Updating the README to add more examples.

README.md

@@ -1,21 +1,20 @@
 # AstraPy
 
-AstraPy is a Pythonic SDK for [DataStax Astra](https://astra.datastax.com) and [Stargate](https://stargate.io/)
+AstraPy is a Pythonic SDK for [DataStax Astra](https://astra.datastax.com)
 
 ## Resources
 
 - [DataStax Astra](https://astra.datastax.com)
-- [Stargate](https://stargate.io/)
 
 ## Getting Started
 
-Install AstraPy
+### Install AstraPy
 
 ```bash
 pip install astrapy
 ```
 
-Setup your Astra client
+### Setup your Astra client
 
 Create a .env file with the appropriate values, or use the 'astra' cli to do the same.
 
@@ -42,22 +41,47 @@ api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
 
 # Initialize our vector db
 astra_db = AstraDB(token=token, api_endpoint=api_endpoint)
+```
+
+## Create a collection
+
+Create a vector collection with dimension of 5
+If you were using OpenAI here you would use 1376 as the value
+
+```
+astra_db.create_collection(collection_name="collection_test", dimension=5)
 
-# Possible Operations
+# Create a collection and then delete it
 astra_db.create_collection(collection_name="collection_test_delete", dimension=5)
 astra_db.delete_collection(collection_name="collection_test_delete")
-astra_db.create_collection(collection_name="collection_test", dimension=5)
 
+# Double check the collections in your vector store
+astra_db.get_collections()
+```
+
+At this point you have a collection named "collection_test" to do the following operations
+
+In the next section, you will be creating the object for your collection
+
+## Create collection object
+
+```
 # Collections
-astra_db_collection = AstraDBCollection(
+collection = AstraDBCollection(
     collection_name="collection_test", astra_db=astra_db
 )
 # Or...
-astra_db_collection = AstraDBCollection(
+collection = AstraDBCollection(
     collection_name="collection_test", token=token, api_endpoint=api_endpoint
 )
+```
+
+## Inserting a document into your collection (vector)
+
+Here is an example of inserting a vector object into your vector store (collection), followed by running a find command to retrieve the document. The first find command fails because that object does not exist. The second find command should succeed.
 
-astra_db_collection.insert_one(
+```
+collection.insert_one(
     {
         "_id": "5",
         "name": "Coded Cleats Copy",
@@ -66,8 +90,203 @@ astra_db_collection.insert_one(
     }
 )
 
