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46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import os
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from langchain_community.document_loaders import UnstructuredFileLoader
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Redis
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from rag_redis.config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL
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def ingest_documents():
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"""
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Ingest PDF to Redis from the data/ directory that
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contains Edgar 10k filings data for Nike.
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"""
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# Load list of pdfs
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company_name = "Nike"
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data_path = "data/"
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doc = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
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print("Parsing 10k filing doc for NIKE", doc)
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1500, chunk_overlap=100, add_start_index=True
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)
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loader = UnstructuredFileLoader(doc, mode="single", strategy="fast")
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chunks = loader.load_and_split(text_splitter)
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print("Done preprocessing. Created", len(chunks), "chunks of the original pdf")
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# Create vectorstore
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embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
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_ = Redis.from_texts(
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# appending this little bit can sometimes help with semantic retrieval
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# especially with multiple companies
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texts=[f"Company: {company_name}. " + chunk.page_content for chunk in chunks],
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metadatas=[chunk.metadata for chunk in chunks],
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embedding=embedder,
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index_name=INDEX_NAME,
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index_schema=INDEX_SCHEMA,
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redis_url=REDIS_URL,
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)
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if __name__ == "__main__":
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ingest_documents()
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