From 5eea01016720889fcd637d641d2193df2ca8495c Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:31:25 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 367 +++++++++++++++--------------------------------------- 1 file changed, 100 insertions(+), 267 deletions(-) diff --git a/README.md b/README.md index 1fd5c95..1639e68 100644 --- a/README.md +++ b/README.md @@ -1,337 +1,175 @@ -
+ +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/assafelovic/gpt-researcher) · [上游 README](https://github.com/assafelovic/gpt-researcher/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 +
+ Logo + #### [![Website](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge&logo=world&logoColor=white&color=0891b2)](https://gptr.dev) [![Documentation](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs&logoColor=white&style=for-the-badge)](https://docs.gptr.dev) -[![Discord](https://img.shields.io/discord/1127851779011391548?logo=discord&logoColor=white&label=Discord&color=34b76a&style=for-the-badge)](https://discord.gg/QgZXvJAccX) - +[![Discord Follow](https://img.shields.io/discord/1127851779011391548?style=for-the-badge&logo=discord&label=Chat%20on%20Discord)](https://discord.gg/QgZXvJAccX) [![PyPI version](https://img.shields.io/pypi/v/gpt-researcher?logo=pypi&logoColor=white&style=flat)](https://badge.fury.io/py/gpt-researcher) ![GitHub Release](https://img.shields.io/github/v/release/assafelovic/gpt-researcher?style=flat&logo=github) [![Open In Colab](https://img.shields.io/static/v1?message=Open%20in%20Colab&logo=googlecolab&labelColor=grey&color=yellow&label=%20&style=flat&logoSize=40)](https://colab.research.google.com/github/assafelovic/gpt-researcher/blob/master/docs/docs/examples/pip-run.ipynb) [![Docker Image Version](https://img.shields.io/docker/v/elestio/gpt-researcher/latest?arch=amd64&style=flat&logo=docker&logoColor=white&color=1D63ED)](https://hub.docker.com/r/gptresearcher/gpt-researcher) -[![Skill](https://img.shields.io/badge/Claude%20Skill-skills.sh-blueviolet?style=flat&logo=anthropic&logoColor=white)](https://skills.sh/assafelovic/gpt-researcher/gpt-researcher) [![Twitter Follow](https://img.shields.io/twitter/follow/assaf_elovic?style=social)](https://twitter.com/assaf_elovic) -[English](README.md) | [中文](README-zh_CN.md) | [日本語](README-ja_JP.md) | [한국어](README-ko_KR.md) - +[English](README.md) | +[中文](README-zh_CN.md) | +[日本語](README-ja_JP.md) | +[한국어](README-ko_KR.md)
# 🔎 GPT Researcher -**GPT Researcher the first open deep research agent designed for both web and local research on any given task.** +**GPT Researcher 是一个智能体代理，专为各种任务的综合在线研究而设计。** -The agent produces detailed, factual, and unbiased research reports with citations. GPT Researcher provides a full suite of customization options to create tailor made and domain specific research agents. Inspired by the recent [Plan-and-Solve](https://arxiv.org/abs/2305.04091) and [RAG](https://arxiv.org/abs/2005.11401) papers, GPT Researcher addresses misinformation, speed, determinism, and reliability by offering stable performance and increased speed through parallelized agent work. +代理可以生成详细、正式且客观的研究报告，并提供自定义选项，专注于相关资源、结构框架和经验报告。受最近发表的[Plan-and-Solve](https://arxiv.org/abs/2305.04091) 和[RAG](https://arxiv.org/abs/2005.11401) 论文的启发，GPT Researcher 解决了速度、确定性和可靠性等问题，通过并行化的代理运行，而不是同步操作，提供了更稳定的性能和更高的速度。 -**Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.