.value -> a3.documents (List[Document])\n", + " - mx.value -> a4.documents (List[Document])\n", + " - a1.output -> fn_llm.generation_kwargs (Dict[str, Any])\n", + " - a2.output -> openapi_container.service_openapi_spec (Dict[str, Any])\n", + " - a3.output -> openapi_container.service_credentials (str)\n", + " - a4.output -> a5.system_message (List[ChatMessage])\n", + " - a5.output -> llm.messages (List[ChatMessage])\n", + " - fn_llm.replies -> openapi_container.messages (List[ChatMessage])\n", + " - openapi_container.service_response -> a5.service_response (Dict[str, Any])" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# pipe.draw(\"/content/pipe.png\")" + ], + "metadata": { + "id": "VGuSsKJ7lpnT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Retrieval, service invocation and LLM generation" + ], + "metadata": { + "id": "ZhpXSiqimXtr" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here is where everything gets interesting! Based on the prompt below, we'll retrieve the top_k=1 document from QdrantDocumentStore. That'll be the relevant service Document. We'll then proceed to use service descriptors from the retrieved Document to initiate:\n", + "\n", + "* Step 1: Function calling to resolve the parameters for service invocation.\n", + "\n", + "\"Step\n", + "\n", + "* Step 2: We'll invoke the OpenAPI-compliant service with those parameters.\n", + "\"Step\n", + "\n", + "* Step 3: The response from the service will be joined with the system prompt for that service.\n", + "\"Step\n", + "\n", + "* Step 4: The final LLM response will be generated.\n", + "\n", + "\n", + "This process involves straightforward intent recognition that utilizes vector similarity to accurately identify and retrieve the appropriate service Document. Theoretically, it's possible to have dozens of services indexed within the QdrantDocumentStore. Once we retrieve the right service Documents, we will rely on the Haystack pipeline to perform the magic and observe the response generated by the LLM." + ], + "metadata": { + "id": "nfWVprj7y5O-" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "user_prompt = \"Compare Github branches main and test/benchmarks2.0, in project deepset-ai, repo haystack\"\n", + "result = pipe.run(data={\"fn_llm\": {\"messages\":[ChatMessage.from_user(user_prompt)]}, \"embedder\": {\"text\": user_prompt}})\n" + ], + "metadata": { + "id": "OHP2Nj79KCdQ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 926, + "referenced_widgets": [ + "bf630f3bcaec48f7bc4129f4c9ef81f4", + "8edbb2f8af7b46a1b89ebe3460ced96b", + "08ba84d84949433b9a58c9b5aab48203", + "2c20892e7894473f8a850d56b7c2074a", + "af7af8a75dc848348a52e28e786a8f89", + "f530f4d88c54422ab4805ad351d5256c", + "fc5eeb2755b243c5b6ed20bf2f497dc2", + "f36c0770ed8a46c6a4adbfb63fd53a1a", + "700b8796103846a1b1a4b63a18bb24b5", + 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+ } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "This notebook demonstrates an OpenAPI service-based Retriever-Augmented Generation (RAG) application using a Qdrant vector database with Haystack.\n", + "\n", + "Starting with a user prompt, we fetch the appropriate OpenAPI specification (OAS) from Qdrant, as well as the matching system message. These retrieved text documents are then injected into the Haystack pipeline, facilitating the dynamic invocation of specific OAS services. By merging any OAS service with Large Language Models (LLMs), this cutting-edge method opens a new phase in RAG systems, enabling the generation of deeply contextual responses from structured OAS data services." + ], + "metadata": { + "id": "J8TDTtQwiSn6" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Setup\n", + "This notebook demos Haystack 2.x service based RAG, using two openapi services: GitHub's compare_branches and SerperDev search API.\n", + "\n", + "Let's install necessary libraries and import key modules to build the foundation for the subsequent steps." + ], + "metadata": { + "id": "6zUWNgF-kUeU" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install --no-deps sentence-transformers\n", + "!pip install \"grpcio-tools==1.42\" jsonref openapi3 qdrant-haystack git+https://github.com/deepset-ai/haystack.git@fcd48d662c17893dea8e899023d72885bd721359" + ], + "metadata": { + "id": "bH8Lo7PSumLG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd24da7b-7f6d-45ac-dae0-88bc8bd63b9a" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting sentence-transformers\n", + " Downloading sentence_transformers-2.6.1-py3-none-any.whl (163 kB)\n", + "\u001b[?25l 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collected packages: monotonic, portalocker, lazy-imports, jsonref, hyperframe, hpack, haystack-bm25, h11, grpcio-tools, boilerpy3, backoff, posthog, openapi3, httpcore, h2, httpx, openai, qdrant-client, haystack-ai, qdrant-haystack\n", + "Successfully installed backoff-2.2.1 boilerpy3-1.0.7 grpcio-tools-1.42.0 h11-0.14.0 h2-4.1.0 haystack-ai-2.1.0rc0 haystack-bm25-1.0.2 hpack-4.0.0 httpcore-1.0.4 httpx-0.27.0 hyperframe-6.0.1 jsonref-1.1.0 lazy-imports-0.3.1 monotonic-1.6 openai-1.14.3 openapi3-1.8.2 portalocker-2.8.2 posthog-3.5.0 qdrant-client-1.8.0 qdrant-haystack-3.1.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import getpass\n", + "import os\n", + "import json\n", + "import jsonref\n", + "import requests\n", + "from typing import List, Dict, Any\n", + "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n", + "from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever\n", + "\n", + "from haystack import Pipeline, Document\n", + "from haystack.components.generators.utils import print_streaming_chunk\n", + "from haystack.components.writers import DocumentWriter\n", + "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n", + "from haystack.components.converters import OpenAPIServiceToFunctions, OutputAdapter\n", + "from haystack.components.generators.chat import OpenAIChatGenerator\n", + "from haystack.components.connectors import OpenAPIServiceConnector\n", + "from haystack.components.fetchers import LinkContentFetcher\n", + "from haystack.components.others import Multiplexer\n", + "from haystack.dataclasses import ChatMessage, ByteStream" + ], + "metadata": { + "id": "RhxdVLCb_D8R" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.1 Let's collect the API keys for LLM provider" + ], + "metadata": { + "id": "m3IcPyJnpPMa" + } + }, + { + "cell_type": "code", + "source": [ + "llm_providers = {\"fireworks\": {\"api_base_url\": \"https://api.fireworks.ai/inference/v1\",\n", + " \"text_model\": \"accounts/fireworks/models/mixtral-8x7b-instruct\",\n", + " \"fc_model\": \"accounts/fireworks/models/firefunction-v1\"},\n", + " \"openai\": {\"api_base_url\": \"https://api.openai.com/v1\",\n", + " \"text_model\": \"gpt-4-turbo-preview\",\n", + " \"fc_model\": \"gpt-3.5-turbo\"}\n", + " }\n", + "\n", + "# change this value to another provider defined above - if needed\n", + "selected_provider = \"openai\"\n", + "llm_api_key = getpass.getpass(f\"Enter {selected_provider} api key:\")\n" + ], + "metadata": { + "id": "_9ySL9WqILJT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb91d07a-2279-48d5-8ac3-348bfff2420b" + }, + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter openai api key:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.2 And OpenAPI services we'll use:" + ], + "metadata": { + "id": "55-vlrpX_W29" + } + }, + { + "cell_type": "code", + "source": [ + "serper_token = getpass.getpass(\"Enter serperdev api key:\")\n", + "github_token = getpass.getpass(\"Enter your github token:\")\n", + "tinurl_token = getpass.getpass(\"Enter your tinyurl token:\")" + ], + "metadata": { + "id": "uqGsQrRYJZ-2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "76e85a25-d275-467b-c0f3-c924b6056c03" + }, + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter serperdev api key:··········\n", + "Enter your github token:··········\n", + "Enter your tinyurl token:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Setup QdrantDocumentStore\n", + "\n", + "QdrantDocumentStore is one of many vector DB integrations we offer via Haystack Core Integrations [project](https://github.com/deepset-ai/haystack-core-integrations/)" + ], + "metadata": { + "id": "m-Po2hm0kzKj" + } + }, + { + "cell_type": "code", + "source": [ + "# Note: for the demo we are using Qdrant in local on-disk mode.