feat: Add crypto-news-aggregator example

crypto-news-aggregator/embedding_generator.py

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+from sentence_transformers import SentenceTransformer
+import numpy as np
+
+model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L3-v2")
+
+def generate_embedding(text):
+    return model.encode(text, convert_to_numpy=True).astype(np.float32)

crypto-news-aggregator/example.py

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+import json
+import asyncio
+import numpy as np
+from redis import Redis
+from datetime import datetime, timedelta
+from news_fetcher import fetch_and_normalize_news_data
+from weight_calculator import calculate_weight
+from embedding_generator import generate_embedding
+from features_dataset_initializer import initialize_features_dataset
+from features_predictor import predict_growth
+
+
+def get_key(article):
+    return f"article::{article['id']}"
+
+
+def get_article(r, key):
+    return json.loads(r.execute_command("JSON.GET", key))
+
+
+def set_article_field(r, key, field, value):
+    return r.execute_command("JSON.SET", key, f"$.{field}", value)
+
+
+async def update_article_weight(r, key):
+    article = get_article(r, key)
+    weight = calculate_weight(article)
+    set_article_field(r, key, "weight", weight)
+
+
+async def update_article_embedding(r, key):
+    article = get_article(r, key)
+    content = article.get("title", "")
+    embedding = str(generate_embedding(content).tolist())
+    set_article_field(r, key, "embedding", embedding)
+
+
+def get_timestamp(days_ago=10):
+    return int((datetime.now() - timedelta(days=days_ago)).timestamp())
+
+
+def create_index(r):
+    return r.execute_command(
+        "FT.CREATE news_idx ON JSON PREFIX 1 article:: SCHEMA $.id AS id NUMERIC $.published_at AS published_at NUMERIC SORTABLE $.votes.negative AS votes_negative NUMERIC $.votes.positive AS votes_positive NUMERIC $.votes.important AS votes_important NUMERIC $.currencies[*].code AS currency_code TAG $.weight AS weight NUMERIC"
+    )
+
+
+def create_news_vector_index(r):
+    return r.execute_command(
+        "FT.CREATE news_vector_idx ON JSON PREFIX 1 article:: SCHEMA $.id AS id NUMERIC $.published_at AS published_at NUMERIC SORTABLE $.title AS title TEXT $.embedding AS embedding VECTOR FLAT 6 TYPE FLOAT32 DIM 384 DISTANCE_METRIC COSINE"
+    )
+
+
+def create_features_index(r):
+    return r.execute_command(
+        "FT.CREATE features_idx ON JSON PREFIX 1 week:: SCHEMA $.currency_code AS currency_code TAG $.features AS features VECTOR FLAT 6 TYPE FLOAT32 DIM 5 DISTANCE_METRIC COSINE $.label AS label NUMERIC"
+    )
+
+
+async def get_most_mentioned_cryptocurrencies(r, from_timestamp, top=10):
+    return r.execute_command(
+        "FT.AGGREGATE",
+        "news_idx",
+        f"@published_at:[{from_timestamp} +inf]",
+        "GROUPBY",
+        1,
+        "@currency_code",
+        "REDUCE",
+        "COUNT",
+        0,
+        "AS",
+        "mentions_count",
+        "SORTBY",
+        2,
+        "@mentions_count",
+        "DESC",
+        "LIMIT",
+        0,
+        top,
+    )
+
+
+async def get_cryptocurrency_recent_mentions(r, currency, from_timestamp, limit=10):
+    return r.execute_command(
+        "FT.SEARCH",
+        "news_idx",
+        f"@currency_code:{{{currency}}} @published_at:[{from_timestamp} +inf]",
+        "SORTBY",
+        "published_at",
+        "DESC",
+        "LIMIT",
+        0,
+        limit,
+    )
+
+
+async def get_cryptocurrency_votes(r, currency, from_timestamp):
+    return r.execute_command(
+        "FT.AGGREGATE",
+        "news_idx",
+        f"@currency_code:{{{currency}}} @published_at:[{from_timestamp} +inf]",
+        "GROUPBY",
+        1,
+        "@currency_code",
+        "REDUCE",
+        "SUM",
+        1,
+        "@votes_positive",
+        "AS",
+        "total_positive_votes",
+        "REDUCE",
+        "SUM",
+        1,
+        "@votes_negative",
+        "AS",
