🚀 **AI 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.
This upgrade brings wxbot integration solution, making it convenient for everyone to add WeChat Official Accounts as information sources. For details, see [weixin_mp/README.md](./weixin_mp/README.md)
We have also provided extractors specifically designed for WeChat Official Account articles, while also designing custom extractor interfaces to allow users to customize according to their actual needs.
This upgrade further strengthens information extraction capabilities, not only greatly optimizing the analysis of links within pages but also enabling models of 7b and 14b scale to better complete extractions based on complex focus points (such as those containing time and metric restrictions in explanations).
-@ourines contributed the install_pocketbase.sh script (docker running solution has been temporarily removed as it wasn't very convenient for users...)
-@ibaoger contributed the pocketbase installation script for Windows
Under the latest extraction strategy, we found that models of 7b scale can also perform link analysis and extraction tasks well. For test results, please refer to [report](./test/reports/wiseflow_report_v037_bigbrother666/README.md)
However, for information summarization tasks, we still recommend using models no smaller than 32b scale. For specific recommendations, please refer to the latest [env_sample](./env_sample)
At this stage, **submitting test results is equivalent to submitting project code**, and will similarly be accepted as a contributor, and may even be invited to participate in commercialization projects! For details, please refer to [test/README.md](./test/README.md)
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.
| **Crawler Tools** | First of all, wiseflow is a project based on a web crawler tool, but traditional crawler tools require manual provision of explicit Xpath information for data extraction... This not only blocks ordinary users but also lacks universality. For different websites (including existing websites after upgrades), manual re-analysis and program updates are required. wiseflow is committed to using LLM to automate the analysis and extraction of web pages. Users only need to tell the program their focus points. Taking Crawl4ai as an example for comparison, Crawl4ai is a crawler that uses LLM for information extraction, while wiseflow is an LLM information extractor that uses 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."|
**The wiseflow 0.4.x version will focus on the integration of downstream tasks, introducing an LLM-driven lightweight knowledge graph to help users gain insights from infos.**
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](./pb) directory) and manually create the superuser (remember to save it in the .env file).
🌟 **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.**
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:
😄 If you'd like, you can use my [siliconflow referral link](https://cloud.siliconflow.cn?referrer=clx6wrtca00045766ahvexw92), which will help me earn more token rewards 🌹
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.
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)
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.
Used to control the number of concurrent LLM requests. Default is 1 if not set (before enabling, please ensure your LLM provider supports the configured concurrency. Use local large models with caution unless you are confident in your hardware capabilities)
Thanks to @tusik for contributing the asynchronous LLM wrapper
✋ 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)
🌟 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.
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.
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.
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](https://github.com/TeamWiseFlow/wiseflow/releases/tag/V0.2.1)
Also inspired by [GNE](https://github.com/GeneralNewsExtractor/GeneralNewsExtractor) [AutoCrawler](https://github.com/kingname/AutoCrawler) [SeeAct](https://github.com/OSU-NLP-Group/SeeAct) .
