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