chore: remove dev artifacts, fix hardcoded path, add tests + license
- Remove agent/agent.md (dev-time agent context dumps), .DS_Store, committed venv configs (pyvenv.cfg), 0-byte runtime cache - Remove hardcoded /home/userpath from cron_scraper feed lookup - Replace ad-hoc test_implementation.py with pytest tests/test_scraper_cache.py - ruff clean (33 fixes: bare excepts, unused Config, whitespace) - Root pyproject.toml (activates shared Gitea CI), MIT LICENSE, README Tests
This commit is contained in:
parent
ef51ef635c
commit
1271f0b21b
6
.gitignore
vendored
6
.gitignore
vendored
@ -24,3 +24,9 @@ nohup.out
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*.pyc
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*.log
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.DS_Store
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pyvenv.cfg
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processed_articles_cache.json
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21
LICENSE
Normal file
21
LICENSE
Normal file
@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2025 Jarian Cottingham
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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@ -1,7 +1,6 @@
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import chromadb
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from flask import Flask, request, jsonify, send_from_directory
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import os
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import json
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import requests
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import logging
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from datetime import datetime
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@ -109,12 +108,12 @@ def get_embedding(text):
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headers = {
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"Content-Type": "application/json"
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}
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# Add API key if available
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api_key = os.getenv("AI_SERVICE_API_KEY")
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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response = requests.post(
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"http://example.com:4000/v1/embeddings",
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json={
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@ -137,23 +136,23 @@ def get_diverse_articles(articles, max_diverse=5):
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"""
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if len(articles) <= max_diverse:
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return articles
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# More sophisticated diversity algorithm
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diverse_articles = []
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source_count = {}
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topic_count = {}
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# First pass: try to get articles from different sources
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for article in articles:
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source = article.get('metadata', {}).get('source', 'unknown')
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topic = article.get('metadata', {}).get('topic', 'unknown')
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# If we haven't reached max diversity and this source is new, add it
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if len(diverse_articles) < max_diverse and source not in source_count:
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diverse_articles.append(article)
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source_count[source] = 1
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topic_count[topic] = topic_count.get(topic, 0) + 1
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# Second pass: fill remaining slots with different topics if possible
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if len(diverse_articles) < max_diverse:
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for article in articles:
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@ -161,17 +160,17 @@ def get_diverse_articles(articles, max_diverse=5):
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break
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source = article.get('metadata', {}).get('source', 'unknown')
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topic = article.get('metadata', {}).get('topic', 'unknown')
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# Add article if it's from a different topic and we haven't seen too many from this topic
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if source not in source_count and topic_count.get(topic, 0) < 2:
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diverse_articles.append(article)
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source_count[source] = 1
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topic_count[topic] = topic_count.get(topic, 0) + 1
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# If we still don't have enough, just return first few
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if len(diverse_articles) < max_diverse:
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return articles[:max_diverse]
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return diverse_articles
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def query_chroma(question, n_results=10):
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@ -180,19 +179,19 @@ def query_chroma(question, n_results=10):
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"""
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if not client:
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return {"error": "ChromaDB connection failed"}
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try:
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# Get embedding for the question
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query_embedding = get_embedding(question)
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if not query_embedding:
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return {"error": "Failed to get embedding"}
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# Query the collection
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results = client.get_or_create_collection("news").query(
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query_embeddings=[query_embedding],
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n_results=n_results,
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)
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return results
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except Exception as e:
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logger.error(f"Error querying ChromaDB: {e}")
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@ -205,16 +204,16 @@ def query_vector_database():
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"""Query the vector database"""
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data = request.get_json()
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question = data.get("question")
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if not question:
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return jsonify({"error": "Missing 'question' in request body"}), 400
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# Query ChromaDB for relevant articles
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results = query_chroma(question, n_results=10)
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if "error" in results:
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return jsonify({"error": results["error"]}), 500
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# Process results to create diverse article set
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mcp_results = []
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for doc, score, meta in zip(
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@ -226,10 +225,10 @@ def query_vector_database():
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"score": float(score),
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"metadata": meta
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})
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# Apply diversity filtering
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diverse_results = get_diverse_articles(mcp_results, 5)
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return jsonify({"results": diverse_results})
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@app.route("/articles/query", methods=["POST"])
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@ -238,16 +237,16 @@ def query_articles():
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data = request.get_json()
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question = data.get("question")
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max_results = data.get("max_results", 5)
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if not question:
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return jsonify({"error": "Missing 'question' in request body"}), 400
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# Query ChromaDB for relevant articles
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results = query_chroma(question, n_results=max_results)
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if "error" in results:
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return jsonify({"error": results["error"]}), 500
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# Process results to create diverse article set
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mcp_results = []
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for doc, score, meta in zip(
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@ -259,10 +258,10 @@ def query_articles():
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"score": float(score),
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"metadata": meta
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})
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# Apply diversity filtering
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diverse_results = get_diverse_articles(mcp_results, max_results)
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return jsonify({
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"results": diverse_results,
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"query": question
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@ -284,7 +283,7 @@ def get_latest_articles(field):
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}
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}
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]
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return jsonify({
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"results": sample_articles,
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"field": field
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@ -296,7 +295,7 @@ def get_company_facts(company_name):
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facts = company_facts.get(company_name, {})
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if not facts:
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return jsonify({"error": f"Company {company_name} not found"}), 404
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return jsonify({
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"company": company_name,
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"facts": facts
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@ -307,10 +306,10 @@ def get_company_products(company_name):
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"""Get products information for a company"""
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facts = company_facts.get(company_name, {})
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products = facts.get("products", [])
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if not products:
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return jsonify({"error": f"No products found for company {company_name}"}), 404
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return jsonify({
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"company": company_name,
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"products": products
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@ -322,13 +321,13 @@ def update_company_facts():
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data = request.get_json()
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company_name = data.get("company_name")
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facts = data.get("facts")
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if not company_name or not facts:
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return jsonify({"error": "Missing 'company_name' or 'facts' in request body"}), 400
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# Update or add company facts
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company_facts[company_name] = facts
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return jsonify({
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"message": "Facts updated successfully",
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"company": company_name,
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12
README.md
12
README.md
@ -131,9 +131,19 @@ docker run -p 5008:5008 stockdocs-article-server
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# Continue for other components...
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```
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## Tests
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```bash
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pip install -r scraper/requirements_clean.txt pytest
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pytest tests/ -v
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```
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Unit tests cover the scraper's article-processing cache: load/save
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roundtrips, processing status tracking, and progress computation.
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## Requirements
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- Python 3.6+
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- Python 3.9+
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- Flask 2.3.3
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- Various NLP and ML libraries
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- Docker (for containerized deployment)
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277
agent
277
agent
@ -1,277 +0,0 @@
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# StockDocs Project Overview
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This document provides a comprehensive overview of the StockDocs financial news analysis platform, including detailed information about each project component, file structures, and how to begin working with the system.
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## Project Architecture
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StockDocs is a sophisticated system designed to collect, process, and analyze financial news from multiple sources. The platform consists of several interconnected components that work together to transform raw news content into valuable market intelligence.
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```
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+--------------+ +--------------+ +-------------------+
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| Scraper | | Article | | AI |
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| (RSS Feeds) |--> | Server |--> | Processor |
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| | | | | |
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+--------------+ +--------------+ +-------------------+
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| |
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v v
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+--------------+ +-------------------+
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| Embedding | | MCPServer |
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| Service | | (Financial Data) |
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| | | |
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+--------------+ +-------------------+
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```
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## Project Components
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### 1. Scraper
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**Location:** `scraper/`
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The Scraper component is responsible for collecting financial news and articles from multiple sources including major news outlets, financial publications, and market analysis services. It uses RSS feeds to gather content and stores the articles in a structured directory hierarchy for easy access by other components of the system.
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#### Key Features:
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- RSS Feed Integration: Supports multiple financial news sources through RSS feeds
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- Automated Scraping: Regularly fetches and processes new articles from configured feeds
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- Structured Storage: Organizes articles in a directory structure by news outlet
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- Duplicate Detection: Prevents re-processing of already collected articles
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- Caching Mechanism: Maintains a cache of processed articles to optimize performance
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#### File Structure:
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```
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scraper/
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├── rss_feeds.json # Configuration file with RSS feed URLs
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├── scraper.py # Main scraping logic
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├── requirements.txt # Python dependencies
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├── dockerfile # Docker configuration
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├── dockerfile-selenium # Dockerfile for selenium-based scraping
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├── articles/ # Directory where scraped articles are stored
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│ ├── Reuters – Business News/
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│ │ ├── article1.txt
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│ │ └── ...
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│ ├── Associated Press – Business/
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│ │ ├── article1.txt
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│ │ └── ...
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│ └── ...
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├── processed_articles_cache.json # Cache of already processed articles
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└── pyvenv.cfg # Python virtual environment configuration
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```
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#### Key Files:
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- `scraper.py`: Main scraping logic with parallel processing capabilities
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- `rss_feeds.json`: Configuration file with RSS feed URLs for 60+ news outlets
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- `articles/`: Directory structure for storing scraped articles organized by news source
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### 2. Article Server
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**Location:** `articleServer/`
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A Flask-based HTTP server that provides access to news articles stored in a directory structure, with time-based filtering and outlet-specific querying capabilities.
|
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#### Key Features:
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- Time-based filtering: Query articles by hour, day, week, or month
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- Outlet-specific querying: Filter articles by news source
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- Content retrieval: Get full article content by file path
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- RESTful API: Clean interfaces for integration with external systems
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#### File Structure:
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```
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articleServer/
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├── app.py # Main Flask application
|
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├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
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||||
├── run_server.py # Server startup script
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├── pyvenv.cfg # Python virtual environment configuration
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||||
└── README.md # Documentation
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```
|
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#### API Endpoints:
|
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- `GET /articles` - Get articles within time range
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- `GET /article/content` - Get full article content by path
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- `GET /outlets` - List all available news outlets
|
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- `GET /health` - Health check endpoint
|
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|
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### 3. AI Processor
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**Location:** `ai_processor/`
|
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|
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An AI-powered analytics engine designed to process financial news articles and extract meaningful insights, sentiment analysis, and market indicators from collected content.
