heymenn commited on
Commit
5e9393d
·
verified ·
1 Parent(s): 331ff0c

Update kig_core/prompts.py

Browse files
Files changed (1) hide show
  1. kig_core/prompts.py +5 -21
kig_core/prompts.py CHANGED
@@ -84,34 +84,18 @@ SUMMARIZER_PROMPT = ChatPromptTemplate.from_template(SUMMARIZER_TEMPLATE)
84
  # This prompt guides the LLM to output structured Key Issues based on gathered context.
85
  # It references the Pydantic model 'KeyIssue' for the desired format.
86
  KEY_ISSUE_STRUCTURING_TEMPLATE = f"""Based on the provided context (summaries of relevant documents, research findings, etc.), identify and formulate distinct Key Issues related to the original user query.
87
- User Query: {user_query}
88
- Context: {context}
89
  For each Key Issue identified, provide the following information in the exact JSON format described below. Output a JSON list containing multiple KeyIssue objects.
90
  JSON Schema for each Key Issue object:
91
- {{
92
  "id": "Sequential integer ID starting from 1",
93
  "title": "Concise title for the key issue (max 15 words)",
94
  "description": "Detailed description of the key issue (2-4 sentences)",
95
  "challenges": ["List of specific challenges related to this issue (strings)", "Each challenge as a separate string"],
96
  "potential_impact": "Brief description of the potential impact if not addressed (optional, max 30 words)"
97
- }}
98
- Example Format:
99
- [
100
- {{
101
- "id": 1,
102
- "title": "Scalability of AI Models in Low-Resource Settings",
103
- "description": "Deploying complex AI models for healthcare diagnostics in areas with limited computational power and data connectivity presents significant scalability challenges. Existing models often require substantial resources.",
104
- "challenges": ["High computational requirements of current models", "Intermittent or low-bandwidth network connectivity", "Lack of large, localized datasets for training/fine-tuning"],
105
- "potential_impact": "Limits equitable access to advanced AI-driven healthcare diagnostics."
106
- }},
107
- {{
108
- "id": 2,
109
- "title": "...",
110
- "description": "...",
111
- "challenges": ["...", "..."],
112
- "potential_impact": "..."
113
- }}
114
- ]
115
  Generate the JSON list of Key Issues based *only* on the provided context and user query. Ensure the output is a valid JSON list.
116
  """
117
  KEY_ISSUE_STRUCTURING_PROMPT = ChatPromptTemplate.from_template(KEY_ISSUE_STRUCTURING_TEMPLATE)
 
84
  # This prompt guides the LLM to output structured Key Issues based on gathered context.
85
  # It references the Pydantic model 'KeyIssue' for the desired format.
86
  KEY_ISSUE_STRUCTURING_TEMPLATE = f"""Based on the provided context (summaries of relevant documents, research findings, etc.), identify and formulate distinct Key Issues related to the original user query.
 
 
87
  For each Key Issue identified, provide the following information in the exact JSON format described below. Output a JSON list containing multiple KeyIssue objects.
88
  JSON Schema for each Key Issue object:
89
+ [{{{{
90
  "id": "Sequential integer ID starting from 1",
91
  "title": "Concise title for the key issue (max 15 words)",
92
  "description": "Detailed description of the key issue (2-4 sentences)",
93
  "challenges": ["List of specific challenges related to this issue (strings)", "Each challenge as a separate string"],
94
  "potential_impact": "Brief description of the potential impact if not addressed (optional, max 30 words)"
95
+ }}}}]
96
+
97
+ User Query: {{user_query}}
98
+ Context: {{context}}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
99
  Generate the JSON list of Key Issues based *only* on the provided context and user query. Ensure the output is a valid JSON list.
100
  """
101
  KEY_ISSUE_STRUCTURING_PROMPT = ChatPromptTemplate.from_template(KEY_ISSUE_STRUCTURING_TEMPLATE)