+        "total_negative_votes",
+        "REDUCE",
+        "SUM",
+        1,
+        "@votes_important",
+        "AS",
+        "total_important_votes",
+    )
+
+
+async def get_similar_articles(r, query_text, from_timestamp, top=10):
+    query_embedding = generate_embedding(query_text).tobytes()
+    return r.execute_command(
+        "FT.SEARCH",
+        "news_vector_idx",
+        f"@published_at:[{from_timestamp} +inf] =>[KNN {top} @embedding $query AS score]",
+        "PARAMS",
+        2,
+        "query",
+        query_embedding,
+        "SORTBY",
+        "score",
+        "ASC",
+        "LIMIT",
+        0,
+        top,
+        "RETURN",
+        3,
+        "title",
+        "published_at",
+        "score",
+    )
+
+
+def to_dict(result):
+    if (len(result) < 2):
+        return {}
+    return {k.decode(): v.decode() for k, v in zip(result[1][::2], result[1][1::2])}
+
+
+async def compute_features(r, currency, from_timestamp):
+    recent_mensions = await get_cryptocurrency_recent_mentions(r, currency, from_timestamp, 1000)
+
+    count_news = float(recent_mensions[0])
+
+    votes = await get_cryptocurrency_votes(r, currency, from_timestamp)
+
+    votes_dict = to_dict(votes)
+
+    sum_positive = float(votes_dict.get("total_positive_votes", 0))
+    sum_negative = float(votes_dict.get("total_negative_votes", 0))
+
+    mean_positive = sum_positive / count_news if count_news > 0.0 else 0.0
+    mean_negative = sum_negative / count_news if count_news > 0.0 else 0.0
+
+    features = [count_news, sum_positive, sum_negative, mean_positive, mean_negative] #etc. 
+    return np.array(features, dtype=np.float32)
+
+
+async def cryptocurrency_will_grow(r, currency, from_timestamp):
+    features = await compute_features(r, currency, from_timestamp)
+    return await predict_growth(r, currency, features)
+
+
+async def main():
+    # Connect to DragonflyDB
+    r = Redis(host="localhost", port=6379)
+
+    r.execute_command("FLUSHALL")
+
+    news_data = fetch_and_normalize_news_data()
+
+    # Store the news data in DragonflyDB
+    for article in news_data:
+        key = get_key(article)
+        value = json.dumps(article)
+        r.execute_command("JSON.SET", key, "$", value)
+
+    for article in news_data:
+        key = get_key(article)
+        await update_article_weight(r, key)
+
+    for article in news_data:
+        key = get_key(article)
+        await update_article_embedding(r, key)
+
+    create_index(r)
+    create_news_vector_index(r)
+
+    timestamp_ten_days_ago = 1700000000
+
+    # Get the most mentioned cryptocurrencies
+    print("Most mentioned cryptocurrencies:")
+    print(await get_most_mentioned_cryptocurrencies(r, timestamp_ten_days_ago))
+    print()
+
+    # Get recent mentions of a cryptocurrency
+    print("Recent mentions of BTC:")
+    print(await get_cryptocurrency_recent_mentions(r, "BTC", timestamp_ten_days_ago, 2))
+    print()
+
+    # Get votes for a cryptocurrency
+    print("Votes for BTC:")
+    print(await get_cryptocurrency_votes(r, "BTC", timestamp_ten_days_ago))
+    print()
+
+    # Get similar articles
+    print("Similar articles to 'Bitcoin crash':")
+    print(await get_similar_articles(r, "Bitcoin crash", timestamp_ten_days_ago, 2))
+    print()
+
+    print("Similar articles to 'Ethereum rally':")
+    print(await get_similar_articles(r, "Ethereum rally", timestamp_ten_days_ago, 2))
+    print()
+
+    print("Similar articles to 'Ripple lawsuit':")
+    print(await get_similar_articles(r, "Ripple lawsuit", timestamp_ten_days_ago, 2))
+    print()
+
+    # Compute features for a cryptocurrency
+    initialize_features_dataset(r)
+    create_features_index(r)
+
+    timestamp_seven_days_ago = 1730000000
+
+    # Predict if a cryptocurrency will grow
+    will_grow = await cryptocurrency_will_grow(r, "BTC", timestamp_seven_days_ago)
+    print(f"Will BTC grow in next seven days? {"Yes" if will_grow else "No"}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

