Delete 9. Image Processing Agent.ipynb
This commit is contained in:
Masih Moafi
GitHub
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c413571f-c824-49be-ba5a-97e9a8476a6d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import httpx\n",
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"import json\n",
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"from crewai import Agent, Task, Crew\n",
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"from langchain.agents import tool\n",
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"from langchain_community.llms import Ollama\n",
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"\n",
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"# Directory for your domain-specific documents (assumed to be .txt files)\n",
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"DOCS_DIR = \"docs\"\n",
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"\n",
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"# Load documents from the 'docs' directory\n",
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"def load_documents():\n",
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" \"\"\"Loads all .txt files from the 'docs' directory and returns a list of (filename, content) tuples.\"\"\"\n",
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" if not os.path.exists(DOCS_DIR):\n",
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" return []\n",
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" documents = []\n",
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" for filename in os.listdir(DOCS_DIR):\n",
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" if filename.endswith(\".txt\"):\n",
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" with open(os.path.join(DOCS_DIR, filename), \"r\", encoding=\"utf-8\") as f:\n",
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" content = f.read()\n",
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" documents.append((filename, content))\n",
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" return documents\n",
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"\n",
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"DOCUMENTS = load_documents()\n",
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"\n",
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"@tool\n",
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"def google_search(query: str) -> str:\n",
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" \"\"\"Searches Google using Serper API for web results based on the query.\n",
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"\n",
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" Args:\n",
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" query (str): The search query.\n",
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"\n",
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" Returns:\n",
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" str: A summary of Google search results from Serper or a message if the search fails.\n",
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" \"\"\"\n",
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" url = \"https://google.serper.dev/search\"\n",
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" headers = {\n",
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" \"X-API-KEY\": \"ebda9508d11e4cc8c5091494a76b783fda7fe6da\",\n",
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" \"Content-Type\": \"application/json\"\n",
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" }\n",
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" response = httpx.post(url, json={\"q\": query}, headers=headers)\n",
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" if response.status_code == 200:\n",
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" data = response.json()\n",
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" results = []\n",
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" for result in data.get(\"organic\", []):\n",
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" title = result.get(\"title\", \"No title\")\n",
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" snippet = result.get(\"snippet\", \"\")\n",
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" link = result.get(\"link\", \"\")\n",
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" results.append(f\"{title}: {snippet} ({link})\")\n",
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" if len(results) >= 3:\n",
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" break\n",
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" return \"\\n\".join(results) if results else \"No results found using Google Serper.\"\n",
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" return \"Error performing Google Serper search.\"\n",
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"\n",
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"@tool\n",
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"def search_docs(query: str) -> str:\n",
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" \"\"\"Searches local documents for relevant information based on the query.\n",
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" \n",
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" Args:\n",
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" query (str): The search query.\n",
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" \n",
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" Returns:\n",
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" str: Snippets from documents matching the query or a message if no documents are found.\n",
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" \"\"\"\n",
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" if not DOCUMENTS:\n",
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" return \"No documents found.\"\n",
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" query_words = query.lower().split()\n",
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" results = []\n",
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" for filename, content in DOCUMENTS:\n",
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" lines = content.split('\\n')\n",
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" for line in lines:\n",
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" if any(word in line.lower() for word in query_words):\n",
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" results.append(f\"From {filename}: {line[:200]}\")\n",
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" if len(results) >= 3:\n",
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" break\n",
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" if len(results) >= 3:\n",
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" break\n",
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" return \"\\n\".join(results) if results else \"No relevant information found in documents.\"\n",
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"\n",
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"@tool\n",
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"def wikipedia(query: str) -> str:\n",
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" \"\"\"Searches Persian Wikipedia for a summary based on the query.\n",
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" \n",
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" Args:\n",
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" query (str): The search query.\n",
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" \n",
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" Returns:\n",
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" str: The snippet from the first Wikipedia search result or a message if no results are found.\n",
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" \"\"\"\n",
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" response = httpx.get(\"https://fa.wikipedia.org/w/api.php\", params={\n",
