adding mongodb vector_io module

updated mongodb.py from print to log

add documentation for mongodb vector search module

changed insert to update mongodb

bug fix mongodb json object conversion error

docs/source/providers/vector_io/mongodb.md

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+---
+orphan: true
+---
+# MongoDB Atlas
+
+[MongoDB Atlas](https://www.mongodb.com/atlas) is a cloud database service that can be used as a vector store provider for Llama Stack. It supports vector search capabilities through its Atlas Vector Search feature, allowing you to store and query vectors within your MongoDB database.
+
+## Features
+MongoDB Atlas Vector Search supports:
+- Store embeddings and their metadata
+- Vector search with multiple algorithms (cosine similarity, euclidean distance, dot product)
+- Hybrid search (combining vector and keyword search)
+- Metadata filtering
+- Scalable vector indexing
+- Managed cloud infrastructure
+
+## Usage
+
+To use MongoDB Atlas in your Llama Stack project, follow these steps:
+
+1. Create a MongoDB Atlas account and cluster.
+2. Configure your Atlas cluster to enable Vector Search.
+3. Configure your Llama Stack project to use MongoDB Atlas.
+4. Start storing and querying vectors.
+
+## Installation
+
+You can install the MongoDB Python driver using pip:
+
+```bash
+pip install pymongo
+```
+
+## Documentation
+See [MongoDB Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/) for more details about vector search capabilities in MongoDB Atlas.