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import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import UnstructuredFileLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
def ingest_documents():
"""
Ingest PDF to Redis from the data/ directory that
contains Edgar 10k filings data for Nike.
"""
# Load list of pdfs
data_path = "data/"
doc = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
print("Parsing 10k filing doc for NIKE", doc)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1500, chunk_overlap=100, add_start_index=True
)
loader = UnstructuredFileLoader(doc, mode="single", strategy="fast")
chunks = loader.load_and_split(text_splitter)
print("Done preprocessing. Created", len(chunks), "chunks of the original pdf")
# Create vectorstore
embedder = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
documents = []
for chunk in chunks:
doc = Document(page_content=chunk.page_content, metadata=chunk.metadata)
documents.append(doc)
# Add to vectorDB
_ = Chroma.from_documents(
documents=documents,
collection_name="xeon-rag",
embedding=embedder,
persist_directory="/tmp/xeon_rag_db",
)
if __name__ == "__main__":
ingest_documents()
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