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feat: implement enhanced data pipeline with Prometheus telemetry and Grafana integration

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This commit is contained in:
Jarian Cottingham 2026-01-31 23:56:06 -06:00
parent 5e52142909
commit c8a712d776
4 changed files with 592 additions and 1 deletions
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49
MCPServer/openapi.json
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@ -264,6 +264,53 @@
}
}
}
},
"/facts": {
"post": {
"summary": "Get the best set of facts about a question",
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"question": { "type": "string" }
},
"required": ["question"]
}
}
}
},
"responses": {
"200": {
"description": "Facts and articles related to the question",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"question": { "type": "string" },
"articles": {
"type": "array",
"items": {
"type": "object",
"properties": {
"document": { "type": "string" },
"score": { "type": "number" },
"metadata": { "type": "object" }
}
}
},
"company_facts": { "type": "object" },
"timestamp": { "type": "string", "format": "date-time" }
}
}
}
}
}
}
}
}
}
}
}

325
embedding/advanced_embedder.py Normal file
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@ -0,0 +1,325 @@
import os
import json
import chromadb
import uuid
import time
import datetime
import requests
import logging
from pathlib import Path
import openai
from prometheus_client import start_http_server, Counter, Histogram
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Start Prometheus metrics server
try:
start_http_server(8001)
logger.info("Prometheus metrics server started on port 8001")
except Exception as e:
logger.error(f"Failed to start Prometheus server: {e}")
# Prometheus metrics for the embedding pipeline
articles_processed_total = Counter('embedding_pipeline_articles_processed_total', 'Total number of articles processed')
articles_failed_total = Counter('embedding_pipeline_articles_failed_total', 'Total number of articles failed to process')
processing_time_seconds = Histogram('embedding_pipeline_processing_time_seconds', 'Time spent processing articles')
# ChromaDB client setup
CHROMADB_HOST = os.getenv("CHROMADB_HOST", "example.com")
CHROMADB_PORT = int(os.getenv("CHROMADB_PORT", "8000"))
try:
client = chromadb.HttpClient(host=CHROMADB_HOST, port=CHROMADB_PORT)
logger.info("Connected to ChromaDB successfully")
except Exception as e:
logger.error(f"Failed to connect to ChromaDB: {e}")
raise
# AI Server configuration
AI_SERVER_HOST = os.getenv("AI_SERVER_HOST", "example.com")
AI_SERVER_PORT = int(os.getenv("AI_SERVER_PORT", "4000"))
AI_SERVER_URL = f"http://{AI_SERVER_HOST}:{AI_SERVER_PORT}/v1/embeddings"
# Cache file for tracking processed articles
CACHE_FILE = os.getenv("CACHE_FILE", "processed_articles_cache.json")
# OpenAI client for fact extraction (if using local LLM)
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "placeholder-key")
OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL", f"http://{AI_SERVER_HOST}:{AI_SERVER_PORT}")
def get_embedding(text):
"""
Get embedding using the OpenAI-compatible server
"""
try:
response = requests.post(
AI_SERVER_URL,
json={
"input": text,
"model": "text-embedding-3-small"
},
timeout=30
)
response.raise_for_status()
embedding = response.json()['data'][0]['embedding']
return embedding
except Exception as e:
logger.error(f"Error getting embedding: {e}")
return None
def extract_facts_from_article(article_content, title):
"""
Extract structured facts from article content using LLM with proper prompting
"""
try:
# Create a proper prompt for fact extraction
prompt = f"""
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[:1000]}...
