Merge pull request #198 from griptape-ai/release/v0.22.0

Release/v0.22.0

.github/actions/init-environment/action.yml

@@ -26,13 +26,9 @@ runs:
 
       - name: Install dependencies
         if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
-        run: poetry install --no-interaction --no-root --with docs --with test
+        run: poetry install --no-interaction --no-root --with docs --with test --all-extras
         shell: bash
 
-      - name: Install project
-        run: poetry install --no-interaction --with docs --with test
-        shell: bash
-
       - name: Activate venv 
         run: |
           source .venv/bin/activate

.github/workflows/integration-tests.yml

@@ -57,21 +57,26 @@ jobs:
       SAGEMAKER_LLAMA_ENDPOINT_NAME: ${{ secrets.INTEG_LLAMA_ENDPOINT_NAME }}
       SAGEMAKER_FALCON_ENDPOINT_NAME: ${{ secrets.INTEG_FALCON_ENDPOINT_NAME }}
       HUGGINGFACE_HUB_ACCESS_TOKEN: ${{ secrets.INTEG_HUGGINGFACE_HUB_ACCESS_TOKEN }}
-      AZURE_OPENAI_API_BASE_1: ${{ secrets.INTEG_AZURE_OPENAI_API_BASE_1 }}
-      AZURE_OPENAI_API_KEY_1: ${{ secrets.INTEG_AZURE_OPENAI_API_KEY_1 }}
-      AZURE_OPENAI_API_BASE_2: ${{ secrets.INTEG_AZURE_OPENAI_API_BASE_2 }}
-      AZURE_OPENAI_API_KEY_2: ${{ secrets.INTEG_AZURE_OPENAI_API_KEY_2 }}
-      AZURE_OPENAI_35_16k_DEPLOYMENT_ID: ${{ secrets.INTEG_OPENAI_35_16k_DEPLOYMENT_ID }}
+      AZURE_OPENAI_ENDPOINT: ${{ secrets.INTEG_AZURE_OPENAI_ENDPOINT }}
+      AZURE_OPENAI_API_KEY: ${{ secrets.INTEG_AZURE_OPENAI_API_KEY}}
+      AZURE_OPENAI_35_TURBO_16k_DEPLOYMENT_ID: ${{ secrets.INTEG_OPENAI_35_TURBO_16k_DEPLOYMENT_ID }}
       AZURE_OPENAI_DAVINCI_DEPLOYMENT_ID: ${{ secrets.INTEG_OPENAI_DAVINCI_DEPLOYMENT_ID }}
       AZURE_OPENAI_4_DEPLOYMENT_ID: ${{ secrets.INTEG_OPENAI_4_DEPLOYMENT_ID }}
       AZURE_OPENAI_4_32k_DEPLOYMENT_ID: ${{ secrets.INTEG_OPENAI_4_32k_DEPLOYMENT_ID }}
+      AZURE_OPENAI_DALL_E_3_DEPLOYMENT_ID: ${{ secrets.INTEG_AZURE_OPENAI_DALL_E_3_DEPLOYMENT_ID }}
+      AZURE_OPENAI_DEV_2_API_BASE: ${{ secrets.INTEG_AZURE_OPENAI_DEV_2_API_BASE }}
+      AZURE_OPENAI_DEV_2_API_KEY: ${{ secrets.INTEG_AZURE_OPENAI_DEV_2_API_KEY }}
       COHERE_API_KEY: ${{ secrets.INTEG_COHERE_API_KEY }}
       GOOGLE_OWNER_EMAIL: ${{ secrets.INTEG_GOOGLE_OWNER_EMAIL }}
+      LEONARDO_API_KEY: ${{ secrets.INTEG_LEONARDO_API_KEY }}
+      LEONARDO_MODEL_ID: ${{ secrets.INTEG_LEONARDO_MODEL_ID }}
+      SAGEMAKER_TENSORFLOW_HUB_MODEL: ${{ secrets.INTEG_SAGEMAKER_TENSORFLOW_HUB_MODEL }}
+      SAGEMAKER_HUGGINGFACE_MODEL: ${{ secrets.INTEG_SAGEMAKER_HUGGINGFACE_MODEL }}
 
     steps:
       - name: Checkout actions
         uses: actions/checkout@v3
       - name: Init environment 
         uses: ./.github/actions/init-environment 
       - name: Run integration tests
-        run: pytest -xvs tests/integration
+        run: pytest -v tests/integration

Makefile

@@ -1,7 +1,20 @@
-publish:
+.PHONY: version
+version: ## Bump version and push to release branch.
 	@poetry version $(v)
 	@git add pyproject.toml
 	@git commit -m "Version bump v$$(poetry version -s)"
+	@git push origin release/v$$(poetry version -s)
+
+.PHONY: publish
+publish: ## Push git tag
 	@git tag v$$(poetry version -s)
-	@git push
 	@git push --tags
+
+.DEFAULT_GOAL := help
+.PHONY: help
+help: ## Print Makefile help text.
+	@# Matches targets with a comment in the format <target>: ## <comment>
+	@# then formats help output using these values.
+	@grep -E '^[a-zA-Z_\/-]+:.*?## .*$$' $(MAKEFILE_LIST) \
+	| awk 'BEGIN {FS = ":.*?## "}; \
+		{printf "\033[36m%-12s\033[0m%s\n", $$1, $$2}'

docs/griptape-cloud/getting-started/quickstart-cli.md

@@ -89,7 +89,7 @@ Step 7. Deploy your application to the cloud
 You can use the Griptape CLI to deploy your application to the cloud.
 
 ```
-user@machine-name demo_app % gt cloud create-deployment --api-id 12345678-8f3e-4028-9934-42e0cd69860a --directory ~/workplace/demo_app
+user@machine-name demo_app % gt cloud create-deployment --app-id 12345678-8f3e-4028-9934-42e0cd69860a --directory ~/workplace/demo_app
 {'app_id': '12345678-8f3e-4028-9934-42e0cd69860a', 'created_at': '2023-12-19T00:16:36.663Z', 'created_by': '12345678-9b16-408a-b5d7-e83044d820f4', 'deployment_id': '12345678-12cd-4003-9ee1-b6b01f7927ea', 'status': 'QUEUED'}
 ```
 

docs/griptape-framework/data/artifacts.md

@@ -32,3 +32,7 @@ Each blob has a [name](../../reference/griptape/artifacts/base_artifact.md#gript
 [dir](../../reference/griptape/artifacts/blob_artifact.md#griptape.artifacts.blob_artifact.BlobArtifact.dir) to uniquely identify stored objects.
 
 [TaskMemory](../../reference/griptape/memory/task/task_memory.md) automatically stores [BlobArtifact](../../reference/griptape/artifacts/blob_artifact.md)s returned by tool activities that can be reused by other tools.
+
+## ImageArtifact
+
+An [ImageArtifact](../../reference/griptape/artifacts/image_artifact.md) is used for passing images back to the LLM. In addition to binary image data, an ImageArtifact includes image metadata like MIME type, dimensions, and prompt and model information for images returned by [image generation Drivers](../structures/image-generation-drivers.md). It inherits from [BlobArtifact](#blobartifact).

docs/griptape-framework/data/embedding-drivers.md

@@ -3,17 +3,17 @@ Embeddings in Griptape are multidimensional representations of text data. Embedd
 
 Griptape provides a way to build Embedding Drivers that are reused in downstream framework components. Every Embedding Driver has two basic methods that can be used to generate embeddings:
 
-* [embed_text_artifact()](../../reference/griptape/drivers/embedding/base_embedding_driver.md#griptape.drivers.embedding.base_embedding_driver.BaseEmbeddingDriver.embed_text_artifact) for [TextArtifact](../../reference/griptape/artifacts/text_artifact.md)s
-* [embed_string()](../../reference/griptape/drivers/embedding/base_embedding_driver.md#griptape.drivers.embedding.base_embedding_driver.BaseEmbeddingDriver.embed_string) for any string
+* [embed_text_artifact()](../../reference/griptape/drivers/embedding/base_embedding_driver.md#griptape.drivers.embedding.base_embedding_driver.BaseEmbeddingDriver.embed_text_artifact) for [TextArtifact](../../reference/griptape/artifacts/text_artifact.md)s.
+* [embed_string()](../../reference/griptape/drivers/embedding/base_embedding_driver.md#griptape.drivers.embedding.base_embedding_driver.BaseEmbeddingDriver.embed_string) for any string.
 
-!!! info
-    More embedding drivers are coming soon.
+You can optionally provide a [Tokenizer](../structures/tokenizers.md) via the [tokenizer](../../reference/griptape/drivers/embedding/base_embedding_driver.md#griptape.drivers.embedding.base_embedding_driver.BaseEmbeddingDriver.tokenizer) field to have the Driver automatically chunk the input text to fit into the token limit.
 
 ## Embedding Drivers
 
 ### OpenAI Embeddings
 
-The [OpenAiEmbeddingDriver](../../reference/griptape/drivers/embedding/openai_embedding_driver.md) uses [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings) to generate embeddings for texts of arbitrary length. This driver automatically chunks the input text to fit into the token limit.
+The [OpenAiEmbeddingDriver](../../reference/griptape/drivers/embedding/openai_embedding_driver.md) uses the [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings).
+
 
 ```python
 from griptape.drivers import OpenAiEmbeddingDriver
@@ -37,12 +37,12 @@ with updated defaults.
 !!! info
     This driver requires the `drivers-embedding-amazon-bedrock` [extra](../index.md#extras).
 
-The [BedrockTitanEmbeddingDriver](../../reference/griptape/drivers/embedding/bedrock_titan_embedding_driver.md) uses the [Amazon Bedrock Embeddings API](https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html).
+The [AmazonBedrockTitanEmbeddingDriver](../../reference/griptape/drivers/embedding/amazon_bedrock_titan_embedding_driver.md) uses the [Amazon Bedrock Embeddings API](https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html).
 
 ```python
-from griptape.drivers import BedrockTitanEmbeddingDriver
+from griptape.drivers import AmazonBedrockTitanEmbeddingDriver
 
-embeddings = BedrockTitanEmbeddingDriver().embed_string("Hello world!")
+embeddings = AmazonBedrockTitanEmbeddingDriver().embed_string("Hello world!")
 
 # display the first 3 embeddings
 print(embeddings[:3])
@@ -78,6 +78,49 @@ driver = HuggingFaceHubEmbeddingDriver(
 
 embeddings = driver.embed_string("Hello world!")
 
+# display the first 3 embeddings
+print(embeddings[:3])
+```
+### Multi Model Embedding Drivers
+Certain embeddings providers such as Amazon SageMaker support many types of models, each with their own slight differences in parameters and response formats. To support this variation across models, these Embedding Drivers takes a [Embedding Model Driver](../../reference/griptape/drivers/embedding_model/base_embedding_model_driver.md)
+through the [embedding_model_driver](../../reference/griptape/drivers/embedding/base_multi_model_embedding_driver.md#griptape.drivers.embedding.base_multi_model_embedding_driver.BaseMultiModelEmbeddingDriver.embedding_model_driver) parameter.
+[Embedding Model Driver](../../reference/griptape/drivers/embedding_model/base_embedding_model_driver.md)s allows for model-specific customization for Embedding Drivers. 
+
+#### SageMaker Embeddings
+
+The [AmazonSageMakerEmbeddingDriver](../../reference/griptape/drivers/embedding/amazon_sagemaker_embedding_driver.md) uses the [Amazon SageMaker Endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) to generate embeddings on AWS.
+
+!!! info
+    This driver requires the `drivers-embedding-amazon-sagemaker` [extra](../index.md#extras).
+
+##### TensorFlow Hub Models
+```python ignore
+import os
+from griptape.drivers import AmazonSageMakerEmbeddingDriver, SageMakerTensorFlowHubEmbeddingModelDriver
+
+driver = AmazonSageMakerEmbeddingDriver(
+    model=os.environ["SAGEMAKER_TENSORFLOW_HUB_MODEL"],
+    embedding_model_driver=SageMakerTensorFlowHubEmbeddingModelDriver(),
+)
+
+embeddings = driver.embed_string("Hello world!")
+
+# display the first 3 embeddings
+print(embeddings[:3])
+```
+
+##### HuggingFace Models
+```python ignore
+import os
+from griptape.drivers import AmazonSageMakerEmbeddingDriver, SageMakerHuggingFaceEmbeddingModelDriver
+
+driver = AmazonSageMakerEmbeddingDriver(
+    model=os.environ["SAGEMAKER_HUGGINGFACE_MODEL"],
+    embedding_model_driver=SageMakerHuggingFaceEmbeddingModelDriver(),
+)
+
+embeddings = driver.embed_string("Hello world!")
+
 # display the first 3 embeddings
 print(embeddings[:3])
 ```
@@ -96,4 +139,3 @@ agent = Agent(
 )
 
 agent.run("based on https://www.griptape.ai/, tell me what Griptape is")
-```

docs/griptape-framework/data/extraction-engines.md

@@ -12,34 +12,28 @@ The CSV Extraction Engine is designed specifically for extracting data from CSV-
 
 ```python
 from griptape.engines import CsvExtractionEngine
-from griptape.artifacts import ListArtifact, ErrorArtifact
 
 # Initialize the CsvExtractionEngine instance
 csv_engine = CsvExtractionEngine()
 
-# Given a sample text which contains CSV content
+# Define some unstructured data
 sample_text = """
-name, age, location
-Alice, 28, New York
-Bob, 35, California
-Charlie, 40, Texas
+Alice, 28, lives in New York.
+Bob, 35 lives in California.
+Charlie is 40 and lives in Texas.
 """
 
 # Extract CSV rows using the engine
 result = csv_engine.extract(sample_text, column_names=["name", "age", "location"])
 
-# Check and display the result
-if isinstance(result, ListArtifact):
-    for row in result.value:
-        print(row.value)  # This will print each row in the CSV content
-elif isinstance(result, ErrorArtifact):
-    print(f"Error: {result.value}")  # Print error if there's any issue in extraction
+for row in result.value:
+    print(row.to_text())
 ```
 ```
-{'name': 'name, age, location'}
-{'name': 'Alice, 28, New York'}
-{'name': 'Bob, 35, California'}
-{'name': 'Charlie, 40, Texas'}
+name,age,location
+Alice,28,New York
+Bob,35,California
+Charlie,40,Texas
 ```
 
 ## JSON Extraction Engine
@@ -51,27 +45,14 @@ The JSON Extraction Engine is tailored for extracting data from JSON-formatted c
 
 ```python
 from griptape.engines import JsonExtractionEngine
-from griptape.artifacts import ListArtifact, ErrorArtifact
 from schema import Schema
 
 json_engine = JsonExtractionEngine()
 
-# Given a sample JSON text
+# Define some unstructured data
 sample_json_text = """
-{
-  "users": [
-    {
-      "name": "Alice",
-      "age": 28,
-      "location": "New York"
-    },
-    {
-      "name": "Bob",
-      "age": 35,
-      "location": "California"
-    }
-  ]
-}
+Alice (Age 28) lives in New York.
+Bob (Age 35) lives in California.
 """
 
 # Define a schema for extraction
@@ -82,12 +63,8 @@ user_schema = Schema(
 # Extract data using the engine
 result = json_engine.extract(sample_json_text, template_schema=user_schema)
 
-# Check and display the result
-if isinstance(result, ListArtifact):
-    for artifact in result.value:
-        print(artifact.value)  # This will print each extracted artifact
-elif isinstance(result, ErrorArtifact):
-    print(f"Error: {result.value}")  # Print error if there's any issue in extraction
+for artifact in result.value:
+    print(artifact.value)
 ```
 ```
 {'name': 'Alice', 'age': 28, 'location': 'New York'}