\n", + "# For production use cases, a Docker container should be preferred.\n", + "# See https://qdrant.tech/documentation/ for more details\n", + "\n", + "document_store = QdrantDocumentStore(\n", + " path=\"./qdrant_1\",\n", + " index=\"Document\",\n", + " embedding_dim=768,\n", + " recreate_index=True\n", + " )" + ], + "metadata": { + "id": "mmVOQAg-C9Zx" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2.1 Add some helper functions we'll use in both indexing and retrieval pipelines" + ], + "metadata": { + "id": "m31frRXzo-W3" + } + }, + { + "cell_type": "code", + "source": [ + "def prepare_fc(openai_functions_schema: Dict[str, Any]) -> Dict[str, Any]:\n", + " return {\n", + " \"tools\": [{\n", + " \"type\": \"function\",\n", + " \"function\": openai_functions_schema\n", + " }],\n", + " \"tool_choice\": {\n", + " \"type\": \"function\",\n", + " \"function\": {\"name\": openai_functions_schema[\"name\"]}\n", + " }\n", + " }\n", + "\n", + "def create_docs(functions, specs, system_messages, service_credentials):\n", + " docs = []\n", + " for function, spec, system_message, service_credential in zip(functions, specs, system_messages, service_credentials):\n", + " d = Document(content=json.dumps(function),\n", + " meta={\"spec\": json.dumps(spec),\n", + " \"system_message\": system_message,\n", + " \"service_credential\": service_credential}) #it's just a demo - relax\n", + " docs.append(d)\n", + " return docs" + ], + "metadata": { + "id": "7U-QKL3nyhX0" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Index OpenAPI specs\n", + "\n", + "Each indexed service Document contains the following descriptors:\n", + "\n", + "* OpenAI function calling JSON\n", + "* OpenAPI specification\n", + "* System message for the service\n", + "* Service credentials" + ], + "metadata": { + "id": "iz57DpxelJKn" + } + }, + { + "cell_type": "code", + "source": [ + "embedding_model = \"sentence-transformers/all-mpnet-base-v2\"\n", + "\n", + "# Note: if you play with the notebook and add more OAS services you are likely to get better service inference\n", + "# results with jina-embeddings-v2-small-en embedding model. jina-embeddings-v2-small-en is an English, monolingual\n", + "# embedding model supporting 8192 sequence length which should be enough for our Document describing OpenAI\n", + "# functions, OAS spec, and sytem message stored.\n", + "\n", + "# embedding_model = \"jina-embeddings-v2-small-en\"\n", + "# os.environ['HF_API_TOKEN'] = getpass.getpass(\"Enter Hugging Face api key:\")" + ], + "metadata": { + "id": "VBRPOeTmZUZO" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "indexing_pipeline = Pipeline()\n", + "indexing_pipeline.add_component(\"fetcher\", LinkContentFetcher())\n", + "indexing_pipeline.add_component(\"mx\", Multiplexer(List[ByteStream]))\n", + "indexing_pipeline.add_component(\"spec_to_functions\", OpenAPIServiceToFunctions())\n", + "indexing_pipeline.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=embedding_model,\n", + " meta_fields_to_embed=[\"spec\", \"system_message\", \"service_credential\"],\n", + " trust_remote_code=True))\n", + "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", + "indexing_pipeline.add_component(\"a1\", OutputAdapter(\"{{functions | create_docs(specs, system_messages, service_credentials)}}\",\n", + " List[Document],\n", + " {\"create_docs\": create_docs}))\n", + "\n", + "indexing_pipeline.add_component(\"a2\", OutputAdapter(\"{{sources | json_objects}}\",\n", + " List[Dict[str, Any]],\n", + " {\"json_objects\": lambda sources: [json.loads(s.to_string()) for s in sources]}))\n", + "\n", + "indexing_pipeline.connect(\"fetcher.streams\", \"mx\")\n", + "indexing_pipeline.connect(\"mx\", \"spec_to_functions.sources\")\n", + "indexing_pipeline.connect(\"mx\", \"a2.sources\")\n", + "indexing_pipeline.connect(\"a2\", \"a1.specs\")\n", + "indexing_pipeline.connect(\"spec_to_functions.functions\", \"a1\")\n", + "indexing_pipeline.connect(\"a1.output\", \"embedder.documents\")\n", + "indexing_pipeline.connect(\"embedder\", \"writer\")" + ], + "metadata": { + "id": "rXrLxTQ8Dl8V", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "18f56665-a7a7-4bde-dd3b-d317152bfa01" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\n", + "🚅 Components\n", + " - fetcher: LinkContentFetcher\n", + " - mx: Multiplexer\n", + " - spec_to_functions: OpenAPIServiceToFunctions\n", + " - embedder: SentenceTransformersDocumentEmbedder\n", + " - writer: DocumentWriter\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + "🛤️ Connections\n", + " - fetcher.streams -> mx.value (List[ByteStream])\n", + " - mx.value -> spec_to_functions.sources (List[ByteStream])\n", + " - mx.value -> a2.sources (List[ByteStream])\n", + " - spec_to_functions.functions -> a1.functions (List[Dict[str, Any]])\n", + " - embedder.documents -> writer.documents (List[Document])\n", + " - a1.output -> embedder.documents (List[Document])\n", + " - a2.output -> a1.specs (List[Dict[str, Any]])" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "indexing_pipeline.draw(\"/content/indexing_pipeline.png\")" + ], + "metadata": { + "id": "u8_d1RrSlN9n" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 We'll use three OAS services: GitHub, SerperDev, TinyURL\n", + "\n", + "\n", + "\n", + "* GitHub - compare branches operation\n", + "* SerperDev - a search engine bridge\n", + "* TinyURL - URL shortener \n", + "\n" + ], + "metadata": { + "id": "Wi6T5IMVZYOW" + } + }, + { + "cell_type": "code", + "source": [ + "services = {\"open_api_specs\": [\"https://bit.ly/github_compare\",\"https://bit.ly/serper_dev_spec\", \"https://bit.ly/tinyurl_spec\"],\n", + " \"system_messages\":[\"https://bit.ly/pr_auto_system\", \"https://bit.ly/serper_system\",\"https://bit.ly/tinyurl_system_prompt\"]}" + ], + "metadata": { + "id": "TPSaQHgKSGvG" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = indexing_pipeline.run(data={\"fetcher\": {\"urls\": services[\"open_api_specs\"]},\n", + " \"a1\":{\"service_credentials\": [github_token, serper_token, tinurl_token],\n", + " \"system_messages\": [requests.get(url).text for url in services[\"system_messages\"]]\n", + " }\n", + " })\n", + "print(f\"\\nWrote {result['writer']['documents_written']} documents\")" + ], + "metadata": { + "id": "F7rKAns3Do6Q", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 582, + "referenced_widgets": [ + "bbf38bbcdee04b2ca8dd9f8bec646229", + "aad17f16c6c9405e95babf428eb34e52", + "039c0dbc5ec64ce5bf6adaceee1c1f63", + "0850b9b7d731484eb006b047037b19ee", + "139ac19fa6014ad5bb46cbed0ab91a7f", + "adcfa266865f47b69e3f332a53d92bda", + "f523c1a8cecb4f44b80562d41ffde4c4", + "16833f1301f5495ea9b114def9f91486", + "b3b6102ffffb4650a0b887692b2f2657", + "734aaa3f1cc54f7f98b6fa12c66d2b40", + "bcc65c59708040c9a9c81a8f0814f66a", + "4c6f7f6be8d74599a5ff5f721f38aa84", + "d72fa84a38974d70a6f8dc722536c107", + "89de052714034a3ca3b1bb1cb434528e", + "f60ec49822ee4e9c9772a753f89b212f", + "68c93e6c0702410ba7e041249832ca0d", + "dcb72ac02fec4e49bde558a28922bcd9", + "499e5b7419ff43e78f046cdce0ed0bc7", + "503a9d7eedad4be8b81caedf625a9d6a", + "4f06b59f01874202ac67ecca8a7c38cb", + 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datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00\n", + "🚅 Components\n", + " - embedder: SentenceTransformersTextEmbedder\n", + " - retriever: QdrantEmbeddingRetriever\n", + " - mx: Multiplexer\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + " - a3: OutputAdapter\n", + " - a4: OutputAdapter\n", + " - a5: OutputAdapter\n", + " - fn_llm: OpenAIChatGenerator\n", + " - openapi_container: OpenAPIServiceConnector\n", + " - llm: OpenAIChatGenerator\n", + "🛤️ Connections\n", + " - embedder.embedding -> retriever.query_embedding (List[float])\n", + " - retriever.documents -> mx.value (List[Document])\n", + " - mx.value -> a1.documents (List[Document])\n", + " - mx.value -> a2.documents (List[Document])\n", + " - mx.value -> a3.documents (List[Document])\n", + " - mx.value -> a4.documents (List[Document])\n", + " - a1.output -> fn_llm.generation_kwargs (Dict[str, Any])\n", + " - a2.output -> openapi_container.service_openapi_spec (Dict[str, Any])\n", + " - a3.output -> openapi_container.service_credentials (str)\n", + " - a4.output -> a5.system_message (List[ChatMessage])\n", + " - a5.output -> llm.messages (List[ChatMessage])\n", + " - fn_llm.replies -> openapi_container.messages (List[ChatMessage])\n", + " - openapi_container.service_response -> a5.service_response (Dict[str, Any])" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "pipe.draw(\"/content/pipe.png\")" + ], + "metadata": { + "id": "VGuSsKJ7lpnT" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Retrieval, service invocation and LLM generation" + ], + "metadata": { + "id": "ZhpXSiqimXtr" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here is where everything gets interesting! Based on the prompt below, we'll retrieve the top_k=1 document from QdrantDocumentStore. That'll be the relevant service Document. We'll then proceed to use service descriptors from the retrieved Document to initiate:\n", + "\n", + "* Step 1: Function calling to resolve the parameters for service invocation.\n", + "\n", + "\"Step\n", + "\n", + "* Step 2: We'll invoke the OpenAPI-compliant service with those parameters.\n", + "\"Step\n", + "\n", + "* Step 3: The response from the service will be joined with the system prompt for that service.\n", + "\"Step\n", + "\n", + "* Step 4: The final LLM response will be generated.\n", + "\n", + "\n", + "This process involves straightforward intent recognition that utilizes vector similarity to accurately identify and retrieve the appropriate service Document. Theoretically, it's possible to have dozens of services indexed within the QdrantDocumentStore. Once we retrieve the right service Documents, we will rely on the Haystack pipeline to perform the magic and observe the response generated by the LLM." + ], + "metadata": { + "id": "nfWVprj7y5O-" + } + }, + { + "cell_type": "code", + "source": [ + "user_prompt = \"Search Google and answer: why did Elon Musk sue OpenAI?\"\n", + "#user_prompt = \"Compare Github branches main and test/benchmarks2.0, in project deepset-ai, repo haystack\"\n", + "#user_prompt = \"Using TinyURL create a shortened URL from this long URL https://haystack.deepset.ai/blog/haystack-2-release\"\n", + "\n", + "result = pipe.run(data={\"fn_llm\": {\"messages\":[ChatMessage.from_user(user_prompt)]}, \"embedder\": {\"text\": user_prompt}})\n" + ], + "metadata": { + "id": "OHP2Nj79KCdQ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "e80ee26b4f0742fc88647e33a4acc558", + "7192e4496a2e448796ef8d4c639f44ba", + "8b2d336e82ea4b4ba8cea4f4dfa4953d", + "aafac7367fc1494385b69349519b1ddd", + "1db719842cde4a6eb5e6a79211409609", + "bd409e60ea5347338db59bf91ebdf31a", + "0ec7f9d4ebfd44f5870dfe5384046373", + "de738a8f9d7344c1ad45fdcbcedd17df", + "d8ccc0bf8cb2450fb2bf0b46510bcacf", + "f01e58958f6d4e739aa1c4ad4df149e0", + "2d65613837af473ab90b733e7ebe4707" + ] + }, + "outputId": "dfb82354-19fc-4997-9fd9-97e23977ac67" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Batches: 0%| | 0/1 [00:00 Date: Tue, 26 Mar 2024 16:41:14 +0100 Subject: [PATCH 3/5] Delete 05-solution-rag-services-with-haystack-and-qdrant.ipynb --- ...ag-services-with-haystack-and-qdrant.ipynb | 5494 ----------------- 1 file changed, 5494 deletions(-) delete mode 100644 05-solution-rag-services-with-haystack-and-qdrant.ipynb diff --git a/05-solution-rag-services-with-haystack-and-qdrant.ipynb b/05-solution-rag-services-with-haystack-and-qdrant.ipynb deleted file mode 100644 index 05c7066..0000000 --- a/05-solution-rag-services-with-haystack-and-qdrant.ipynb +++ /dev/null @@ -1,5494 +0,0 @@ -{ - 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"model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## Introduction\n", - "\n", - "This notebook demonstrates an OpenAPI service-based Retriever-Augmented Generation (RAG) application using a Qdrant vector database with Haystack.\n", - "\n", - "Starting with a user prompt, we fetch the appropriate OpenAPI specification (OAS) from Qdrant, as well as the matching system message. These retrieved text documents are then injected into the Haystack pipeline, facilitating the dynamic invocation of specific OAS services. By merging any OAS service with Large Language Models (LLMs), this cutting-edge method opens a new phase in RAG systems, enabling the generation of deeply contextual responses from structured OAS data services." - ], - "metadata": { - "id": "J8TDTtQwiSn6" - } - }, - { - "cell_type": "markdown", - "source": [ - "## 1. Setup\n", - "This notebook demos Haystack 2.x service based RAG, using two openapi services: GitHub's compare_branches and SerperDev search API.\n", - "\n", - "Let's install necessary libraries and import key modules to build the foundation for the subsequent steps." - ], - "metadata": { - "id": "6zUWNgF-kUeU" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install --no-deps sentence-transformers\n", - "!pip install \"grpcio-tools==1.42\" jsonref openapi3 qdrant-haystack git+https://github.com/deepset-ai/haystack.git@fcd48d662c17893dea8e899023d72885bd721359" - ], - "metadata": { - "id": "bH8Lo7PSumLG", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "cd24da7b-7f6d-45ac-dae0-88bc8bd63b9a" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting sentence-transformers\n", - " Downloading sentence_transformers-2.6.1-py3-none-any.whl (163 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/163.