crypto-news-aggregator/features_dataset.json

@@ -0,0 +1,92 @@
+[
+    {
+        "week": "2024-10-27",
+        "currency_code": "BTC",
+        "features": [10.0, 5.0, 2.0, 0.5, 0.2],
+        "label": 1
+    },
+    {
+        "week": "2024-11-03",
+        "currency_code": "BTC",
+        "features": [12.0, 3.0, 4.0, 0.25, 0.333],
+        "label": 1
+    },
+    {
+        "week": "2024-11-10",
+        "currency_code": "BTC",
+        "features": [8.0, 7.0, 1.5, 0.875, 0.1875],
+        "label": 0
+    },
+    {
+        "week": "2024-11-17",
+        "currency_code": "BTC",
+        "features": [9.0, 2.0, 2.0, 0.222, 0.222],
+        "label": 0
+    },
+    {
+        "week": "2024-11-24",
+        "currency_code": "BTC",
+        "features": [14.0, 4.0, 3.5, 0.286, 0.25],
+        "label": 1
+    },
+    {
+        "week": "2024-12-01",
+        "currency_code": "BTC",
+        "features": [16.0, 5.0, 4.0, 0.3125, 0.25],
+        "label": 1
+    },
+    {
+        "week": "2024-12-08",
+        "currency_code": "BTC",
+        "features": [11.0, 6.0, 2.0, 0.545, 0.181],
+        "label": 1
+    },
+    {
+        "week": "2024-12-15",
+        "currency_code": "BTC",
+        "features": [10.5, 2.0, 3.0, 0.190, 0.285],
+        "label": 0
+    },
+    {
+        "week": "2024-12-22",
+        "currency_code": "BTC",
+        "features": [13.0, 8.0, 2.5, 0.615, 0.192],
+        "label": 0
+    },
+    {
+        "week": "2024-12-29",
+        "currency_code": "BTC",
+        "features": [15.0, 5.0, 2.5, 0.333, 0.167],
+        "label": 1
+    },
+    {
+        "week": "2025-01-05",
+        "currency_code": "BTC",
+        "features": [10.0, 7.0, 3.0, 0.7, 0.3],
+        "label": 0
+    },
+    {
+        "week": "2025-01-12",
+        "currency_code": "BTC",
+        "features": [12.0, 4.0, 5.0, 0.333, 0.417],
+        "label": 1
+    },
+    {
+        "week": "2024-10-27",
+        "currency_code": "ETH",
+        "features": [8.0, 3.0, 6.0, 0.375, 0.75],
+        "label": 0
+    },
+    {
+        "week": "2025-01-05",
+        "currency_code": "ETH",
+        "features": [14.0, 6.0, 1.0, 0.428, 0.071],
+        "label": 1
+    },
+    {
+        "week": "2025-01-12",
+        "currency_code": "ETH",
+        "features": [11.0, 7.5, 2.0, 0.682, 0.182],
+        "label": 1
+    }
+]

crypto-news-aggregator/features_dataset_initializer.py

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+import json
+import os
+
+def get_dataset():
+    json_file_path = os.getenv("FEATURES_DATASET_PATH", "features_dataset.json")
+
+    try:
+        with open(json_file_path, 'r', encoding='utf-8') as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Error while reading features dataset from ({json_file_path}): {e}")
+        return []
+
+
+def initialize_features_dataset(r):
+    dataset = get_dataset()
+
+    for doc in dataset:
+        key = f"week::{doc["week"]}::{doc["currency_code"]}"
+        value = json.dumps(doc)
+        r.execute_command("JSON.SET", key, "$", value)

crypto-news-aggregator/features_predictor.py

@@ -0,0 +1,22 @@
+import json
+
+
+def parse_search_result(result):
+    docs = result[2::2]
+    return [json.loads(doc[1].decode()) for doc in docs]
+
+
+# Simple KNN-based predictor
+async def predict_growth(r, currency, new_features, k=5):
+    query_vector = new_features.tobytes()
+
+    search_result = r.execute_command(
+        "FT.SEARCH", "features_idx", "*",  
+        f"@currency_code:{{{currency}}} =>[KNN {k} @features $query_vec AS score]",
+        "PARAMS", "2", "query_vec", query_vector
+    )
+
+    results = parse_search_result(search_result)
+
+    knn_labels = [doc["label"] for doc in results]
+    return 1 if sum(knn_labels) > len(knn_labels) / 2 else 0