Update README.md

README.md

@@ -1,3 +1,80 @@
 ---
 license: apache-2.0
+task_categories:
+  - question-answering
+  - summarization
+  - conversational
+  - sentence-similarity
+language:
+  - en
+pretty_name: FAISS Vector Store of Embeddings of the Chartered Financial Analysts Level 1 Curriculum
+tags:
+  - faiss
+  - langchain
+  - instructor embeddings
+  - vector stores
+  - LLM
 ---
+Vector store of embeddings for CFA Level 1 Curriculum
+
+This is a faiss vector store created with Sentence Transformer embeddings using LangChain . Use it for similarity search, question answering or anything else that leverages embeddings! 😃
+
+Creating these embeddings can take a while so here's a convenient, downloadable one 🤗
+
+How to use
+
+Download data
+Load to use with LangChain
+pip install -qqq langchain sentence_transformers faiss-cpu huggingface_hub
+import os
+from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
+
+from langchain.vectorstores.faiss import FAISS
+from huggingface_hub import snapshot_download
+
+# download the vectorstore for the book you want
+
+cache_dir="cfa_level_1_cache"
+vectorstore = snapshot_download(repo_id="nickmuchi/CFA_Level_1_Text_Embeddings",
+                                repo_type="dataset",
+                                revision="main",
+                                allow_patterns=f"books/{book}/*", # to download only the one book
+                                cache_dir=cache_dir,
+                                )
+
+# get path to the `vectorstore` folder that you just downloaded
+# we'll look inside the `cache_dir` for the folder we want
+target_dir = f"cfa/cfa_level_1"
+
+# Walk through the directory tree recursively
+for root, dirs, files in os.walk(cache_dir):
+    # Check if the target directory is in the list of directories
+    if target_dir in dirs:
+        # Get the full path of the target directory
+        target_path = os.path.join(root, target_dir)
+
+# load embeddings
+# this is what was used to create embeddings for the text
+
+embed_instruction = "Represent the financial paragraph for document retrieval: "
+query_instruction = "Represent the question for retrieving supporting documents: "
+
+model_sbert = "sentence-transformers/all-mpnet-base-v2"
+sbert_emb = HuggingFaceEmbeddings(model_name=model_sbert)
+
+model_instr = "hkunlp/instructor-large"
+instruct_emb = HuggingFaceInstructEmbeddings(model_name=model_instr,
+                                             embed_instruction=embed_instruction,
+                                             query_instruction=query_instruction)
+
+# load vector store to use with langchain
+docsearch = FAISS.load_local(folder_path=target_path, embeddings=sbert_emb)
+
+# similarity search
+question = "How do you hedge the interest rate risk of an MBS?"
+search = docsearch.similarity_search(question, k=4)
+
+for item in search:
+    print(item.page_content)
+    print(f"From page: {item.metadata['page']}")
+    print("---")