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m122.9/163.3 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.3/163.3 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"Successfully installed backoff-2.2.1 boilerpy3-1.0.7 grpcio-tools-1.42.0 h11-0.14.0 h2-4.1.0 haystack-ai-2.1.0rc0 haystack-bm25-1.0.2 hpack-4.0.0 httpcore-1.0.4 httpx-0.27.0 hyperframe-6.0.1 jsonref-1.1.0 lazy-imports-0.3.1 monotonic-1.6 openai-1.14.3 openapi3-1.8.2 portalocker-2.8.2 posthog-3.5.0 qdrant-client-1.8.0 qdrant-haystack-3.1.0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import getpass\n", - "import os\n", - "import json\n", - "import jsonref\n", - "import requests\n", - "from typing import List, Dict, Any\n", - "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n", - "from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever\n", - "\n", - "from haystack import Pipeline, Document\n", - "from haystack.components.generators.utils import print_streaming_chunk\n", - "from haystack.components.writers import DocumentWriter\n", - "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n", - "from haystack.components.converters import OpenAPIServiceToFunctions, OutputAdapter\n", - "from haystack.components.generators.chat import OpenAIChatGenerator\n", - "from haystack.components.connectors import OpenAPIServiceConnector\n", - "from haystack.components.fetchers import LinkContentFetcher\n", - "from haystack.components.others import Multiplexer\n", - "from haystack.dataclasses import ChatMessage, ByteStream" - ], - "metadata": { - "id": "RhxdVLCb_D8R" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### 1.1 Let's collect the API keys for LLM provider" - ], - "metadata": { - "id": "m3IcPyJnpPMa" - } - }, - { - "cell_type": "code", - "source": [ - "llm_providers = {\"fireworks\": {\"api_base_url\": \"https://api.fireworks.ai/inference/v1\",\n", - " \"text_model\": \"accounts/fireworks/models/mixtral-8x7b-instruct\",\n", - " \"fc_model\": \"accounts/fireworks/models/firefunction-v1\"},\n", - " \"openai\": {\"api_base_url\": \"https://api.openai.com/v1\",\n", - " \"text_model\": \"gpt-4-turbo-preview\",\n", - " \"fc_model\": \"gpt-3.5-turbo\"}\n", - " }\n", - "\n", - "# change this value to another provider defined above - if needed\n", - "selected_provider = \"openai\"\n", - "llm_api_key = getpass.getpass(f\"Enter {selected_provider} api key:\")\n" - ], - "metadata": { - "id": "_9ySL9WqILJT", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "eb91d07a-2279-48d5-8ac3-348bfff2420b" - }, - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter openai api key:··········\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### 1.2 And OpenAPI services we'll use:" - ], - "metadata": { - "id": "55-vlrpX_W29" - } - }, - { - "cell_type": "code", - "source": [ - "serper_token = getpass.getpass(\"Enter serperdev api key:\")\n", - "github_token = getpass.getpass(\"Enter your github token:\")\n", - "tinurl_token = getpass.getpass(\"Enter your tinyurl token:\")" - ], - "metadata": { - "id": "uqGsQrRYJZ-2", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "76e85a25-d275-467b-c0f3-c924b6056c03" - }, - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter serperdev api key:··········\n", - "Enter your github token:··········\n", - "Enter your tinyurl token:··········\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 2. Setup QdrantDocumentStore\n", - "\n", - "QdrantDocumentStore is one of many vector DB integrations we offer via Haystack Core Integrations [project](https://github.com/deepset-ai/haystack-core-integrations/)" - ], - "metadata": { - "id": "m-Po2hm0kzKj" - } - }, - { - "cell_type": "code", - "source": [ - "# Note: for the demo we are using Qdrant in local on-disk mode.\n", - "# For production use cases, a Docker container should be preferred.\n", - "# See https://qdrant.tech/documentation/ for more details\n", - "\n", - "document_store = QdrantDocumentStore(\n", - " path=\"./qdrant_1\",\n", - " index=\"Document\",\n", - " embedding_dim=768,\n", - " recreate_index=True\n", - " )" - ], - "metadata": { - "id": "mmVOQAg-C9Zx" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### 2.1 Add some helper functions we'll use in both indexing and retrieval pipelines" - ], - "metadata": { - "id": "m31frRXzo-W3" - } - }, - { - "cell_type": "code", - "source": [ - "def prepare_fc(openai_functions_schema: Dict[str, Any]) -> Dict[str, Any]:\n", - " return {\n", - " \"tools\": [{\n", - " \"type\": \"function\",\n", - " \"function\": openai_functions_schema\n", - " }],\n", - " \"tool_choice\": {\n", - " \"type\": \"function\",\n", - " \"function\": {\"name\": openai_functions_schema[\"name\"]}\n", - " }\n", - " }\n", - "\n", - "def create_docs(functions, specs, system_messages, service_credentials):\n", - " docs = []\n", - " for function, spec, system_message, service_credential in zip(functions, specs, system_messages, service_credentials):\n", - " d = Document(content=json.dumps(function),\n", - " meta={\"spec\": json.dumps(spec),\n", - " \"system_message\": system_message,\n", - " \"service_credential\": service_credential}) #it's just a demo - relax\n", - " docs.append(d)\n", - " return docs" - ], - "metadata": { - "id": "7U-QKL3nyhX0" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## 3. Index OpenAPI specs\n", - "\n", - "Each indexed service Document contains the following descriptors:\n", - "\n", - "* OpenAI function calling JSON\n", - "* OpenAPI specification\n", - "* System message for the service\n", - "* Service credentials" - ], - "metadata": { - "id": "iz57DpxelJKn" - } - }, - { - "cell_type": "code", - "source": [ - "embedding_model = \"sentence-transformers/all-mpnet-base-v2\"\n", - "\n", - "# Note: if you play with the notebook and add more OAS services you are likely to get better service inference\n", - "# results with jina-embeddings-v2-small-en embedding model. jina-embeddings-v2-small-en is an English, monolingual\n", - "# embedding model supporting 8192 sequence length which should be enough for our Document describing OpenAI\n", - "# functions, OAS spec, and sytem message stored.\n", - "\n", - "# embedding_model = \"jina-embeddings-v2-small-en\"\n", - "# os.environ['HF_API_TOKEN'] = getpass.getpass(\"Enter Hugging Face api key:\")" - ], - "metadata": { - "id": "VBRPOeTmZUZO" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "indexing_pipeline = Pipeline()\n", - "indexing_pipeline.add_component(\"fetcher\", LinkContentFetcher())\n", - "indexing_pipeline.add_component(\"mx\", Multiplexer(List[ByteStream]))\n", - "indexing_pipeline.add_component(\"spec_to_functions\", OpenAPIServiceToFunctions())\n", - "indexing_pipeline.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=embedding_model,\n", - " meta_fields_to_embed=[\"spec\", \"system_message\", \"service_credential\"],\n", - " trust_remote_code=True))\n", - "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", - "indexing_pipeline.add_component(\"a1\", OutputAdapter(\"{{functions | create_docs(specs, system_messages, service_credentials)}}\",\n", - " List[Document],\n", - " {\"create_docs\": create_docs}))\n", - "\n", - "indexing_pipeline.add_component(\"a2\", OutputAdapter(\"{{sources | json_objects}}\",\n", - " List[Dict[str, Any]],\n", - " {\"json_objects\": lambda sources: [json.loads(s.to_string()) for s in sources]}))\n", - "\n", - "indexing_pipeline.connect(\"fetcher.streams\", \"mx\")\n", - "indexing_pipeline.connect(\"mx\", \"spec_to_functions.sources\")\n", - "indexing_pipeline.connect(\"mx\", \"a2.sources\")\n", - "indexing_pipeline.connect(\"a2\", \"a1.specs\")\n", - "indexing_pipeline.connect(\"spec_to_functions.functions\", \"a1\")\n", - "indexing_pipeline.connect(\"a1.output\", \"embedder.documents\")\n", - "indexing_pipeline.connect(\"embedder\", \"writer\")" - ], - "metadata": { - "id": "rXrLxTQ8Dl8V", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "18f56665-a7a7-4bde-dd3b-d317152bfa01" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\n", - "🚅 Components\n", - " - fetcher: LinkContentFetcher\n", - " - mx: Multiplexer\n", - " - spec_to_functions: OpenAPIServiceToFunctions\n", - " - embedder: SentenceTransformersDocumentEmbedder\n", - " - writer: DocumentWriter\n", - " - a1: OutputAdapter\n", - " - a2: OutputAdapter\n", - "🛤️ Connections\n", - " - fetcher.streams -> mx.value (List[ByteStream])\n", - " - mx.value -> spec_to_functions.sources (List[ByteStream])\n", - " - mx.value -> a2.sources (List[ByteStream])\n", - " - spec_to_functions.functions -> a1.functions (List[Dict[str, Any]])\n", - " - embedder.documents -> writer.documents (List[Document])\n", - " - a1.output -> embedder.documents (List[Document])\n", - " - a2.output -> a1.specs (List[Dict[str, Any]])" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "source": [ - "indexing_pipeline.draw(\"/content/indexing_pipeline.png\")" - ], - "metadata": { - "id": "u8_d1RrSlN9n" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### 3.1 We'll use three OAS services: GitHub, SerperDev, TinyURL\n", - "\n", - "\n", - "\n", - "* GitHub - compare branches operation\n", - "* SerperDev - a search engine bridge\n", - "* TinyURL - URL shortener \n", - "\n" - ], - "metadata": { - "id": "Wi6T5IMVZYOW" - } - }, - { - "cell_type": "code", - "source": [ - "services = {\"open_api_specs\": [\"https://bit.ly/github_compare\",\"https://bit.ly/serper_dev_spec\", \"https://bit.ly/tinyurl_spec\"],\n", - " \"system_messages\":[\"https://bit.ly/pr_auto_system\", \"https://bit.ly/serper_system\",\"https://bit.ly/tinyurl_system_prompt\"]}" - ], - "metadata": { - "id": "TPSaQHgKSGvG" - }, - "execution_count": 10, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "result = indexing_pipeline.run(data={\"fetcher\": {\"urls\": services[\"open_api_specs\"]},\n", - " \"a1\":{\"service_credentials\": [github_token, serper_token, tinurl_token],\n", - " \"system_messages\": [requests.get(url).text for url in services[\"system_messages\"]]\n", - " }\n", - " })\n", - "print(f\"\\nWrote {result['writer']['documents_written']} documents\")" - ], - "metadata": { - "id": "F7rKAns3Do6Q", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 582, - "referenced_widgets": [ - "bbf38bbcdee04b2ca8dd9f8bec646229", - "aad17f16c6c9405e95babf428eb34e52", - "039c0dbc5ec64ce5bf6adaceee1c1f63", - "0850b9b7d731484eb006b047037b19ee", - "139ac19fa6014ad5bb46cbed0ab91a7f", - "adcfa266865f47b69e3f332a53d92bda", - "f523c1a8cecb4f44b80562d41ffde4c4", - "16833f1301f5495ea9b114def9f91486", - "b3b6102ffffb4650a0b887692b2f2657", - "734aaa3f1cc54f7f98b6fa12c66d2b40", - "bcc65c59708040c9a9c81a8f0814f66a", - "4c6f7f6be8d74599a5ff5f721f38aa84", - "d72fa84a38974d70a6f8dc722536c107", - "89de052714034a3ca3b1bb1cb434528e", - "f60ec49822ee4e9c9772a753f89b212f", - "68c93e6c0702410ba7e041249832ca0d", - "dcb72ac02fec4e49bde558a28922bcd9", - "499e5b7419ff43e78f046cdce0ed0bc7", - "503a9d7eedad4be8b81caedf625a9d6a", - "4f06b59f01874202ac67ecca8a7c38cb", - "3d35f66542664c899f9fe797e3806216", - "97f03ec78f95457e898674afac83f376", - "607b5128277c460bb186be2df5db0258", - "199216b018474b8c84a051451bfdc75f", - 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] - }, - "outputId": "3d620104-3e81-4e9e-cec0-1ff24a24f0ff" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "modules.json: 0%| | 0.00/349 [00:00\n", - "🚅 Components\n", - " - embedder: SentenceTransformersTextEmbedder\n", - " - retriever: QdrantEmbeddingRetriever\n", - " - mx: Multiplexer\n", - " - a1: OutputAdapter\n", - " - a2: OutputAdapter\n", - " - a3: OutputAdapter\n", - " - a4: OutputAdapter\n", - " - a5: OutputAdapter\n", - " - fn_llm: OpenAIChatGenerator\n", - " - openapi_container: OpenAPIServiceConnector\n", - " - llm: OpenAIChatGenerator\n", - "🛤️ Connections\n", - " - embedder.embedding -> retriever.query_embedding (List[float])\n", - " - retriever.documents -> mx.value (List[Document])\n", - " - mx.value -> a1.documents (List[Document])\n", - " - mx.value -> a2.documents (List[Document])\n", - " - mx.value -> a3.documents (List[Document])\n", - " - mx.value -> a4.documents (List[Document])\n", - " - a1.output -> fn_llm.generation_kwargs (Dict[str, Any])\n", - " - a2.output -> openapi_container.service_openapi_spec (Dict[str, Any])\n", - " - a3.output -> openapi_container.service_credentials (str)\n", - " - a4.output -> a5.system_message (List[ChatMessage])\n", - " - a5.output -> llm.messages (List[ChatMessage])\n", - " - fn_llm.replies -> openapi_container.messages (List[ChatMessage])\n", - " - openapi_container.service_response -> a5.service_response (Dict[str, Any])" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "source": [ - "pipe.draw(\"/content/pipe.png\")" - ], - "metadata": { - "id": "VGuSsKJ7lpnT" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## 5. Retrieval, service invocation and LLM generation" - ], - "metadata": { - "id": "ZhpXSiqimXtr" - } - }, - { - "cell_type": "markdown", - "source": [ - "Here is where everything gets interesting! Based on the prompt below, we'll retrieve the top_k=1 document from QdrantDocumentStore. That'll be the relevant service Document. We'll then proceed to use service descriptors from the retrieved Document to initiate:\n", - "\n", - "* Step 1: Function calling to resolve the parameters for service invocation.\n", - "\n", - "\"Step\n", - "\n", - "* Step 2: We'll invoke the OpenAPI-compliant service with those parameters.\n", - "\"Step\n", - "\n", - "* Step 3: The response from the service will be joined with the system prompt for that service.\n", - "\"Step\n", - "\n", - "* Step 4: The final LLM response will be generated.\n", - "\n", - "\n", - "This process involves straightforward intent recognition that utilizes vector similarity to accurately identify and retrieve the appropriate service Document. Theoretically, it's possible to have dozens of services indexed within the QdrantDocumentStore. Once we retrieve the right service Documents, we will rely on the Haystack pipeline to perform the magic and observe the response generated by the LLM." - ], - "metadata": { - "id": "nfWVprj7y5O-" - } - }, - { - "cell_type": "code", - "source": [ - "user_prompt = \"Search Google and answer: why did Elon Musk sue OpenAI?\"\n", - "#user_prompt = \"Compare Github branches main and test/benchmarks2.0, in project deepset-ai, repo haystack\"\n", - "#user_prompt = \"Using TinyURL create a shortened URL from this long URL https://haystack.deepset.ai/blog/haystack-2-release\"\n", - "\n", - "result = pipe.run(data={\"fn_llm\": {\"messages\":[ChatMessage.from_user(user_prompt)]}, \"embedder\": {\"text\": user_prompt}})\n" - ], - "metadata": { - "id": "OHP2Nj79KCdQ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67, - "referenced_widgets": [ - "e80ee26b4f0742fc88647e33a4acc558", - "7192e4496a2e448796ef8d4c639f44ba", - "8b2d336e82ea4b4ba8cea4f4dfa4953d", - 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These retrieved text documents are then injected into the Haystack pipeline, facilitating the dynamic invocation of specific OAS services. By merging any OAS service with Large Language Models (LLMs), this cutting-edge method opens a new phase in RAG systems, enabling the generation of deeply contextual responses from structured OAS data services." + ], + "metadata": { + "id": "J8TDTtQwiSn6" + } + }, + { + "cell_type": "markdown", + "source": [ + "## 1. Setup\n", + "This notebook demos Haystack 2.x service based RAG, using two openapi services: GitHub's compare_branches and SerperDev search API.