|
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|
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#### Key Features:
|
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- Sentiment Analysis: Determine positive, negative, or neutral sentiment of news articles
|
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- Topic Classification: Categorize articles by financial topics
|
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- Entity Extraction: Identify key entities mentioned in articles (stocks, companies, people, organizations)
|
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- Market Indicator Detection: Extract quantitative indicators that may affect stock prices
|
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- Insight Generation: Automated generation of actionable intelligence from news content
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- Batch Processing: Process large volumes of articles efficiently
|
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|
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#### File Structure:
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```
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ai_processor/
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├── ai_processor.py # Main AI processing application
|
||||
├── requirements.txt # Python dependencies
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├── Dockerfile # Docker configuration
|
||||
├── README.md # Documentation
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├── models/ # Machine learning models and NLP components
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│ ├── sentiment_analyzer.py # Sentiment analysis module
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│ ├── topic_classifier.py # News categorization module
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│ └── entity_extractor.py # Named entity recognition
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├── processors/ # Article processing pipelines
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│ ├── text_processor.py # Text cleaning and preprocessing
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│ └── analysis_pipeline.py # Full analysis pipeline
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└── output/ # Processed data storage
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├── insights/
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└── reports/
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```
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|
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#### API Endpoints:
|
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- `POST /api/analyze/article` - Analyze a single article for insights
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- `POST /api/analyze/batch` - Process multiple articles in batch mode
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- `GET /api/insights/latest` - Get latest analysis insights
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- `GET /api/models` - List available AI models
|
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### 4. Embedding Service
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**Location:** `embedding/`
|
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|
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An embedding service that converts text content into numerical vectors for machine learning and data analysis purposes. This component transforms financial news articles into vector representations that can be used for similarity comparisons, clustering, and other AI tasks.
|
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|
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#### Key Features:
|
||||
- Multiple Model Support: Integrates with various embedding models including BERT, Sentence-BERT, and other transformer-based models
|
||||
- Batch Processing: Efficient processing of large volumes of articles
|
||||
- Caching Mechanism: Caches generated embeddings to avoid reprocessing
|
||||
- Real-time Generation: Generates embeddings on-demand for new content
|
||||
- Vector Similarity: Computes similarity between different pieces of content
|
||||
- Storage Management: Organizes and stores embeddings efficiently
|
||||
|
||||
#### File Structure:
|
||||
```
|
||||
embedding/
|
||||
├── embedder.py # Main embedding application
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── README.md # Documentation
|
||||
├── models/ # Pre-trained embedding models
|
||||
│ ├── sentence_transformer.py # Sentence transformer implementation
|
||||
│ └── model_loader.py # Model loading utilities
|
||||
├── processors/ # Text processing pipeline
|
||||
│ ├── text_cleaner.py # Text cleaning and preprocessing
|
||||
│ └── embedding_generator.py # Embedding generation
|
||||
└── data/ # Processed embeddings storage
|
||||
├── cache/
|
||||
└── outputs/
|
||||
```
|
||||
|
||||
#### API Endpoints:
|
||||
- `POST /api/embeddings/generate` - Generate embeddings for text content
|
||||
- `POST /api/embeddings/batch` - Generate embeddings for multiple texts in batch
|
||||
- `GET /api/embeddings/similarity` - Calculate similarity between two pieces of text/content
|
||||
|
||||
### 5. MCPServer
|
||||
**Location:** `MCPServer/`
|
||||
|
||||
A Flask-based server that provides an API for accessing and analyzing financial data, with integration for stock analysis and news processing. Serves as the backend service for processing financial information and making it available through HTTP endpoints.
|
||||
|
||||
#### Key Features:
|
||||
- Stock Analysis: Financial metrics calculation and market data processing
|
||||
- News Integration: APIs for retrieving and processing financial news
|
||||
- Data Aggregation: Consolidation of multiple data sources into unified responses
|
||||
- RESTful API: Clean HTTP interface for external services to consume data
|
||||
|
||||
#### File Structure:
|
||||
```
|
||||
MCPServer/
|
||||
├── server.py # Main Flask application
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── openapi.json # API specification
|
||||
├── README.md # Documentation
|
||||
└── api/ # API endpoints and handlers
|
||||
├── stock_analysis.py # Stock analysis functions
|
||||
└── news_processing.py # News processing functions
|
||||
```
|
||||
|
||||
#### API Endpoints:
|
||||
- `POST /query` - Query the vector database with a question
|
||||
- `GET /health` - Server health check
|
||||
- `GET /info` - Returns model and service metadata
|
||||
- `GET /tools` - List available endpoints and their descriptions
|
||||
|
||||
## Setup and Installation
|
||||
|
||||
### Prerequisites
|
||||
- Python 3.6+
|
||||
- Docker (for containerized deployment)
|
||||
- Internet connection for RSS feed access
|
||||
|
||||
### Installation Steps
|
||||
|
||||
1. **Clone the repository:**
|
||||
```bash
|
||||
git clone <repository-url>
|
||||
cd StockDocs
|
||||
```
|
||||
|
||||
2. **Set up each component:**
|
||||
```bash
|
||||
# For each component, follow specific installation instructions
|
||||
cd scraper && pip install -r requirements.txt
|
||||
cd articleServer && pip install -r requirements.txt
|
||||
cd ai_processor && pip install -r requirements.txt
|
||||
cd embedding && pip install -r requirements.txt
|
||||
cd MCPServer && pip install -r requirements.txt
|
||||
```
|
||||
|
||||
3. **Configure environment variables as needed for each component**
|
||||
|
||||
4. **Run individual services:**
|
||||
```bash
|
||||
python scraper/scraper.py # Start scraping
|
||||
python articleServer/run_server.py # Start article server
|
||||
python ai_processor/app.py # Start AI processor
|
||||
python embedding/app.py # Start embedding service
|
||||
python MCPServer/app.py # Start MCP server
|
||||
```
|
||||
|
||||
## Deployment
|
||||
|
||||
Each component can be run independently or containerized using the provided Dockerfiles:
|
||||
```bash
|
||||
# Build and run each component in Docker
|
||||
docker build -t stockdocs-scraper ./scraper
|
||||
docker run -p 5000:5000 stockdocs-scraper
|
||||
|
||||
docker build -t stockdocs-article-server ./articleServer
|
||||
docker run -p 5008:5008 stockdocs-article-server
|
||||
|
||||
# Continue for other components...
|
||||
```
|
||||
|
||||
## Key Technical Details
|
||||
|
||||
### Data Flow
|
||||
1. **Scraper** collects articles from RSS feeds and stores them in `scraper/articles/`
|
||||
2. **Article Server** provides API access to these articles
|
||||
3. **AI Processor** analyzes articles and generates insights in `ai_processor/output/`
|
||||
4. **Embedding Service** converts article content into vector representations and stores in ChromaDB
|
||||
5. **MCPServer** provides API access to the vector database for querying
|
||||
|
||||
### Environment Variables
|
||||
Each component may require specific environment variables:
|
||||
- `ARTICLE_DIR`: Path to article directory
|
||||
- `AI_SERVICE_URL`: URL for local AI service
|
||||
- `CHROMADB_HOST` and `CHROMADB_PORT`: ChromaDB connection settings
|
||||
- `FEED_FILE`: Path to RSS feed configuration file
|
||||
|
||||
### Directory Structure
|
||||
- `scraper/articles/`: Stores raw scraped articles organized by news source
|
||||
- `ai_processor/output/`: Stores processed AI analysis results
|
||||
- `embedding/data/cache/`: Stores cached embeddings
|
||||
- `MCPServer/`: Contains server configuration and API endpoints
|
||||
|
||||
## Getting Started Guide
|
||||
|
||||
To begin working with the StockDocs platform:
|
||||
|
||||
1. **Start the Scraper** to collect news articles
|
||||
2. **Run the Article Server** to make articles accessible via API
|
||||
3. **Launch the AI Processor** to analyze articles and generate insights
|
||||
4. **Initialize the Embedding Service** to create vector representations
|
||||
5. **Start MCPServer** to query the vector database for insights
|
||||
|
||||
Each component can be run independently or as part of a complete pipeline for comprehensive financial news analysis.
|
||||
304
agent.md
304
agent.md
@ -1,304 +0,0 @@
|
||||
# StockDocs Project Overview
|
||||
|
||||
This document provides a comprehensive overview of the StockDocs repository, which contains multiple interconnected projects for processing, scraping, and serving financial articles and embeddings.
|
||||
|
||||
## Repository Structure
|
||||
|
||||
The repository contains 5 main projects:
|
||||
|
||||
1. **ai_processor/** - AI processing component
|
||||
2. **articleServer/** - Article serving component
|
||||
3. **embedding/** - Embedding functionality
|
||||
4. **MCPServer/** - MCP server component
|
||||
5. **scraper/** - Web scraping component
|
||||
|
||||
## Project Details
|
||||
|
||||
### 1. ai_processor/
|
||||
The AI processing component handles artificial intelligence operations for processing articles and generating insights.
|
||||
|
||||
**Key Files:**
|
||||
- `ai_processor.py` - Main AI processing logic
|
||||
- `requirements.txt` - Python dependencies
|
||||
- `Dockerfile` - Container configuration
|
||||
|
||||
**Purpose:** Processes articles using AI models to extract key information, generate summaries, and create embeddings.
|
||||
|
||||
**Detailed Structure:**
|
||||
```
|
||||
ai_processor/
|
||||
├── app.py # Main AI processing application
|
||||
├── config.py # Configuration settings
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── README.md # This file
|
||||
├── models/ # Machine learning models and NLP components
|
||||
│ ├── __init__.py
|
||||
│ ├── sentiment_analyzer.py # Sentiment analysis module
|
||||
│ ├── topic_classifier.py # News categorization module
|
||||
│ └── entity_extractor.py # Named entity recognition
|
||||
├── processors/ # Article processing pipelines
|
||||
│ ├── __init__.py
|
||||
│ ├── text_processor.py # Text cleaning and preprocessing
|
||||
│ └── analysis_pipeline.py # Full analysis pipeline
|
||||
└── data/ # Processed data storage
|
||||
├── insights/
|
||||
└── reports/
|
||||
```
|
||||
|
||||
**Endpoints:**
|
||||
- POST `/api/analyze/article` - Analyze a single article for insights
|
||||
- POST `/api/analyze/batch` - Process multiple articles in batch mode
|
||||
- GET `/api/analyze/status/{task_id}` - Check processing status
|
||||
- GET `/api/insights/latest` - Get latest analysis insights
|
||||
- GET `/api/insights/articles/{article_path}` - Get insights for specific article
|
||||
- GET `/api/reports/generate` - Generate comprehensive market analysis report
|
||||
- GET `/api/models` - List available AI models
|
||||
- POST `/api/models/update` - Update or retrain models with new data
|
||||
|
||||
### 2. articleServer/
|
||||
The article serving component provides an API for accessing processed articles.
