416 lines
20 KiB
Plaintext
416 lines
20 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyOe5BsaH0aplNCjknkFtnjg",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/mshumer/OpenDeepResearcher/blob/main/open_deep_researcher.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install nest_asyncio\n",
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"import nest_asyncio\n",
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"nest_asyncio.apply()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "y7cTpP9rDZW-",
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"outputId": "5a443ad2-7a8d-4fef-f315-12108c28f1a2"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: nest_asyncio in /usr/local/lib/python3.11/dist-packages (1.6.0)\n"
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]
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}
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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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||
"metadata": {
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"id": "GJTo96a7DGUz"
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},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"import aiohttp\n",
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"import json\n",
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"\n",
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"# =======================\n",
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"# Configuration Constants\n",
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"# =======================\n",
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"OPENROUTER_API_KEY = \"REDACTED\" # Replace with your OpenRouter API key\n",
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"SERPAPI_API_KEY = \"REDACTED\" # Replace with your SERPAPI API key\n",
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"JINA_API_KEY = \"REDACTED\" # Replace with your JINA API key\n",
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"\n",
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"# Endpoints\n",
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"OPENROUTER_URL = \"https://openrouter.ai/api/v1/chat/completions\"\n",
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"SERPAPI_URL = \"https://serpapi.com/search\"\n",
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"JINA_BASE_URL = \"https://r.jina.ai/\"\n",
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"\n",
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"# Default LLM model (can be changed if desired)\n",
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"DEFAULT_MODEL = \"anthropic/claude-3.5-haiku\"\n",
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"\n",
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"\n",
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"# ============================\n",
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"# Asynchronous Helper Functions\n",
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"# ============================\n",
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"\n",
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"async def call_openrouter_async(session, messages, model=DEFAULT_MODEL):\n",
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" \"\"\"\n",
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" Asynchronously call the OpenRouter chat completion API with the provided messages.\n",
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" Returns the content of the assistant’s reply.\n",
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" \"\"\"\n",
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" headers = {\n",
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" \"Authorization\": f\"Bearer {OPENROUTER_API_KEY}\",\n",
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" \"X-Title\": \"OpenDeepResearcher, by Matt Shumer\",\n",
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" \"Content-Type\": \"application/json\"\n",
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" }\n",
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" payload = {\n",
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" \"model\": model,\n",
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" \"messages\": messages\n",
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" }\n",
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" try:\n",
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" async with session.post(OPENROUTER_URL, headers=headers, json=payload) as resp:\n",
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" if resp.status == 200:\n",
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" result = await resp.json()\n",
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" try:\n",
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" return result['choices'][0]['message']['content']\n",
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" except (KeyError, IndexError) as e:\n",
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" print(\"Unexpected OpenRouter response structure:\", result)\n",
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" return None\n",
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" else:\n",
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" text = await resp.text()\n",
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" print(f\"OpenRouter API error: {resp.status} - {text}\")\n",
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" return None\n",
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" except Exception as e:\n",
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" print(\"Error calling OpenRouter:\", e)\n",
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" return None\n",
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"\n",
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"\n",
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"async def generate_search_queries_async(session, user_query):\n",
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" \"\"\"\n",
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" Ask the LLM to produce up to four precise search queries (in Python list format)\n",
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" based on the user’s query.\n",
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" \"\"\"\n",
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" prompt = (\n",
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" \"You are an expert research assistant. Given the user's query, generate up to four distinct, \"\n",
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" \"precise search queries that would help gather comprehensive information on the topic. \"\n",
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" \"Return only a Python list of strings, for example: ['query1', 'query2', 'query3'].\"\n",
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" )\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful and precise research assistant.\"},\n",
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" {\"role\": \"user\", \"content\": f\"User Query: {user_query}\\n\\n{prompt}\"}\n",
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" ]\n",
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" response = await call_openrouter_async(session, messages)\n",
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" if response:\n",
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" try:\n",
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" # Expect exactly a Python list (e.g., \"['query1', 'query2']\")\n",
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" search_queries = eval(response)\n",
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" if isinstance(search_queries, list):\n",
