Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+tags:
+- agent
+- chemistry
+---
+## Model Overview
+This model is an **Contaminants of Emerging Concern Annotation Intelligent Agent** built on the Dify platform, integrated with the Norman knowledge base, Pubchemlite_exposomics database, and Invitrodb_v4.3 database. It enables high-throughput, large-scale annotation of emerging contaminants, including **usage classification**, **toxicity endpoints**, and corresponding **AC50 value queries** by inputting the IUPAC name of the target contaminant.
+
+## Model Purpose
+To construct a specialized knowledge database for emerging contaminants usage classification, which combines multi-source chemical/toxicological databases and AI agents. The core goals are:
+1. Realize fast and large-scale annotation of emerging contaminants' usage categories.
+2. Provide efficient query services for toxicity endpoints and their corresponding AC50 values.
+3. Support high-throughput data analysis scenarios for emerging contaminants in environmental chemistry and toxicology research.
+
+## Key Definitions
+| Term | Definition |
+|------|------------|
+| **System** | Refers to the *Emerging Contaminants Annotation Intelligent Agent Based on Dify Platform* |
+| **User** | Anyone authorized to use the functions of this system |
+| **IUPAC Name** | A systematic naming convention formulated by IUPAC for accurately describing the composition and structure of chemical substances |
+| **AI Agent** | A system based on large language models (LLMs) that understands user intentions and invokes multiple tools to solve complex tasks; in this system, it accepts IUPAC names of emerging contaminants and outputs usage classification, toxicity endpoints, and AC50 values |
+| **Norman Database** | A network for monitoring and evaluating environmental pollutants, facilitating European and international cooperation and data sharing in environmental pollution monitoring; classifies over 100,000 chemicals |
+| **Pubchemlite_exposomics Database** | An open-source organic molecule information database derived from PubChem, applicable for mass spectrometry analysis and non-targeted identification of unknown pollutants |
+| **Invitrodb_v4.3 Database** | The core database of US EPA ToxCast, storing a large amount of biological activity data, analysis workflows, and metadata of compounds generated by high-throughput screening (HTS) |
+
+## System Architecture & Components
+### Core Databases Deployment
+The system integrates three core databases with differentiated deployment strategies:
+1. **Norman Chemical Classification Database**
+   - Serves as a relational knowledge base, uploaded and parsed on the FastGPT platform, then embedded into the Dify platform.
+   - Optimized classification: Integrated or removed redundant categories, finally categorized chemicals into **9 classes** (see Figure 1).
+2. **Pubchemlite_exposomics & Invitrodb_v4.3 Databases**
+   - Deployed in local SQL databases to support efficient local query and invocation.
+   - Query workflow: GPT-4o generates SQL statements → Extract valid SQL queries → Backend executes database queries and returns results.
+
+### AI Workflow Design (Dify Chatflow)
+1. **Base Model**: GPT-4o is used to generate SQL query statements and organize output data in JSON format for subsequent data extraction.
+2. **Custom Schema Tool**
+   - Created on the Dify platform to standardize SQL statement generation and API invocation logic.
+   - Implementation steps: Create custom tool → Configure tool name and Schema rules (see Appendix for details).
+3. **Knowledge Base Integration (FastGPT + Dify)**
+   - **FastGPT Knowledge Base Construction**
+     1. Log in to FastGPT (https://fastgpt.aiown.top/) and enter the main interface.
+     2. Import dataset (50,000+ chemicals with IUPAC names and categories from the Norman database).
+     3. Connect the dataset to a FastGPT application and configure prompts (consistent with Few-shot prompts).
+     4. Publish the application and export the API key for subsequent calls.
+   - **Fast-Dify Adaptor (FDA) Plugin**
+     - Resolves API incompatibility between FastGPT and Dify.
+     - Deployment steps: Create `docker-compose.yml` for FDA → Run `docker-compose up -d` in the configuration file directory to deploy the plugin.
+   - **Dify External Knowledge Base Connection**: Link the trained FastGPT knowledge base to Dify by importing the FastGPT API key and knowledge base ID.
+
+## System Requirements
+| Category | Specification |
+|----------|---------------|
+| Operating System | Windows 10 |
+| Python Version | Python 3.8 or higher |
+| Dependencies | Docker (for FDA plugin deployment), FastGPT access rights, Dify platform account |
+
+## Usage Instructions
+### Preparations
+1. Complete Norman database classification optimization (merge into 9 categories).
+2. Deploy FDA plugin to connect FastGPT and Dify.
+3. Import the Norman dataset into FastGPT, configure the application, and export the API key.
+4. Create a custom Schema tool on Dify and configure SQL invocation rules.
+5. Deploy Pubchemlite_exposomics and Invitrodb_v4.3 databases to local SQL and test query connectivity.
+
+### Inference Workflow
+1. Input the **IUPAC name** of the emerging contaminant into the Dify chat interface.
+2. The AI agent invokes:
+   - FastGPT knowledge base for **usage classification** via FDA plugin.
+   - Local SQL databases for **toxicity endpoints and AC50 values** via GPT-4o-generated SQL queries.
+3. Receive the structured output (JSON format) containing usage category, toxicity endpoints, and corresponding AC50 values.
+
+## Limitations
+1. The accuracy of annotations depends on the completeness of the Norman, Pubchemlite_exposomics, and Invitrodb_v4.3 databases; unrecorded emerging contaminants may return empty results.
+2. Requires local deployment of SQL databases and FDA plugins, which has a certain threshold for environment configuration.
+3. Currently only supports input of **IUPAC names**; other naming formats (e.g., common names) are not supported.
+
+## Contact
+laquh1086@163.com