Update README.md

README.md

@@ -1,3 +1,77 @@
 ---
 license: apache-2.0
+task_categories:
+- question-answering
+- summarization
+- conversational
+- sentence-similarity
+language:
+- en
+pretty_name: Embeddings for George Orwell's 1984
+tags:
+- faiss
+- langchain
+- instructor embeddings
+- vector stores
+- books
+- LLM
 ---
+# Vector store of embeddings for the book "1984" by George Orwell
+
+This is a [faiss](https://github.com/facebookresearch/faiss) vector store created with [instructor embeddings](https://github.com/HKUNLP/instructor-embedding) using [LangChain](https://langchain.readthedocs.io/en/latest/modules/indexes/examples/embeddings.html#instructembeddings) . 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
+
+1. Download data
+2. Load to use with LangChain
+
+pip install -qqq langchain InstructorEmbedding sentence_transformers faiss-cpu huggingface_hub
+
+```python
+import os
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.vectorstores.faiss import FAISS
+from huggingface_hub import snapshot_download
+
+# download the `vectorstore` folder
+cache_dir="orwell_faiss"
+vectorstore = snapshot_download(repo_id="calmgoose/orwell-1984_faiss-instructembeddings",
+                                repo_type="dataset",
+                                revision="main",
+                                allow_patterns="vectorstore/*",
+                                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 = "vectorstore"
+
+# 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 book
+embeddings = HuggingFaceInstructEmbeddings(
+    embed_instruction="Represent the book passage for retrieval: ",
+    query_instruction="Represent the question for retrieving supporting texts from the book passage: "
+    )
+
+# load vector store to use with langchain
+docsearch = FAISS.load_local(folder_path=target_path, embeddings=embeddings)
+
+# similarity search
+question = "Who is big brother?"
+search = docsearch.similarity_search(question, k=4)
+
+for item in search:
+    print(item.page_content)
+    print(f"From page: {item.metadata['page']}")
+    print("---")
+```