** +**我们的使命是利用人工智能的力量，为个人和组织提供准确、客观和事实的信息。** -## Why GPT Researcher? +## 为什么选择GPT Researcher? -- Objective conclusions for manual research can take weeks, requiring vast resources and time. -- LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks. -- Current LLMs have token limitations, insufficient for generating long research reports. -- Limited web sources in existing services lead to misinformation and shallow results. -- Selective web sources can introduce bias into research tasks. +- 因为人工研究任务形成客观结论可能需要时间和经历，有时甚至需要数周才能找到正确的资源和信息。 +- 目前的LLM是根据历史和过时的信息进行训练的，存在严重的幻觉风险，因此几乎无法胜任研究任务。 +- 网络搜索的解决方案（例如 ChatGPT + Web 插件）仅考虑有限的资源和内容，在某些情况下会导致肤浅的结论或不客观的答案。 +- 只使用部分资源可能会在确定研究问题或任务的正确结论时产生偏差。 -## Demo - - Demo video - - -## Install as Claude Skill - -Extend Claude's deep research capabilities by installing GPT Researcher as a [Claude Skill](https://skills.sh/assafelovic/gpt-researcher/gpt-researcher): - -```bash -npx skills add assafelovic/gpt-researcher -``` - -Once installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations. - -## Architecture - -The core idea is to utilize 'planner' and 'execution' agents. The planner generates research questions, while the execution agents gather relevant information. The publisher then aggregates all findings into a comprehensive report. +## 架构 +主要思想是运行“**计划者**”和“**执行**”代理，而**计划者**生成问题进行研究，“**执行**”代理根据每个生成的研究问题寻找最相关的信息。最后，“**计划者**”过滤和聚合所有相关信息并创建研究报告。

+代理同时利用 gpt-40-mini 和 gpt-4o（128K 上下文）来完成一项研究任务。我们仅在必要时使用这两种方法对成本进行优化。**研究任务平均耗时约 3 分钟，成本约为 ~0.1 美元**。
- +
-Steps: -* Create a task-specific agent based on a research query. -* Generate questions that collectively form an objective opinion on the task. -* Use a crawler agent for gathering information for each question. -* Summarize and source-track each resource. -* Filter and aggregate summaries into a final research report. -## Tutorials - - [How it Works](https://docs.gptr.dev/blog/building-gpt-researcher) - - [How to Install](https://www.loom.com/share/04ebffb6ed2a4520a27c3e3addcdde20?sid=da1848e8-b1f1-42d1-93c3-5b0b9c3b24ea) - - [Live Demo](https://www.loom.com/share/6a3385db4e8747a1913dd85a7834846f?sid=a740fd5b-2aa3-457e-8fb7-86976f59f9b8) +详细说明: +* 根据研究搜索或任务创建特定领域的代理。 +* 生成一组研究问题，这些问题共同形成答案对任何给定任务的客观意见。 +* 针对每个研究问题，触发一个爬虫代理，从在线资源中搜索与给定任务相关的信息。 +* 对于每一个抓取的资源，根据相关信息进行汇总，并跟踪其来源。 +* 最后，对所有汇总的资料来源进行过滤和汇总，并生成最终研究报告。 -## Features +## 演示 +https://github.com/assafelovic/gpt-researcher/assets/13554167/a00c89a6-a295-4dd0-b58d-098a31c40fda -- 📝 Generate detailed research reports using web and local documents. -- 🖼️ Smart image scraping and filtering for reports. -- 🍌 **AI-generated inline images** using Google Gemini (Nano Banana) for visual illustrations. -- 📜 Generate detailed reports exceeding 2,000 words. -- 🌐 Aggregate over 20 sources for objective conclusions. -- 🖥️ Frontend available in lightweight (HTML/CSS/JS) and production-ready (NextJS + Tailwind) versions. -- 🔍 JavaScript-enabled web scraping. -- 📂 Maintains memory and context throughout research. -- 📄 Export reports to PDF, Word, and other formats. +## 教程 + - [运行原理](https://docs.gptr.dev/blog/building-gpt-researcher) + - [如何安装](https://www.loom.com/share/04ebffb6ed2a4520a27c3e3addcdde20?sid=da1848e8-b1f1-42d1-93c3-5b0b9c3b24ea) + - [现场演示](https://www.loom.com/share/6a3385db4e8747a1913dd85a7834846f?sid=a740fd5b-2aa3-457e-8fb7-86976f59f9b8) -## 📖 Documentation +## 