|
||||
|
||||
**Key Files:**
|
||||
- `app.py` - Main Flask application
|
||||
- `run_server.py` - Server startup script
|
||||
- `requirements.txt` - Python dependencies
|
||||
- `Dockerfile` - Container configuration
|
||||
|
||||
**Purpose:** Exposes processed articles through a REST API for client applications to consume.
|
||||
|
||||
**Detailed Structure:**
|
||||
```
|
||||
articleServer/
|
||||
├── app.py # Main Flask application
|
||||
├── run_server.py # Server startup script
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── README.md # This file
|
||||
└── templates/ # HTML templates (if any)
|
||||
```
|
||||
|
||||
**Endpoints:**
|
||||
- GET `/articles` - Get articles within time range
|
||||
- GET `/article/content` - Get full article content by path
|
||||
- GET `/outlets` - List all available news outlets
|
||||
- GET `/health` - Health check endpoint
|
||||
|
||||
### 3. embedding/
|
||||
The embedding functionality handles vector embeddings for articles and documents.
|
||||
|
||||
**Key Files:**
|
||||
- `embedder.py` - Embedding generation logic
|
||||
- `requirements.txt` - Python dependencies
|
||||
- `Dockerfile` - Container configuration
|
||||
|
||||
**Purpose:** Converts articles into vector embeddings for semantic search and similarity operations.
|
||||
|
||||
**Detailed Structure:**
|
||||
```
|
||||
embedding/
|
||||
├── app.py # Main embedding application
|
||||
├── config.py # Configuration settings
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── README.md # This file
|
||||
├── models/ # Pre-trained embedding models
|
||||
│ ├── __init__.py
|
||||
│ ├── sentence_transformer.py # Sentence transformer implementation
|
||||
│ └── model_loader.py # Model loading utilities
|
||||
├── processors/ # Text processing pipeline
|
||||
│ ├── __init__.py
|
||||
│ ├── text_cleaner.py # Text cleaning and preprocessing
|
||||
│ └── embedding_generator.py # Embedding generation
|
||||
└── data/ # Processed embeddings storage
|
||||
├── cache/
|
||||
└── outputs/
|
||||
```
|
||||
|
||||
**Endpoints:**
|
||||
- POST `/api/embeddings/generate` - Generate embeddings for text content
|
||||
- POST `/api/embeddings/batch` - Generate embeddings for multiple texts in batch
|
||||
- GET `/api/embeddings/similarity` - Calculate similarity between two pieces of text/content
|
||||
- GET `/api/embeddings/status` - Check service status
|
||||
- GET `/api/embeddings/models` - List available embedding models
|
||||
- DELETE `/api/embeddings/cache/clear` - Clear the embedding cache
|
||||
|
||||
### 4. MCPServer/
|
||||
The MCP (Model Context Protocol) server component provides external API access.
|
||||
|
||||
**Key Files:**
|
||||
- `server.py` - Main MCP server implementation
|
||||
- `openapi.json` - API specification
|
||||
- `requirements.txt` - Python dependencies
|
||||
- `Dockerfile` - Container configuration
|
||||
|
||||
**Purpose:** Exposes functionality through the Model Context Protocol for integration with other systems.
|
||||
|
||||
**Detailed Structure:**
|
||||
```
|
||||
MCPServer/
|
||||
├── server.py # Main Flask application
|
||||
├── config.py # Configuration settings
|
||||
├── requirements.txt # Python dependencies
|
||||
├── Dockerfile # Docker configuration
|
||||
├── README.md # This file
|
||||
└── api/ # API endpoints and handlers
|
||||
├── __init__.py
|
||||
├── stock_analysis.py # Stock analysis functions
|
||||
└── news_processing.py # News processing functions
|
||||
```
|
||||
|
||||
**Endpoints:**
|
||||
- GET `/api/stock/metrics` - Get financial metrics for a stock
|
||||
- GET `/api/stock/history` - Get historical price data
|
||||
- POST `/api/stock/analyze` - Perform comprehensive stock analysis
|
||||
- GET `/api/news` - Retrieve news articles related to stocks
|
||||
- GET `/api/news/outlets` - List available news sources
|
||||
- POST `/api/news/process` - Process and categorize news content
|
||||
- GET `/api/data/refresh` - Refresh data from sources
|
||||
- GET `/api/status` - Server health check
|
||||
|
||||
### 5. scraper/
|
||||
The web scraping component collects articles from various sources.
|
||||
|
||||
**Key Files:**
|
||||
- `scraper.py` - Main scraping logic
|
||||
- `rss_feeds.json` - RSS feed configuration
|
||||
- `requirements.txt` - Python dependencies
|
||||
- `Dockerfile` - Container configuration
|
||||
|
||||
**Purpose:** Collects financial articles from various sources including RSS feeds and web scraping.
|
||||
|
||||
**Detailed Structure:**
|
||||
```
|
||||
scraper/
|
||||
├── rss_feeds.json # Configuration file with RSS feed URLs
|
||||
├── scraper.py # Main scraping logic
|
||||
├── requirements.txt # Python dependencies
|
||||
├── dockerfile # Docker configuration
|
||||
├── dockerfile-selenium # Dockerfile for selenium-based scraping
|
||||
├── articles/ # Directory where scraped articles are stored
|
||||
│ ├── Reuters – Business News/
|
||||
│ │ ├── article1.txt
|
||||
│ │ └── ...
|
||||
│ ├── Associated Press – Business/
|
||||
│ │ ├── article1.txt
|
||||
│ │ └── ...
|
||||
│ └── ...
|
||||
├── processed_articles_cache.json # Cache of already processed articles
|
||||
└── pyvenv.cfg # Python virtual environment configuration
|
||||
```
|
||||
|
||||
**Features:**
|
||||
- RSS Feed Integration: Supports multiple financial news sources through RSS feeds
|
||||
- Automated Scraping: Regularly fetches and processes new articles from configured feeds
|
||||
- Structured Storage: Organizes articles in a directory structure by news outlet
|
||||
- Duplicate Detection: Prevents re-processing of already collected articles
|
||||
- Caching Mechanism: Maintains a cache of processed articles to optimize performance
|
||||
|
||||
## Getting Started
|
||||
|
||||
### Prerequisites
|
||||
- Docker installed
|
||||
- Python 3.8+
|
||||
- Git
|
||||
|
||||
### Setup Instructions
|
||||
|
||||
1. **Clone the repository:**
|
||||
```bash
|
||||
git clone <repository-url>
|
||||
cd StockDocs
|
||||
```
|
||||
|
||||
2. **Build and run containers:**
|
||||
```bash
|
||||
docker-compose up --build
|
||||
```
|
||||
|
||||
3. **Project-specific setup:**
|
||||
- Each project has its own `README.md` with detailed setup instructions
|
||||
- Check individual project directories for specific requirements
|
||||
|
||||
## Project Dependencies
|
||||
|
||||
### Common Dependencies
|
||||
- Python 3.8+
|
||||
- Docker
|
||||
- Various Python packages (listed in requirements.txt files)
|
||||
|
||||
### Inter-project Relationships
|
||||
- `scraper/` feeds articles to `ai_processor/`
|
||||
- `ai_processor/` generates embeddings that `embedding/` processes
|
||||
- `articleServer/` serves articles processed by `ai_processor/`
|
||||
- `MCPServer/` provides API access to all components
|
||||
|
||||
## Development Workflow
|
||||
|
||||
1. **Start all services:**
|
||||
```bash
|
||||
docker-compose up --build
|
||||
```
|
||||
|
||||
2. **Work with individual projects:**
|
||||
- Navigate to project directory
|
||||
- Check `README.md` for specific instructions
|
||||
- Make changes and rebuild as needed
|
||||
|
||||
3. **Testing:**
|
||||
- Each project includes its own testing setup
|
||||
- Integration tests may be needed for cross-project functionality
|
||||
|
||||
## API Endpoints
|
||||
|
||||
### articleServer/
|
||||
- `/articles` - Get all articles
|
||||
- `/articles/<id>` - Get specific article
|
||||
- `/search` - Search articles by query
|
||||
|
||||
### MCPServer/
|
||||
- Exposes various endpoints through Model Context Protocol
|
||||
- See `openapi.json` for complete specification
|
||||
|
||||
## Configuration
|
||||
|
||||
### Environment Variables
|
||||
Each project may require specific environment variables. Check individual `README.md` files for details.
|
||||
|
||||
### Authentication
|
||||
The system now supports API key authentication for connections to the centralized AI service at `http://example.com:4000`. To enable authentication:
|
||||
|
||||
1. Set the `AI_SERVICE_API_KEY` environment variable with your API key
|
||||
2. All connections to the AI service will automatically include the `Authorization: Bearer {api_key}` header
|
||||
|
||||
### Data Storage
|
||||
- Articles are stored in the scraper component
|
||||
- Processed data flows through the ai_processor
|
||||
- Embeddings are generated and stored in the embedding component
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
1. **Docker build failures:** Ensure Docker is running and check `Dockerfile` syntax
|
||||
2. **Python dependency issues:** Run `pip install -r requirements.txt` in each project
|
||||
3. **Port conflicts:** Check `docker-compose.yml` for port mappings
|
||||
4. **Service startup issues:** Check individual project logs
|
||||
|
||||
### Logs
|
||||
- View logs with `docker-compose logs <service-name>`
|
||||
- Check individual project logs for detailed error information
|
||||
|
||||
## Contributing
|
||||
|
||||
1. Fork the repository
|
||||
2. Create feature branch
|
||||
3. Make changes
|
||||
4. Test thoroughly
|
||||
5. Submit pull request
|
||||
|
||||
## Support
|
||||
|
||||
For issues or questions, please check:
|
||||
- Individual project README.md files
|
||||
- Docker logs for runtime errors
|
||||
- GitHub issues for known problems
|
||||
@ -2,4 +2,4 @@
|
||||
AI Processor for extracting facts from articles and preparing them for embedding.
|
||||
This module handles the intelligent processing of news articles to extract structured facts
|
||||
that can be used for querying and analysis.
|
||||
"""
|
||||
"""
|
||||
|
||||
@ -60,7 +60,7 @@ class ArticleProcessor:
|
||||
scraper_dir = alt_path
|
||||
break
|
||||
else:
|
||||
logger.error(f"No valid articles directory found")
|
||||
logger.error("No valid articles directory found")
|
||||
return []
|
||||
|
||||
# Log cache state before scanning
|
||||
@ -69,7 +69,7 @@ class ArticleProcessor:
|
||||
f"Cache state before scanning: {cache_stats['processed_files']} files marked as processed"
|
||||
)
|
||||
|
||||
logger.info(f"Directory exists, walking through files...")
|
||||
logger.info("Directory exists, walking through files...")