-astra_db_collection.find_one({"name": "potato"})  # Not found
-astra_db_collection.find_one({"name": "Coded Cleats Copy"})
+collection.find_one({"name": "potato"})  # Not found
+collection.find_one({"name": "Coded Cleats Copy"})
+```
+
+## Inserting multiple documents into your collection (non-vector)
+
+Here is an example of inserting a number of documents into your collection. Note that the json object is 'documents' here, not 'document' as it is in insert_one.
+
+In the first insert, the default behavior is in place. If you are inserting documents that already exist, you will get an error and the process will end.
+
+These two examples are using non-vector objects.
+
+```
+    documents = [
+        {
+            "_id": "id_1",
+            "first_name": "Dang",
+            "last_name": "Son",
+        },
+        {
+            "_id": "id_2",
+            "first_name": "Yep",
+            "last_name": "Boss",
+        },
+    ]
+    response = collection.insert_many(documents=documents)
+```
+
+In the following insert_many example, options are set so that it skips errors and only inserts successful entries.
+
+```
+    documents2 = [
+        {
+            "_id": "id_2",
+            "first_name": "Yep",
+            "last_name": "Boss",
+        },
+        {
+            "_id": "id_3",
+            "first_name": "Miv",
+            "last_name": "Fuff",
+        },
+    ]
+    response = collection.insert_many(
+        documents=documents2,
+        partial_failures_allowed=True,
+    )
+```
+
+## Insert many (vector)
+
+The following code inserts vector objects into the collection in your vector store.
+
+    json_query = [
+        {
+            "_id": str(uuid.uuid4()),
+            "name": "Coded Cleats",
+            "description": "ChatGPT integrated sneakers that talk to you",
+            "$vector": [0.1, 0.15, 0.3, 0.12, 0.05],
+        },
+        {
+            "_id": str(uuid.uuid4()),
+            "name": "Logic Layers",
+            "description": "An AI quilt to help you sleep forever",
+            "$vector": [0.45, 0.09, 0.01, 0.2, 0.11],
+        },
+        {
+            "_id": vv_uuid,
+            "name": "Vision Vector Frame",
+            "description": "Vision Vector Frame - A deep learning display that controls your mood",
+            "$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
+        },
+    ]
+
+    res = collection.insert_many(documents=json_query)
+
+## Create a subdocument
+
+The following code uses update to create or update a sub-document under one of your existing documents.
+
+```
+    document = collection.update_one(
+        filter={"_id": "id_1"},
+        update={"$set": {"name": "Eric"}},
+    )
+
+    document = collection.find_one(filter={"_id": "id_1"})
+```
+
+## Create a document without an ID
+
+```
+response = collection.insert_one(
+        document={
+            "first_name": "New",
+            "last_name": "Guy",
+        }
+    )
+
+document = collection.find_one(filter={"first_name": "New"})
+```
+
+## Update a document
+
+```
+collection.update_one(
+    filter={"_id": cliff_uuid},
+    update={"$set": {"name": "Bob"}},
+)
+
+document = collection.find_one(filter={"_id": "id_1"})
+```
+
+## Replace a non-vector document
+
+```
+collection.find_one_and_replace(
+        filter={"_id": "id_1"},
+        replacement={
+            "_id": "id_1",
+            "addresses": {
+                "work": {
+                    "city": "New York",
+                    "state": "NY",
+                }
+            },
+        },
+    )
+document = collection.find_one(filter={"_id": "id_1"})
+document_2 = collection.find_one(
+        filter={"_id": cliff_uuid}, projection={"addresses.work.city": 1}
+    )
+```
+
+## Delete a subdocument
+
+```
+@pytest.mark.describe("should delete a subdocument")
+def test_delete_subdocument(collection, "id_1"):
+    response = collection.delete_subdocument(id="id_1", subdoc="addresses")
+    document = collection.find(filter={"_id": "id_1"})
+```
+
+## Delete a document
+
+```
+response = collection.delete(id="id_1")
+```
+
+## Find documents using vector search
+
+```
+sort = {"$vector": [0.15, 0.1, 0.1, 0.35, 0.55]}
+options = {"limit": 100}
+
+document = collection.find(sort=sort, options=options)
+```
+
+## Find documents using vector search and projection"
+
+```
+sort = {"$vector": [0.15, 0.1, 0.1, 0.35, 0.55]}
+options = {"limit": 100}
+projection = {"$vector": 1, "$similarity": 1}
+
+document = collection.find(sort=sort, options=options, projection=projection)
+```
+
+## Find one and update with vector search
+
+@pytest.mark.describe("Find one and update with vector search")
+
+```
+sort = {"$vector": [0.15, 0.1, 0.1, 0.35, 0.55]}
+update = {"$set": {"status": "active"}}
+options = {"returnDocument": "after"}
+
+result = collection.find_one_and_update(sort=sort, update=update, options=options)
+
+document = collection.find_one(filter={"status": "active"})
+```
+
+## Find one and replace with vector search
+
+```
+sort = {"$vector": [0.15, 0.1, 0.1, 0.35, 0.55]}
+replacement = {
+    "_id": vv_uuid,
+    "name": "Vision Vector Frame",
+    "description": "Vision Vector Frame - A deep learning display that controls your mood",
+    "$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
+    "status": "inactive",
+}
+options = {"returnDocument": "after"}
+
+collection.find_one_and_replace(sort=sort, replacement=replacement, options=options)
+document = collection.find_one(filter={"name": "Vision Vector Frame"})
 ```
 
 ### More Information