Python: Adding MongoDB Atlas Vector Search Connector (#2818)

### Motivation and Context
Resolves: #2591 

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### Description

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* Added in the `MongoDBAtlasMemoryStore`. A memory store abstraction
that facilitates connections between a MongoDB Atlas cluster to conduct
[Atlas Vector
Search](https://www.mongodb.com/products/platform/atlas-vector-search)
on the Microsoft Semantic Kernel.

* Leverages our async python driver
[motor](https://motor.readthedocs.io/en/stable/) to keep with the
heuristic of asynchronous code

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

### Callout
* When writing my test, I noticed there's a habit of other tests
conducting a `try` on imports. Grepping through those implementations, I
see some of them [encapsulate their respective libraries in function
calls rather than doing direct imports in the
module.](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/memory/chroma/chroma_memory_store.py#L55-L63)
Is this the general code pattern we should follow? Or is it fine to have
our imports at the top of the module fine?

---------

Co-authored-by: Steven Silvester <steven.silvester@ieee.org>
Co-authored-by: Abby Harrison <54643756+awharrison-28@users.noreply.github.com>
Co-authored-by: Abby Harrison <abby.harrison@microsoft.com>

.github/workflows/python-integration-tests.yml

@@ -90,6 +90,7 @@ jobs:
           Postgres__Connectionstr: ${{secrets.POSTGRES__CONNECTIONSTR}}
           AZURE_COGNITIVE_SEARCH_ADMIN_KEY: ${{secrets.AZURE_COGNITIVE_SEARCH_ADMIN_KEY}}
           AZURE_COGNITIVE_SEARCH_ENDPOINT: ${{secrets.AZURE_COGNITIVE_SEARCH_ENDPOINT}}
+          MONGODB_ATLAS_CONNECTION_STRING: ${{secrets.MONGODB_ATLAS_CONNECTION_STRING}}
         run: |
           if ${{ matrix.os == 'ubuntu-latest' }}; then
             docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
@@ -152,6 +153,7 @@ jobs:
           Postgres__Connectionstr: ${{secrets.POSTGRES__CONNECTIONSTR}}
           AZURE_COGNITIVE_SEARCH_ADMIN_KEY: ${{secrets.AZURE_COGNITIVE_SEARCH_ADMIN_KEY}}
           AZURE_COGNITIVE_SEARCH_ENDPOINT: ${{secrets.AZURE_COGNITIVE_SEARCH_ENDPOINT}}
+          MONGODB_ATLAS_CONNECTION_STRING: ${{secrets.MONGODB_ATLAS_CONNECTION_STRING}}
         run: |
           if ${{ matrix.os == 'ubuntu-latest' }}; then
             docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest

python/.env.example

@@ -5,6 +5,7 @@ AZURE_OPENAI_ENDPOINT=""
 AZURE_OPENAI_API_KEY=""
 AZURE_COGNITIVE_SEARCH_ENDPOINT=""
 AZURE_COGNITIVE_SEARCH_ADMIN_KEY=""
+MONGODB_ATLAS_CONNECTION_STRING=""
 PINECONE_API_KEY=""
 PINECONE_ENVIRONMENT=""
 POSTGRES_CONNECTION_STRING=""

python/pyproject.toml

@@ -16,6 +16,7 @@ regex = "^2023.6.3"
 openapi_core = "^0.18.0"
 prance = "^23.6.21.0"
 pydantic = "<2"
+motor = "^3.3.1"
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "3.3.3"

python/semantic_kernel/__init__.py

@@ -18,6 +18,7 @@
     azure_openai_settings_from_dot_env,
     bing_search_settings_from_dot_env,
     google_palm_settings_from_dot_env,
+    mongodb_atlas_settings_from_dot_env,
     openai_settings_from_dot_env,
     pinecone_settings_from_dot_env,
     postgres_settings_from_dot_env,
@@ -32,6 +33,7 @@
     "postgres_settings_from_dot_env",
     "pinecone_settings_from_dot_env",
     "bing_search_settings_from_dot_env",
+    "mongodb_atlas_settings_from_dot_env",
     "google_palm_settings_from_dot_env",
     "redis_settings_from_dot_env",
     "PromptTemplateConfig",

python/semantic_kernel/connectors/memory/mongodb_atlas/README.md

@@ -0,0 +1,52 @@
+# microsoft.semantic_kernel.connectors.memory.mongodb_atlas
+
+This connector uses [MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to implement Semantic Memory.
+
+## Quick Start
+
+1. Create [Atlas cluster](https://www.mongodb.com/docs/atlas/getting-started/)
+
+2. Create a collection
+
+3. Create [Vector Search Index](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector/) for the collection.
+The index has to be defined on a field called ```embedding```. For example:
+```
+{
+  "mappings": {
+    "dynamic": true,
+    "fields": {
+      "embedding": {
+        "dimension": 1024,
+        "similarity": "cosine",
+        "type": "knnVector"
+      }
+    }
+  }
+}
+```
+
+4. Create the MongoDB memory store
+```python
+import semantic_kernel as sk
+import semantic_kernel.connectors.ai.open_ai
+from semantic_kernel.connectors.memory.mongodb_atlas import (
+    MongoDBAtlasMemoryStore
+)
+
+kernel = sk.Kernel()
+
+...
+
+kernel.register_memory_store(memory_store=MongoDBAtlasMemoryStore(
+    # connection_string = if not provided pull from .env
+))
+...
+
+```
+
+## Important Notes
+
+### Vector search indexes
+In this version, vector search index management is outside of ```MongoDBAtlasMemoryStore``` scope.
+Creation and maintenance of the indexes have to be done by the user. Please note that deleting a collection
+(```memory_store.delete_collection_async```) will delete the index as well.

python/semantic_kernel/connectors/memory/mongodb_atlas/__init__.py

@@ -0,0 +1,5 @@
+from semantic_kernel.connectors.memory.mongodb_atlas.mongodb_atlas_memory_store import (
+    MongoDBAtlasMemoryStore,
+)
+
+__all__ = ["MongoDBAtlasMemoryStore"]