|
||||
file_count = 0
|
||||
article_file_count = 0
|
||||
already_processed_count = 0
|
||||
@ -136,7 +136,7 @@ class ArticleProcessor:
|
||||
try:
|
||||
logger.debug(f"Processing article file: {filename}")
|
||||
logger.debug(f"File path: {file_path}")
|
||||
|
||||
|
||||
with open(file_path, "r", encoding="utf-8") as f:
|
||||
article_data = json.load(f)
|
||||
|
||||
|
||||
@ -6,7 +6,7 @@ import json
|
||||
import logging
|
||||
import os
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional
|
||||
from typing import Dict, List
|
||||
|
||||
from config import CACHE_FILE
|
||||
|
||||
@ -31,7 +31,7 @@ class CacheManager:
|
||||
|
||||
try:
|
||||
logger.debug(f"Attempting to load cache from: {self.cache_file}")
|
||||
|
||||
|
||||
if not os.path.exists(self.cache_file):
|
||||
logger.info(
|
||||
f"Cache file does not exist: {self.cache_file}. Creating new empty cache."
|
||||
@ -104,7 +104,7 @@ class CacheManager:
|
||||
|
||||
with open(self.cache_file, "w", encoding="utf-8") as f:
|
||||
json.dump(self.cache, f, indent=2, ensure_ascii=False)
|
||||
|
||||
|
||||
logger.debug(f"Successfully saved cache to {self.cache_file}")
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving cache file {self.cache_file}: {e}")
|
||||
|
||||
@ -32,4 +32,4 @@ CHROMADB_PORT = int(os.getenv("CHROMADB_PORT", "8000"))
|
||||
|
||||
# Collection names
|
||||
FACTS_COLLECTION_NAME = "facts"
|
||||
ARTICLES_COLLECTION_NAME = "articles"
|
||||
ARTICLES_COLLECTION_NAME = "articles"
|
||||
|
||||
@ -6,7 +6,7 @@ Uses the gpt-oss model via the centralized AI service.
|
||||
import json
|
||||
import logging
|
||||
import requests
|
||||
from typing import Dict, Any, Optional
|
||||
from typing import Dict, Any
|
||||
|
||||
from config import AI_SERVER_URL, AI_SERVICE_API_KEY, FACT_EXTRACTION_MODEL
|
||||
from metrics_collector import metrics_collector
|
||||
@ -15,12 +15,12 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
class FactExtractor:
|
||||
"""Extracts structured facts from article content using AI models."""
|
||||
|
||||
|
||||
def __init__(self):
|
||||
self.ai_server_url = AI_SERVER_URL
|
||||
self.api_key = AI_SERVICE_API_KEY
|
||||
self.model = FACT_EXTRACTION_MODEL
|
||||
|
||||
|
||||
def _get_headers(self) -> Dict[str, str]:
|
||||
"""Get headers with authentication."""
|
||||
headers = {
|
||||
@ -29,36 +29,36 @@ class FactExtractor:
|
||||
if self.api_key:
|
||||
headers["Authorization"] = f"Bearer {self.api_key}"
|
||||
return headers
|
||||
|
||||
|
||||
def extract_facts_from_article(self, article_content: str, title: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Extract structured facts from article content using gpt-oss model.
|
||||
|
||||
|
||||
Args:
|
||||
article_content (str): The full content of the article
|
||||
title (str): The title of the article
|
||||
|
||||
|
||||
Returns:
|
||||
Dict containing extracted facts
|
||||
"""
|
||||
try:
|
||||
extraction_url = f"{self.ai_server_url}/v1/chat/completions"
|
||||
|
||||
|
||||
# Create a proper prompt for fact extraction
|
||||
prompt = f"""
|
||||
Extract key facts from the following article in structured JSON format.
|
||||
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
|
||||
|
||||
|
||||
Format the response as a JSON object with these fields:
|
||||
{{
|
||||
"title": "{title}",
|
||||
@ -70,12 +70,12 @@ class FactExtractor:
|
||||
"main_points": ["point1", "point2", "point3"]
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
# Log the request details for debugging
|
||||
logger.debug(f"Preparing AI request for article: {title}")
|
||||
logger.debug(f"AI Server URL: {extraction_url}")
|
||||
logger.debug(f"Request payload preview: {str({'model': self.model, 'messages': [{'role': 'system', 'content': 'You are a helpful assistant that extracts structured facts from articles.'}, {'role': 'user', 'content': prompt[:200]}]}[:300])}...")
|
||||
|
||||
|
||||
# Call the AI service with gpt-oss model for fact extraction
|
||||
try:
|
||||
response = requests.post(
|
||||
@ -92,12 +92,12 @@ class FactExtractor:
|
||||
headers=self._get_headers(),
|
||||
timeout=60
|
||||
)
|
||||
|
||||
|
||||
# Log response details for debugging
|
||||
logger.debug(f"AI service response status: {response.status_code}")
|
||||
logger.debug(f"AI service response headers: {dict(response.headers)}")
|
||||
logger.debug(f"AI service response text preview: {response.text[:500]}...")
|
||||
|
||||
|
||||
response.raise_for_status()
|
||||
except requests.exceptions.RequestException as e:
|
||||
logger.error(f"REQUEST FAILED for article '{title}' - URL: {extraction_url}")
|
||||
@ -106,7 +106,7 @@ class FactExtractor:
|
||||
logger.error(f"Response text (if available): {response.text[:500] if 'response' in locals() else 'No response available'}")
|
||||
# Return basic structure if request fails
|
||||
return self._create_basic_fact_structure(article_content, title)
|
||||
|
||||
|
||||
# Parse the response
|
||||
try:
|
||||
result = response.json()
|
||||
@ -121,7 +121,7 @@ class FactExtractor:
|
||||
logger.error(f"Article content preview: {article_content[:200]}...")
|
||||
# Return basic structure if response parsing fails
|
||||
return self._create_basic_fact_structure(article_content, title)
|
||||
|
||||
|
||||
# Check if the response is empty or invalid
|
||||
if not extracted_text or extracted_text.strip() == "":
|
||||
logger.warning(f"Empty response from AI service for article '{title}'")
|
||||
@ -141,21 +141,21 @@ class FactExtractor:
|
||||
logger.error(f"Response status: {response.status_code}")
|
||||
logger.error(f"Response text (full): {response.text}")
|
||||
facts = self._create_basic_fact_structure(article_content, title)
|
||||
|
||||
|
||||
# Ensure all required fields are present
|
||||
facts = self._ensure_required_fields(facts, title, article_content)
|
||||
|
||||
|
||||
metrics_collector.increment_facts_extracted()
|
||||
logger.info(f"Successfully extracted facts from article: {title}")
|
||||
return facts
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"UNEXPECTED ERROR extracting facts from article '{title}': {e}")
|
||||
logger.error(f"Error type: {type(e).__name__}")
|
||||
logger.error(f"Article content preview: {article_content[:200]}...")
|
||||
# Return basic structure if extraction fails
|
||||
return self._create_basic_fact_structure(article_content, title)
|
||||
|
||||
|
||||
def _create_basic_fact_structure(self, article_content: str, title: str) -> Dict[str, Any]:
|
||||
"""Create a basic fact structure when AI extraction fails."""
|
||||
return {
|
||||
@ -171,7 +171,7 @@ class FactExtractor:
|
||||
"Third key fact from the article"
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
def _ensure_required_fields(self, facts: Dict[str, Any], title: str, article_content: str) -> Dict[str, Any]:
|
||||
"""Ensure all required fields are present in the facts structure."""
|
||||
required_fields = {
|
||||
@ -183,11 +183,11 @@ class FactExtractor:
|
||||
"key_dates": [],
|
||||
"main_points": []
|
||||
}
|
||||
|
||||
|
||||
for field, default_value in required_fields.items():
|
||||
if field not in facts:
|
||||
facts[field] = default_value
|
||||
elif not facts[field]: # If field is empty
|
||||
facts[field] = default_value
|
||||
|
||||
return facts
|
||||
|
||||
return facts
|
||||
|
||||
@ -6,11 +6,9 @@ Handles the orchestration of article processing and fact extraction.
|
||||
import logging
|
||||
import sys
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
from article_processor import ArticleProcessor
|
||||
from metrics_collector import metrics_collector
|
||||
from cache_manager import CacheManager
|
||||
from config import CACHE_FILE, LOG_FILE, LOG_LEVEL
|
||||
|
||||
# Setup logging
|
||||
@ -31,7 +29,7 @@ def setup_logging():
|
||||
log_dir = os.path.dirname(LOG_FILE)
|
||||
if log_dir:
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
|
||||
|
||||
# Ensure output directory exists for cache files
|
||||
output_dir = os.path.dirname(CACHE_FILE)
|
||||
if output_dir:
|
||||
@ -40,18 +38,18 @@ if output_dir:
|
||||
def main():
|
||||
"""Main function to run the AI processor."""
|
||||
logger.info("Starting AI Processor")
|
||||
|
||||
|
||||
try:
|
||||
# Setup logging
|
||||
setup_logging()
|
||||
|
||||
|
||||
# Create processor instance
|
||||
processor = ArticleProcessor()
|
||||
|
||||
|
||||
# Process all articles
|
||||
logger.info("Starting article processing...")
|
||||
stats = processor.process_all_articles()
|
||||
|
||||
|
||||
# Log final statistics
|
||||
logger.info("Processing completed")
|
||||
logger.info(f"Total processed: {stats['total_processed']}")
|
||||
@ -61,13 +59,13 @@ def main():
|
||||
logger.info(f"Cache stats: {stats['cache_stats']}")
|
||||
else:
|
||||
logger.info("No cache stats available")
|
||||
|
||||
|
||||
# Print metrics summary
|
||||
metrics_summary = metrics_collector.get_metrics_summary()
|
||||
logger.info(f"Metrics summary: {metrics_summary}")
|
||||
|
||||
|
||||
logger.info("AI Processor completed successfully")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in main function: {e}")
|
||||
raise
|
||||
@ -75,15 +73,15 @@ def main():
|
||||
def process_new_articles():
|
||||
"""Process only new articles (for real-time processing)."""
