Chief Intelligence Officer (Wiseflow)

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🚀 Chief Intelligence Officer (Wiseflow) is an agile information mining tool that can precisely extract specific information from various given sources by leveraging the thinking and analytical capabilities of large models, requiring no human intervention throughout the process.

What we lack is not information, but the ability to filter out noise from massive information, thereby revealing valuable information.

🌱 See how AI Intelligence Officer helps you save time, filter irrelevant information, and organize key points of interest! 🌱

https://github.com/user-attachments/assets/fc328977-2366-4271-9909-a89d9e34a07b

🔥 Test Scripts and Test Reports Release

We have horizontally tested and compared the performance of deepseekV2.5, Qwen2.5-32B-Instruct, Qwen2.5-14B-Instruct, and Qwen2.5-coder-7B-Instruct models provided by siliconflow across four real-case tasks and a total of ten real webpage samples. Please refer to the report for test results.

We have also open-sourced the test scripts and welcome everyone to actively submit more test results. Wiseflow is an open-source project, and we hope to create an "information crawling tool that everyone can use" through our collective contributions!

For details, please refer to test/README_EN.md

At this stage, submitting test results is equivalent to submitting project code, and you will similarly be accepted as a contributor and may even be invited to participate in commercialization projects!

Additionally, we have improved the download and username/password configuration solution for pocketbase. Thanks to @ourines for contributing the install_pocketbase.sh script.

(The docker deployment solution has been temporarily removed as we felt it wasn't very convenient for users...)

🌟 V0.3.6 Version Preview

Version V0.3.6 is planned for release before December 30, 2024. This version is a performance optimization of v0.3.5, with significant improvements in information extraction quality. It will also introduce visual large models to extract page image information as supplementary when webpage information is insufficient.

V0.3.x Plan

• Attempt to support WeChat official account subscription without wxbot (V0.3.7)
• Introduce support for RSS information sources (V0.3.8)
• Attempt to introduce LLM-driven lightweight knowledge graphs to help users build insights from infos (V0.3.9)

Starting from version V0.3.5, wiseflow uses a completely new architecture and introduces Crawlee as the basic crawler and task management framework, significantly improving page acquisition capabilities. We will continue to enhance wiseflow's page acquisition capabilities. If you encounter pages that cannot be properly acquired, please provide feedback in issue #136.

✋ How is wiseflow Different from Traditional Crawler Tools, AI Search, and Knowledge Base (RAG) Projects?

Since the release of version V0.3.0 in late June 2024, wiseflow has received widespread attention from the open-source community, attracting even some self-media reports. First of all, we would like to express our gratitude!

However, we have also noticed some misunderstandings about the functional positioning of wiseflow among some followers. The following table, through comparison with traditional crawler tools, AI search, and knowledge base (RAG) projects, represents our current thinking on the latest product positioning of wiseflow.

	Comparison with Chief Intelligence Officer (Wiseflow)
Crawler Tools	First, wiseflow is a project based on crawler tools (in the current version, we use the crawler framework Crawlee). However, traditional crawler tools require manual intervention in information extraction, providing explicit Xpath, etc. This not only blocks ordinary users but also lacks generality. For different websites (including upgraded websites), manual reanalysis and updating of extraction code are required. Wiseflow is committed to automating web analysis and extraction using LLM. Users only need to tell the program their focus points. From this perspective, wiseflow can be simply understood as an "AI agent that can automatically use crawler tools."
AI Search	AI search is mainly used for instant question-and-answer scenarios, such as "Who is the founder of XX company?" or "Where can I buy the xx product under the xx brand?" Users want a single answer; wiseflow is mainly used for continuous information collection in certain areas, such as tracking related information of XX company, continuously tracking market behavior of XX brand, etc. In these scenarios, users can provide focus points (a company, a brand) or even information sources (site URLs, etc.), but cannot pose specific search questions. Users want a series of related information.
Knowledge Base (RAG) Projects	Knowledge base (RAG) projects are generally based on downstream tasks of existing information and usually face private knowledge (such as operation manuals, product manuals, government documents within enterprises, etc.); wiseflow currently does not integrate downstream tasks and faces public information on the internet. From the perspective of "agents," the two belong to agents built for different purposes. RAG projects are "internal knowledge assistant agents," while wiseflow is an "external information collection agent."