\n", + "\n", + "Let's install necessary libraries and import key modules to build the foundation for the subsequent steps." + ], + "metadata": { + "id": "6zUWNgF-kUeU" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install --no-deps sentence-transformers\n", + "!pip install \"grpcio-tools==1.42\" jsonref openapi3 qdrant-haystack git+https://github.com/deepset-ai/haystack.git@fcd48d662c17893dea8e899023d72885bd721359" + ], + "metadata": { + "id": "bH8Lo7PSumLG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cd24da7b-7f6d-45ac-dae0-88bc8bd63b9a" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting sentence-transformers\n", + " Downloading sentence_transformers-2.6.1-py3-none-any.whl (163 kB)\n", + "\u001b[?25l 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collected packages: monotonic, portalocker, lazy-imports, jsonref, hyperframe, hpack, haystack-bm25, h11, grpcio-tools, boilerpy3, backoff, posthog, openapi3, httpcore, h2, httpx, openai, qdrant-client, haystack-ai, qdrant-haystack\n", + "Successfully installed backoff-2.2.1 boilerpy3-1.0.7 grpcio-tools-1.42.0 h11-0.14.0 h2-4.1.0 haystack-ai-2.1.0rc0 haystack-bm25-1.0.2 hpack-4.0.0 httpcore-1.0.4 httpx-0.27.0 hyperframe-6.0.1 jsonref-1.1.0 lazy-imports-0.3.1 monotonic-1.6 openai-1.14.3 openapi3-1.8.2 portalocker-2.8.2 posthog-3.5.0 qdrant-client-1.8.0 qdrant-haystack-3.1.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import getpass\n", + "import os\n", + "import json\n", + "import jsonref\n", + "import requests\n", + "from typing import List, Dict, Any\n", + "from haystack_integrations.document_stores.qdrant import QdrantDocumentStore\n", + "from haystack_integrations.components.retrievers.qdrant import QdrantEmbeddingRetriever\n", + "\n", + "from haystack import Pipeline, Document\n", + "from haystack.components.generators.utils import print_streaming_chunk\n", + "from haystack.components.writers import DocumentWriter\n", + "from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder\n", + "from haystack.components.converters import OpenAPIServiceToFunctions, OutputAdapter\n", + "from haystack.components.generators.chat import OpenAIChatGenerator\n", + "from haystack.components.connectors import OpenAPIServiceConnector\n", + "from haystack.components.fetchers import LinkContentFetcher\n", + "from haystack.components.others import Multiplexer\n", + "from haystack.dataclasses import ChatMessage, ByteStream" + ], + "metadata": { + "id": "RhxdVLCb_D8R" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.1 Let's collect the API keys for LLM provider" + ], + "metadata": { + "id": "m3IcPyJnpPMa" + } + }, + { + "cell_type": "code", + "source": [ + "llm_providers = {\"fireworks\": {\"api_base_url\": \"https://api.fireworks.ai/inference/v1\",\n", + " \"text_model\": \"accounts/fireworks/models/mixtral-8x7b-instruct\",\n", + " \"fc_model\": \"accounts/fireworks/models/firefunction-v1\"},\n", + " \"openai\": {\"api_base_url\": \"https://api.openai.com/v1\",\n", + " \"text_model\": \"gpt-4-turbo-preview\",\n", + " \"fc_model\": \"gpt-3.5-turbo\"}\n", + " }\n", + "\n", + "# change this value to another provider defined above - if needed\n", + "selected_provider = \"openai\"\n", + "llm_api_key = getpass.getpass(f\"Enter {selected_provider} api key:\")\n" + ], + "metadata": { + "id": "_9ySL9WqILJT", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb91d07a-2279-48d5-8ac3-348bfff2420b" + }, + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter openai api key:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 1.2 And OpenAPI services we'll use:" + ], + "metadata": { + "id": "55-vlrpX_W29" + } + }, + { + "cell_type": "code", + "source": [ + "serper_token = getpass.getpass(\"Enter serperdev api key:\")\n", + "github_token = getpass.getpass(\"Enter your github token:\")\n", + "tinurl_token = getpass.getpass(\"Enter your tinyurl token:\")" + ], + "metadata": { + "id": "uqGsQrRYJZ-2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "76e85a25-d275-467b-c0f3-c924b6056c03" + }, + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter serperdev api key:··········\n", + "Enter your github token:··········\n", + "Enter your tinyurl token:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## 2. Setup QdrantDocumentStore\n", + "\n", + "QdrantDocumentStore is one of many vector DB integrations we offer via Haystack Core Integrations [project](https://github.com/deepset-ai/haystack-core-integrations/)" + ], + "metadata": { + "id": "m-Po2hm0kzKj" + } + }, + { + "cell_type": "code", + "source": [ + "# Note: for the demo we are using Qdrant in local on-disk mode.\n", + "# For production use cases, a Docker container should be preferred.\n", + "# See https://qdrant.tech/documentation/ for more details\n", + "\n", + "document_store = QdrantDocumentStore(\n", + " path=\"./qdrant_1\",\n", + " index=\"Document\",\n", + " embedding_dim=768,\n", + " recreate_index=True\n", + " )" + ], + "metadata": { + "id": "mmVOQAg-C9Zx" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2.1 Add some helper functions we'll use in both indexing and retrieval pipelines" + ], + "metadata": { + "id": "m31frRXzo-W3" + } + }, + { + "cell_type": "code", + "source": [ + "def prepare_fc(openai_functions_schema: Dict[str, Any]) -> Dict[str, Any]:\n", + " return {\n", + " \"tools\": [{\n", + " \"type\": \"function\",\n", + " \"function\": openai_functions_schema\n", + " }],\n", + " \"tool_choice\": {\n", + " \"type\": \"function\",\n", + " \"function\": {\"name\": openai_functions_schema[\"name\"]}\n", + " }\n", + " }\n", + "\n", + "def create_docs(functions, specs, system_messages, service_credentials):\n", + " docs = []\n", + " for function, spec, system_message, service_credential in zip(functions, specs, system_messages, service_credentials):\n", + " d = Document(content=json.dumps(function),\n", + " meta={\"spec\": json.dumps(spec),\n", + " \"system_message\": system_message,\n", + " \"service_credential\": service_credential}) #it's just a demo - relax\n", + " docs.append(d)\n", + " return docs" + ], + "metadata": { + "id": "7U-QKL3nyhX0" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 3. Index OpenAPI specs\n", + "\n", + "Each indexed service Document contains the following descriptors:\n", + "\n", + "* OpenAI function calling JSON\n", + "* OpenAPI specification\n", + "* System message for the service\n", + "* Service credentials" + ], + "metadata": { + "id": "iz57DpxelJKn" + } + }, + { + "cell_type": "code", + "source": [ + "embedding_model = \"sentence-transformers/all-mpnet-base-v2\"\n", + "\n", + "# Note: if you play with the notebook and add more OAS services you are likely to get better service inference\n", + "# results with jina-embeddings-v2-small-en embedding model. jina-embeddings-v2-small-en is an English, monolingual\n", + "# embedding model supporting 8192 sequence length which should be enough for our Document describing OpenAI\n", + "# functions, OAS spec, and sytem message stored.\n", + "\n", + "# embedding_model = \"jina-embeddings-v2-small-en\"\n", + "# os.environ['HF_API_TOKEN'] = getpass.getpass(\"Enter Hugging Face api key:\")" + ], + "metadata": { + "id": "VBRPOeTmZUZO" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "indexing_pipeline = Pipeline()\n", + "indexing_pipeline.add_component(\"fetcher\", LinkContentFetcher())\n", + "indexing_pipeline.add_component(\"mx\", Multiplexer(List[ByteStream]))\n", + "indexing_pipeline.add_component(\"spec_to_functions\", OpenAPIServiceToFunctions())\n", + "indexing_pipeline.