|
||||
logger.info("Starting real-time processing of new articles")
|
||||
|
||||
|
||||
try:
|
||||
processor = ArticleProcessor()
|
||||
stats = processor.process_new_articles()
|
||||
|
||||
|
||||
logger.info("Real-time processing completed")
|
||||
logger.info(f"Total processed: {stats['total_processed']}")
|
||||
logger.info(f"Total failed: {stats['total_failed']}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in real-time processing: {e}")
|
||||
raise
|
||||
|
||||
@ -20,38 +20,38 @@ except Exception as e:
|
||||
|
||||
# AI Processor Metrics
|
||||
articles_processed_total = Counter(
|
||||
'ai_processor_articles_processed_total',
|
||||
'ai_processor_articles_processed_total',
|
||||
'Total number of articles processed by AI processor'
|
||||
)
|
||||
|
||||
articles_failed_total = Counter(
|
||||
'ai_processor_articles_failed_total',
|
||||
'ai_processor_articles_failed_total',
|
||||
'Total number of articles failed to process by AI processor'
|
||||
)
|
||||
|
||||
facts_extracted_total = Counter(
|
||||
'ai_processor_facts_extracted_total',
|
||||
'ai_processor_facts_extracted_total',
|
||||
'Total number of facts extracted by AI processor'
|
||||
)
|
||||
|
||||
processing_time_seconds = Histogram(
|
||||
'ai_processor_processing_time_seconds',
|
||||
'ai_processor_processing_time_seconds',
|
||||
'Time spent processing articles in AI processor'
|
||||
)
|
||||
|
||||
cache_hits_total = Counter(
|
||||
'ai_processor_cache_hits_total',
|
||||
'ai_processor_cache_hits_total',
|
||||
'Total number of cache hits in AI processor'
|
||||
)
|
||||
|
||||
cache_misses_total = Counter(
|
||||
'ai_processor_cache_misses_total',
|
||||
'ai_processor_cache_misses_total',
|
||||
'Total number of cache misses in AI processor'
|
||||
)
|
||||
|
||||
# Current processing status
|
||||
current_processing_status = Gauge(
|
||||
'ai_processor_current_status',
|
||||
'ai_processor_current_status',
|
||||
'Current processing status of AI processor (0=inactive, 1=active)'
|
||||
)
|
||||
|
||||
@ -59,18 +59,18 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
class MetricsCollector:
|
||||
"""Collects and reports metrics for the AI processor."""
|
||||
|
||||
|
||||
def __init__(self):
|
||||
self.start_time = None
|
||||
self.active = False
|
||||
|
||||
|
||||
def start_processing(self):
|
||||
"""Mark processing as started."""
|
||||
self.start_time = time.time()
|
||||
self.active = True
|
||||
current_processing_status.set(1)
|
||||
logger.info("AI processor started processing")
|
||||
|
||||
|
||||
def stop_processing(self):
|
||||
"""Mark processing as stopped."""
|
||||
self.active = False
|
||||
@ -78,42 +78,42 @@ class MetricsCollector:
|
||||
if self.start_time:
|
||||
total_time = time.time() - self.start_time
|
||||
logger.info(f"AI processor stopped after {total_time:.2f} seconds")
|
||||
|
||||
|
||||
def increment_articles_processed(self, count: int = 1):
|
||||
"""Increment articles processed counter."""
|
||||
articles_processed_total.inc(count)
|
||||
logger.info(f"Articles processed: {count}")
|
||||
|
||||
|
||||
def increment_articles_failed(self, count: int = 1):
|
||||
"""Increment articles failed counter."""
|
||||
articles_failed_total.inc(count)
|
||||
logger.error(f"Articles failed: {count}")
|
||||
|
||||
|
||||
def increment_facts_extracted(self, count: int = 1):
|
||||
"""Increment facts extracted counter."""
|
||||
facts_extracted_total.inc(count)
|
||||
logger.info(f"Facts extracted: {count}")
|
||||
|
||||
|
||||
def record_processing_time(self, duration: float):
|
||||
"""Record processing time."""
|
||||
processing_time_seconds.observe(duration)
|
||||
logger.info(f"Processing time: {duration:.2f} seconds")
|
||||
|
||||
|
||||
def increment_cache_hit(self):
|
||||
"""Increment cache hit counter."""
|
||||
cache_hits_total.inc()
|
||||
logger.debug("Cache hit")
|
||||
|
||||
|
||||
def increment_cache_miss(self):
|
||||
"""Increment cache miss counter."""
|
||||
cache_misses_total.inc()
|
||||
logger.debug("Cache miss")
|
||||
|
||||
|
||||
def log_status(self, message: str, level: str = "info"):
|
||||
"""Log status message with appropriate level."""
|
||||
log_method = getattr(logger, level)
|
||||
log_method(message)
|
||||
|
||||
|
||||
def get_metrics_summary(self) -> Dict[str, Any]:
|
||||
"""Get current metrics summary."""
|
||||
return {
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
home = /usr/bin
|
||||
include-system-site-packages = false
|
||||
version = 3.12.3
|
||||
executable = /usr/bin/python3.12
|
||||
command = /usr/bin/python3 -m venv /home/user/StockDocs/articleServer
|
||||
@ -2,24 +2,22 @@ import os
|
||||
import json
|
||||
import chromadb
|
||||
import uuid
|
||||
import time
|
||||
import datetime
|
||||
import requests
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from prometheus_client import start_http_server, Counter, Histogram
|
||||
|
||||
# Setup logging with better error handling
|
||||
try:
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler('embedding_pipeline.log'),
|
||||
logging.StreamHandler()
|
||||
]
|
||||
)
|
||||
except Exception as e:
|
||||
except Exception:
|
||||
# Fallback if file logging fails
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
@ -77,15 +75,15 @@ def get_embedding(text):
|
||||
"""
|
||||
try:
|
||||
embedding_url = f"{AI_SERVER_URL}/v1/embeddings"
|
||||
|
||||
|
||||
# Build headers with authentication if available
|
||||
headers = {
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
|
||||
if AI_SERVICE_API_KEY:
|
||||
headers["Authorization"] = f"Bearer {AI_SERVICE_API_KEY}"
|
||||
|
||||
|
||||
response = requests.post(
|
||||
embedding_url,
|
||||
json={
|
||||
@ -109,30 +107,30 @@ def extract_facts_from_article(article_content, title):
|
||||
try:
|
||||
# Use the centralized AI endpoint for fact extraction
|
||||
extraction_url = f"{AI_SERVER_URL}/v1/chat/completions"
|
||||
|
||||
|
||||
# Build headers with authentication if available
|
||||
headers = {
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
|
||||
if AI_SERVICE_API_KEY:
|
||||
headers["Authorization"] = f"Bearer {AI_SERVICE_API_KEY}"
|
||||
|
||||
|
||||
# Create a proper prompt for fact extraction
|
||||
prompt = f"""
|
||||
Extract key facts from the following article in structured JSON format.
|
||||
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[:2000]}...
|
||||
|
||||
|
||||
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": "{title}",
|
||||
@ -144,7 +142,7 @@ def extract_facts_from_article(article_content, title):
|
||||
"main_points": ["point1", "point2", "point3"]
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
# Call the AI service with gpt-oss model for fact extraction
|
||||
response = requests.post(
|
||||
extraction_url,
|
||||
@ -160,13 +158,13 @@ def extract_facts_from_article(article_content, title):
|
||||
headers=headers,
|
||||
timeout=60
|
||||
)
|
||||
|
||||
|
||||
response.raise_for_status()
|
||||
|
||||
|
||||
# Parse the response
|
||||
result = response.json()
|
||||
extracted_text = result['choices'][0]['message']['content'].strip()
|
||||
|
||||
|
||||
# Try to parse the JSON from the response
|
||||
try:
|
||||
facts = json.loads(extracted_text)
|
||||
@ -185,9 +183,9 @@ def extract_facts_from_article(article_content, title):
|
||||
"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
|
||||
@ -208,19 +206,19 @@ def process_article_file(file_path):
|
||||
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('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}")
|
||||
@ -232,10 +230,10 @@ def create_collections():
|
||||
"""
|
||||
# Collection for extracted facts (now with entity support)
|
||||
facts_collection = client.get_or_create_collection("facts")
|
||||
|
||||
|
||||
# Collection for full articles
|
||||
articles_collection = client.get_or_create_collection("articles")
|
||||
|
||||
|
||||
# Remove company collection - now using entity tracking in facts collection
|
||||
return facts_collection, articles_collection
|
||||
|
||||
@ -260,7 +258,7 @@ def embed_and_store_facts(facts, facts_collection, articles_collection):
|
||||
"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)
|
||||
@ -281,10 +279,10 @@ def embed_and_store_facts(facts, facts_collection, articles_collection):
|
||||
"financial_impact": facts.get('financial_impact', 'neutral')
|
||||
}]
|
||||
)
|
||||
|
||||
|
||||
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
|
||||
@ -294,22 +292,22 @@ def main():
|
||||
Main embedding pipeline function with batch processing
|
||||
"""
|
||||
logger.info("Starting advanced embedding pipeline")
|
||||
|
||||
|
||||
# Create collections
|
||||
facts_collection, articles_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()
|
||||
|
||||
|
||||
# Collect all articles to process
|
||||
articles_to_process = []
|
||||
for root, dirs, files in os.walk(scraper_articles_dir):
|
||||
@ -319,20 +317,20 @@ def main():
|
||||
# Check if already processed
|
||||
if file_path not in processed_cache:
|
||||
articles_to_process.append((file_path, file))
|
||||
|
||||
|
||||
logger.info(f"Found {len(articles_to_process)} articles to process in batches of {BATCH_SIZE}")
|
||||
|
||||
|
||||
# Process articles in batches
|
||||
total_processed = 0
|
||||
total_failed = 0
|
||||
|
||||
|
||||
for i in range(0, len(articles_to_process), BATCH_SIZE):
|
||||
batch = articles_to_process[i:i + BATCH_SIZE]
|
||||
logger.info(f"Processing batch {i//BATCH_SIZE + 1} with {len(batch)} articles")
|
||||
|
||||
|
||||
batch_processed = 0
|
||||
batch_failed = 0
|
||||
|
||||
|
||||
for file_path, file in batch:
|
||||
try:
|
||||
# Process the article
|
||||
@ -340,11 +338,11 @@ def main():
|
||||
if facts:
|
||||
# Embed and store in appropriate collections
|
||||
success = embed_and_store_facts(
|
||||
facts,
|
||||
facts_collection,
|
||||
facts,
|
||||
facts_collection,
|
||||
articles_collection
|
||||
)
|
||||
|
||||
|
||||
if success:
|
||||
# Update cache with detailed status tracking
|
||||
processed_cache[file_path] = {
|
||||
@ -366,15 +364,15 @@ def main():
|
||||
logger.error(f"Failed to extract facts for {file}")
|
||||
batch_failed += 1
|
||||
total_failed += 1
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing article {file}: {e}")
|
||||
articles_failed_total.inc()
|
||||
batch_failed += 1
|
||||
total_failed += 1
|
||||
|
||||
|
||||
logger.info(f"Batch {i//BATCH_SIZE + 1} completed: {batch_processed} successful, {batch_failed} failed")
|
||||
|
||||
|
||||
# Save cache periodically during batch processing
|
||||
try:
|
||||
with open(CACHE_FILE, 'w', encoding='utf-8') as f:
|
||||
@ -382,7 +380,7 @@ def main():
|
||||
logger.info(f"Cache updated after batch {i//BATCH_SIZE + 1}")
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving cache file: {e}")
|
||||
|
||||
|
||||
# Final cache save
|
||||
try:
|
||||
with open(CACHE_FILE, 'w', encoding='utf-8') as f:
|
||||
@ -390,14 +388,14 @@ def main():
|
||||
logger.info(f"Final cache saved with {len(processed_cache)} entries")
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving final 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(f"Total processed: {total_processed}, Total failed: {total_failed}")
|
||||
|
||||
|
||||
logger.info("Embedding pipeline completed")
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
import math
|
||||
import os
|
||||
import json
|
||||
import chromadb
|
||||
@ -15,8 +16,6 @@ model = SentenceTransformer(
|
||||
trust_remote_code=True
|
||||
)
|
||||
|
||||
|
||||
import math
|
||||
def embed_text(text, specific_context, max_tokens=2048, overlap=256):
|
||||
"""
|
||||
Embeds the given text using the SentenceTransformer model.
