src/models/prompt_template.py

from langchain.prompts import PromptTemplate
from .prompt_example import *

# ==================================================================== #  
#                           SCHEMA AGENT                               #  
# ==================================================================== #  

# Get Text Analysis
TEXT_ANALYSIS_INSTRUCTION = """
**Instruction**: Please analyze and categorize the given text.
{examples}
**Text**: {text}

**Output Shema**: {schema}
"""

text_analysis_instruction = PromptTemplate(
    input_variables=["examples", "text", "schema"],
    template=TEXT_ANALYSIS_INSTRUCTION,
)

# Get Deduced Schema Json
DEDUCE_SCHEMA_JSON_INSTRUCTION = """
**Instruction**: Generate an output format that meets the requirements as described in the task. Pay attention to the following requirements:
    - Format: Return your responses in dictionary format as a JSON object. 
    - Content: Do not include any actual data; all attributes values should be set to None.
    - Note: Attributes not mentioned in the task description should be ignored. 
{examples}
**Task**: {instruction}

**Text**: {distilled_text}
{text}

Now please deduce the output schema in json format. All attributes values should be set to None.
**Output Schema**:
"""

deduced_schema_json_instruction = PromptTemplate(
    input_variables=["examples", "instruction", "distilled_text", "text", "schema"],
    template=DEDUCE_SCHEMA_JSON_INSTRUCTION,
)

# Get Deduced Schema Code
DEDUCE_SCHEMA_CODE_INSTRUCTION = """
**Instruction**: Based on the provided text and task description, Define the output schema in Python using Pydantic. Name the final extraction target class as 'ExtractionTarget'.
{examples}
**Task**: {instruction}

**Text**: {distilled_text}
{text}

Now please deduce the output schema. Ensure that the output code snippet is wrapped in '```',and can be directly parsed by the Python interpreter.
**Output Schema**: """
deduced_schema_code_instruction = PromptTemplate(
    input_variables=["examples", "instruction", "distilled_text", "text"],
    template=DEDUCE_SCHEMA_CODE_INSTRUCTION,
)


# ==================================================================== #  
#                         EXTRACTION AGENT                             #  
# ==================================================================== #  

EXTRACT_INSTRUCTION = """
**Instruction**: You are an agent skilled in information extarction. {instruction}
{examples}
**Text**: {text}
{additional_info}
**Output Schema**: {schema}

Now please extract the corresponding information from the text. Ensure that the information you extract has a clear reference in the given text. Set any property not explicitly mentioned in the text to null.
"""

extract_instruction = PromptTemplate(
    input_variables=["instruction", "examples", "text", "schema", "additional_info"],
    template=EXTRACT_INSTRUCTION,
)

SUMMARIZE_INSTRUCTION = """
**Instruction**: Below is a list of results obtained after segmenting and extracting information from a long article. Please consolidate all the answers to generate a final response.
{examples}
**Task**: {instruction}

**Result List**: {answer_list}

**Output Schema**: {schema}
Now summarize all the information from the Result List. 
"""
summarize_instruction = PromptTemplate(
    input_variables=["instruction", "examples", "answer_list", "schema"],
    template=SUMMARIZE_INSTRUCTION,
)


# ==================================================================== #  
#                          REFLECION AGENT                             #  
# ==================================================================== #  
REFLECT_INSTRUCTION = """**Instruction**: You are an agent skilled in reflection and optimization based on the original result. Refer to **Reflection Reference** to identify potential issues in the current extraction results.

**Reflection Reference**: {examples}

Now please review each element in the extraction result. Identify and improve any potential issues in the result based on the reflection. NOTE: If the original result is correct, no modifications are needed!

**Task**: {instruction}

**Text**: {text}

**Output Schema**: {schema}

**Original Result**: {result}

"""
reflect_instruction = PromptTemplate(
    input_variables=["instruction", "examples", "text", "schema", "result"],
    template=REFLECT_INSTRUCTION,
)

SUMMARIZE_INSTRUCTION = """
**Instruction**: Below is a list of results obtained after segmenting and extracting information from a long article. Please consolidate all the answers to generate a final response.

**Task**: {instruction}

**Result List**: {answer_list}
{additional_info}
**Output Schema**: {schema}
Now summarize the information from the Result List.
"""
summarize_instruction = PromptTemplate(
    input_variables=["instruction", "answer_list", "additional_info", "schema"],
    template=SUMMARIZE_INSTRUCTION,
)



# ==================================================================== #  
#                            CASE REPOSITORY                           #  
# ==================================================================== #  

GOOD_CASE_ANALYSIS_INSTRUCTION = """
**Instruction**: Below is an information extraction task and its corresponding correct answer. Provide the reasoning steps that led to the correct answer, along with brief explanation of the answer. Your response should be brief and organized.

**Task**: {instruction}

**Text**: {text}
{additional_info}
**Correct Answer**: {result}

Now please generate the reasoning steps and breif analysis of the **Correct Answer** given above. DO NOT generate your own extraction result.
**Analysis**:
"""
good_case_analysis_instruction = PromptTemplate(
    input_variables=["instruction", "text", "result", "additional_info"],
    template=GOOD_CASE_ANALYSIS_INSTRUCTION,
)

BAD_CASE_REFLECTION_INSTRUCTION = """
**Instruction**: Based on the task description, compare the original answer with the correct one. Your output should be a brief reflection or concise summarized rules.

**Task**: {instruction}

**Text**: {text}
{additional_info}
**Original Answer**: {original_answer}

**Correct Answer**: {correct_answer}

Now please generate a brief and organized reflection. DO NOT generate your own extraction result.
**Reflection**:
"""

bad_case_reflection_instruction = PromptTemplate(
    input_variables=["instruction", "text", "original_answer", "correct_answer", "additional_info"],
    template=BAD_CASE_REFLECTION_INSTRUCTION,
)