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PIP Package

PyPI version Open In Colab

🌟 Exciting News! Now, you can integrate gpt-researcher with your apps seamlessly!

Steps to Install GPT Researcher​

Follow these easy steps to get started:

  1. Pre-requisite: Ensure Python 3.10+ is installed on your machine 💻
  2. Install gpt-researcher: Grab the official package from PyPi.
pip install gpt-researcher
  1. Environment Variables: Create a .env file with your OpenAI API key or simply export it
export OPENAI_API_KEY={Your OpenAI API Key here}
export TAVILY_API_KEY={Your Tavily API Key here}
  1. Start using GPT Researcher in your own codebase

Example Usage​

from gpt_researcher import GPTResearcher
import asyncio

async def get_report(query: str, report_type: str):
    researcher = GPTResearcher(query, report_type)
    research_result = await researcher.conduct_research()
    report = await researcher.write_report()
    
    # Get additional information
    research_context = researcher.get_research_context()
    research_costs = researcher.get_costs()
    research_images = researcher.get_research_images()
    research_sources = researcher.get_research_sources()
    
    return report, research_context, research_costs, research_images, research_sources

if __name__ == "__main__":
    query = "what team may win the NBA finals?"
    report_type = "research_report"

    report, context, costs, images, sources = asyncio.run(get_report(query, report_type))
    
    print("Report:")
    print(report)
    print("\nResearch Costs:")
    print(costs)
    print("\nNumber of Research Images:")
    print(len(images))
    print("\nNumber of Research Sources:")
    print(len(sources))

Specific Examples​

Example 1: Research Report​

query = "Latest developments in renewable energy technologies"
report_type = "research_report"

Example 2: Resource Report​

query = "List of top AI conferences in 2023"
report_type = "resource_report"

Example 3: Outline Report​

query = "Outline for an article on the impact of AI in education"
report_type = "outline_report"

Integration with Web Frameworks​

FastAPI Example​

from fastapi import FastAPI
from gpt_researcher import GPTResearcher
import asyncio

app = FastAPI()

@app.get("/report/{report_type}")
async def get_report(query: str, report_type: str) -> dict:
    researcher = GPTResearcher(query, report_type)
    research_result = await researcher.conduct_research()
    report = await researcher.write_report()
    
    source_urls = researcher.get_source_urls()
    research_costs = researcher.get_costs()
    research_images = researcher.get_research_images()
    research_sources = researcher.get_research_sources()
    
    return {
        "report": report,
        "source_urls": source_urls,
        "research_costs": research_costs,
        "num_images": len(research_images),
        "num_sources": len(research_sources)
    }

# Run the server
# uvicorn main:app --reload

Flask Example​

Pre-requisite: Install flask with the async extra.

pip install 'flask[async]'
from flask import Flask, request, jsonify
from gpt_researcher import GPTResearcher

app = Flask(__name__)

@app.route('/report/<report_type>', methods=['GET'])
async def get_report(report_type):
    query = request.args.get('query')
    researcher = GPTResearcher(query, report_type)
    research_result = await researcher.conduct_research()
    report = await researcher.write_report()
    
    source_urls = researcher.get_source_urls()
    research_costs = researcher.get_costs()
    research_images = researcher.get_research_images()
    research_sources = researcher.get_research_sources()
    
    return jsonify({
        "report": report,
        "source_urls": source_urls,
        "research_costs": research_costs,
        "num_images": len(research_images),
        "num_sources": len(research_sources)
    })

# Run the server
# flask run

Run the server

flask run

Example Request

curl -X GET "http://localhost:5000/report/research_report?query=what team may win the nba finals?"

Getters and Setters​

GPT Researcher provides several methods to retrieve additional information about the research process:

Get Research Sources​

Sources are the URLs that were used to gather information for the research.

source_urls = researcher.get_source_urls()

Get Research Context​

Context is all the retrieved information from the research. It includes the sources and their corresponding content.

research_context = researcher.get_research_context()

Get Research Costs​

Costs are the number of tokens consumed during the research process.

research_costs = researcher.get_costs()

Get Research Images​

Retrieves a list of images found during the research process.

research_images = researcher.get_research_images()

Get Research Sources​

Retrieves a list of research sources, including title, content, and images.

research_sources = researcher.get_research_sources()

Set Verbose​

You can set the verbose mode to get more detailed logs.

researcher.set_verbose(True)

Add Costs​

You can also add costs to the research process if you want to track the costs from external usage.

researcher.add_costs(0.22)

Advanced Usage​

Customizing the Research Process​

You can customize various aspects of the research process by passing additional parameters when initializing the GPTResearcher:

researcher = GPTResearcher(
    query="Your research query",
    report_type="research_report",
    report_format="APA",
    tone="formal and objective",
    max_subtopics=5,
    verbose=True
)

Handling Research Results​

After conducting research, you can process the results in various ways:

# Conduct research
research_result = await researcher.conduct_research()

# Generate a standard report
report = await researcher.write_report()

# Generate a customized report with specific formatting requirements
custom_report = await researcher.write_report(custom_prompt="Answer in short, 2 paragraphs max without citations.")

# Generate a focused report for a specific audience
executive_summary = await researcher.write_report(custom_prompt="Create an executive summary focused on business impact and ROI. Keep it under 500 words.")

# Generate a report with specific structure requirements
technical_report = await researcher.write_report(custom_prompt="Create a technical report with problem statement, methodology, findings, and recommendations sections.")

# Generate a conclusion
conclusion = await researcher.write_report_conclusion(report)

# Get subtopics
subtopics = await researcher.get_subtopics()

# Get draft section titles for a subtopic
draft_titles = await researcher.get_draft_section_titles("Subtopic name")

Customizing Report Generation with Custom Prompts​

The write_report method accepts a custom_prompt parameter that gives you complete control over how your research is presented:

# After conducting research
research_result = await researcher.conduct_research()

# Generate a report with a custom prompt
report = await researcher.write_report(
    custom_prompt="Based on the research, provide a bullet-point summary of the key findings."
)

Custom prompts can be used for various purposes:

  1. Format Control: Specify the structure, length, or style of your report

    report = await researcher.write_report(
        custom_prompt="Write a blog post in a conversational tone using the research. Include headings and a conclusion."
    )

  2. Audience Targeting: Tailor the content for specific readers

    report = await researcher.write_report(
        custom_prompt="Create a report for technical stakeholders, focusing on methodologies and implementation details."
    )

  3. Specialized Outputs: Generate specific types of content

    report = await researcher.write_report(
        custom_prompt="Create a FAQ section based on the research with at least 5 questions and detailed answers."
    )

The custom prompt will be combined with the research context to generate your customized report.

Working with Research Context​

You can use the research context for further processing or analysis:

# Get the full research context
context = researcher.get_research_context()

# Get similar written contents based on draft section titles
similar_contents = await researcher.get_similar_written_contents_by_draft_section_titles(
    current_subtopic="Subtopic name",
    draft_section_titles=["Title 1", "Title 2"],
    written_contents=some_written_contents,
    max_results=10
)

This comprehensive documentation should help users understand and utilize the full capabilities of the GPT Researcher package.