|
||||
@ -59,7 +58,7 @@ output_folder = "/app/output"
|
||||
|
||||
while True:
|
||||
print(f"Starting embedding run at {datetime.datetime.now()}")
|
||||
|
||||
|
||||
embedded_cache = {}
|
||||
if os.path.exists(cache_file):
|
||||
with open(cache_file, 'r', encoding='utf-8') as f:
|
||||
@ -74,7 +73,7 @@ while True:
|
||||
if filename in embedded_cache:
|
||||
print(f"Article {filename} already embedded, skipping.")
|
||||
continue
|
||||
|
||||
|
||||
with open(os.path.join(output_folder, filename), 'r', encoding='utf-8') as f:
|
||||
prev_proc = json.load(f)
|
||||
output_articles.append(prev_proc)
|
||||
@ -92,7 +91,7 @@ while True:
|
||||
try:
|
||||
specific_context = prev_proc['summary'] + ",".join([' '.join(makeTickerMovement(*x)) for x in prev_proc['tickersAndMovements']])
|
||||
chunks, embedded_content = embed_text(prev_proc['original_content'], prev_proc['summary'])
|
||||
|
||||
|
||||
print(f"Embedded content for {filename}: \n {embedded_content[:10]}...") # Print first 10 values for preview
|
||||
|
||||
collection.upsert(
|
||||
@ -105,11 +104,11 @@ while True:
|
||||
"filename": prev_proc.get('filename', 'Unknown') # Add filename for traceability
|
||||
} for d in chunks], # Metadata for each embedded content
|
||||
)
|
||||
|
||||
|
||||
# Add to processed list for cache update
|
||||
processed_articles.append(filename)
|
||||
print(f"Successfully embedded {filename}")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error embedding content for {filename}: {e}")
|
||||
continue
|
||||
|
||||
@ -38,7 +38,7 @@ def check_chromadb_connection():
|
||||
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()
|
||||
@ -57,7 +57,7 @@ def check_ai_server_connection():
|
||||
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(
|
||||
@ -87,23 +87,23 @@ def check_scraper_directory():
|
||||
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")
|
||||
logger.warning("⚠ 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)
|
||||
@ -148,16 +148,16 @@ def main():
|
||||
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:
|
||||
@ -166,17 +166,17 @@ def main():
|
||||
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
|
||||
|
||||
@ -6,8 +6,6 @@ This shows how to connect and query your ChromaDB database
|
||||
|
||||
import os
|
||||
import chromadb
|
||||
import json
|
||||
from pathlib import Path
|
||||
from chromadb.config import Settings
|
||||
|
||||
def connect_to_chromadb():
|
||||
@ -16,9 +14,9 @@ def connect_to_chromadb():
|
||||
# Get connection details from environment or use defaults
|
||||
CHROMADB_HOST = os.getenv("CHROMADB_HOST", "example.com")
|
||||
CHROMADB_PORT = int(os.getenv("CHROMADB_PORT", "8000"))
|
||||
|
||||
|
||||
print(f"Connecting to ChromaDB at {CHROMADB_HOST}:{CHROMADB_PORT}")
|
||||
|
||||
|
||||
# Create client connection using Settings for remote server
|
||||
settings = Settings(
|
||||
chroma_api_impl="rest",
|
||||
@ -26,18 +24,18 @@ def connect_to_chromadb():
|
||||
chroma_server_http_port=CHROMADB_PORT,
|
||||
chroma_server_ssl_enabled=False
|
||||
)
|
||||
|
||||
|
||||
print(f"Creating client with settings: {settings}")
|
||||
client = chromadb.Client(settings=settings)
|
||||
|
||||
|
||||
# Test connection
|
||||
collections = client.list_collections()
|
||||
print(f"Successfully connected! Found {len(collections)} collections:")
|
||||
for collection in collections:
|
||||
print(f" - {collection.name}")
|
||||
|
||||
|
||||
return client
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Failed to connect to ChromaDB: {e}")
|
||||
return None
|
||||
@ -47,9 +45,9 @@ def query_facts_collection(client):
|
||||
try:
|
||||
# Get the facts collection
|
||||
facts_collection = client.get_collection("facts")
|
||||
|
||||
|
||||
print("\n=== Querying Facts Collection ===")
|
||||
|
||||
|
||||
# Example queries that would work with your data
|
||||
example_queries = [
|
||||
"Unrivaled attendance records",
|
||||
@ -57,17 +55,17 @@ def query_facts_collection(client):
|
||||
"David Levy on Unrivaled",
|
||||
"Fox Business coverage of Unrivaled"
|
||||
]
|
||||
|
||||
|
||||
for i, query in enumerate(example_queries, 1):
|
||||
print(f"\n{i}. Query: '{query}'")
|
||||
|
||||
|
||||
# Perform similarity search
|
||||
results = facts_collection.query(
|
||||
query_texts=[query],
|
||||
n_results=2,
|
||||
include=["documents", "metadatas", "distances"]
|
||||
)
|
||||
|
||||
|
||||
if results['documents'] and len(results['documents'][0]) > 0:
|
||||
print(" Results:")
|
||||
for j, doc in enumerate(results['documents'][0], 1):
|
||||
@ -76,7 +74,7 @@ def query_facts_collection(client):
|
||||
break
|
||||
else:
|
||||
print(" No results found")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error querying facts collection: {e}")
|
||||
|
||||
@ -85,27 +83,27 @@ def query_articles_collection(client):
|
||||
try:
|
||||
# Get the articles collection
|
||||
articles_collection = client.get_collection("articles")
|
||||
|
||||
|
||||
print("\n=== Querying Articles Collection ===")
|
||||
|
||||
|
||||
# Example query
|
||||
query = "Unrivaled women's basketball"
|
||||
print(f"Query: '{query}'")
|
||||
|
||||
|
||||
# Perform similarity search
|
||||
results = articles_collection.query(
|
||||
query_texts=[query],
|
||||
n_results=2,
|
||||
include=["documents", "metadatas", "distances"]
|
||||
)
|
||||
|
||||
|
||||
if results['documents'] and len(results['documents'][0]) > 0:
|
||||
print("Results:")
|
||||
for j, doc in enumerate(results['documents'][0], 1):
|
||||
print(f" {j}. {doc[:100]}...")
|
||||
else:
|
||||
print("No results found")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error querying articles collection: {e}")
|
||||
|
||||
@ -114,20 +112,20 @@ def main():
|
||||
print("=== ChromaDB Query Demonstration ===")
|
||||
print("This script shows how to connect and query your ChromaDB instance")
|
||||
print()
|
||||
|
||||
|
||||
# Connect to ChromaDB
|
||||
client = connect_to_chromadb()
|
||||
|
||||
|
||||
if client:
|
||||
print("\n=== Available Collections ===")
|
||||
collections = client.list_collections()
|
||||
for collection in collections:
|
||||
print(f" - {collection.name} ({collection.count()} items)")
|
||||
|
||||
|
||||
# Query each collection
|
||||
query_facts_collection(client)
|
||||
query_articles_collection(client)
|
||||
|
||||
|
||||
print("\n=== Query Capabilities ===")
|
||||
print("The system supports:")
|
||||
print("✓ Semantic similarity search across facts")
|
||||
@ -137,7 +135,7 @@ def main():
|
||||
print("✓ Multi-entity queries")
|
||||
print()
|
||||
print("All queries use the qwen3:8b embedding model for efficient searching")
|
||||
|
||||
|
||||
else:
|
||||
print("Cannot connect to ChromaDB. Please ensure:")
|
||||
print("1. ChromaDB server is running")
|
||||
@ -145,4 +143,4 @@ def main():
|
||||
print("3. Correct host/port configuration")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
|
||||
@ -5,8 +5,6 @@ Test script to demonstrate end-to-end pipeline with a single article
|
||||
|
||||
import os
|
||||
import json
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
# Add the current directory to Python path to import our modules
|
||||
import sys
|
||||
@ -14,17 +12,14 @@ sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
from advanced_embedder import (
|
||||
extract_facts_from_article,
|
||||
get_embedding,
|
||||
create_collections,
|
||||
embed_and_store_facts,
|
||||
process_article_file
|
||||
get_embedding
|
||||
)
|
||||
|
||||
def test_end_to_end():
|
||||
"""Test the complete pipeline with a single article"""
|
||||
|
||||
|
||||
print("=== End-to-End Pipeline Test ===")
|
||||
|
||||
|
||||
# Your provided article content
|
||||
article_content = """SOURCE:Fox Business – Headlines
|
||||
Unrivaled started out as an idea, and it has turned into a phenomenon.
|
||||
@ -58,31 +53,31 @@ Clark and A'ja Wilson, arguably the WNBA's two biggest stars, have yet to join t
|
||||
"It's every single great player playing in a three-on-three league. It is absolutely a huge opportunity, and that's why I think it just rose so fast. The quality of play, the names on the back of the jerseys, the social strategy is amazing. These women, they all have equity. Everyone has a following; women athletes completely engage with their fans. The breadth of impressions, I think, is a phenomenal one. I think that's why the league is as successful as it is after just a year and three weeks." """
|
||||
|
||||
title = "Unrivaled Women's Basketball League Breaks Attendance Records"
|
||||
|
||||
|
||||
print(f"Testing with article: {title}")
|
||||
print("-" * 50)
|
||||
|
||||
|
||||
# Test fact extraction
|
||||
print("1. Extracting facts...")
|
||||
facts = extract_facts_from_article(article_content, title)
|
||||
print("Extracted facts:")
|
||||
print(json.dumps(facts, indent=2))
|
||||
print()
|
||||
|
||||
|
||||
# Test embedding creation
|
||||
print("2. Creating embeddings...")
|
||||
facts_text = json.dumps(facts, indent=2)
|
||||
embedding = get_embedding(facts_text)
|
||||
print(f"Created embedding with {len(embedding)} dimensions")
|
||||
print()
|
||||
|
||||
|
||||
# Test storage (this would normally connect to ChromaDB)
|
||||
print("3. Testing storage structure...")
