[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

examples/cloud/pdf-rag/embedding.py

@@ -1,10 +1,8 @@
 import json
 import os
-import time
 
 import numpy as np
 from FlagEmbedding import FlagAutoModel
-from sklearn.metrics.pairwise import cosine_similarity
 
 
 def load_model(model_name="BAAI/bge-base-en-v1.5", use_fp16=True):
@@ -12,27 +10,32 @@ def load_model(model_name="BAAI/bge-base-en-v1.5", use_fp16=True):
         model_name,
         query_instruction_for_retrieval="Represent this sentence for searching relevant passages:",
         # device='cpu', # Uncomment this line if you want to use GPU.
-        use_fp16=use_fp16
+        use_fp16=use_fp16,
     )
 
+
 def load_data(file_path):
     try:
-        with open(file_path, 'r', encoding='utf-8') as f:
+        with open(file_path, "r", encoding="utf-8") as f:
             return json.load(f)
     except (FileNotFoundError, json.JSONDecodeError):
         print("Error loading data from", file_path)
         return []
 
+
 def extract_texts(data):
-    return [doc.get("content", '').strip() for doc in data]
+    return [doc.get("content", "").strip() for doc in data]
+
 
 def generate_embeddings(model, texts):
     return np.array(model.encode(texts))
 
+
 def save_embeddings(embeddings, output_path):
     os.makedirs(os.path.dirname(output_path), exist_ok=True)
     np.save(output_path, embeddings)
 
+
 def load_embeddings(file_path):
     try:
         return np.load(file_path)
@@ -47,13 +50,10 @@ def main():
         "json_path": "#PATH_TO_YOUR_JSON.json#",
         "embedding_path": "#PATH_TO_YOUR_EMBEDDING.npy#",
         "use_fp16": True,
-        "use_precomputed_embeddings": False
+        "use_precomputed_embeddings": False,
     }
 
-    model = load_model(
-        model_name=config["model_name"],
-        use_fp16=config["use_fp16"]
-    )
+    model = load_model(model_name=config["model_name"], use_fp16=config["use_fp16"])
 
     if config["use_precomputed_embeddings"]:
         embeddings = load_embeddings(config["embedding_path"])
@@ -68,7 +68,7 @@ def main():
         embeddings = generate_embeddings(model, texts)
         save_embeddings(embeddings, config["embedding_path"])
 
-##### Test demo with simple KNN cosine_similarity
+    ##### Test demo with simple KNN cosine_similarity
     # query='This is a test query to find relevant documents.'
     # query_embedding=np.array(model.encode(query))
     # similarity_scores = cosine_similarity([query_embedding], embeddings)
@@ -76,5 +76,6 @@ def main():
 
     return embeddings
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()

examples/cloud/pdf-rag/parse.py

@@ -2,7 +2,7 @@ import json
 import time
 from pathlib import Path
 
-from haystack import Document, Pipeline
+from haystack import Pipeline
 from haystack.components.converters import PyPDFToDocument
 from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
 from haystack.components.writers import DocumentWriter
@@ -29,6 +29,7 @@ def create_indexing_pipeline():
 
     return indexing_pipeline, document_store
 
+
 def process_pdfs(pdf_directory, indexing_pipeline):
     papers_dir = Path(pdf_directory)
     pdf_files = list(papers_dir.glob("*.pdf"))
@@ -38,20 +39,23 @@ def process_pdfs(pdf_directory, indexing_pipeline):
         except:
             pass
 
+
 def save_to_json(document_store, output_path):
     all_documents = document_store.filter_documents()
     docs_list = [doc.to_dict() for doc in all_documents]
     with open(output_path, "w", encoding="utf-8") as f:
         json.dump(docs_list, f, ensure_ascii=False, indent=2)
 
+
 def main():
     PDF_DIRECTORY = "#PATH_TO_YOUR_PDF_DIRECTORY#"
     OUTPUT_JSON = "#PATH_TO_YOUR_JSON#"
-    
-    start_time = time.time()
+
+    time.time()
     indexing_pipeline, document_store = create_indexing_pipeline()
     process_pdfs(PDF_DIRECTORY, indexing_pipeline)
     save_to_json(document_store, OUTPUT_JSON)
 
+
 if __name__ == "__main__":
     main()