prevectorchunks-core 0.1.3

A Python module that allows conversion of a document into chunks to be inserted into Pinecone vector database

📚 PreVectorChunks

A lightweight utility for document chunking and vector database upserts — designed for developers building RAG (Retrieval-Augmented Generation) solutions.

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✨ Who Needs This Module?

Any developer working with:

• RAG pipelines
• Vector Databases (like Pinecone, Weaviate, etc.)
• AI applications requiring similar content retrieval

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🎯 What Does This Module Do?

This module helps you:

• Chunk documents into smaller fragments
• Insert (upsert) fragments into a vector database
• Fetch & update existing chunks from a vector database

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📦 Installation

pip install prevectorchunks-core

How to import in a file:

from PreVectorChunks.services import chunk_documents_crud_vdb

#How to use Pinecone and OpenAI:
#Use a .env file in your project root to configure API keys:

PINECONE_API_KEY=YOUR_API_KEY
OPENAI_API_KEY=YOUR_API_KEY

#how to call relevant functions:
#Four key functions that you can call are below: 
#function that chunks any document 

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📄 Functions

1. chunk_documents

chunk_documents(instructions, file_path="content_playground/content.json", chunk_size=200)

Splits the content of a document into smaller, manageable chunks.

Parameters

• instructions (dict or str): Additional rules or guidance for how the document should be split.
  • Example: "split my content by biggest headings"
• file_path (str): Path to the input JSON/text file containing the content or content of the file. Default: "content_playground/content.json".
• chunk_size (int): Number of words per chunk (default = 200).

Returns

• A list of chunked strings including a unique id, a meaningful title and chunked text

Use Cases

• Preparing text for LLM ingestion
• Splitting text by structure (headings, paragraphs)
• Vector database indexing

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2. chunk_and_upsert_to_vdb

chunk_and_upsert_to_vdb(index_n, instructions, file_path="content_playground/content.json", chunk_size=200)

Splits a document into chunks (via chunk_documents) and inserts them into a Vector Database.

Parameters

• index_n (str): The name of the VDB index where chunks should be stored. for example, in pinecone, we can have an index 'dl-doco'
• instructions (dict or str): Rules for splitting content (same as chunk_documents).
• file_path (str): Path to the document file or content of the file. Default: "content_playground/content.json".
• chunk_size (int): Max words per chunk. Default: 200.

Returns

• Confirmation of successful insert into the VDB.

Use Cases

• Automated document preprocessing and storage for vector search
• Preparing embeddings for semantic search

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3. fetch_vdb_chunks_grouped_by_document_name

fetch_vdb_chunks_grouped_by_document_name(index_n)

Fetches existing chunks stored in the Vector Database, grouped by document name.

Parameters

• index_n (str): The name of the VDB index.

Returns

• A dictionary or list of chunks grouped by document name.

Use Cases

• Retrieving all chunks of a specific document
• Verifying what content has been ingested into the VDB

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4. update_vdb_chunks_grouped_by_document_name

update_vdb_chunks_grouped_by_document_name(index_n, dataset)

Updates existing chunks in the Vector Database by document name.

Parameters

• index_n (str): The name of the VDB index.
• dataset (dict or list): The new data (chunks) to update existing entries.

Returns

• Confirmation of update status.

Use Cases

• Keeping VDB chunks up to date when documents change
• Re-ingesting revised or corrected content

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🚀 Example Workflow

# Step 1: Chunk a document
chunks = chunk_documents(
    instructions="split my content by biggest headings",
    file_path="content_playground/content.json",
    chunk_size=150
)

# Step 2: Insert chunks into VDB
chunk_and_upsert_to_vdb("my_index", instructions="split by headings")

# Step 3: Fetch stored chunks
docs = fetch_vdb_chunks_grouped_by_document_name("my_index")

# Step 4: Update chunks if needed
update_vdb_chunks_grouped_by_document_name("my_index", dataset=docs)

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🛠 Use Cases

• Preprocessing documents for LLM ingestion
• Semantic search and Q&A systems
• Vector database indexing and retrieval
• Maintaining versioned document chunks