feat(vector): add Elasticsearch backend with hybrid similarity search

[vigneshn16]

Elasticsearch as a vector store backend, enabling hybrid search functionality — combining both semantic vector similarity and text-based scoring. It enhances document retrieval quality, especially when dealing with large documents or unstructured data chunks.

Changes Introduced

• Added elasticsearch_vector.py with support for vector storage and similarity search using Elasticsearch
• Updated factory.py to register and initialize the Elasticsearch vector store
• Modified document_routes.py to route document operations through the new vector store
• Extended config.py to include Elasticsearch-specific settings
• Updated .gitignore, requirements.txt, and requirements.lite.txt with necessary additions