Update README.md

README.md

@@ -2,4 +2,68 @@
 license: other
 license_name: wikimedia
 license_link: https://dumps.wikimedia.org/legal.html
+language:
+- 'no'
+- nb
 ---
+
+# This is a FAISS vectordb from a Norwegian Wikipedia dump from 2023-09 embedded with NbAiLab/nb-sbert-base.
+### This can be used to augment a chatbot with RAG (norwegian bokmål language). 
+
+Only the article abstracts are processed, but they seemed detailed enough. The 'url' in the metadata points to the original article.
+Each abstract is embedded as a 768 dimensional vector with this model: NbAiLab/nb-sbert-base
+
+### Example usage (Python):
+
+```python
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import time
+
+embedder = HuggingFaceEmbeddings(model_name='NbAiLab/nb-sbert-base')
+
+qs = [
+  'Kven er Beyonce?',
+  'Hva skjedde i 2012?',
+  'Hvilke musikkfestivalar kan du anbefale?',
+]
+
+db = FAISS.load_local('nowiki_faiss_sbert_all', embedder, allow_dangerous_deserialization=True)
+
+starttime=time.time()
+
+for q in qs :
+  print('----\n',q)
+  r = db.similarity_search_with_score(q)
+  print(r)
+
+print('questions took ',time.time()-starttime,' s. ')
+
+```
+
+### More info about the Wikipedia source:
+
+https://dumps.wikimedia.org/
+
+https://dumps.wikimedia.org/other/enterprise_html/
+
+### License and guidelines:
+
+https://dumps.wikimedia.org/legal.html
+
+https://foundation.wikimedia.org/wiki/Legal:Developer_app_guidelines
+
+### Embedder model:
+
+https://huggingface.co/NbAiLab/nb-sbert-base
+
+### FAISS vectordb:
+
+https://python.langchain.com/docs/integrations/vectorstores/faiss
+
+---
+license: other
+license_name: wikimedia
+license_link: https://dumps.wikimedia.org/legal.html
+---