特性 +- 📝 生成研究问题、大纲、资源和课题报告 +- 🌐 每项研究汇总超过20个网络资源，形成客观和真实的结论 +- 🖥️ 包括易于使用的web界面 (HTML/CSS/JS) +- 🔍 支持JavaScript网络资源抓取功能 +- 📂 追踪访问过和使用过的网络资源和来源 +- 📄 将研究报告导出为PDF或其他格式... -See the [Documentation](https://docs.gptr.dev/docs/gpt-researcher/getting-started) for: -- Installation and setup guides -- Configuration and customization options -- How-To examples -- Full API references +## 📖 文档 -## ⚙️ Getting Started +请参阅[此处](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction)，了解完整文档： -### Installation +- 入门（安装、设置环境、简单示例） +- 操作示例（演示、集成、docker 支持） +- 参考资料（API完整文档） +- Tavily 应用程序接口集成（核心概念的高级解释） -1. Install Python 3.11 or later. [Guide](https://www.tutorialsteacher.com/python/install-python). -2. Clone the project and navigate to the directory: +## 快速开始 +> **步骤 0** - 安装 Python 3.11 或更高版本。[参见此处](https://www.tutorialsteacher.com/python/install-python) 获取详细指南。 - ```bash - git clone https://github.com/assafelovic/gpt-researcher.git - cd gpt-researcher - ``` +
-3. Set up API keys by exporting them or storing them in a `.env` file. - - ```bash - export OPENAI_API_KEY={Your OpenAI API Key here} - export TAVILY_API_KEY={Your Tavily API Key here} - ``` - - (Optional) For enhanced tracing and observability, you can also set: - - ```bash - # export LANGCHAIN_TRACING_V2=true - # export LANGCHAIN_API_KEY={Your LangChain API Key here} - ``` - - For custom OpenAI-compatible APIs (e.g., local models, other providers), you can also set: - - ```bash - export OPENAI_BASE_URL={Your custom API base URL here} - ``` - -4. Install dependencies and start the server: - - ```bash - pip install -r requirements.txt - python -m uvicorn main:app --reload - ``` - -Visit [http://localhost:8000](http://localhost:8000) to start. - -For other setups (e.g., Poetry or virtual environments), check the [Getting Started page](https://docs.gptr.dev/docs/gpt-researcher/getting-started). - -## Run as PIP package -```bash -pip install gpt-researcher - -``` -### Example Usage: -```python -... -from gpt_researcher import GPTResearcher - -query = "why is Nvidia stock going up?" -researcher = GPTResearcher(query=query) -# Conduct research on the given query -research_result = await researcher.conduct_research() -# Write the report -report = await researcher.write_report() -... -``` - -**For more examples and configurations, please refer to the [PIP documentation](https://docs.gptr.dev/docs/gpt-researcher/gptr/pip-package) page.** - -### 🔧 MCP Client -GPT Researcher supports MCP integration to connect with specialized data sources like GitHub repositories, databases, and custom APIs. This enables research from data sources alongside web search. +> **步骤 1** - 下载项目 ```bash -export RETRIEVER=tavily,mcp # Enable hybrid web + MCP research +$ git clone https://github.com/assafelovic/gpt-researcher.git +$ cd gpt-researcher ``` -```python -from gpt_researcher import GPTResearcher -import asyncio -import os +
-async def mcp_research_example(): - # Enable MCP with web search - os.environ["RETRIEVER"] = "tavily,mcp" - - researcher = GPTResearcher( - query="What are the top open source web research agents?", - mcp_configs=[ - { - "name": "github", - "command": "npx", - "args": ["-y", "@modelcontextprotocol/server-github"], - "env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")} - } - ] - ) - - research_result = await researcher.conduct_research() - report = await researcher.write_report() - return report +> **步骤2** -安装依赖项 +```bash +$ pip install -r requirements.txt ``` +
