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Implement optimized database schema and AI prompt - removed title field, summary to fact, removed financial_impact and main_points

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Jarian Cottingham 2026-02-02 20:48:59 -06:00
parent 9344a69636
commit 332f49eeee
2 changed files with 12 additions and 35 deletions
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19
factsdb/ai_processor.py
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@ -41,29 +41,22 @@ class AIEndpointClient:
def extract_facts(self, text_content: str, prompt: str, model: str = "gpt-oss") -> Dict[str, Any]:
"""Extract facts from text using AI"""
# Default prompt from requirements
# Default prompt from requirements - optimized for facts extraction
default_prompt = """Extract key facts from the following article in structured JSON format.
Return only valid JSON without any additional text.
Article Title: {title}
Article Content: {article_content[:3000]}...
Extract the following information:
1. Main topic/subject
2. Key entities (companies, people, locations, organizations)
3. Financial impact or implications
4. Key dates or time periods mentioned
5. Summary of main points
1. Key entities (companies, people, locations, organizations)
2. Key dates or time periods mentioned
3. Main facts from the article
Format the response as a JSON object with these fields:
{
"title": "{title}",
"summary": "brief summary",
"main_topic": "main topic",
"fact": "main fact extracted from the article",
"key_entities": ["entity1", "entity2"],
"financial_impact": "positive/negative/neutral",
"key_dates": ["date1", "date2"],
"main_points": ["point1", "point2", "point3"]
"key_dates": ["date1", "date2"]
}"""
# Use provided prompt or default

28
factsdb/database.py
View File

@ -29,13 +29,9 @@ class DatabaseManager:
CREATE TABLE IF NOT EXISTS facts (
id INTEGER PRIMARY KEY AUTOINCREMENT,
table_name TEXT NOT NULL,
title TEXT,
summary TEXT,
main_topic TEXT,
fact TEXT,
key_entities TEXT,
financial_impact TEXT,
key_dates TEXT,
main_points TEXT,
file_path TEXT,
extracted_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
processed BOOLEAN DEFAULT FALSE
@ -125,22 +121,16 @@ class DatabaseManager:
# Convert lists to JSON strings for storage
key_entities = str(fact_data.get('key_entities', []))
key_dates = str(fact_data.get('key_dates', []))
main_points = str(fact_data.get('main_points', []))
cursor.execute('''
INSERT INTO facts (
table_name, title, summary, main_topic, key_entities,
financial_impact, key_dates, main_points, file_path
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
table_name, fact, key_entities, key_dates, file_path
) VALUES (?, ?, ?, ?, ?)
''', (
table_name,
fact_data.get('title'),
fact_data.get('summary'),
fact_data.get('main_topic'),
fact_data.get('fact'),
key_entities,
fact_data.get('financial_impact'),
key_dates,
main_points,
fact_data.get('file_path')
))
@ -166,8 +156,7 @@ class DatabaseManager:
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute('''
SELECT id, table_name, title, summary, main_topic, key_entities,
financial_impact, key_dates, main_points, file_path, extracted_at
SELECT id, table_name, fact, key_entities, key_dates, file_path, extracted_at
FROM facts
WHERE table_name = ?
ORDER BY extracted_at DESC
@ -185,8 +174,6 @@ class DatabaseManager:
fact['key_entities'] = eval(fact['key_entities'])
if fact['key_dates']:
fact['key_dates'] = eval(fact['key_dates'])
if fact['main_points']:
fact['main_points'] = eval(fact['main_points'])
facts.append(fact)
return facts
@ -196,8 +183,7 @@ class DatabaseManager:
with self.get_connection() as conn:
cursor = conn.cursor()
cursor.execute('''
SELECT id, table_name, title, summary, main_topic, key_entities,
financial_impact, key_dates, main_points, file_path, extracted_at
SELECT id, table_name, fact, key_entities, key_dates, file_path, extracted_at
FROM facts
WHERE id = ?
''', (fact_id,))
@ -211,8 +197,6 @@ class DatabaseManager:
fact['key_entities'] = eval(fact['key_entities'])
if fact['key_dates']:
fact['key_dates'] = eval(fact['key_dates'])
if fact['main_points']:
fact['main_points'] = eval(fact['main_points'])
return fact
return None