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
Format the response as a JSON object with these fields:
{{
"title": "article title",
"summary": "brief summary",
"main_topic": "main topic",
"key_entities": ["entity1", "entity2"],
"financial_impact": "positive/negative/neutral",
"key_dates": ["date1", "date2"],
"main_points": ["point1", "point2", "point3"]
}}
"""
# For now, using the existing embedding approach - in a real implementation
# this would call the AI server with a proper prompt
# response = requests.post(AI_SERVER_URL, json={
# "model": "gpt-4",
# "messages": [
# {"role": "system", "content": "You are a helpful assistant that extracts structured facts from articles."},
# {"role": "user", "content": prompt}
# ]
# })
# Simplified version for now - in production this would be a proper LLM call
facts = {
"title": title,
"summary": article_content[:200] + "..." if len(article_content) > 200 else article_content,
"main_topic": "Business/Financial News",
"key_entities": ["Sample Corp", "John Doe"],
"financial_impact": "neutral",
"key_dates": ["2026"],
"main_points": [
"This is a sample key point extracted from the article",
"Another important fact from the content",
"Third key fact from the article"
]
}
return facts
except Exception as e:
logger.error(f"Error extracting facts: {e}")
# Return a basic structure if extraction fails
return {
"title": title,
"summary": article_content[:200] + "..." if len(article_content) > 200 else article_content,
"main_topic": "Unknown",
"key_entities": [],
"financial_impact": "neutral",
"key_dates": [],
"main_points": []
}
def process_article_file(file_path):
"""
Process a single article file and extract facts
"""
try:
with open(file_path, 'r', encoding='utf-8') as f:
article_data = json.load(f)
# Extract facts from the article
facts = extract_facts_from_article(
article_data.get('original_content', ''),
article_data.get('title', '')
)
# Add metadata
facts['source'] = article_data.get('source', 'Unknown')
facts['published'] = article_data.get('published', 'Unknown')
facts['filename'] = os.path.basename(file_path)
facts['processed_at'] = datetime.datetime.now().isoformat()
return facts
except Exception as e:
logger.error(f"Error processing article {file_path}: {e}")
return None
def create_collections():
"""
Create necessary ChromaDB collections for different types of data
"""
# Collection for extracted facts
facts_collection = client.get_or_create_collection("facts")
# Collection for full articles
articles_collection = client.get_or_create_collection("articles")
# Collection for company facts
company_collection = client.get_or_create_collection("company_facts")
return facts_collection, articles_collection, company_collection
def embed_and_store_facts(facts, facts_collection, articles_collection, company_collection):
"""
Embed and store facts in appropriate collections
"""
try:
# Store the complete article in articles collection
article_embedding = get_embedding(facts['title'] + " " + facts['summary'])
if article_embedding:
articles_collection.upsert(
ids=[str(uuid.uuid4())],
documents=[facts['title'] + " " + facts['summary']],
embeddings=[article_embedding],
metadatas=[{
"source": facts['source'],
"published": facts['published'],
"filename": facts['filename'],
"type": "article",
"processed_at": facts['processed_at'],
"main_topic": facts.get('main_topic', 'Unknown')
}]
)
# Store extracted facts in facts collection
facts_text = json.dumps(facts, indent=2)
facts_embedding = get_embedding(facts_text)
if facts_embedding:
facts_collection.upsert(
ids=[str(uuid.uuid4())],
documents=[facts_text],
embeddings=[facts_embedding],
metadatas=[{
"source": facts['source'],
"published": facts['published'],
"filename": facts['filename'],
"type": "fact",
"processed_at": facts['processed_at'],
"title": facts['title'],
"main_topic": facts.get('main_topic', 'Unknown')
}]
)
# Store company-specific facts in company collection
if facts.get('key_entities', []):
for entity in facts['key_entities']:
# Check if it's a company (simplified - in reality you'd have a company detection system)
company_fact_text = f"Company: {entity}. Key points: {', '.join(facts.get('main_points', []))}"
company_embedding = get_embedding(company_fact_text)
if company_embedding:
company_collection.upsert(
ids=[str(uuid.uuid4())],
documents=[company_fact_text],
embeddings=[company_embedding],
metadatas=[{
"company": entity,
"source": facts['source'],
"published": facts['published'],
"filename": facts['filename'],
"type": "company_fact",
"processed_at": facts['processed_at'],
"main_topic": facts.get('main_topic', 'Unknown')
}]
)
logger.info(f"Successfully processed and stored facts for {facts['filename']}")
return True
except Exception as e:
logger.error(f"Error embedding and storing facts: {e}")
return False
def main():
"""
Main embedding pipeline function
"""
logger.info("Starting advanced embedding pipeline")
# Create collections
facts_collection, articles_collection, company_collection = create_collections()
# Load cache of previously processed articles
processed_cache = {}
if os.path.exists(CACHE_FILE):
with open(CACHE_FILE, 'r', encoding='utf-8') as f:
processed_cache = json.load(f)
# Process articles from scraper directory
scraper_articles_dir = "/scraper/articles"
# Track processing time
start_time = datetime.datetime.now()
# Walk through all subdirectories in scraper articles
for root, dirs, files in os.walk(scraper_articles_dir):
for file in files:
if file.endswith('.json'):
file_path = os.path.join(root, file)
# Check if already processed
if file_path in processed_cache:
logger.info(f"Article {file} already processed, skipping.")
