├── .env.example ├── LICENSE ├── README.md ├── app.py ├── chainlit.md ├── demo.mp4 └── requirements.txt /.env.example: -------------------------------------------------------------------------------- 1 | OPENAI_API_KEY= 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ResearchAgent 2 | 3 | Welcome to ResearchAgent! 4 | ResearchAgent is not just any web search tool – it's your personal research assistant powered by the mighty GPT-3.5/GPT-4 at its core. It not only provides concise and accurate answers but also offers references to the extracted data sources. With its memory capabilities, you can ask follow-up questions, enabling a continuous flow of insightful conversations. 5 | 6 | 7 | 8 | 9 | https://github.com/Shivanandroy/ResearchAgent/assets/25221992/d9e1740c-339b-4f2b-b38f-8476164cb3f0 10 | 11 | 12 | 13 | ### Installation 14 | 15 | 1. Clone this repository to your local machine: 16 | ```python 17 | git clone https://github.com/Shivanandroy/ResearchAgent.git 18 | ``` 19 | 2. Install the required dependencies using pip: 20 | ```python 21 | pip install -r requirement.txt 22 | ``` 23 | 24 | 3. Add your OPENAI_API_KEY to the .env file: 25 | ```python 26 | OPENAI_API_KEY= 27 | ``` 28 | 29 | 4. Run the application: 30 | ```python 31 | chainlit run app.py 32 | ``` 33 | 34 | ### Support 35 | If you encounter any problems or have questions regarding ResearchAgent, please create an issue in the GitHub repository. 36 | 37 | ### License 38 | ResearchAgent is released under the APACHE License. 39 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | from langchain import PromptTemplate 3 | from langchain.agents import initialize_agent, Tool 4 | from langchain.agents import AgentType 5 | from langchain.chat_models import ChatOpenAI 6 | from langchain.prompts import MessagesPlaceholder 7 | from langchain.memory import ConversationSummaryBufferMemory 8 | from langchain.text_splitter import RecursiveCharacterTextSplitter 9 | from langchain.chains.summarize import load_summarize_chain 10 | from langchain.tools import BaseTool 11 | from pydantic import BaseModel, Field 12 | from typing import Type 13 | import requests 14 | import json 15 | from langchain.schema import SystemMessage 16 | import chainlit as cl 17 | from newsplease import NewsPlease 18 | import time 19 | from duckduckgo_search import DDGS 20 | from itertools import islice 21 | from dotenv import load_dotenv 22 | load_dotenv() 23 | 24 | 25 | def search(query, max_retries=5): 26 | """ 27 | Search the given query using DuckDuckGo. 28 | Args: 29 | - query (str): The search query. 30 | - max_retries (int): Maximum number of retries in case of request failure. 31 | Returns: 32 | - list[dict]: A list of search results with 'title' and 'url'. 33 | """ 34 | for attempt in range(max_retries): 35 | try: 36 | result = [] 37 | 38 | # Initialize the DuckDuckGo search object. 39 | with DDGS() as ddgs: 40 | response = ddgs.text(query, region='wt-wt', safesearch='Off', timelimit='y') 41 | for r in islice(response, 20): 42 | result.append({'title': r['title'], 'url': r['href']}) 43 | return result 44 | 45 | except requests.RequestException as e: 46 | # Handle request exceptions. 47 | print(f"Attempt {attempt + 1} raised an error: {e}. Retrying...") 48 | if attempt < max_retries - 1: 49 | time.sleep(1) 50 | 51 | except Exception as e: 52 | # Handle other exceptions. 53 | print(f"An unexpected error occurred on attempt {attempt + 1}: {e}. Retrying...") 54 | if attempt < max_retries - 1: 55 | time.sleep(1) 56 | 57 | else: 58 | # If max retries reached, exit the function. 59 | print("Max retries reached. Exiting...") 60 | return None 61 | 62 | def scrape_website(objective: str, url: str): 63 | """ 64 | Scrape and potentially summarize the content of a website based on a given objective. 65 | Args: 66 | - objective (str): The objective & task that users give to the agent. 67 | - url (str): The URL of the website to be scraped. 68 | Returns: 69 | - str: Extracted or summarized content of the website. 70 | """ 71 | print("Scraping website...") 72 | try: 73 | # Use NewsPlease to scrape the website. 74 | article = NewsPlease.from_url(url) 75 | print(f'{article.title} - {article.url}') 76 | text = article.maintext 77 | # Summarize if content is too large. 78 | if len(text) > 10000: 79 | output = summary(objective, text) 80 | return output 81 | else: 82 | return text 83 | except: 84 | pass 85 | 86 | def summary(objective, content): 87 | """ 88 | Generate a summary for a given content based on the objective. 89 | Args: 90 | - objective (str): The objective for the summary. 91 | - content (str): The content to be summarized. 92 | Returns: 93 | - str: Summarized content. 94 | """ 95 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613", streaming=True) 96 | 97 | # Split the content into manageable chunks. 98 | text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500) 99 | docs = text_splitter.create_documents([content]) 100 | 101 | map_prompt = """ 102 | Write a summary of the following text for {objective}: 103 | "{text}" 104 | SUMMARY: 105 | """ 106 | map_prompt_template = PromptTemplate(template=map_prompt, input_variables=["text", "objective"]) 107 | 108 | # Load the summary chain with necessary configurations. 