Fix: Qdrant query count not optional (#972)

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Changed
 
 ### Fixed
+- Parameter `count` for `QdrantVectorStoreDriver.query` now optional as per documentation.
 
 ## [0.28.2] - 2024-07-12
 ### Fixed

docs/griptape-framework/drivers/vector-store-drivers.md

@@ -24,7 +24,6 @@ The [LocalVectorStoreDriver](../../reference/griptape/drivers/vector/local_vecto
 
 ```python
 import os 
-from griptape.artifacts import BaseArtifact
 from griptape.drivers import LocalVectorStoreDriver, OpenAiEmbeddingDriver
 from griptape.loaders import WebLoader
 
@@ -40,16 +39,11 @@ artifacts = WebLoader(max_tokens=100).load("https://www.griptape.ai")
 # Upsert Artifacts into the Vector Store Driver
 [vector_store_driver.upsert_text_artifact(a, namespace="griptape") for a in artifacts]
 
-results = vector_store_driver.query(
-    "creativity",
-    count=3,
-    namespace="griptape"
-)
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
 print("\n\n".join(values))
-
 ```
 
 ### Griptape Cloud Knowledge Base
@@ -58,7 +52,6 @@ The [GriptapeCloudKnowledgeBaseVectorStoreDriver](../../reference/griptape/drive
 
 ```python
 import os 
-from griptape.artifacts import BaseArtifact
 from griptape.drivers import GriptapeCloudKnowledgeBaseVectorStoreDriver
 
 
@@ -68,12 +61,11 @@ gt_cloud_knowledge_base_id = os.environ["GRIPTAPE_CLOUD_KB_ID"]
 
 vector_store_driver = GriptapeCloudKnowledgeBaseVectorStoreDriver(api_key=gt_cloud_api_key, knowledge_base_id=gt_cloud_knowledge_base_id)
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
 print("\n\n".join(values))
-
 ```
 
 ### Pinecone
@@ -86,50 +78,28 @@ The [PineconeVectorStoreDriver](../../reference/griptape/drivers/vector/pinecone
 Here is an example of how the Driver can be used to load and query information in a Pinecone cluster:
 
 ```python
-import os
-import hashlib
-import json
-from urllib.request import urlopen
+import os 
 from griptape.drivers import PineconeVectorStoreDriver, OpenAiEmbeddingDriver
+from griptape.loaders import WebLoader
 
-def load_data(driver: PineconeVectorStoreDriver) -> None:
-    response = urlopen(
-        "https://raw.githubusercontent.com/wedeploy-examples/"
-        "supermarket-web-example/master/products.json"
-    )
-
-    for product in json.loads(response.read()):
-        driver.upsert_text(
-            product["description"],
-            vector_id=hashlib.md5(product["title"].encode()).hexdigest(),
-            meta={
-                "title": product["title"],
-                "description": product["description"],
-                "type": product["type"],
-                "price": product["price"],
-                "rating": product["rating"],
-            },
-            namespace="supermarket-products",
-        )
 
 # Initialize an Embedding Driver
 embedding_driver = OpenAiEmbeddingDriver(api_key=os.environ["OPENAI_API_KEY"])
 
 vector_store_driver = PineconeVectorStoreDriver(
     api_key=os.environ["PINECONE_API_KEY"],
     environment=os.environ["PINECONE_ENVIRONMENT"],
-    index_name=os.environ['PINECONE_INDEX_NAME'],
+    index_name=os.environ["PINECONE_INDEX_NAME"],
     embedding_driver=embedding_driver,
 )
 
-load_data(vector_store_driver)
+# Load Artifacts from the web
+artifacts = WebLoader(max_tokens=100).load("https://www.griptape.ai")
 
-results = vector_store_driver.query(
-    "fruit",
-    count=3,
-    filter={"price": {"$lte": 15}, "rating": {"$gte": 4}},
-    namespace="supermarket-products",
-)
+# Upsert Artifacts into the Vector Store Driver
+[vector_store_driver.upsert_text_artifact(a, namespace="griptape") for a in artifacts]
+
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -175,7 +145,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -227,7 +197,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -298,7 +268,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -341,7 +311,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -388,7 +358,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -450,7 +420,7 @@ vector_store_driver.upsert_text_artifacts(
     }
 )
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 
@@ -468,54 +438,40 @@ Here is an example of how the Driver can be used to query information in a Qdran
 
 ```python
 import os
-from griptape.drivers import QdrantVectorStoreDriver, HuggingFaceHubEmbeddingDriver
-from griptape.tokenizers import HuggingFaceTokenizer
+from griptape.drivers import QdrantVectorStoreDriver, OpenAiEmbeddingDriver
 from griptape.loaders import WebLoader
 
 # Set up environment variables
-embedding_model_name = "sentence-transformers/all-MiniLM-L6-v2"
 host = os.environ["QDRANT_CLUSTER_ENDPOINT"]
-huggingface_token = os.environ["HUGGINGFACE_HUB_ACCESS_TOKEN"]
+api_key = os.environ["QDRANT_CLUSTER_API_KEY"]
 
-# Initialize HuggingFace Embedding Driver
-embedding_driver = HuggingFaceHubEmbeddingDriver(
-    api_token=huggingface_token,
-    model=embedding_model_name,
-    tokenizer=HuggingFaceTokenizer(model=embedding_model_name, max_output_tokens=512),
-)
+# Initialize an Embedding Driver.
+embedding_driver = OpenAiEmbeddingDriver(api_key=os.environ["OPENAI_API_KEY"])
 
-# Initialize Qdrant Vector Store Driver
 vector_store_driver = QdrantVectorStoreDriver(
     url=host,
     collection_name="griptape",
     content_payload_key="content",
     embedding_driver=embedding_driver,
-    api_key=os.environ["QDRANT_CLUSTER_API_KEY"],
+    api_key=api_key,
 )
 
 # Load Artifacts from the web
 artifacts = WebLoader().load("https://www.griptape.ai")
 
-# Encode text to get embeddings
-embeddings = embedding_driver.embed_text_artifact(artifacts[0])
-
 # Recreate Qdrant collection
 vector_store_driver.client.recreate_collection(
     collection_name=vector_store_driver.collection_name,
     vectors_config={
-        "size": len(embeddings),
+        "size": 1536,
         "distance": vector_store_driver.distance
     },
 )
 
-# Upsert vector into Qdrant
-vector_store_driver.upsert_vector(
-    vector=embeddings,
-    vector_id=str(artifacts[0].id),
-    content=artifacts[0].value
-)
+# Upsert Artifacts into the Vector Store Driver
+[vector_store_driver.upsert_text_artifact(a, namespace="griptape") for a in artifacts]
 
-results =vector_store_driver.query(query="What is griptape?")
+results = vector_store_driver.query(query="What is griptape?")
 
 values = [r.to_artifact().value for r in results]
 

griptape/drivers/vector/qdrant_vector_store_driver.py

@@ -106,7 +106,9 @@ def query(
         query_vector = self.embedding_driver.embed_string(query)
 
         # Create a search request
-        results = self.client.search(collection_name=self.collection_name, query_vector=query_vector, limit=count)
+        request = {"collection_name": self.collection_name, "query_vector": query_vector, "limit": count}
+        request = {k: v for k, v in request.items() if v is not None}
+        results = self.client.search(**request)
 
         # Convert results to QueryResult objects
         query_results = [