|
||||
print("Storage would create entries in:")
|
||||
print("- Facts collection: structured facts with entity tracking")
|
||||
print("- Articles collection: complete article content")
|
||||
print()
|
||||
|
||||
|
||||
print("=== Test Complete ===")
|
||||
print("The pipeline successfully demonstrates:")
|
||||
print("✓ Fact extraction with entity identification")
|
||||
@ -91,4 +86,4 @@ Clark and A'ja Wilson, arguably the WNBA's two biggest stars, have yet to join t
|
||||
print("✓ Ready for /facts endpoint queries")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_end_to_end()
|
||||
test_end_to_end()
|
||||
|
||||
48
pyproject.toml
Normal file
48
pyproject.toml
Normal file
@ -0,0 +1,48 @@
|
||||
[project]
|
||||
name = "stockdocs"
|
||||
version = "1.0.0"
|
||||
description = "Financial news analysis platform: RSS scraping, AI processing, embeddings, and MCP server"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.9"
|
||||
license = { text = "MIT" }
|
||||
authors = [{ name = "Jarian Cottingham", email = "jarianc@proton.me" }]
|
||||
keywords = ["finance", "news", "scraping", "nlp", "embeddings", "mcp"]
|
||||
classifiers = [
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Programming Language :: Python :: 3",
|
||||
]
|
||||
dependencies = [
|
||||
"feedparser>=6.0,<7.0",
|
||||
"requests>=2.31,<3.0",
|
||||
"beautifulsoup4>=4.12,<5.0",
|
||||
"lxml>=5.0",
|
||||
"nltk>=3.8",
|
||||
"newspaper4k>=0.2.8,<0.3",
|
||||
"selenium>=4.15",
|
||||
"pandas>=2.0",
|
||||
"pyyaml>=6.0",
|
||||
"tqdm>=4.64",
|
||||
"tldextract>=5.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=7.0",
|
||||
"ruff>=0.1.0",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["setuptools>=61.0"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 120
|
||||
target-version = "py39"
|
||||
exclude = [".git", "articles", "ai_processor/ai_processor"]
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = ["E", "F", "W"]
|
||||
ignore = ["E501"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
BIN
scraper/.DS_Store
vendored
BIN
scraper/.DS_Store
vendored
Binary file not shown.
@ -6,7 +6,6 @@ that can be scheduled via cron job.
|
||||
"""
|
||||
|
||||
import newspaper
|
||||
from newspaper import Config
|
||||
import json
|
||||
import feedparser
|
||||
import time
|
||||
@ -14,7 +13,6 @@ import os
|
||||
import requests
|
||||
import logging
|
||||
import random
|
||||
from datetime import datetime
|
||||
from selenium import webdriver
|
||||
from selenium.webdriver.firefox.options import Options as FirefoxOptions
|
||||
from selenium.webdriver.common.by import By
|
||||
@ -50,16 +48,15 @@ def get_feed_file_path():
|
||||
possible_paths = [
|
||||
"./rss_feeds.json", # Current directory
|
||||
"../rss_feeds.json", # Parent directory
|
||||
"/home/user/StockDocs/scraper/rss_feeds.json", # Explicit path
|
||||
"/app/rss_feeds.json", # Docker path
|
||||
"./scraper/rss_feeds.json" # Scraper subdirectory
|
||||
]
|
||||
|
||||
|
||||
for path in possible_paths:
|
||||
if os.path.exists(path):
|
||||
print(f"Found feed file at: {path}")
|
||||
return path
|
||||
|
||||
|
||||
# If no file found, exit the program
|
||||
print("Error: RSS feed file not found in any expected location")
|
||||
print("Exiting program...")
|
||||
@ -81,10 +78,10 @@ def load_rss_feed_sources(feed_file=FEED_FILE):
|
||||
Loads the RSS feed sources from a JSON file.
|
||||
"""
|
||||
logger.info(f"Loading RSS feed sources from {feed_file}...")
|
||||
|
||||
|
||||
# Debug: Print current working directory
|
||||
logger.debug(f"Current working directory: {os.getcwd()}")
|
||||
|
||||
|
||||
try:
|
||||
with open(feed_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
@ -113,12 +110,12 @@ def mine_all_articles(rss_feed_sources, limit=None):
|
||||
Returns a list of (site, title, link) tuples.
|
||||
"""
|
||||
all_links = []
|
||||
|
||||
|
||||
# Check if rss_feed_sources is a valid dict with rss_feeds key
|
||||
if not isinstance(rss_feed_sources, dict):
|
||||
logger.warning(f"rss_feed_sources is not a dict, it's {type(rss_feed_sources)}")
|
||||
return all_links
|
||||
|
||||
|
||||
if "rss_feeds" not in rss_feed_sources:
|
||||
logger.warning("rss_feeds key not found in rss_feed_sources")
|
||||
return all_links
|
||||
@ -172,7 +169,7 @@ def save_article_to_file(article, filename, source="Unfiltered"):
|
||||
with open(file_path, "w", encoding="utf-8") as f:
|
||||
f.write(f"SOURCE:{source}\n")
|
||||
f.write(article)
|
||||
|
||||
|
||||
# Only log when a new file is actually created (not cached)
|
||||
logger.info(f"New article saved: {safe_filename} from {source}")
|
||||
|
||||
@ -204,7 +201,7 @@ def get_article_with_selenium(url):
|
||||
WebDriverWait(driver, 15).until(
|
||||
EC.presence_of_element_located((By.TAG_NAME, "body"))
|
||||
)
|
||||
except:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
time.sleep(random.uniform(1, 3))
|
||||
@ -224,7 +221,7 @@ def get_article_with_selenium(url):
|
||||
if driver:
|
||||
try:
|
||||
driver.quit()
|
||||
except:
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
@ -293,7 +290,6 @@ def pull_article(link, source, title=None, save_to_file=True):
|
||||
try:
|
||||
# Try newspaper4k first with proper User-Agent to bypass bot detection
|
||||
ua = get_random_ua()
|
||||
config = Config(browser_user_agent=ua)
|
||||
article = newspaper.article(link, browser_user_agent=ua)
|
||||
article.download()
|
||||
article.parse()
|
||||
@ -315,7 +311,7 @@ def pull_article(link, source, title=None, save_to_file=True):
|
||||
logger.info(f"Successfully pulled article from {link} with Playwright")
|
||||
|
||||
if not text or len(text) < 200:
|
||||
logger.warning(f"Playwright article too short, falling back to Selenium.")
|
||||
logger.warning("Playwright article too short, falling back to Selenium.")
|
||||
# Fallback to Selenium with better error handling
|
||||
text = get_article_with_selenium(link)
|
||||
logger.info(f"Successfully pulled article from {link} with Selenium")
|
||||
@ -377,20 +373,20 @@ def gather_new_articles():
|
||||
Gather list of all newly downloaded articles and format them for webhook.
|
||||
"""
|
||||
new_articles = []
|
||||
|
||||
|
||||
# Walk through all article directories
|
||||
for root, dirs, files in os.walk("articles"):
|
||||
for file in files:
|
||||
if file != "processed_articles_cache.json": # Skip cache file
|
||||
# Get the full file path
|
||||
file_path = os.path.join(root, file)
|
||||
|
||||
|
||||
# Get the outlet name from the directory path
|
||||
outlet = os.path.basename(root)
|
||||
|
||||
|
||||
# Create the relative path for the article
|
||||
relative_path = os.path.relpath(file_path, "scraper")
|
||||
|
||||
|
||||
# Create article data structure
|
||||
article_data = {
|
||||
"created_at": time.strftime("%Y-%m-%dT%H:%M:%S.%f", time.localtime(os.path.getctime(file_path))),
|
||||
@ -398,9 +394,9 @@ def gather_new_articles():
|
||||
"outlet": outlet,
|
||||
"path": f"../{relative_path}"
|
||||
}
|
||||
|
||||
|
||||
new_articles.append(article_data)
|
||||
|
||||
|
||||
return new_articles
|
||||
|
||||
|
||||
@ -412,7 +408,7 @@ def send_to_webhook(articles):
|
||||
headers = {
|
||||
"StockDocsN8NAuthToken": "ganvT4gsgRjWpGE8FMw9uCzFjZrTx8RZCoVm2Dh7skbZecov"
|
||||
}
|
||||
|
||||
|
||||
try:
|
||||
response = requests.post(webhook_url, json=articles, headers=headers, timeout=30)
|
||||
if response.status_code == 200:
|
||||
@ -464,7 +460,7 @@ def main():
|
||||
with open("errors.txt", "w", encoding="utf-8") as f:
|
||||
for error in errors:
|
||||
f.write(str(error) + "\n")
|
||||
logger.info(f"Errors logged to errors.txt")
|
||||
logger.info("Errors logged to errors.txt")
|
||||
|
||||
# Print all results to a log file
|
||||
with open("results.txt", "w", encoding="utf-8") as f:
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
home = /usr/bin
|
||||
include-system-site-packages = false
|
||||
version = 3.12.3
|
||||
executable = /usr/bin/python3.12
|
||||
command = /usr/bin/python3 -m venv /home/user/StockDocs/scraper
|
||||
@ -1,5 +1,4 @@
|
||||
import newspaper
|
||||
from newspaper import Config
|
||||
import json
|
||||
import feedparser
|
||||
import time
|
||||
@ -87,14 +86,14 @@ def mark_article_processed(article_path, status="completed", embedding_status="p
|
||||
"last_updated": datetime.now().isoformat()
|
||||
}
|
||||
save_processed_cache(cache_data)
|
||||
|
||||
|
||||
def get_processing_progress():
|
||||
"""Get overall processing progress"""
|
||||
cache_data = load_processed_cache()
|
||||
total_articles = len(cache_data)
|
||||
completed_articles = sum(1 for data in cache_data.values() if data.get('status') == 'completed')
|
||||
embedded_articles = sum(1 for data in cache_data.values() if data.get('embedding_status') == 'completed')
|
||||
|
||||
|
||||
return {
|
||||
"total_articles": total_articles,
|
||||
"completed_articles": completed_articles,
|
||||
@ -128,12 +127,12 @@ def mine_all_articles(rss_feed_sources, limit=None):
|
||||
Returns a list of (site, title, link) tuples.