📥 Installation and Usage

1. Clone the Code Repository

🌹 Starring and forking are good habits 🌹

windows users please download git bash tool first link

git clone https://github.com/TeamWiseFlow/wiseflow.git

2. Execute the install_pocketbase.sh script in the root directory

This script will guide you through downloading and configuring pocketbase (version 0.23.4), and create a .env file under core.

chmod +x install_pocketbase.sh
./install_pocketbase.sh

Wiseflow 0.3.x uses pocketbase as its database. You can also manually download the pocketbase client (remember to download version 0.23.4 and place it in the pb directory) and manually create the superuser (remember to save it in the .env file).

For details, please refer to pb/README.md

3. Continue Configuring the core/.env File

🌟 This is different from previous versions - starting from V0.3.5, the .env file needs to be placed in the core folder.

3.1 Large Language Model Configuration

Wiseflow is a LLM native application, so please ensure you provide stable LLM service for the program.

🌟 Wiseflow does not restrict the source of model services - as long as the service is compatible with the openAI SDK, including locally deployed services like ollama, Xinference, etc.

Recommendation 1: Use MaaS Service Provided by Siliconflow

Siliconflow provides online MaaS services for most mainstream open-source models. With its accumulated acceleration inference technology, its service has great advantages in both speed and price. When using siliconflow's service, the .env configuration can refer to the following:

export LLM_API_KEY=Your_API_KEY
export LLM_API_BASE="https://api.siliconflow.cn/v1"
export PRIMARY_MODEL="Qwen/Qwen2.5-32B-Instruct"
export SECONDARY_MODEL="Qwen/Qwen2.5-7B-Instruct"
export VL_MODEL="OpenGVLab/InternVL2-26B"

😄 If you'd like, you can use my siliconflow referral link, which will help me earn more token rewards 🌹

Recommendation 2: Use AiHubMix Proxy for Closed-Source Commercial Models like OpenAI, Claude, Gemini

If your information sources are mostly non-Chinese pages and you don't require the extracted info to be in Chinese, then it's recommended to use closed-source commercial models like OpenAI, Claude, Gemini. You can try the third-party proxy AiHubMix, seamlessly access a wide range of leading AI models like OpenAI, Claude, Google, Llama, and more with just one API.

When using AiHubMix models, the .env configuration can refer to the following:

export LLM_API_KEY=Your_API_KEY
export LLM_API_BASE="https://aihubmix.com/v1" # refer to https://doc.aihubmix.com/
export PRIMARY_MODEL="gpt-4o"
export SECONDARY_MODEL="gpt-4o-mini"
export VL_MODEL="gpt-4o"

😄 Welcome to register using the AiHubMix referral link 🌹

Use Local Large Language Model Service

Taking Xinference as an example, the .env configuration can refer to the following:

# LLM_API_KEY='' no need for local service, please comment out or delete
export LLM_API_BASE='http://127.0.0.1:9997'
export PRIMARY_MODEL=launched_model_id
export SECONDARY_MODEL=launched_model_id
export VL_MODEL=launched_model_id

3.2 Pocketbase Account and Password Configuration

export PB_API_AUTH="test@example.com|1234567890" 

This is where you set the superuser username and password for the pocketbase database, remember to separate them with | (if the install_pocketbase.sh script executed successfully, this should already exist)

3.3 Other Optional Configurations

The following are all optional configurations:

• #VERBOSE="true"

  Whether to enable observation mode. When enabled, debug information will be recorded in the logger file (by default only output to console);

• #PROJECT_DIR="work_dir"

  Project runtime data directory. If not configured, defaults to core/work_dir. Note: Currently the entire core directory is mounted under the container, meaning you can access it directly.

• #PB_API_BASE=""

  Only needs to be configured if your pocketbase is not running on the default IP or port. Under default circumstances, you can ignore this.

