zencognify 0.1.2

Document ingestion and processing package with vector store and graph capabilities

Zencognify

A Python package for document ingestion and processing with vector store capabilities.

Installation

pip install zencognify

Features

• Document processing from URLs (PDF, DOC, etc.)
• Vector storage with Qdrant
• Bulk document upload
• Document chunking and metadata management
• OpenAI embeddings integration

Usage

Basic Setup

from cognify import VectorStore

vector_store = VectorStore(qdrant_url="http://localhost:6333")

Create Collection

vector_store.create_collection("my_documents")

Bulk Upload Documents

urls = [
    "https://example.com/document1.pdf",
    "https://example.com/document2.pdf"
]

results = vector_store.bulk_url_upload("my_documents", urls)
print(f"Uploaded {results['successful_uploads']} documents")

Retrieve Document Chunks

chunks = vector_store.get_document_chunks("my_documents", "document_id")

Semantic Search

results = vector_store.search("my_documents", "machine learning algorithms", top_k=5)
for doc, score in results:
    print(f"Score: {score:.3f}")
    print(f"Content: {doc.page_content[:200]}...")
    print(f"Source: {doc.metadata['source']}")
    print("---")

document_id = "your-document-id"
results = vector_store.search_by_document("my_documents", "neural networks", document_id, top_k=3)

Delete Documents

chunk_ids = ["chunk_id1", "chunk_id2"]
vector_store.delete_documents("my_documents", chunk_ids)

Requirements

• Python 3.12+
• Qdrant vector database
• OpenAI API key (for embeddings)

Dependencies

• langchain
• langchain-openai
• langchain-qdrant
• qdrant-client
• docling
• python-dotenv
• openai
• tiktoken

Environment Variables

Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key-here"

License

MIT License