-> For comprehensive MCP documentation and advanced examples, visit the [MCP Integration Guide](https://docs.gptr.dev/docs/gpt-researcher/retrievers/mcp-configs). - -## 🍌 Inline Image Generation - -GPT Researcher can automatically generate and embed AI-created illustrations in your research reports using Google's Gemini models (Nano Banana). +> **第 3 步** - 使用 OpenAI 密钥和 Tavily API 密钥创建 .env 文件，或直接导出该文件 ```bash -# Enable in your .env file -IMAGE_GENERATION_ENABLED=true -GOOGLE_API_KEY=your_google_api_key -IMAGE_GENERATION_MODEL=models/gemini-2.5-flash-image +$ export OPENAI_API_KEY={Your OpenAI API Key here} +``` +```bash +$ export TAVILY_API_KEY={Your Tavily API Key here} ``` -When enabled, the system will: -1. Analyze your research context to identify visualization opportunities -2. Pre-generate 2-3 relevant images during the research phase -3. Embed them inline as the report is written - -Images are generated with dark-mode styling that matches the GPT Researcher UI, featuring professional infographic aesthetics with teal accents. - -[Learn more about Image Generation](https://docs.gptr.dev/docs/gpt-researcher/gptr/image_generation) in our documentation. - -## ✨ Deep Research - -GPT Researcher now includes Deep Research - an advanced recursive research workflow that explores topics with agentic depth and breadth. This feature employs a tree-like exploration pattern, diving deeper into subtopics while maintaining a comprehensive view of the research subject. - -- 🌳 Tree-like exploration with configurable depth and breadth -- ⚡️ Concurrent processing for faster results -- 🤝 Smart context management across research branches -- ⏱️ Takes ~5 minutes per deep research -- 💰 Costs ~$0.4 per research (using `o3-mini` on "high" reasoning effort) - -[Learn more about Deep Research](https://docs.gptr.dev/docs/gpt-researcher/gptr/deep_research) in our documentation. - -## Run with Docker - -> **Step 1** - [Install Docker](https://docs.gptr.dev/docs/gpt-researcher/getting-started/getting-started-with-docker) - -> **Step 2** - Clone the '.env.example' file, add your API Keys to the cloned file and save the file as '.env' - -> **Step 3** - Within the docker-compose file comment out services that you don't want to run with Docker. +（可选）如需开启全链路追踪和可观测性，可设置： ```bash -docker-compose up --build +# $ export LANGCHAIN_TRACING_V2=true +# $ export LANGCHAIN_API_KEY={Your LangChain API Key here} ``` -If that doesn't work, try running it without the dash: -```bash -docker compose up --build -``` +- **LLM，我们推荐使用 [OpenAI GPT](https://platform.openai.com/docs/guides/gpt)**，但您也可以使用 [Langchain Adapter](https://python.langchain.com/docs/guides/adapters/openai) 支持的任何其他 LLM 模型（包括开源），只需在 gpt_researcher/config/variables/default.py 中更改 llm 模型和提供者即可。请按照 [这份指南](https://python.langchain.com/docs/integrations/llms/) 学习如何将 LLM 与 Langchain 集成。 +- **对于搜索引擎，我们推荐使用 [Tavily Search API](https://app.tavily.com)（已针对 LLM 进行优化）**，但您也可以选择其他搜索引擎，只需将 gpt_researcher/config/variables/default.py 中的搜索提供程序更改为 "duckduckgo"、"googleAPI"、"searchapi"、"googleSerp "或 "searx "即可。然后在 gpt_researcher/config/variables/default.py 文件中添加相应的 env API 密钥。 +- **我们强烈建议使用 [OpenAI GPT](https://platform.openai.com/docs/guides/gpt) 模型和 [Tavily Search API](https://app.tavily.com) 以获得最佳性能。** +