4. Running the Program

✋ The V0.3.5 version architecture and dependencies are significantly different from previous versions. Please make sure to re-pull the code, delete (or rebuild) pb_data

It is recommended to use conda to build a virtual environment (of course you can skip this step, or use other Python virtual environment solutions)

conda create -n wiseflow python=3.10
conda activate wiseflow

then

cd wiseflow
cd core
pip install -r requirements.txt
chmod +x run.sh
./run_task.sh # if you just want to scan sites one-time (no loop), use ./run.sh

🌟 This script will automatically determine if pocketbase is already running. If not, it will automatically start. However, please note that when you terminate the process with ctrl+c or ctrl+z, the pocketbase process will not be terminated until you close the terminal.

run_task.sh will periodically execute crawling-extraction tasks (it will execute immediately at startup, then every hour after that). If you only need to execute once, you can use the run.sh script.

5. Adding Focus Points and Scheduled Scanning of Information Sources

After starting the program, open the pocketbase Admin dashboard UI (http://127.0.0.1:8090/_/)

5.1 Open the focus_point Form

Through this form, you can specify your focus points, and LLM will refine, filter, and categorize information accordingly.

Field description:

• focuspoint, focus point description (required), such as "Shanghai elementary to junior high school information," "cryptocurrency prices"
• explanation, detailed explanation or specific conventions of the focus point, such as "Only official junior high school admission information released by Shanghai" or "Current price, price change data of BTC, ETH"
• activated, whether to activate. If closed, this focus point will be ignored, and it can be re-enabled later.

Note: After updating the focus_point settings (including activated adjustments), the program needs to be restarted for the changes to take effect.

5.2 Open the sites Form

Through this form, you can specify custom information sources. The system will start background scheduled tasks to scan, parse, and analyze the information sources locally.

sites field description:

• url, the URL of the information source. The information source does not need to be given a specific article page, just the article list page.
• per_hours, scanning frequency, in hours, integer type (1~24 range, we recommend not exceeding once a day, i.e., set to 24)
• activated, whether to activate. If closed, this information source will be ignored, and it can be re-enabled later.

Adjustments to sites settings do not require restarting the program.

📚 How to Use the Data Crawled by wiseflow in Your Own Program

1. Refer to the dashboard part of the source code for secondary development.

Note that the core part of wiseflow does not require a dashboard, and the current product does not integrate a dashboard. If you need a dashboard, please download V0.2.1 version

2. Directly obtain data from Pocketbase

All crawled data by wiseflow will be instantly stored in pocketbase, so you can directly operate the pocketbase database to obtain data.

PocketBase, as a popular lightweight database, currently has SDKs for Go/Javascript/Python and other languages.

• Go : https://pocketbase.io/docs/go-overview/
• Javascript : https://pocketbase.io/docs/js-overview/
• python : https://github.com/vaphes/pocketbase

🛡️ License

This project is open-source under the Apache2.0 license.

For cooperation, please contact Email: zm.zhao@foxmail.com

• Commercial customers, please contact us for registration. The product promises to be forever free.

📬 Contact

If you have any questions or suggestions, please feel free to leave a message via issue.

🤝 This Project is Based on the Following Excellent Open-Source Projects:

• crawlee-python (A web scraping and browser automation library for Python to build reliable crawlers. Works with BeautifulSoup, Playwright, and raw HTTP. Both headful and headless mode. With proxy rotation.) https://github.com/apify/crawlee-python
• json_repair (Repair invalid JSON documents) https://github.com/josdejong/jsonrepair/tree/main
• python-pocketbase (pocketBase client SDK for python) https://github.com/vaphes/pocketbase
• SeeAct (a system for generalist web agents that autonomously carry out tasks on any given website, with a focus on large multimodal models (LMMs) such as GPT-4Vision.) https://github.com/OSU-NLP-Group/SeeAct

Also inspired by GNE and AutoCrawler.

Citation

If you reference or cite part or all of this project in your related work, please specify the following information:

Author: Wiseflow Team
https://github.com/TeamWiseFlow/wiseflow
Licensed under Apache2.0