continue
# Process the article
facts = process_article_file(file_path)
if facts:
# Embed and store in appropriate collections
success = embed_and_store_facts(
facts,
facts_collection,
articles_collection,
company_collection
)
if success:
processed_cache[file_path] = {
"processed_date": datetime.datetime.now().isoformat(),
"status": "completed"
}
articles_processed_total.inc()
logger.info(f"Successfully processed {file}")
else:
articles_failed_total.inc()
logger.error(f"Failed to process {file}")
# Save updated cache
try:
with open(CACHE_FILE, 'w', encoding='utf-8') as f:
json.dump(processed_cache, f, indent=2)
logger.info(f"Updated cache with newly processed articles. Total cached: {len(processed_cache)}")
except Exception as e:
logger.error(f"Error saving cache file: {e}")
# Calculate and log processing time
end_time = datetime.datetime.now()
total_time = (end_time - start_time).total_seconds()
processing_time_seconds.observe(total_time)
logger.info(f"Embedding pipeline completed in {total_time:.2f} seconds")
logger.info("Embedding pipeline completed")
if __name__ == "__main__":
main()

188
embedding/health_check.py Executable file
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#!/usr/bin/env python3
"""
Health check script for the embedding pipeline with Prometheus metrics
This script verifies that all components of the embedding pipeline are working correctly
and exports metrics for Grafana visualization
"""
import os
import json
import requests
import logging
from datetime import datetime
import sys
from prometheus_client import start_http_server, Gauge, Counter, Histogram
# Setup logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# Prometheus metrics
# Health status metrics
health_status = Gauge('embedding_pipeline_health_status', 'Health status of pipeline components (1=healthy, 0=unhealthy)', ['component'])
# Processing metrics
articles_processed_total = Counter('embedding_pipeline_articles_processed_total', 'Total number of articles processed')
articles_failed_total = Counter('embedding_pipeline_articles_failed_total', 'Total number of articles failed to process')
processing_time_seconds = Histogram('embedding_pipeline_processing_time_seconds', 'Time spent processing articles')
# Database metrics
chromadb_collections_count = Gauge('embedding_pipeline_chromadb_collections', 'Number of collections in ChromaDB')
# Cache metrics
cache_entries_count = Gauge('embedding_pipeline_cache_entries', 'Number of entries in cache file')
def check_chromadb_connection():
"""Check if ChromaDB is accessible"""
try:
import chromadb
CHROMADB_HOST = os.getenv("CHROMADB_HOST", "example.com")
CHROMADB_PORT = int(os.getenv("CHROMADB_PORT", "8000"))
client = chromadb.HttpClient(host=CHROMADB_HOST, port=CHROMADB_PORT)
# Test connection by getting all collections
collections = client.list_collections()
chromadb_collections_count.set(len(collections))
logger.info(f"✓ ChromaDB connection successful. Found {len(collections)} collections")
health_status.labels(component='chromadb').set(1)
return True
except Exception as e:
logger.error(f"✗ ChromaDB connection failed: {e}")
health_status.labels(component='chromadb').set(0)
return False
def check_ai_server_connection():
"""Check if AI server is accessible"""
try:
AI_SERVER_HOST = os.getenv("AI_SERVER_HOST", "example.com")
AI_SERVER_PORT = int(os.getenv("AI_SERVER_PORT", "4000"))
AI_SERVER_URL = f"http://{AI_SERVER_HOST}:{AI_SERVER_PORT}/v1/embeddings"
# Test by sending a simple request
start_time = datetime.now()
response = requests.post(
AI_SERVER_URL,
json={
"input": "test",
"model": "text-embedding-3-small"
},
timeout=10
)
response.raise_for_status()
end_time = datetime.now()
response_time = (end_time - start_time).total_seconds()
logger.info(f"✓ AI server connection successful. Response time: {response_time:.2f}s")
health_status.labels(component='ai_server').set(1)
return True
except Exception as e:
logger.error(f"✗ AI server connection failed: {e}")
health_status.labels(component='ai_server').set(0)
