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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
from pathlib import Path
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from langchain.text_splitter import CharacterTextSplitter
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import faiss
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from langchain.vectorstores import FAISS
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from langchain.embeddings import OpenAIEmbeddings
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import pickle
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import dotenv
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import os
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dotenv.load_dotenv()
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# Here we load in the data in the format that Notion exports it in.
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ps = list(Path("pandasdocs/").glob("**/*.rst"))
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# parse all child directories
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data = []
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sources = []
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for p in ps:
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with open(p) as f:
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data.append(f.read())
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sources.append(p)
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# Here we split the documents, as needed, into smaller chunks.
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# We do this due to the context limits of the LLMs.
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text_splitter = CharacterTextSplitter(chunk_size=1500, separator="\n")
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docs = []
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metadatas = []
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for i, d in enumerate(data):
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splits = text_splitter.split_text(d)
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docs.extend(splits)
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metadatas.extend([{"source": sources[i]}] * len(splits))
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# Here we create a vector store from the documents and save it to disk.
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store = FAISS.from_texts(docs, OpenAIEmbeddings(), metadatas=metadatas)
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faiss.write_index(store.index, "docs.index")
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store.index = None
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with open("faiss_store.pkl", "wb") as f:
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pickle.dump(store, f)
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