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""" |
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Agent used to evaluate the state of the research report (typically done in a loop) and identify knowledge gaps that still |
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need to be addressed. |
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The Agent takes as input a string in the following format: |
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=========================================================== |
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ORIGINAL QUERY: <original user query> |
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CURRENT DRAFT: <most recent draft of the research output> |
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PREVIOUS EVALUATION: <the KnowledgeGapOutput from the previous iteration> |
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=========================================================== |
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The Agent then: |
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1. Carefully reviews the current draft and assesses its completeness in answering the original query |
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2. Identifies specific knowledge gaps that still exist and need to be filled |
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3. Returns a KnowledgeGapOutput object |
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""" |
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try: |
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from ..utils.llm_client import model_supports_structured_output, qianwen_plus_model |
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from ..utils.baseclass import ResearchAgent |
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from ..utils.parse_output import create_type_parser |
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except ImportError: |
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from utils.llm_client import model_supports_structured_output, qianwen_plus_model |
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from utils.baseclass import ResearchAgent |
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from utils.parse_output import create_type_parser |
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from datetime import datetime |
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from typing import List |
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from pydantic import BaseModel, Field |
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class KnowledgeGapOutput(BaseModel): |
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"""Output from the Knowledge Gap Agent""" |
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research_complete: bool = Field( |
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description="Whether the research and findings are complete enough to end the research loop" |
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) |
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outstanding_gaps: List[str] = Field( |
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description="List of knowledge gaps that still need to be addressed" |
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) |
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INSTRUCTIONS = f""" |
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You are a Research State Evaluator. Today's date is {datetime.now().strftime("%Y-%m-%d")}. |
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Your job is to critically analyze the current state of a research report, |
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identify what knowledge gaps still exist and determine the best next step to take. |
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You will be given: |
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1. The original user query and any relevant background context to the query |
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2. A full history of the tasks, actions, findings and thoughts you've made up until this point in the research process |
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Your task is to: |
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1. Carefully review the findings and thoughts, particularly from the latest iteration, and assess their completeness in answering the original query |
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2. Determine if the findings are sufficiently complete to end the research loop |
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3. If not, identify up to 3 knowledge gaps that need to be addressed in sequence in order to continue with research - these should be relevant to the original query |
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Be specific in the gaps you identify and include relevant information as this will be passed onto another agent to process without additional context. |
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Only output JSON and follow the JSON schema below. Do not output anything else. I will be parsing this with Pydantic so output valid JSON only: |
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{KnowledgeGapOutput.model_json_schema()} |
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""" |
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selected_model = qianwen_plus_model |
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knowledge_gap_agent = ResearchAgent( |
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name="KnowledgeGapAgent", |
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instructions=INSTRUCTIONS, |
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model=selected_model, |
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output_type=KnowledgeGapOutput |
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if model_supports_structured_output(selected_model) |
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else None, |
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output_parser=( |
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create_type_parser(KnowledgeGapOutput) |
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if not model_supports_structured_output(selected_model) |
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else None |
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), |
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) |
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