109 | summary_chain = load_summarize_chain(llm=llm, chain_type='map_reduce', map_prompt=map_prompt_template, combine_prompt=map_prompt_template, verbose=True) 110 | 111 | output = summary_chain.run(input_documents=docs, objective=objective) 112 | return output 113 | 114 | class ScrapeWebsiteInput(BaseModel): 115 | """Inputs for scrape_website function.""" 116 | objective: str = Field(description="The objective & task that users give to the agent") 117 | url: str = Field(description="The url of the website to be scraped") 118 | 119 | class ScrapeWebsiteTool(BaseTool): 120 | """ 121 | A tool that provides functionality to scrape a website. 122 | """ 123 | name = "scrape_website" 124 | description = "useful when you need to get data from a website url, passing both url and objective to the function; DO NOT make up any url, the url should only be from the search results" 125 | args_schema: Type[BaseModel] = ScrapeWebsiteInput 126 | 127 | def _run(self, objective: str, url: str): 128 | """Runs the scrape_website function.""" 129 | return scrape_website(objective, url) 130 | 131 | def _arun(self, url: str): 132 | """Asynchronous version of _run. (Currently not implemented)""" 133 | raise NotImplementedError("error here") 134 | 135 | @cl.langchain_factory(use_async=False) 136 | def run(): 137 | """ 138 | Initialize and return a langchain agent with search and scraping tools. 139 | Returns: 140 | - Agent: Initialized langchain agent. 141 | """ 142 | tools = [ 143 | Tool(name="Search", func=search, description="useful for when you need to answer questions about current events, data. You should ask targeted questions"), 144 | ScrapeWebsiteTool(), 145 | ] 146 | 147 | system_message = SystemMessage( 148 | content="""You are a world class researcher, who can do detailed research on any topic and produce facts based results; 149 | you do not make things up, you will try as hard as possible to gather facts & data to back up the research 150 | 151 | Please make sure you complete the objective above with the following rules: 152 | 1/ You should do enough research to gather as much information as possible about the objective 153 | 2/ If there are url of relevant links & articles, you will scrape it to gather more information 154 | 3/ After scraping & search, you should think "is there any new things i should search & scraping based on the data I collected to increase research quality?" If answer is yes, continue; But don't do this more than 3 iteratins 155 | 4/ You should not make things up, you should only write facts & data that you have gathered 156 | 5/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research 157 | 6/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research""" 158 | ) 159 | agent_kwargs = { 160 | "extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")], 161 | "system_message": system_message, 162 | } 163 | 164 | # Initialize the ChatOpenAI model. 165 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613", streaming=True) 166 | memory = ConversationSummaryBufferMemory(memory_key="memory", return_messages=True, llm=llm) 167 | 168 | # Initialize the agent with tools and other configurations. 169 | return initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, agent_kwargs=agent_kwargs, memory=memory) 170 | -------------------------------------------------------------------------------- /chainlit.md: -------------------------------------------------------------------------------- 1 | 🔍 Welcome to ResearchAgent! 🔍 2 | Dive into the vast expanse of the web with unparalleled precision and intelligence. With the power of GPT-4 at its core, ResearchAgent is not just any web search tool – it's your personal research assistant. 3 | 4 | 🌟 Features: 5 | Advanced Web Search: Go beyond regular search engines. Get concise, relevant, and accurate results tailored to your needs. 6 | Smart Data Scraping: Extract critical data from websites effortlessly, with results curated and summarized just for you. 7 | Dynamic Query Understanding: ResearchAgent understands the context and nuances of your query, ensuring you get the most accurate and relevant information. 8 | Embark on a journey to redefine web research. Whether you're a student, a professional, or just curious, ResearchAgent is here to transform the way you gather and comprehend information. 9 | 10 | Start your quest for knowledge with ResearchAgent today! 11 | -------------------------------------------------------------------------------- /demo.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shivanandroy/ResearchAgent/b39c75263c5ca18f900f728a1ce78a51a15a821e/demo.mp4 -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | langchain 2 | openai 3 | tiktoken 4 | chainlit==0.5.2 5 | news-please 6 | duckduckgo_search 7 | python-dotenv 8 | --------------------------------------------------------------------------------