How can I load various document types with Langchain's DirectoryLoader for Pinecone embedding?

Below is one approach using DirectoryLoader’s ability to map file extensions to specific loader classes. In this example, we define a loader mapping for different file types (PDF, Word, Excel, text, CSV, etc.) so that DirectoryLoader can select the right loader based on each file’s extension.

For example:

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from langchain.document_loaders import (
    DirectoryLoader,
    UnstructuredPDFLoader,
    UnstructuredWordDocumentLoader,
    UnstructuredExcelLoader,
    TextLoader,
)

# Define a mapping between file extensions and loader classes
loader_map = {
    ".pdf": UnstructuredPDFLoader,
    ".docx": UnstructuredWordDocumentLoader,
    ".txt": TextLoader,
    ".csv": TextLoader,        # Alternatively, if you have a CSVLoader, you can use that.
    ".xlsx": UnstructuredExcelLoader,
    ".xls": UnstructuredExcelLoader,
}

# Initialize DirectoryLoader with the loader_map and a glob that matches all files
loader = DirectoryLoader("path/to/files", glob="**/*", loader_map=loader_map)

# Load the documents
documents = loader.load()

Explanation

• loader_map: This dictionary specifies which loader should process files based on their extension. Adjust the loaders as needed for your use case.
• DirectoryLoader: When initialized with a glob pattern of "**/*", it will traverse the file directory recursively and automatically pick the appropriate loader class for each file using the mapping.
• Embeddings Process: Once the documents are loaded into LangChain Document objects, you can then pass them to the Pinecone retriever along with your vector embeddings process, as you usually do.

This setup allows you to work with a variety of file types while keeping your ingestion pipeline robust and easily extendable.