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=embedding_model,\n", + " meta_fields_to_embed=[\"spec\", \"system_message\", \"service_credential\"],\n", + " trust_remote_code=True))\n", + "indexing_pipeline.add_component(\"writer\", DocumentWriter(document_store=document_store))\n", + "indexing_pipeline.add_component(\"a1\", OutputAdapter(\"{{functions | create_docs(specs, system_messages, service_credentials)}}\",\n", + " List[Document],\n", + " {\"create_docs\": create_docs}))\n", + "\n", + "indexing_pipeline.add_component(\"a2\", OutputAdapter(\"{{sources | json_objects}}\",\n", + " List[Dict[str, Any]],\n", + " {\"json_objects\": lambda sources: [json.loads(s.to_string()) for s in sources]}))\n", + "\n", + "indexing_pipeline.connect(\"fetcher.streams\", \"mx\")\n", + "indexing_pipeline.connect(\"mx\", \"spec_to_functions.sources\")\n", + "indexing_pipeline.connect(\"mx\", \"a2.sources\")\n", + "indexing_pipeline.connect(\"a2\", \"a1.specs\")\n", + "indexing_pipeline.connect(\"spec_to_functions.functions\", \"a1\")\n", + "indexing_pipeline.connect(\"a1.output\", \"embedder.documents\")\n", + "indexing_pipeline.connect(\"embedder\", \"writer\")" + ], + "metadata": { + "id": "rXrLxTQ8Dl8V", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "18f56665-a7a7-4bde-dd3b-d317152bfa01" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\n", + "🚅 Components\n", + " - fetcher: LinkContentFetcher\n", + " - mx: Multiplexer\n", + " - spec_to_functions: OpenAPIServiceToFunctions\n", + " - embedder: SentenceTransformersDocumentEmbedder\n", + " - writer: DocumentWriter\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + "🛤️ Connections\n", + " - fetcher.streams -> mx.value (List[ByteStream])\n", + " - mx.value -> spec_to_functions.sources (List[ByteStream])\n", + " - mx.value -> a2.sources (List[ByteStream])\n", + " - spec_to_functions.functions -> a1.functions (List[Dict[str, Any]])\n", + " - embedder.documents -> writer.documents (List[Document])\n", + " - a1.output -> embedder.documents (List[Document])\n", + " - a2.output -> a1.specs (List[Dict[str, Any]])" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "indexing_pipeline.draw(\"/content/indexing_pipeline.png\")" + ], + "metadata": { + "id": "u8_d1RrSlN9n" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 3.1 We'll use three OAS services: GitHub, SerperDev, TinyURL\n", + "\n", + "\n", + "\n", + "* GitHub - compare branches operation\n", + "* SerperDev - a search engine bridge\n", + "* TinyURL - URL shortener \n", + "\n" + ], + "metadata": { + "id": "Wi6T5IMVZYOW" + } + }, + { + "cell_type": "code", + "source": [ + "services = {\"open_api_specs\": [\"https://bit.ly/github_compare\",\"https://bit.ly/serper_dev_spec\", \"https://bit.ly/tinyurl_spec\"],\n", + " \"system_messages\":[\"https://bit.ly/pr_auto_system\", \"https://bit.ly/serper_system\",\"https://bit.ly/tinyurl_system_prompt\"]}" + ], + "metadata": { + "id": "TPSaQHgKSGvG" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "result = indexing_pipeline.run(data={\"fetcher\": {\"urls\": services[\"open_api_specs\"]},\n", + " \"a1\":{\"service_credentials\": [github_token, serper_token, tinurl_token],\n", + " \"system_messages\": [requests.get(url).text for url in services[\"system_messages\"]]\n", + " }\n", + " })\n", + 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datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00\n", + "🚅 Components\n", + " - embedder: SentenceTransformersTextEmbedder\n", + " - retriever: QdrantEmbeddingRetriever\n", + " - mx: Multiplexer\n", + " - a1: OutputAdapter\n", + " - a2: OutputAdapter\n", + " - a3: OutputAdapter\n", + " - a4: OutputAdapter\n", + " - a5: OutputAdapter\n", + " - fn_llm: OpenAIChatGenerator\n", + " - openapi_container: OpenAPIServiceConnector\n", + " - llm: OpenAIChatGenerator\n", + "🛤️ Connections\n", + " - embedder.embedding -> retriever.query_embedding (List[float])\n", + " - retriever.documents -> mx.value (List[Document])\n", + " - mx.value -> a1.documents (List[Document])\n", + " - mx.value -> a2.documents (List[Document])\n", + " - mx.value -> a3.documents (List[Document])\n", + " - mx.value -> a4.documents (List[Document])\n", + " - a1.output -> fn_llm.generation_kwargs (Dict[str, Any])\n", + " - a2.output -> openapi_container.service_openapi_spec (Dict[str, Any])\n", + " - a3.output -> openapi_container.service_credentials (str)\n", + " - a4.output -> a5.system_message (List[ChatMessage])\n", + " - a5.output -> llm.messages (List[ChatMessage])\n", + " - fn_llm.replies -> openapi_container.messages (List[ChatMessage])\n", + " - openapi_container.service_response -> a5.service_response (Dict[str, Any])" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "pipe.draw(\"/content/pipe.png\")" + ], + "metadata": { + "id": "VGuSsKJ7lpnT" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## 5. Retrieval, service invocation and LLM generation" + ], + "metadata": { + "id": "ZhpXSiqimXtr" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here is where everything gets interesting! Based on the prompt below, we'll retrieve the top_k=1 document from QdrantDocumentStore. That'll be the relevant service Document. We'll then proceed to use service descriptors from the retrieved Document to initiate:\n", + "\n", + "* Step 1: Function calling to resolve the parameters for service invocation.\n", + "\n", + "\"Step\n", + "\n", + "* Step 2: We'll invoke the OpenAPI-compliant service with those parameters.\n", + "\"Step\n", + "\n", + "* Step 3: The response from the service will be joined with the system prompt for that service.\n", + "\"Step\n", + "\n", + "* Step 4: The final LLM response will be generated.\n", + "\n", + "\n", + "This process involves straightforward intent recognition that utilizes vector similarity to accurately identify and retrieve the appropriate service Document. Theoretically, it's possible to have dozens of services indexed within the QdrantDocumentStore. Once we retrieve the right service Documents, we will rely on the Haystack pipeline to perform the magic and observe the response generated by the LLM." + ], + "metadata": { + "id": "nfWVprj7y5O-" + } + }, + { + "cell_type": "code", + "source": [ + "user_prompt = \"Search Google and answer: why did Elon Musk sue OpenAI?\"\n", + "#user_prompt = \"Compare Github branches main and test/benchmarks2.0, in project deepset-ai, repo haystack\"\n", + "#user_prompt = \"Using TinyURL create a shortened URL from this long URL https://haystack.deepset.ai/blog/haystack-2-release\"\n", + "\n", + "result = pipe.run(data={\"fn_llm\": {\"messages\":[ChatMessage.from_user(user_prompt)]}, \"embedder\": {\"text\": user_prompt}})\n" + ], + "metadata": { + "id": "OHP2Nj79KCdQ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "e80ee26b4f0742fc88647e33a4acc558", + "7192e4496a2e448796ef8d4c639f44ba", + "8b2d336e82ea4b4ba8cea4f4dfa4953d", + "aafac7367fc1494385b69349519b1ddd", + "1db719842cde4a6eb5e6a79211409609", + "bd409e60ea5347338db59bf91ebdf31a", + "0ec7f9d4ebfd44f5870dfe5384046373", + "de738a8f9d7344c1ad45fdcbcedd17df", + "d8ccc0bf8cb2450fb2bf0b46510bcacf", + "f01e58958f6d4e739aa1c4ad4df149e0", + "2d65613837af473ab90b733e7ebe4707" + ] + }, + "outputId": "dfb82354-19fc-4997-9fd9-97e23977ac67" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Batches: 0%| | 0/1 [00:00 Date: Tue, 26 Mar 2024 17:08:13 +0100 Subject: [PATCH 5/5] Add HyDE notebook --- ...06-using-hyde-for-improved-retrieval.ipynb | 6491 +++++++++++++++++ 1 file changed, 6491 insertions(+) create mode 100644 notebooks/06-using-hyde-for-improved-retrieval.ipynb diff --git a/notebooks/06-using-hyde-for-improved-retrieval.ipynb b/notebooks/06-using-hyde-for-improved-retrieval.ipynb new file mode 100644 index 0000000..ba201af --- /dev/null +++ b/notebooks/06-using-hyde-for-improved-retrieval.ipynb @@ -0,0 +1,6491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "chBgl-RHFAKi" + }, + "source": [ + "# Using Hypothetical Document Embeddings (HyDE) to Improve Retrieval\n", + "\n", + "> 📚 This cookbook has an accompanying article with a complete walkthrough [\"Optimizing Retrival with HyDE\"](https://haystack.deepset.ai/blog/optimizing-retrieval-with-hyde)\n", + "\n", + "In this coookbook, we are building Haystack components that allow us to easily incorporate HyDE into our RAG pipelines, to optimize retrieval.