Added three new keys on hypothesis reasoning (#138)

* Added three new keys on hypothesis reasoning

* Updated two scenario rich text

rdagent/app/model_extraction_and_code/GeneralModel.py

@@ -31,34 +31,22 @@ class GeneralModelScenario(Scenario):
     @property
     def rich_style_description(self) -> str:
         return """
-# General Model Scenario
-
-## Overview
-
-This demo automates the extraction and iterative development of models from academic papers, ensuring functionality and correctness.
-
-### Scenario: Auto-Developing Model Code from Academic Papers
-
-#### Overview
-
-This scenario automates the development of PyTorch models by reading academic papers or other sources. It supports various data types, including tabular, time-series, and graph data. The primary workflow involves two main components: the Reader and the Coder.
-
-#### Workflow Components
-
-1. **Reader**
-    - Parses and extracts relevant model information from academic papers or sources, including architectures, parameters, and implementation details.
-    - Uses Large Language Models to convert content into a structured format for the Coder.
-
-2. **Evolving Coder**
-    - Translates structured information from the Reader into executable PyTorch code.
-    - Utilizes an evolving coding mechanism to ensure correct tensor shapes, verified with sample input tensors.
-    - Iteratively refines the code to align with source material specifications.
-
-#### Supported Data Types
-
-- **Tabular Data:** Structured data with rows and columns, such as spreadsheets or databases.
-- **Time-Series Data:** Sequential data points indexed in time order, useful for forecasting and temporal pattern recognition.
-- **Graph Data:** Data structured as nodes and edges, suitable for network analysis and relational tasks.
+# [General Model Research Co-Pilot] (#_scenario)
+ 
+## [Overview](#_summary)
+ 
+This demo automates the extraction and development of PyTorch models from academic papers. It supports various model types through two main components: Reader and Coder.
+ 
+#### [Workflow Components](#_rdloops)
+ 
+1. **[Reader](#_research)**
+    - Extracts model information from papers, including architectures and parameters.
+    - Converts content into a structured format using Large Language Models.
+ 
+2. **[Evolving Coder](#_development)**
+    - Translates structured information into executable PyTorch code.
+    - Ensures correct tensor shapes with an evolving coding mechanism.
+    - Refines the code to match source specifications.
 
         """
 

rdagent/components/proposal/prompts.yaml

@@ -14,7 +14,7 @@ hypothesis_gen:
     {{ hypothesis_and_feedback }}
     To help you generate new {{targets}}, we have prepared the following information for you:
     {{ RAG }}
-    Please generate the new hypothesis based on the information above.
+    Please generate the new hypothesis based on the information above. Also generate the relevant keys for the reasoning and the distilled knowledge that follows. 
 
 hypothesis2experiment:
   system_prompt: |-

rdagent/core/proposal.py

@@ -25,15 +25,21 @@ class Hypothesis:
     - Belief
     """
 
-    def __init__(self, hypothesis: str, reason: str, concise_reason: str) -> None:
+    def __init__(self, hypothesis: str, reason: str, concise_reason: str, concise_observation: str, concise_justification: str, concise_knowledge: str) -> None:
         self.hypothesis: str = hypothesis
         self.reason: str = reason
         self.concise_reason: str = concise_reason
+        self.concise_observation: str = concise_observation
+        self.concise_justification: str = concise_justification 
+        self.concise_knowledge: str = concise_knowledge
 
     def __str__(self) -> str:
         return f"""Hypothesis: {self.hypothesis}
                 Reason: {self.reason}
                 Concise Reason & Knowledge: {self.concise_reason}
+                Concise Observation: {self.concise_observation}
+                Concise Justification: {self.concise_justification}
+                Concise Knowledge: {self.concise_knowledge}
                 """
 
     # source: data_ana | model_nan = None

rdagent/scenarios/qlib/experiment/model_experiment.py

@@ -43,39 +43,24 @@ class QlibModelScenario(Scenario):
     def rich_style_description(self) -> str:
         return """
 ### Qlib Model Evolving Automatic R&D Demo
-
-#### Overview
-
-The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making. It highlights how models evolve through continuous feedback and refinement.
-
-#### Key Steps
-
-1. **Hypothesis**
-   - Generate and propose initial hypotheses based on data and domain knowledge.
-
-2. **Knowledge Construction**
-   - Develop, test, and refine models to gather empirical results.
-   - Analyze feedback and incorporate new insights.
-
-3. **Decision Pair**
-   - Make data-driven decisions based on validated hypotheses and constructed knowledge.
-   - Iterate the process to evolve and improve models continuously.
-
-#### Automated R&D
-
-- **R (Research)**
+ 
+#### [Overview](#_summary)
+ 
+The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making in model construction in quantitative finance. It highlights how models evolve through continuous feedback and refinement.
+ 
+#### [Automated R&D](#_rdloops)
+ 
+- **[R (Research)](#_research)**
   - Iteration of ideas and hypotheses.
   - Continuous learning and knowledge construction.
-
-- **D (Development)**
+ 
+- **[D (Development)](#_development)**
   - Evolving code generation and model refinement.
   - Automated implementation and testing of models.
-
-#### Objective
-
-To demonstrate the dynamic evolution of models through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting models.
-
-        """
+ 
+#### [Objective](#_summary)
+ 
+To demonstrate the dynamic evolution of models through the Qlib platform, emphasizing how each iteration enhances the accuracy and reliability of the resulting models.    """
 
     def get_scenario_all_desc(self) -> str:
         return f"""Background of the scenario:

rdagent/scenarios/qlib/prompts.yaml

@@ -13,8 +13,11 @@ hypothesis_output_format: |-
   The output should follow JSON format. The schema is as follows:
   {
     "hypothesis": "The new hypothesis generated based on the information provided.", Note that this should focus on model architecture, not training process or feature engineering or anything else
-    "reason": "The reason why you generate this hypothesis.",
-    "concise_reason": Two line summary. First line focuses on the a concise justification for the change. 2nd Line learns from first line and previous experiences (hypothesis & experiments & code & feedbacks) to generalise a knowledge statement (use tend to/because/if/generally/etc. ). 
+    "reason": "The reason why you generate this hypothesis. It should be comprehensive and logical. It should cover the other keys below and extend them.",
+    "concise_reason": Two line summary. First line focuses on the a concise justification for the change. 2nd Line learns from first line and previous experiences (hypothesis & experiments & code & feedbacks) to generalise a knowledge statement (use tend to/because/if/generally/etc. ). This is the summary of the three keys below. 
+    "concise_observation": One line summary. It focuses on the observation of the given scenario, data characteristics, or previous experiences (failures & succeses).
+    "concise_justification": One line summary. It focuses on the justification for the change in new hypothesis and the route of exploration supporting the growth of the hypothesis, based on the observation. 
+    "concise_knowledge": One line summary. It focuses on a transferable knowledege that comes with the new hypothesis. Use conditional grammar. eg. "If...., ..; When..., .; and etc"
   }
 
 model_hypothesis_specification: |-

rdagent/scenarios/qlib/proposal/model_proposal.py

@@ -43,6 +43,9 @@ class QlibModelHypothesisGen(ModelHypothesisGen):
             hypothesis=response_dict["hypothesis"],
             reason=response_dict["reason"],
             concise_reason=response_dict["concise_reason"],
+            concise_observation=response_dict["concise_observation"],
+            concise_justification=response_dict["concise_justification"],
+            concise_knowledge=response_dict["concise_knowledge"],
         )
         return hypothesis