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" \"action\": \"query\",\n",
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" \"list\": \"search\",\n",
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" \"srsearch\": query,\n",
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" \"format\": \"json\",\n",
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" \"utf8\": 1\n",
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" })\n",
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" results = response.json().get(\"query\", {}).get(\"search\", [])\n",
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" if results:\n",
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" return results[0].get(\"snippet\", \"اطلاعاتی یافت نشد.\")\n",
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" return \"اطلاعاتی یافت نشد در ویکیپدیا.\"\n",
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"\n",
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"@tool\n",
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"def duckduckgo(query: str) -> str:\n",
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" \"\"\"Searches DuckDuckGo for web results based on the query.\n",
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" \n",
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" Args:\n",
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" query (str): The search query.\n",
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" \n",
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" Returns:\n",
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" str: A placeholder message with search results or a message if the search fails.\n",
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" \"\"\"\n",
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" response = httpx.get(\"https://duckduckgo.com/html/\", params={\"q\": query})\n",
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" if response.status_code == 200:\n",
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" return \"نتایج جستجوی DuckDuckGo: [صفحه 1, صفحه 2, صفحه 3]\"\n",
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" return \"نتایجی از DuckDuckGo یافت نشد.\"\n",
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"\n",
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"# Set up the language model (using Llama3.2 for now)\n",
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"llm = Ollama(model=\"llama3.2\")\n",
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"\n",
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"# Create the agent with CrewAI prioritizing Google Serper search first\n",
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"agent = Agent(\n",
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" role=\"researcher\",\n",
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" goal=\"Answer the user's question by primarily using Google search results from the Serper API, then using local documents, Persian Wikipedia, and DuckDuckGo if needed.\",\n",
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" backstory=\"You are an AI assistant designed to efficiently gather and synthesize information from multiple sources, prioritizing Google search results from the Serper API.\",\n",
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" tools=[google_search, search_docs, wikipedia, duckduckgo],\n",
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" llm=llm,\n",
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" verbose=True\n",
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")\n",
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"\n",
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"def query(question):\n",
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" \"\"\"Runs the query through the agent and saves the result.\n",
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" \n",
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" Args:\n",
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" question (str): The user's question.\n",
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" \"\"\"\n",
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" task = Task(\n",
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" description=question,\n",
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" expected_output=\"A detailed answer to the user's question.\",\n",
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" agent=agent\n",
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" )\n",
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" crew = Crew(\n",
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" agents=[agent],\n",
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" tasks=[task],\n",
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" verbose=2\n",
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" )\n",
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" result = crew.kickoff()\n",
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" print(result)\n",
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" save_to_file(str(result))\n",
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"\n",
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"def save_to_file(text, path=\"C:/Users/ASUS/Downloads/Month 2/agents.txt\"):\n",
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" \"\"\"Saves the result to a file.\n",
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" \n",
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" Args:\n",
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" text (str): The text to save.\n",
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" path (str): The file path to save the text.\n",
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" \"\"\"\n",
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" with open(path, 'a', encoding='utf-8') as f:\n",
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" f.write(text + \"\\n\" + \"=\"*50 + \"\\n\")\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" # Example queries to test the agent\n",
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" q1 = \"شاه عباس صفوی آدم خوبی بوده؟ چرا؟\"\n",
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" q2 = \"وقتی چراغ DNS مودم قطع میشه به چه معنیه؟\"\n",
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" query(q1)\n",
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" query(q2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "052e7c32-65c4-40ce-8454-3c404075a80f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"\n",
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"url = \"https://api.va.landing.ai/v1/tools/agentic-document-analysis\"\n",
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"files = {\n",
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" \"image\": open(\"C:/Users/ASUS/Desktop/benefits of RAG.png\", \"rb\")\n",
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"}\n",
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"headers = {\n",
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" \"Authorization\": \"Basic YWdmbjl0d200emV3cjVicHFzeXpuOmFBWGJCTGZRUFBsMzZVVWF3aXFVYVduY0hGVUdIQmhq\",\n",
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"}\n",
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"response = requests.post(url, files=files, headers=headers)\n",
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"print(response.json())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b48cc6f-81be-48f0-be88-1de07a0f47f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"with open(\"C:/Users/ASUS/Desktop/today.txt\", \"r\") as f:\n",
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" data = json.load(f)\n",
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"\n",
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"print(data[\"data\"][\"markdown\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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