|
||||
"""
|
||||
all_links = []
|
||||
|
||||
|
||||
# Check if rss_feed_sources is a valid dict with rss_feeds key
|
||||
if not isinstance(rss_feed_sources, dict):
|
||||
logger.warning(f"rss_feed_sources is not a dict, it's {type(rss_feed_sources)}")
|
||||
return all_links
|
||||
|
||||
|
||||
if "rss_feeds" not in rss_feed_sources:
|
||||
logger.warning("rss_feeds key not found in rss_feed_sources")
|
||||
return all_links
|
||||
@ -148,18 +147,18 @@ def mine_all_articles(rss_feed_sources, limit=None):
|
||||
# Add more aggressive timeout settings with fallback
|
||||
# Use a wrapper to ensure we don't hang indefinitely
|
||||
import signal
|
||||
|
||||
|
||||
def timeout_handler(signum, frame):
|
||||
raise TimeoutError(f"Timeout parsing feed: {site}")
|
||||
|
||||
|
||||
# Set up signal-based timeout (this is a fallback for truly hanging requests)
|
||||
old_handler = signal.signal(signal.SIGALRM, timeout_handler)
|
||||
signal.alarm(10) # 10 second alarm
|
||||
|
||||
|
||||
feed = feedparser.parse(data["rss_url"], timeout=8) # 8 second timeout
|
||||
signal.alarm(0) # Cancel the alarm
|
||||
signal.signal(signal.SIGALRM, old_handler)
|
||||
|
||||
|
||||
feed_entries = feed.entries[:limit] if limit else feed.entries
|
||||
|
||||
entries = []
|
||||
@ -187,14 +186,14 @@ def mine_all_articles(rss_feed_sources, limit=None):
|
||||
# Use ThreadPoolExecutor for parallel RSS feed parsing
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
max_workers = min(10, len(sources)) # Limit concurrent workers
|
||||
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
# Submit all feed parsing tasks
|
||||
future_to_site = {
|
||||
executor.submit(parse_single_feed, site, data): site
|
||||
executor.submit(parse_single_feed, site, data): site
|
||||
for site, data in sources.items()
|
||||
}
|
||||
|
||||
|
||||
# Collect results as they complete
|
||||
for future in as_completed(future_to_site, timeout=30): # 30 second overall timeout
|
||||
try:
|
||||
@ -244,7 +243,7 @@ def save_article_to_file(article, filename, source="Unfiltered"):
|
||||
with open(file_path, "w", encoding="utf-8") as f:
|
||||
f.write(f"SOURCE:{source}\n")
|
||||
f.write(article)
|
||||
|
||||
|
||||
# Only log when a new file is actually created (not cached)
|
||||
logger.info(f"New article saved: {safe_filename} from {source}")
|
||||
|
||||
@ -275,7 +274,7 @@ def get_article_with_selenium(url):
|
||||
driver = webdriver.Firefox(options=options)
|
||||
else:
|
||||
raise e
|
||||
|
||||
|
||||
driver.set_page_load_timeout(30) # 30 seconds timeout
|
||||
|
||||
# Navigate to URL
|
||||
@ -286,7 +285,7 @@ def get_article_with_selenium(url):
|
||||
WebDriverWait(driver, 15).until(
|
||||
EC.presence_of_element_located((By.TAG_NAME, "body"))
|
||||
)
|
||||
except:
|
||||
except Exception:
|
||||
pass # Continue even if wait times out
|
||||
|
||||
time.sleep(random.uniform(1, 3)) # Random wait to mimic human behavior
|
||||
@ -313,7 +312,7 @@ def get_article_with_selenium(url):
|
||||
if driver:
|
||||
try:
|
||||
driver.quit()
|
||||
except:
|
||||
except Exception:
|
||||
pass # Ignore errors in cleanup
|
||||
|
||||
|
||||
@ -387,7 +386,6 @@ def pull_article(link, source, title=None, save_to_file=True):
|
||||
try:
|
||||
# Try newspaper4k first with proper User-Agent to bypass bot detection
|
||||
ua = get_random_ua()
|
||||
config = Config(browser_user_agent=ua)
|
||||
article = newspaper.article(link, browser_user_agent=ua)
|
||||
article.download()
|
||||
article.parse()
|
||||
@ -409,7 +407,7 @@ def pull_article(link, source, title=None, save_to_file=True):
|
||||
logger.info(f"Successfully pulled article from {link} with Playwright")
|
||||
|
||||
if not text or len(text) < 200:
|
||||
logger.warning(f"Playwright article too short, falling back to Selenium.")
|
||||
logger.warning("Playwright article too short, falling back to Selenium.")
|
||||
# Fallback to Selenium with better error handling
|
||||
text = get_article_with_selenium(link)
|
||||
logger.info(f"Successfully pulled article from {link} with Selenium")
|
||||
@ -437,7 +435,7 @@ def is_article_downloaded(source, title, link):
|
||||
# Generate the same filename that would be used for saving
|
||||
filename = title if title else link
|
||||
safe_filename = generate_filename_from_url(filename)
|
||||
|
||||
|
||||
# Check if file exists in the articles directory
|
||||
file_path = os.path.join("articles", source, safe_filename)
|
||||
return os.path.exists(file_path)
|
||||
@ -453,16 +451,16 @@ def safe_pull_articles(article_list):
|
||||
# Filter out articles that are already downloaded
|
||||
filtered_article_list = []
|
||||
total_articles = len(article_list)
|
||||
|
||||
|
||||
for source, title, link in article_list:
|
||||
if not is_article_downloaded(source, title, link):
|
||||
filtered_article_list.append((source, title, link))
|
||||
else:
|
||||
logger.info(f"Skipping already downloaded article: {title[:50]}... from {source}")
|
||||
|
||||
|
||||
logger.info(f"Filtered out {total_articles - len(filtered_article_list)} articles that were already downloaded")
|
||||
logger.info(f"Processing {len(filtered_article_list)} remaining articles")
|
||||
|
||||
|
||||
if not filtered_article_list:
|
||||
logger.info("No new articles to process")
|
||||
return [], []
|
||||
@ -474,7 +472,7 @@ def safe_pull_articles(article_list):
|
||||
# Use configurable worker setting
|
||||
max_workers = min(MAX_ARTICLE_WORKERS, len(filtered_article_list)) # Cap at configured workers, but don't exceed article count
|
||||
batch_size = max(1, min(20, len(filtered_article_list) // 4)) # Dynamic batch size
|
||||
|
||||
|
||||
logger.info(f"Starting parallel article pulling with {max_workers} workers and batch size {batch_size}")
|
||||
|
||||
# Process all articles in parallel with proper error handling
|
||||
@ -542,14 +540,14 @@ def main():
|
||||
with open("errors.txt", "w", encoding="utf-8") as f:
|
||||
for error in errors:
|
||||
f.write(str(error) + "\n")
|
||||
logger.info(f"Errors logged to errors.txt")
|
||||
logger.info("Errors logged to errors.txt")
|
||||
|
||||
# Print all results to a log file
|
||||
with open("results.txt", "w", encoding="utf-8") as f:
|
||||
for result in results:
|
||||
f.write(result + "\n")
|
||||
logger.info("All articles pulled successfully.")
|
||||
|
||||
|
||||
# Log processing progress
|
||||
progress = get_processing_progress()
|
||||
logger.info(f"Processing progress - Total: {progress['total_articles']}, "
|
||||
@ -567,4 +565,4 @@ def main():
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
|
||||
@ -1,43 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to verify the enhanced cache system implementation
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import datetime
|
||||
from scraper.scraper import load_processed_cache, save_processed_cache, get_processing_progress
|
||||
|
||||
def test_cache_system():
|
||||
"""Test the enhanced cache system"""
|
||||
print("Testing enhanced cache system...")
|
||||
|
||||
# Test loading cache (should work even if file doesn't exist)
|
||||
cache = load_processed_cache()
|
||||
print(f"Initial cache loaded: {len(cache)} entries")
|
||||
|
||||
# Test saving cache
|
||||
test_entry = {
|
||||
"test_article_path": {
|
||||
"processed_date": datetime.datetime.now().isoformat(),
|
||||
"status": "completed",
|
||||
"embedding_status": "pending",
|
||||
"last_updated": datetime.datetime.now().isoformat()
|
||||
}
|
||||
}
|
||||
|
||||
save_processed_cache(test_entry)
|
||||
print("Cache saved successfully")
|
||||
|
||||
# Test loading again
|
||||
cache = load_processed_cache()
|
||||
print(f"Cache loaded after save: {len(cache)} entries")
|
||||
|
||||
# Test progress tracking
|
||||
progress = get_processing_progress()
|
||||
print(f"Progress tracking: {progress}")
|
||||
|
||||
print("Cache system test completed successfully!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_cache_system()
|
||||
61
tests/test_scraper_cache.py
Normal file
61
tests/test_scraper_cache.py
Normal file
@ -0,0 +1,61 @@
|
||||
"""Tests for the scraper article-processing cache."""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
from scraper.scraper import ( # noqa: E402
|
||||
get_processing_progress,
|
||||
load_processed_cache,
|
||||
mark_article_processed,
|
||||
save_processed_cache,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def cache_dir(tmp_path, monkeypatch):
|
||||
monkeypatch.chdir(tmp_path)
|
||||
(tmp_path / "articles").mkdir()
|
||||
return tmp_path
|
||||
|
||||
|
||||
def test_load_missing_cache_returns_empty(cache_dir):
|
||||
assert load_processed_cache() == {}
|
||||
|
||||
|
||||
def test_save_and_load_roundtrip(cache_dir):
|
||||
save_processed_cache({"a.html": {"status": "completed"}})
|
||||
cache = load_processed_cache()
|
||||
assert cache["a.html"]["status"] == "completed"
|
||||
|
||||
|
||||
def test_mark_article_processed(cache_dir):
|
||||
mark_article_processed("articles/x/article.html")
|
||||
cache = load_processed_cache()
|
||||
entry = cache["articles/x/article.html"]
|
||||
assert entry["status"] == "completed"
|
||||
assert entry["embedding_status"] == "pending"
|
||||
assert "processed_date" in entry
|
||||
assert "last_updated" in entry
|
||||
|
||||
|
||||
def test_processing_progress_empty(cache_dir):
|
||||
progress = get_processing_progress()
|
||||
assert progress["total_articles"] == 0
|
||||
assert progress["completed_articles"] == 0
|
||||
assert progress["embedded_articles"] == 0
|
||||
assert progress["completion_rate"] == 0
|
||||
|
||||
|
||||
def test_processing_progress_mixed(cache_dir):
|
||||
mark_article_processed("a.html")
|
||||
mark_article_processed("b.html", status="failed")
|
||||
mark_article_processed("c.html", embedding_status="completed")
|
||||
progress = get_processing_progress()
|
||||
assert progress["total_articles"] == 3
|
||||
assert progress["completed_articles"] == 2
|
||||
assert progress["embedded_articles"] == 1
|
||||
assert progress["completion_rate"] == pytest.approx(2 / 3 * 100)
|
||||
Loading…
x
Reference in New Issue
Block a user