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code for vector store
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innovation_pathfinder_ai/vector_store/chroma_vector_store.py
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# got some of the code from https://diptimanrc.medium.com/rapid-q-a-on-multiple-pdfs-using-langchain-and-chromadb-as-local-disk-vector-store-60678328c0df
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import PyPDF2
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import io
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import os
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from langchain_community.vectorstores import Chroma
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.vectorstores import Chroma
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import chromadb
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from langchain_community.embeddings import HuggingFaceEmbeddings
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import dotenv
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dotenv.load_dotenv()
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VECTOR_DATABASE_LOCATION = os.getenv("VECTOR_DATABASE_LOCATION")
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def extract_text_from_pdf(file) -> list[str]:
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documents = []
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try:
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reader = PyPDF2.PdfReader(file)
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num_pages = len(reader.pages)
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for page_num in range(num_pages):
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page = reader.pages[page_num]
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text= page.extract_text() + "\n"
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documents.append(text)
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except Exception as e:
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print(e)
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finally:
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return documents
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def add_pdf_to_vector_store(
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vector_store:Chroma.from_documents,
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pdf_file_location:str,
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text_chunk_size=1000,
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text_chunk_overlap=10,
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) -> None:
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"""
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## Summary
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given the location of a pdf file this will chunk it's contents
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and store it the given vectorstore
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## Arguments
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vectorstore (vectorstore):
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pdf_file_location (str) : location of pdf file
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## Return
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None
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"""
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documents = []
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loader = PyPDFLoader(pdf_file_location)
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# documents.extend(loader.load())
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text_splitter = CharacterTextSplitter(
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chunk_size=text_chunk_size,
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chunk_overlap=text_chunk_overlap,
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)
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documents.extend(loader.load())
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chunked_documents = text_splitter.split_documents(documents)
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vectordb = Chroma.from_documents(
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documents=chunked_documents,
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embedding = HuggingFaceEmbeddings(),
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persist_directory=VECTOR_DATABASE_LOCATION,
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)
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def load_chunk_persist_pdf(
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pdf_folder_path: str = "mydir",
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vector_db_location:str = VECTOR_DATABASE_LOCATION,
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) -> Chroma:
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documents = []
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for file in os.listdir(pdf_folder_path):
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if file.endswith('.pdf'):
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pdf_path = os.path.join(pdf_folder_path, file)
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loader = PyPDFLoader(pdf_path)
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documents.extend(loader.load())
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
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chunked_documents = text_splitter.split_documents(documents)
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client = chromadb.Client()
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if client.list_collections():
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consent_collection = client.create_collection("consent_collection")
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else:
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print("Collection already exists")
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vectordb = Chroma.from_documents(
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documents=chunked_documents,
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embedding = HuggingFaceEmbeddings(),
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persist_directory=VECTOR_DATABASE_LOCATION,
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)
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vectordb.persist()
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return vectordb
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def load_vector_store(
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vector_store_location=os.getenv("VECTOR_DATABASE_LOCATION"),
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embeddings:chromadb.utils.embedding_functions = HuggingFaceEmbeddings(),
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) -> Chroma:
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"""
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## Summary
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get the vector_store
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## Arguments
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vector_store_location (str) : the location of the vector store
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embeddings (chromadb.utils.embedding_functions) : the function for embedding the data
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## Return
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returns the chroma db vector store
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"""
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db = Chroma(
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persist_directory=vector_store_location,
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embedding_function=embeddings,
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)
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return db
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if __name__ == "__main__":
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vector_db = load_vector_store()
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# pdf_file_location = "mydir/181000551.pdf"
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pdf_file_location = "/workspaces/InnovationPathfinderAI/2402.17764.pdf"
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add_pdf_to_vector_store(
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vector_store=vector_db,
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pdf_file_location=pdf_file_location,
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)
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