Utilize diffbot-kg client

api/app/enhance.py

@@ -1,33 +1,32 @@
-import logging
 import os
 from datetime import datetime
-from typing import Any, Dict, List, Optional, Union
-from urllib.parse import urlencode
+from typing import Dict, List, Literal, Optional, Tuple, Union
 
-import requests
+from diffbot_kg import DiffbotEnhanceClient
 from utils import graph
 
 CATEGORY_THRESHOLD = 0.50
 params = []
 
 DIFF_TOKEN = os.environ["DIFFBOT_API_KEY"]
 
+client = DiffbotEnhanceClient(DIFF_TOKEN)
 
 def get_datetime(value: Optional[Union[str, int, float]]) -> datetime:
     if not value:
         return value
     return datetime.fromtimestamp(float(value) / 1000.0)
 
 
-def process_entities(entity: str, type: str) -> Dict[str, Any]:
+
+async def process_entities(entity: str, type: str) -> Tuple[str, List[Dict]]:
     """
     Fetch relevant articles from Diffbot KG endpoint
     """
-    search_host = "https://kg.diffbot.com/kg/v3/enhance?"
-    params = {"type": type, "name": entity, "token": DIFF_TOKEN}
-    encoded_query = urlencode(params)
-    url = f"{search_host}{encoded_query}"
-    return entity, requests.get(url).json()
+    params = {"type": type, "name": entity}
+    response = await client.enhance(params)
+
+    return entity, response.entities
 
 
 def get_people_params(row: Dict) -> Optional[Dict]:

api/app/importing.py

@@ -1,33 +1,40 @@
 import logging
 import os
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional
 
-import requests
+from diffbot_kg import DiffbotSearchClient
 from utils import embeddings, text_splitter
 
 CATEGORY_THRESHOLD = 0.50
 params = []
 
 DIFF_TOKEN = os.environ["DIFFBOT_API_KEY"]
 
+client = DiffbotSearchClient(token=DIFF_TOKEN)
 
-def get_articles(
-    query: Optional[str], tag: Optional[str], size: int = 5, offset: int = 0
-) -> Dict[str, Any]:
+
+async def get_articles(
+    query: Optional[str],
+    tag: Optional[str],
+    size: int = 5,
+    offset: int = 0,
+) -> List[Dict]:
     """
     Fetch relevant articles from Diffbot KG endpoint
     """
+    search_query = "type:Article language:en sortBy:date"
+    if query:
+        search_query += f' strict:text:"{query}"'
+    if tag:
+        search_query += f' tags.label:"{tag}"'
+
+    params = {"query": search_query, "size": size, "offset": offset}
+
+    logging.info(f"Fetching articles with params: {params}")
+
     try:
-        search_host = "https://kg.diffbot.com/kg/v3/dql?"
-        search_query = f'query=type%3AArticle+strict%3Alanguage%3A"en"+sortBy%3Adate'
-        if query:
-            search_query += f'+text%3A"{query}"'
-        if tag:
-            search_query += f'+tags.label%3A"{tag}"'
-        url = (
-            f"{search_host}{search_query}&token={DIFF_TOKEN}&from={offset}&size={size}"
-        )
-        return requests.get(url).json()
+        response = await client.search(params)
+        return response.entities
     except Exception as ex:
         raise ex
 

api/app/main.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 import os
 from concurrent.futures import ThreadPoolExecutor
@@ -20,7 +21,7 @@
 )
 
 # Multithreading for Diffbot API
-MAX_WORKERS = min(os.cpu_count() * 5, 20)
+MAX_WORKERS = min((os.cpu_count() or 1) * 5, 20)
 
 app = FastAPI()
 
@@ -35,21 +36,24 @@
 
 
 @app.post("/import_articles/")
-def import_articles_endpoint(article_data: ArticleData) -> int:
+async def import_articles_endpoint(article_data: ArticleData) -> int:
     logging.info(f"Starting to process article import with params: {article_data}")
-    if not article_data.query and not article_data.tag:
+    if not article_data.query and not article_data.category and not article_data.tag:
         raise HTTPException(
-            status_code=500, detail="Either `query` or `tag` must be provided"
+            status_code=500,
+            detail="Either `query` or `category` or `tag` must be provided",
         )
-    data = get_articles(article_data.query, article_data.tag, article_data.size)
-    logging.info(f"Articles fetched: {len(data['data'])} articles.")
+    articles = await get_articles(
+        article_data.query, article_data.category, article_data.tag, article_data.size
+    )
+    logging.info(f"Articles fetched: {len(articles)} articles.")
     try:
-        params = process_params(data)
+        params = process_params(articles)
     except Exception as e:
         # You could log the exception here if needed
-        raise HTTPException(status_code=500, detail=e)
+        raise HTTPException(status_code=500, detail=e) from e
     graph.query(import_cypher_query, params={"data": params})
-    logging.info(f"Article import query executed successfully.")
+    logging.info("Article import query executed successfully.")
     return len(params)
 
 
@@ -124,26 +128,41 @@ def fetch_unprocessed_count(count_data: CountData) -> int:
 
 
 @app.post("/enhance_entities/")
-def enhance_entities(entity_data: EntityData) -> str:
+async def enhance_entities(entity_data: EntityData) -> str:
     entities = graph.query(
         "MATCH (a:Person|Organization) WHERE a.processed IS NULL "
         "WITH a LIMIT toInteger($limit) "
         "RETURN [el in labels(a) WHERE el <> '__Entity__' | el][0] "
         "AS label, collect(a.name) AS entities",
         params={"limit": entity_data.size},
     )
-    enhanced_data = []
-    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
-        # Submitting all tasks and creating a list of future objects
-        for row in entities:
-            futures = [
-                executor.submit(process_entities, el, row["label"])
-                for el in row["entities"]
-            ]
-
-            for future in futures:
-                response = future.result()
-                enhanced_data.append(response)
+    enhanced_data = {}
+
+    # Run the process_entities function in a TaskGroup
+
+    queue = asyncio.Queue()
+    for row in entities:
+        for el in row["entities"]:
+            await queue.put((el, row["label"]))
+
+    async def worker():
+        while True:
+            el, label = await queue.get()
+            try:
+                response = await process_entities(el, label)
+                enhanced_data[response[0]] = response[1]
+            finally:
+                queue.task_done()
+
+    tasks = []
+    for _ in range(4):  # Number of workers
+        tasks.append(asyncio.create_task(worker()))
+
+    await queue.join()
+
+    for task in tasks:
+        task.cancel()
+
     store_enhanced_data(enhanced_data)
     return "Finished enhancing entities."