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" return search_queries\n",
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" else:\n",
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" print(\"LLM did not return a list. Response:\", response)\n",
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" return []\n",
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" except Exception as e:\n",
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" print(\"Error parsing search queries:\", e, \"\\nResponse:\", response)\n",
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" return []\n",
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" return []\n",
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"\n",
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"\n",
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"async def perform_search_async(session, query):\n",
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" \"\"\"\n",
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" Asynchronously perform a Google search using SERPAPI for the given query.\n",
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" Returns a list of result URLs.\n",
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" \"\"\"\n",
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" params = {\n",
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" \"q\": query,\n",
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" \"api_key\": SERPAPI_API_KEY,\n",
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" \"engine\": \"google\"\n",
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" }\n",
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" try:\n",
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" async with session.get(SERPAPI_URL, params=params) as resp:\n",
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" if resp.status == 200:\n",
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" results = await resp.json()\n",
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" if \"organic_results\" in results:\n",
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" links = [item.get(\"link\") for item in results[\"organic_results\"] if \"link\" in item]\n",
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" return links\n",
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" else:\n",
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" print(\"No organic results in SERPAPI response.\")\n",
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" return []\n",
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" else:\n",
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" text = await resp.text()\n",
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" print(f\"SERPAPI error: {resp.status} - {text}\")\n",
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" return []\n",
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" except Exception as e:\n",
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" print(\"Error performing SERPAPI search:\", e)\n",
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" return []\n",
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"\n",
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"\n",
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"async def fetch_webpage_text_async(session, url):\n",
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" \"\"\"\n",
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" Asynchronously retrieve the text content of a webpage using Jina.\n",
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" The URL is appended to the Jina endpoint.\n",
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" \"\"\"\n",
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" full_url = f\"{JINA_BASE_URL}{url}\"\n",
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" headers = {\n",
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" \"Authorization\": f\"Bearer {JINA_API_KEY}\"\n",
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" }\n",
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" try:\n",
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" async with session.get(full_url, headers=headers) as resp:\n",
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" if resp.status == 200:\n",
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" return await resp.text()\n",
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" else:\n",
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" text = await resp.text()\n",
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" print(f\"Jina fetch error for {url}: {resp.status} - {text}\")\n",
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" return \"\"\n",
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" except Exception as e:\n",
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" print(\"Error fetching webpage text with Jina:\", e)\n",
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" return \"\"\n",
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"\n",
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"\n",
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"async def is_page_useful_async(session, user_query, page_text):\n",
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" \"\"\"\n",
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" Ask the LLM if the provided webpage content is useful for answering the user's query.\n",
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" The LLM must reply with exactly \"Yes\" or \"No\".\n",
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" \"\"\"\n",
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" prompt = (\n",
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" \"You are a critical research evaluator. Given the user's query and the content of a webpage, \"\n",
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" \"determine if the webpage contains information relevant and useful for addressing the query. \"\n",
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" \"Respond with exactly one word: 'Yes' if the page is useful, or 'No' if it is not. Do not include any extra text.\"\n",
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" )\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a strict and concise evaluator of research relevance.\"},\n",
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" {\"role\": \"user\", \"content\": f\"User Query: {user_query}\\n\\nWebpage Content (first 20000 characters):\\n{page_text[:20000]}\\n\\n{prompt}\"}\n",
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" ]\n",
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" response = await call_openrouter_async(session, messages)\n",
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" if response:\n",
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" answer = response.strip()\n",
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" if answer in [\"Yes\", \"No\"]:\n",
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" return answer\n",
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" else:\n",
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" # Fallback: try to extract Yes/No from the response.\n",
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" if \"Yes\" in answer:\n",
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" return \"Yes\"\n",
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" elif \"No\" in answer:\n",
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" return \"No\"\n",
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" return \"No\"\n",
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"\n",
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"\n",
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"async def extract_relevant_context_async(session, user_query, search_query, page_text):\n",