\n", + "\n", + "> To learn more about HyDE and when it's useful, check out our [guide to Hypothetical Document Embeddings (HyDE)](https://docs.haystack.deepset.ai/v2.0/docs/hypothetical-document-embeddings-hyde)\n", + "\n", + "## Install Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_ZIUJlvQCDvF", + "outputId": 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haystack-ai-2.0.0 haystack-bm25-1.0.2 httpcore-1.0.4 httpx-0.27.0 lazy-imports-0.3.1 monotonic-1.6 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 openai-1.14.3 posthog-3.5.0 sentence-transformers-2.6.1 xxhash-3.4.1\n" + ] + } + ], + "source": [ + "!pip install haystack-ai sentence-transformers datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kr6_UhFyFh-c" + }, + "source": [ + "In the following sections, we will be using the `OpenAIGenerator`, so we need to provide our API key 👇" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J5l8zljUB1wc", + "outputId": "be344d0b-fa6e-4a7f-efb2-8cb37f9d1009" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter your openAI key:··········\n" + ] + } + ], + "source": [ + "from getpass import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your openAI key:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yiIk0Bt2GVbl" + }, + "source": [ + "## Building a Pipeline for Hypothetical Document Embeddings\n", + "\n", + "We will build a Haystack pipeline that generates 'fake' documents.\n", + "For this part, we are using the `OpenAIGenerator` with a `PromptBuilder` that instructs the model to generate paragraphs." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "q7vs3ubjB7PB" + }, + "outputs": [], + "source": [ + "from haystack.components.generators.openai import OpenAIGenerator\n", + "from haystack.components.builders import PromptBuilder\n", + "\n", + "generator = OpenAIGenerator(\n", + " model=\"gpt-3.5-turbo\",\n", + " generation_kwargs={\"n\": 5, \"temperature\": 0.75, \"max_tokens\": 400},\n", + ")\n", + "\n", + "template=\"\"\"Given a question, generate a paragraph of text that answers the question.\n", + " Question: {{question}}\n", + " Paragraph:\"\"\"\n", + "\n", + "prompt_builder = PromptBuilder(template=template)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dJVyxytsG6Zc" + }, + "source": [ + "Next, we use the `OutputAdapter` to transform the generated paragraphs into a List of Documents. This way, we will be able to use the `SentenceTransformersDocumentEmbedder` to create embeddings, since this component expects `List[Document]`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "K72gcW84CFtT", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 496, + "referenced_widgets": [ + "70f85c1914584880ae27be13baaabe2e", + "bc0389bebe184adc93251f201eb87945", + "61521a273f5849489981968cef911b84", + "cb7bf9dd8eee4682b9e4f8c561fc6c23", + "da8959ad254147b9ae43034b53b237e6", + "7321f7b1e347443cbd436bba9cc971aa", + "37d0b989680744e391d892ec06840b7f", + "e1e4e465a1bc443491761e33b5d37cd1", + "ddb8e28c75f24582b14467a5b7ea1c42", + "5d2292e1f7414c3999ffd0088b2f5d7b", + "ff7a46270fda41478428069049028510", + "fe83b59f0ef54706b8d3f74ae091733d", + "78e207e503eb4449b46b680b55efcf39", + "5209821174b547aa9df0bcfdda2cc3cf", + "b5a1d7196b994ec98f05d29aabe6da72", + "b227227c195c4d1f969130ae713af2aa", + "a92c3699d7234aad932a113798e5641d", + 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"outputId": "65714284-8687-478b-cdf1-fd7bc98e6202" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00 Dict[str, Any]:\n", + " data = default_to_dict(\n", + " self,\n", + " instruct_llm=self.instruct_llm,\n", + " instruct_llm_api_key=self.instruct_llm_api_key,\n", + " nr_completions=self.nr_completions,\n", + " embedder_model=self.embedder_model,\n", + " )\n", + " data[\"pipeline\"] = self.pipeline.to_dict()\n", + " return data\n", + "\n", + " @classmethod\n", + " def from_dict(cls, data: Dict[str, Any]) -> \"HypotheticalDocumentEmbedder\":\n", + " hyde_obj = default_from_dict(cls, data)\n", + " hyde_obj.pipeline = Pipeline.from_dict(data[\"pipeline\"])\n", + " return hyde_obj\n", + "\n", + " @component.output_types(hypothetical_embedding=List[float])\n", + " def run(self, query: str):\n", + " result = self.pipeline.run(data={\"prompt_builder\": {\"question\": query}})\n", + " # return a single query vector embedding representing the average of the hypothetical document embeddings\n", + " stacked_embeddings = array([doc.embedding for doc in result[\"embedder\"][\"documents\"]])\n", + " avg_embeddings = mean(stacked_embeddings, axis=0)\n", + " hyde_vector = avg_embeddings.reshape((1, len(avg_embeddings)))\n", + " return {\"hypothetical_embedding\": hyde_vector[0].tolist()}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z3BTZF6qMtTY" + }, + "source": [ + "## Use HyDE For Retrieval\n", + "\n", + "Let's see how we can use this component in a full pipeline. First, let's index some documents into an `InMemoryDocumentStore`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "da921e5a58b04aaab790bbd11aff0c63", + "b3c4f1f692474691b341047813284e9a", + "1fad28421fc74de883684b3448a0ee43", + "38d62c26e52442289766295e69093cdc", + "cf698c3ce16f4897931c9599d38c1297", + "d507bd2b396c4eca81ba2e7286d5eca7", + "a97ba5fbf2534774a3e4889db8a3cb8f", + "e919f6b33b454b978e13321cfd8ff9d7", + "ad33be2811a44f41ac59853e771e818f", + "6d08bafa3bf64cca834f311e4fc58be8", + "28b66f2ec805426586a5618c772b778d", + "b2bda02f72be431088b345ce75f67ac2", + "16c94d58f95741e79f82b6f0ffd506f0", + "eacc2a82775f4709ac758d9f1a61c958", + "039605fb2fb74ce2881e8123085a1afe", + "07da8983958b49e18bfce7bb993b2e7d", + "a80fde83c29545ae8c763dff84d2707d", + "781272e339c84bb5af97d3b1500dce56", + "36137ed83ea646959679167c950f7345", + "523881cd144a49f3a152d80dd7d2a908", + "4cfe59251b6746a5af7cbb4c063b9983", + "7497b56780a94d50bea7b003837f47d7", + "7dde19e7eea248c589206b43b605811d", + "8e08c7c339c445dd92d5aa6eccbdd72b", + "8e4860cf07664a37904435786f867baa", + "2ab884009bb24b99ba2d49204bce0e83", + "e905202e49834b848d15529537a60f08", + "5e3bacee89874841ad095310a11cab82", + "2d9dd9bc0fca44f6a236b0af8ae5340a", + "a157de5086a1451db85fe3cd69dc96a4", + "b63e7854d909473ba637e3280f1a7bb4", + "3afd2cebdd674571b7df7c3f5331f94e", + "75bbf1fe2ffb40008f36edc2a6083c1e", + "42c8cafec745416484b6b38289751d62", + "6281728c2e164b75939e16d4e45e1c0b", + "e47de34568794c2a81597ad061106558", + "6401bf7915eb43df99b9de5448f85a8c", + "02bb5711ecf541d1abfa97b32864ce3e", + "8085df4e1983458e87bdc5295ddc2bd7", + "93561b8f3d434f22bed883db6e550f84", + "af469b7ad3124c588ccaf36886e915b5", + "a660f6eb3b354fb190a46d8a6af83e72", + "5be1ef85e3dd42a6a509e3ce677bd8df", + "837dd02f91e648cd944005dff0aa98e3" + ] + }, + "id": "9tb9zO_vMzuf", + "outputId": "e173caf2-a11f-4663-e05c-86988a5bb16f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading metadata: 0%| | 0.00/1.51k [00:00