expand matric

.github/workflows/mongodb_atlas.yml

@@ -30,9 +30,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # MongoDB Atlas run against a live instance so we should avoid overwhelming it
-        os: [ubuntu-latest]
-        python-version: ['3.10']
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ['3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v4

integrations/mongodb_atlas/examples/example.py

@@ -1,12 +1,42 @@
-from pymongo.mongo_client import MongoClient
-from pymongo.server_api import ServerApi
-
-uri = "mongodb+srv://sarazanzottera:[email protected]/?retryWrites=true&w=majority"
-# Create a new client and connect to the server
-client = MongoClient(uri, server_api=ServerApi("1"))
-# Send a ping to confirm a successful connection
-try:
-    client.admin.command("ping")
-    print("Pinged your deployment. You successfully connected to MongoDB!")
-except Exception as e:
-    print(e)
+# Install required packages for this example, including mongodb-atlas-haystack and other libraries needed
+# for Markdown conversion and embeddings generation. Use the following command:
+#
+# pip install mongodb-atlas-haystack markdown-it-py mdit_plain "sentence-transformers>=2.2.0"
+#
+# Download some Markdown files to index.
+# git clone https://github.com/anakin87/neural-search-pills
+
+import glob
+from haystack import Pipeline
+from haystack.components.converters import MarkdownToDocument
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
+
+# Provide your connection string
+connection_string = input("Enter your MongoDB Atlas connection string: ")
+
+# Initialize the document store
+document_store = MongoDBAtlasDocumentStore(
+    mongo_connection_string=connection_string,
+    database_name="haystack_test",
+    collection_name="test_collection",
+)
+
+# Create the indexing Pipeline and index some documents
+file_paths = glob.glob("neural-search-pills/pills/*.md")
+
+
+indexing = Pipeline()
+indexing.add_component("converter", MarkdownToDocument())
+indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))
+indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "splitter")
+indexing.connect("splitter", "embedder")
+indexing.connect("embedder", "writer")
+
+indexing.run({"converter": {"sources": file_paths}})
+
+print("Indexed documents:" + document_store.count_documents() + "\n - ".join(document_store.filter_documents()))