Library API Documentation

The page contains simple library usage examples and the module-level documentation for each of the importable modules in docp-dbi.

Use Cases

To save digging through the documentation for each module and cobbling together what a ‘standard use case’ may look like, a couple have been provided here.

Create a ChromaDB interface

>>> from docp_dbi import ChromaDB

>>> db = ChromaDB(path='/path/to/chromadb', collection='test-collection')

# Display a list of all collections in the database.
>>> db.client.list_collections()

# Debug: Retrieve records from the database.
>>> records = db.show_all()

Load a new PDF document into ChromaDB

>>> from docp_dbi import ChromaDB
>>> from docp_parsers import PDFParser
>>> from langchain_text_splitters import RecursiveCharacterTextSplitter

# Parse the PDF document.
>>> pdf = PDFParser(path='/path/to/documents/rag-pipelines-how-to.pdf')
>>> pdf.extract_text()

# Setup a text splitter (for chunking the document).
>>> splitter = RecursiveCharacterTextSplitter(
...     separators=['\n\n\n', '\n\n', '\n', '.'],
...     chunk_size=512,
...     chunk_overlap=128
... )
# Split the document for storage.
>>> docs = splitter.split_documents(pdf.doc.documents)

# Create a database interface, using an offline, local user-defined embedding model.
>>> db = ChromaDB(path='/path/to/databases/chroma/',
...               collection='test',
...               embedding_model_path='/path/to/models/sentence-transformers/all-MiniLM-L6-v2',
...               offline=True)
# Embed and store the document chunks.
>>> db.add_documents(documents=docs)

# Run your first query.
>>> result = db.collection.query(query_texts=['How do I implement a RAG pipeline?'])

Module Documentation

In addition to the module-level documentation, most of the public classes and/or methods come with one or more usage examples and access to the source code itself.

There are two type of modules listed here:

  • Those whose API is designed to be accessed by the user/caller

  • Those which are designated ‘private’ and designed only for internal use

We’ve exposed both here for completeness and to aid in understanding how the library is implemented:

Last updated: 24 Jan 2026