-> **Step 4** - By default, if you haven't uncommented anything in your docker-compose file, this flow will start 2 processes: - - the Python server running on localhost:8000
- - the React app running on localhost:3000
- -Visit localhost:3000 on any browser and enjoy researching! - - -## 📄 Research on Local Documents - -You can instruct the GPT Researcher to run research tasks based on your local documents. Currently supported file formats are: PDF, plain text, CSV, Excel, Markdown, PowerPoint, and Word documents. - -Step 1: Add the env variable `DOC_PATH` pointing to the folder where your documents are located. +> **第 4 步** - 使用 FastAPI 运行代理 ```bash -export DOC_PATH="./my-docs" +$ uvicorn main:app --reload ``` +
-Step 2: - - If you're running the frontend app on localhost:8000, simply select "My Documents" from the "Report Source" Dropdown Options. - - If you're running GPT Researcher with the [PIP package](https://docs.tavily.com/guides/gpt-researcher/gpt-researcher#pip-package), pass the `report_source` argument as "local" when you instantiate the `GPTResearcher` class [code sample here](https://docs.gptr.dev/docs/gpt-researcher/context/tailored-research). +> **第 5 步** - 在任何浏览器上访问 http://localhost:8000，享受研究乐趣！ +要了解如何开始使用 Docker 或了解有关功能和服务的更多信息，请访问 [documentation](https://docs.gptr.dev) 页面。 -## 🤖 MCP Server +## 🔍 可观测性 -We've moved our MCP server to a dedicated repository: [gptr-mcp](https://github.com/assafelovic/gptr-mcp). +GPT Researcher 支持 **LangSmith** 以增强链路追踪和可观测性，特别适用于调试和优化复杂的多智能体工作流。 -The GPT Researcher MCP Server enables AI applications like Claude to conduct deep research. While LLM apps can access web search tools with MCP, GPT Researcher MCP delivers deeper, more reliable research results. - -Features: -- Deep research capabilities for AI assistants -- Higher quality information with optimized context usage -- Comprehensive results with better reasoning for LLMs -- Claude Desktop integration - -For detailed installation and usage instructions, please visit the [official repository](https://github.com/assafelovic/gptr-mcp). - - -## 👪 Multi-Agent Assistant -As AI evolves from prompt engineering and RAG to multi-agent systems, we're excited to introduce multi-agent assistants built with [LangGraph](https://python.langchain.com/v0.1/docs/langgraph/) and [AG2](https://github.com/ag2ai/ag2). - -By using multi-agent frameworks, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills. Inspired by the recent [STORM](https://arxiv.org/abs/2402.14207) paper, this project showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication. - -An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown. - -Check it out [here](https://github.com/assafelovic/gpt-researcher/tree/master/multi_agents) or head over to our documentation for [LangGraph](https://docs.gptr.dev/docs/gpt-researcher/multi_agents/langgraph) and [AG2](https://docs.gptr.dev/docs/gpt-researcher/multi_agents/ag2) for more information. - -## 🔍 Observability - -GPT Researcher supports **LangSmith** for enhanced tracing and observability, making it easier to debug and optimize complex multi-agent workflows. - -To enable tracing: -1. Set the following environment variables: +要开启追踪： +1. 设置以下环境变量： ```bash export LANGCHAIN_TRACING_V2=true - export LANGCHAIN_API_KEY=your_api_key + export LANGCHAIN_API_KEY=您的_API_KEY export LANGCHAIN_PROJECT="gpt-researcher" ``` -2. Run your research tasks as usual. All LangGraph-based agent interactions will be automatically traced and visualized in your LangSmith dashboard. +2. 