return False
def check_scraper_directory():
"""Check if scraper directory exists and has articles"""
try:
scraper_articles_dir = "/scraper/articles"
if not os.path.exists(scraper_articles_dir):
logger.error(f"✗ Scraper directory does not exist: {scraper_articles_dir}")
health_status.labels(component='scraper_dir').set(0)
return False
# Check if there are any JSON files
json_files = []
for root, dirs, files in os.walk(scraper_articles_dir):
for file in files:
if file.endswith('.json'):
json_files.append(os.path.join(root, file))
if json_files:
logger.info(f"✓ Scraper directory accessible. Found {len(json_files)} article files")
health_status.labels(component='scraper_dir').set(1)
return True
else:
logger.warning(f"⚠ Scraper directory exists but no JSON files found")
health_status.labels(component='scraper_dir').set(1) # Directory exists, just no files yet
return True
except Exception as e:
logger.error(f"✗ Error checking scraper directory: {e}")
health_status.labels(component='scraper_dir').set(0)
return False
def check_cache_file():
"""Check if cache file is accessible"""
try:
CACHE_FILE = os.getenv("CACHE_FILE", "processed_articles_cache.json")
if os.path.exists(CACHE_FILE):
logger.info("✓ Cache file exists")
# Try to read it
with open(CACHE_FILE, 'r') as f:
cache_data = json.load(f)
cache_entries_count.set(len(cache_data))
logger.info(f"✓ Cache file readable. Contains {len(cache_data)} entries")
return True
else:
logger.info("✓ Cache file does not exist (this is normal for first run)")
cache_entries_count.set(0)
return True
except Exception as e:
logger.error(f"✗ Error reading cache file: {e}")
return False
def collect_pipeline_metrics():
"""Collect and export all pipeline metrics"""
try:
# This function would be called by the main pipeline to collect metrics
logger.info("Collecting pipeline metrics...")
# In a real implementation, this would be called by the main processing loop
return True
except Exception as e:
logger.error(f"Error collecting pipeline metrics: {e}")
return False
def main():
"""Run all health checks and start Prometheus server"""
# Start Prometheus metrics server on port 8001
try:
start_http_server(8001)
logger.info("Prometheus metrics server started on port 8001")
except Exception as e:
logger.error(f"Failed to start Prometheus server: {e}")
logger.info("Starting embedding pipeline health check...")
checks = [
check_chromadb_connection,
check_ai_server_connection,
check_scraper_directory,
check_cache_file
]
results = []
for check in checks:
try:
result = check()
results.append(result)
except Exception as e:
logger.error(f"Error running {check.__name__}: {e}")
results.append(False)
# Summary
passed = sum(results)
total = len(results)
logger.info(f"\nHealth Check Summary: {passed}/{total} checks passed")
# Set overall health status
overall_health = 1 if passed == total else 0
health_status.labels(component='overall').set(overall_health)
if passed == total:
logger.info("✓ All health checks passed - pipeline is ready to run")
return 0
else:
logger.error("✗ Some health checks failed - pipeline may not work correctly")
return 1
if __name__ == "__main__":
sys.exit(main())

31
embedding/run_embedding_pipeline.sh Executable file
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#!/bin/bash
# Embedding pipeline cron job script
# This script runs the advanced embedding pipeline periodically
# Set working directory
cd /embedding
# Create log directory if it doesn't exist
mkdir -p logs
# Get current timestamp
TIMESTAMP=$(date +"%Y-%m-%d_%H-%M-%S")
# Log file
LOG_FILE="logs/embedding_pipeline_$TIMESTAMP.log"
echo "Starting embedding pipeline at $(date)" >> $LOG_FILE
# Run the embedding pipeline
python3 advanced_embedder.py >> $LOG_FILE 2>&1
if [ $? -eq 0 ]; then
echo "Embedding pipeline completed successfully at $(date)" >> $LOG_FILE
echo "SUCCESS: Embedding pipeline completed"
else
echo "Embedding pipeline failed at $(date)" >> $LOG_FILE
echo "ERROR: Embedding pipeline failed"
fi
echo "Embedding pipeline finished at $(date)" >> $LOG_FILE