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" \"\"\"\n",
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" Given the original query, the search query used, and the page content,\n",
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" have the LLM extract all information relevant for answering the query.\n",
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" \"\"\"\n",
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" prompt = (\n",
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" \"You are an expert information extractor. Given the user's query, the search query that led to this page, \"\n",
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" \"and the webpage content, extract all pieces of information that are relevant to answering the user's query. \"\n",
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" \"Return only the relevant context as plain text without commentary.\"\n",
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" )\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are an expert in extracting and summarizing relevant information.\"},\n",
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" {\"role\": \"user\", \"content\": f\"User Query: {user_query}\\nSearch Query: {search_query}\\n\\nWebpage Content (first 20000 characters):\\n{page_text[:20000]}\\n\\n{prompt}\"}\n",
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" ]\n",
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" response = await call_openrouter_async(session, messages)\n",
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" if response:\n",
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" return response.strip()\n",
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" return \"\"\n",
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"\n",
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"\n",
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"async def get_new_search_queries_async(session, user_query, previous_search_queries, all_contexts):\n",
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" \"\"\"\n",
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" Based on the original query, the previously used search queries, and all the extracted contexts,\n",
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" ask the LLM whether additional search queries are needed. If yes, return a Python list of up to four queries;\n",
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" if the LLM thinks research is complete, it should return \"<done>\".\n",
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" \"\"\"\n",
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" context_combined = \"\\n\".join(all_contexts)\n",
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" prompt = (\n",
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" \"You are an analytical research assistant. Based on the original query, the search queries performed so far, \"\n",
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" \"and the extracted contexts from webpages, determine if further research is needed. \"\n",
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" \"If further research is needed, provide up to four new search queries as a Python list (for example, \"\n",
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" \"['new query1', 'new query2']). If you believe no further research is needed, respond with exactly <done>.\"\n",
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" \"\\nOutput only a Python list or the token <done> without any additional text.\"\n",
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" )\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a systematic research planner.\"},\n",
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" {\"role\": \"user\", \"content\": f\"User Query: {user_query}\\nPrevious Search Queries: {previous_search_queries}\\n\\nExtracted Relevant Contexts:\\n{context_combined}\\n\\n{prompt}\"}\n",
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" ]\n",
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" response = await call_openrouter_async(session, messages)\n",
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" if response:\n",
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" cleaned = response.strip()\n",
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" if cleaned == \"<done>\":\n",
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" return \"<done>\"\n",
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" try:\n",
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" new_queries = eval(cleaned)\n",
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" if isinstance(new_queries, list):\n",
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" return new_queries\n",
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" else:\n",
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" print(\"LLM did not return a list for new search queries. Response:\", response)\n",
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" return []\n",
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" except Exception as e:\n",
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" print(\"Error parsing new search queries:\", e, \"\\nResponse:\", response)\n",
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" return []\n",
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" return []\n",
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"\n",
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"\n",
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"async def generate_final_report_async(session, user_query, all_contexts):\n",
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" \"\"\"\n",
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" Generate the final comprehensive report using all gathered contexts.\n",
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" \"\"\"\n",
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" context_combined = \"\\n\".join(all_contexts)\n",
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" prompt = (\n",
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" \"You are an expert researcher and report writer. Based on the gathered contexts below and the original query, \"\n",
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" \"write a comprehensive, well-structured, and detailed report that addresses the query thoroughly. \"\n",
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" \"Include all relevant insights and conclusions without extraneous commentary.\"\n",
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" )\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": \"You are a skilled report writer.\"},\n",
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" {\"role\": \"user\", \"content\": f\"User Query: {user_query}\\n\\nGathered Relevant Contexts:\\n{context_combined}\\n\\n{prompt}\"}\n",
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" ]\n",
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" report = await call_openrouter_async(session, messages)\n",
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" return report\n",
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"\n",
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"\n",
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"async def process_link(session, link, user_query, search_query):\n",
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" \"\"\"\n",
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" Process a single link: fetch its content, judge its usefulness, and if useful, extract the relevant context.\n",
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" \"\"\"\n",
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" print(f\"Fetching content from: {link}\")\n",
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" page_text = await fetch_webpage_text_async(session, link)\n",
