Create README.md

README.md

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+import os
+import pandas as pd
+from pathlib import Path
+
+
+import retriv
+retriv.set_base_path("./retriv_wiki_de")
+
+
+from retriv import DenseRetriever
+
+
+"""
+# Uncomment if you wanna make your own index
+dr = DenseRetriever(
+  index_name="wiki_de-index_sentence_transf-BAAI/bge-m3_title_only_fullarticles",
+  model="BAAI/bge-m3",  
+  normalize=True,
+  max_length=512,
+  use_ann=True,
+)
+
+
+dr = dr.index_file(
+  path="./wikipedia_de_filtered_fullarticles.csv",  # File kind is automatically inferred
+  embeddings_path=None,       # Default value
+  use_gpu=True,              # Default value
+  batch_size=32,             # Default value
+  show_progress=True,         # Default value
+  callback=lambda doc: {      # Callback defaults to None.
+    "id": doc["id"],
+    "text": doc["title"],          
+  },
+)
+"""
+
+from retriv import DenseRetriever
+
+# loading the wikipedia de text data 
+file_path = "./wikipedia_de_filtered_fullarticles.csv"   # CSV with fulltext
+df = pd.read_csv(file_path)
+
+file_path = "./wikipedia_de_filtered_300wordchunks.csv"   # CSV with fulltext
+df2 = pd.read_csv(file_path)
+
+
+# loading the retrievers
+dr = DenseRetriever.load("wiki_de-index_sentence_transf-BAAI/bge-m3_title_only_fullarticles")  # the embeddings here are made from the titles of the wikipedia pages, but can be matched to the full texts in the wikipedia_de_filtered_fullarticles.csv
+
+
+result =  dr.search(
+  query="was is der doppelspaltversuch?",    # What to search for        
+  return_docs=True,          # Default value, return the text of the documents
+  cutoff=3,                # Default value, number of results to return
+)
+print(df)
+
+for res in result:
+
+  id_query = int(res["id"])-1
+  row = df.iloc[id_query]
+
+  print(row)
+  # Extracting 'text' and 'url' from the resulting row
+  result_text = row['text']
+  result_url = row['url']
+  print(result_url,result_text[:1000])
+
+
+
+print("###################")
+print("+++++++++++++++++++")
+
+
+
+dr2 = DenseRetriever.load("wiki_de-index_sentence_transf-BAAI/bge-m3") # the embeddings here are made from 300 word segments of the articles. The IDs point to wikipedia_de_filtered_300wordchunks.csv
+
+
+
+result =  dr2.search(
+  query="was is der doppelspaltversuch?",    # What to search for        
+  return_docs=True,          # Default value, return the text of the documents
+  cutoff=3,                # Default value, number of results to return
+)
+
+
+for res in result:
+
+  id_query = int(res["id"])-1    # the "id" values start with 1, not 0 , -> need to substract 1 ;)
+  row = df2.iloc[id_query]
+
+  print(row)
+  # Extracting 'text' and 'url' from the resulting row
+  result_text = row['text']
+  result_url = row['url']
+  print(result_url,result_text)
+
+
+print("########")