From 460f5581fef1d800e2a96fbf10f30b242921ac60 Mon Sep 17 00:00:00 2001 From: Stijn Smits <167638923+s-smits@users.noreply.github.com> Date: Mon, 7 Oct 2024 12:17:47 +0200 Subject: [PATCH] Add files via upload --- ...website_qa_with_gemini_flash_caching.ipynb | 166 ++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 examples/website_qa_with_gemini_caching/website_qa_with_gemini_flash_caching.ipynb diff --git a/examples/website_qa_with_gemini_caching/website_qa_with_gemini_flash_caching.ipynb b/examples/website_qa_with_gemini_caching/website_qa_with_gemini_flash_caching.ipynb new file mode 100644 index 00000000..19d72c9d --- /dev/null +++ b/examples/website_qa_with_gemini_caching/website_qa_with_gemini_flash_caching.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ericciarla/projects/python_projects/agents_testing/.conda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "import datetime\n", + "import time\n", + "import google.generativeai as genai\n", + "from google.generativeai import caching\n", + "from dotenv import load_dotenv\n", + "from firecrawl import FirecrawlApp\n", + "import json\n", + "\n", + "# Load environment variables\n", + "load_dotenv()\n", + "\n", + "# Retrieve API keys from environment variables\n", + "google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n", + "firecrawl_api_key = os.getenv(\"FIRECRAWL_API_KEY\")\n", + "\n", + "# Configure the Google Generative AI module with the API key\n", + "genai.configure(api_key=google_api_key)\n", + "\n", + "# Initialize the FirecrawlApp with your API key\n", + "app = FirecrawlApp(api_key=firecrawl_api_key)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data returned from crawl.\n" + ] + } + ], + "source": [ + "# Crawl a website\n", + "crawl_url = 'https://dify.ai/'\n", + "params = {\n", + " \n", + " 'crawlOptions': {\n", + " 'limit': 100\n", + " }\n", + "}\n", + "crawl_result = app.crawl_url(crawl_url, params=params)\n", + "\n", + "if crawl_result is not None:\n", + " # Convert crawl results to JSON format, excluding 'content' field from each entry\n", + " cleaned_crawl_result = [{k: v for k, v in entry.items() if k != 'content'} for entry in crawl_result]\n", + "\n", + " # Save the modified results as a text file containing JSON data\n", + " with open('crawl_result.txt', 'w') as file:\n", + " file.write(json.dumps(cleaned_crawl_result, indent=4))\n", + "else:\n", + " print(\"No data returned from crawl.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Upload the video using the Files API\n", + "text_file = genai.upload_file(path=\"crawl_result.txt\")\n", + "\n", + "# Wait for the file to finish processing\n", + "while text_file.state.name == \"PROCESSING\":\n", + " print('Waiting for file to be processed.')\n", + " time.sleep(2)\n", + " text_file = genai.get_file(text_file.name)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a cache with a 5 minute TTL\n", + "cache = caching.CachedContent.create(\n", + " model=\"models/gemini-1.5-flash-002\",\n", + " display_name=\"website crawl testing again\", # used to identify the cache\n", + " system_instruction=\"You are an expert at this website, and your job is to answer user's query based on the website you have access to.\",\n", + " contents=[text_file],\n", + " ttl=datetime.timedelta(minutes=15),\n", + ")\n", + "# Construct a GenerativeModel which uses the created cache.\n", + "model = genai.GenerativeModel.from_cached_content(cached_content=cache)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dify.AI utilizes the **Firecrawl** service for website scraping. This service can crawl and convert any website into clean markdown or structured data that's ready for use in building RAG applications. \n", + "\n", + "Here's how Firecrawl helps:\n", + "\n", + "* **Crawling and Conversion:** Firecrawl crawls the website and converts the content into a format that is easily understood by LLMs, such as markdown or structured data.\n", + "* **Clean Output:** Firecrawl ensures the data is clean and free of errors, making it easier to use in Dify's RAG engine.\n", + "* **Parallel Crawling:** Firecrawl efficiently crawls web pages in parallel, delivering results quickly.\n", + "\n", + "You can find Firecrawl on their website: [https://www.firecrawl.dev/](https://www.firecrawl.dev/)\n", + "\n", + "Firecrawl offers both a cloud service and an open-source software (OSS) edition. \n", + "\n" + ] + } + ], + "source": [ + "# Query the model\n", + "response = model.generate_content([\"What powers website scraping with Dify?\"])\n", + "response_dict = response.to_dict()\n", + "response_text = response_dict['candidates'][0]['content']['parts'][0]['text']\n", + "print(response_text)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}