正常运行研究任务。所有基于 LangGraph 的智能体交互将自动被追踪，并可在您的 LangSmith 控制台中查看可视化结果。 -## 🖥️ Frontend Applications +## 🚀 贡献 +我们非常欢迎您的贡献！如果您感兴趣，请查看 [contributing](CONTRIBUTING.md)。 -GPT-Researcher now features an enhanced frontend to improve the user experience and streamline the research process. The frontend offers: +如果您有兴趣加入我们的任务，请查看我们的 [路线图](https://trello.com/b/3O7KBePw/gpt-researcher-roadmap) 页面，并通过我们的 [Discord 社区](https://discord.gg/QgZXvJAccX) 联系我们。 -- An intuitive interface for inputting research queries -- Real-time progress tracking of research tasks -- Interactive display of research findings -- Customizable settings for tailored research experiences +## ✉️ 支持 / 联系我们 +- [社区讨论区](https://discord.gg/spBgZmm3Xe) +- 我们的邮箱: support@tavily.com -Two deployment options are available: -1. A lightweight static frontend served by FastAPI -2. A feature-rich NextJS application for advanced functionality +## 🛡 免责声明 -For detailed setup instructions and more information about the frontend features, please visit our [documentation page](https://docs.gptr.dev/docs/gpt-researcher/frontend/introduction). +本项目 "GPT Researcher "是一个实验性应用程序，按 "现状 "提供，不做任何明示或暗示的保证。我们根据 MIT 许可分享用于学术目的的代码。本文不提供任何学术建议，也不建议在学术或研究论文中使用。 -## 🚀 Contributing -We highly welcome contributions! Please check out [contributing](https://github.com/assafelovic/gpt-researcher/blob/master/CONTRIBUTING.md) if you're interested. +我们对客观研究主张的看法： +1. 我们抓取系统的全部目的是减少不正确的事实。如何解决？我们抓取的网站越多，错误数据的可能性就越小。我们每项研究都会收集20条信息，它们全部错误的可能性极低。 +2. 我们的目标不是消除偏见，而是尽可能减少偏见。**作为一个社区，我们在这里探索最有效的人机互动**。 +3. 在研究过程中，人们也容易产生偏见，因为大多数人对自己研究的课题都有自己的看法。这个工具可以搜罗到许多观点，并均匀地解释各种不同的观点，而有偏见的人是绝对读不到这些观点的。 -Please check out our [roadmap](https://trello.com/b/3O7KBePw/gpt-researcher-roadmap) page and reach out to us via our [Discord community](https://discord.gg/QgZXvJAccX) if you're interested in joining our mission. - - - -## ✉️ Support / Contact us -- [Community Discord](https://discord.gg/spBgZmm3Xe) -- Author Email: assaf.elovic@gmail.com - -## 🛡 Disclaimer - -This project, GPT Researcher, is an experimental application and is provided "as-is" without any warranty, express or implied. We are sharing codes for academic purposes under the Apache 2 license. Nothing herein is academic advice, and NOT a recommendation to use in academic or research papers. - -Our view on unbiased research claims: -1. The main goal of GPT Researcher is to reduce incorrect and biased facts. How? We assume that the more sites we scrape the less chances of incorrect data. By scraping multiple sites per research, and choosing the most frequent information, the chances that they are all wrong is extremely low. -2. We do not aim to eliminate biases; we aim to reduce it as much as possible. **We are here as a community to figure out the most effective human/llm interactions.** -3. In research, people also tend towards biases as most have already opinions on the topics they research about. This tool scrapes many opinions and will evenly explain diverse views that a biased person would never have read. +**请注意，使用 GPT-4 语言模型可能会因使用令牌而产生高昂费用**。使用本项目即表示您承认有责任监控和管理自己的令牌使用情况及相关费用。强烈建议您定期检查 OpenAI API 的使用情况，并设置任何必要的限制或警报，以防止发生意外费用。 --- @@ -344,8 +182,3 @@ Our view on unbiased research claims:

- - -

- ⬆️ Back to Top -