Update README.md

README.md

@@ -69,13 +69,14 @@ The system integrates three core databases with differentiated deployment strate
 3. Import the Norman dataset(knowledge_database_input_iupac.csv) 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(PubChemLite_exposomics_20251226.csv) and Invitrodb_v4.3 databases to local SQL and test query connectivity.
+6. Create a backend program(step1_pubchemlite_invitrodb_to_dify_en.py) to connect Dify and the SQL databases.
 
 ### 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** via GPT-4o-generated SQL queries.
-3. Receive the structured output (JSON format) containing usage category, toxicity endpoints, and corresponding AC50 values.
+3. Receive the structured output (JSON format) containing usage category and toxicity endpoints.
 
 ## 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.