Data Characteristics
Lead compound screening data comes from high-throughput screening reports, compound structure databases, and biological activity tests. This data is typically structured or semi-structured, found in CSV, SDF, JSON files, or database records. Update frequency depends on experimental progress, potentially weekly or monthly. Document structure usually includes a unique compound identifier (compound_id), chemical structure (SMILES or InChI encoding), molecular weight, LogP values, and activity data (e.g., IC50, EC50 values) across different targets or cell lines. Activity data often includes metadata like detection methods and experimental conditions. Field units are generally consistent; for example, IC50 values are typically in nanomoles (nM), and molecular weight in Daltons (Da).
Constraints Imposed by Data Characteristics on Tool Calling and Plugins
The structured nature of lead compound screening data makes tool calling for data parsing and field mapping relatively straightforward. However, data source diversity requires flexible data access capabilities, such as support for parsing multiple file formats. The update frequency of activity data determines the knowledge base synchronization cycle, ensuring the model always bases decisions on the latest experimental results. Chemical structure information is core data; it requires specialized handling during tool calls, such as molecular feature calculation or structural similarity searches using cheminformatics toolkits. Activity data metadata, like experimental conditions, must pass as context to the tool to prevent misinterpretation. Furthermore, large volumes of compound data can lead to excessive data processing in a single tool call, necessitating pagination or batch processing mechanisms.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chunk_size | 500 characters | Ensures each knowledge chunk contains complete key compound information, preventing context loss. |
overlap_size | 50 characters | Provides smooth transitions between knowledge chunks, enhancing recall continuity. |
retrieval_top_k | 8 entries | Balances retrieval precision and computational overhead, covering potentially relevant results. |
max_tokens | 4096 | Adapts to common large model context windows, handling complex queries and structural information. |
timeout | 60 seconds | Addresses potential delays from external tool calls (e.g., molecular descriptor calculation). |
tool_output_format | JSON | Ensures the model can reliably parse structured data returned by tools, facilitating subsequent processing. |
Common Pitfalls
- After calling a tool, the chat page does not display line graphs. This usually occurs because the chart data format returned by the tool does not meet FastGPT's rendering requirements. It needs conversion to a front-end supported image URL or a specific JSON structure.
- Text extraction encounters a
Your model may not support tool_call SyntaxErrorerror. This typically indicates an outdated model version or that thetool_callfeature is not enabled, preventing correct recognition and execution of tool call instructions. - After exporting a workflow and importing it into a new environment, referenced plugins are not found. This means the plugin is not correctly registered or its path configuration is incorrect in the target environment, preventing the workflow from resolving its dependencies.
Verification Steps
- For typical lead compound queries, verify that tool calls execute successfully and return expected activity data and structural information.
- Check that
compound_id, SMILES encoding, and key activity values for compound entries in the knowledge base are complete and accurate, with no data truncation or parsing errors. - Enter chart-requesting queries in the chat interface and confirm that charts returned by the tool render and display correctly.
- Simulate abnormal data or edge cases to observe whether tool call failures provide clear error messages.
The values provided are common starting points. Measure them against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.