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" if not page_text:\n",
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" return None\n",
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" usefulness = await is_page_useful_async(session, user_query, page_text)\n",
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" print(f\"Page usefulness for {link}: {usefulness}\")\n",
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" if usefulness == \"Yes\":\n",
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" context = await extract_relevant_context_async(session, user_query, search_query, page_text)\n",
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" if context:\n",
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" print(f\"Extracted context from {link} (first 200 chars): {context[:200]}\")\n",
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" return context\n",
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" return None\n",
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"\n",
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"\n",
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"# =========================\n",
|
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"# Main Asynchronous Routine\n",
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"# =========================\n",
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"\n",
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"async def async_main():\n",
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" user_query = input(\"Enter your research query/topic: \").strip()\n",
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" iter_limit_input = input(\"Enter maximum number of iterations (default 10): \").strip()\n",
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" iteration_limit = int(iter_limit_input) if iter_limit_input.isdigit() else 10\n",
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"\n",
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" aggregated_contexts = [] # All useful contexts from every iteration\n",
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" all_search_queries = [] # Every search query used across iterations\n",
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" iteration = 0\n",
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"\n",
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" async with aiohttp.ClientSession() as session:\n",
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" # ----- INITIAL SEARCH QUERIES -----\n",
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" new_search_queries = await generate_search_queries_async(session, user_query)\n",
|
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" if not new_search_queries:\n",
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" print(\"No search queries were generated by the LLM. Exiting.\")\n",
|
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" return\n",
|
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" all_search_queries.extend(new_search_queries)\n",
|
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"\n",
|
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" # ----- ITERATIVE RESEARCH LOOP -----\n",
|
||
" while iteration < iteration_limit:\n",
|
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" print(f\"\\n=== Iteration {iteration + 1} ===\")\n",
|
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" iteration_contexts = []\n",
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"\n",
|
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" # For each search query, perform SERPAPI searches concurrently.\n",
|
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" search_tasks = [perform_search_async(session, query) for query in new_search_queries]\n",
|
||
" search_results = await asyncio.gather(*search_tasks)\n",
|
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"\n",
|
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" # Aggregate all unique links from all search queries of this iteration.\n",
|
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" # Map each unique link to the search query that produced it.\n",
|
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" unique_links = {}\n",
|
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" for idx, links in enumerate(search_results):\n",
|
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" query = new_search_queries[idx]\n",
|
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" for link in links:\n",
|
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" if link not in unique_links:\n",
|
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" unique_links[link] = query\n",
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"\n",
|
||
" print(f\"Aggregated {len(unique_links)} unique links from this iteration.\")\n",
|
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"\n",
|
||
" # Process each link concurrently: fetch, judge, and extract context.\n",
|
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" link_tasks = [\n",
|
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" process_link(session, link, user_query, unique_links[link])\n",
|
||
" for link in unique_links\n",
|
||
" ]\n",
|
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" link_results = await asyncio.gather(*link_tasks)\n",
|
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"\n",
|
||
" # Collect non-None contexts.\n",
|
||
" for res in link_results:\n",
|
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" if res:\n",
|
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" iteration_contexts.append(res)\n",
|
||
"\n",
|
||
" if iteration_contexts:\n",
|
||
" aggregated_contexts.extend(iteration_contexts)\n",
|
||
" else:\n",
|
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" print(\"No useful contexts were found in this iteration.\")\n",
|
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"\n",
|
||
" # ----- ASK THE LLM IF MORE SEARCHES ARE NEEDED -----\n",
|
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" new_search_queries = await get_new_search_queries_async(session, user_query, all_search_queries, aggregated_contexts)\n",
|
||
" if new_search_queries == \"<done>\":\n",
|
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" print(\"LLM indicated that no further research is needed.\")\n",
|
||
" break\n",
|
||
" elif new_search_queries:\n",
|
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" print(\"LLM provided new search queries:\", new_search_queries)\n",
|
||
" all_search_queries.extend(new_search_queries)\n",
|
||
" else:\n",
|
||
" print(\"LLM did not provide any new search queries. Ending the loop.\")\n",
|
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" break\n",
|
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"\n",
|
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" iteration += 1\n",
|
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"\n",
|
||
" # ----- FINAL REPORT -----\n",
|
||
" print(\"\\nGenerating final report...\")\n",
|
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" final_report = await generate_final_report_async(session, user_query, aggregated_contexts)\n",
|
||
" print(\"\\n==== FINAL REPORT ====\\n\")\n",
|
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" print(final_report)\n",
|
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"\n",
|
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"\n",
|
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"def main():\n",
|
||
" asyncio.run(async_main())\n",
|
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"\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
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" main()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "46Q5XpapDJZT"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |