feat: update api schema

Author: jameszyao

./workflows/release-publish.yml

@@ -3,73 +3,72 @@ name: publish-and-release
 on:
 create:
 tags:
-- 'v*'
+- "v*"
 push:
-branches: [ "master", "dev" ]
+branches: ["master", "dev"]
 
 jobs:
 publish-to-pypi:
 runs-on: ubuntu-latest
 steps:
+- uses: actions/checkout@master
 
-- uses: actions/checkout@master
+- name: Set up Python 3.10
+uses: actions/setup-python@v1
+with:
+python-version: "3.10.13"
 
-- name: Set up Python 3.10
-uses: actions/setup-python@v1
-with:
-python-version: '3.10.13'
+- name: Install build
+run: >-
+pip install -r requirements.txt &&
+python setup.py sdist
 
-- name: Install build
-run: >-
-pip install -r requirements.txt &&
-python setup.py sdist
+ - name: Publish distribution 📦 to Test PyPI
+if: startsWith(.ref, 'refs/dev')
+uses: pypa/gh-action-pypi-publish@master
+with:
+password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+repository_url: https://test.pypi.org/legacy/
+skip_existing: true
 
- - name: Publish distribution 📦 to Test PyPI
-if: startsWith(.ref, 'refs/dev')
-uses: pypa/gh-action-pypi-publish@master
-with:
-password: ${{ secrets.TEST_PYPI_API_TOKEN }}
-repository_url: https://test.pypi.org/legacy/
-skip_existing: true
-
-- name: Publish distribution 📦 to PyPI
-if: startsWith(.ref, 'refs/tags/')
-uses: pypa/gh-action-pypi-publish@master
-with:
-password: ${{ secrets.PYPI_API_TOKEN }}
+- name: Publish distribution 📦 to PyPI
+if: startsWith(.ref, 'refs/tags/')
+uses: pypa/gh-action-pypi-publish@master
+with:
+password: ${{ secrets.PYPI_API_TOKEN }}
 
 -release:
 name: -release
 needs:
-- publish-to-pypi
+- publish-to-pypi
 runs-on: ubuntu-latest
 permissions:
 contents: write
 id-token: write
 steps:
-- name: Download all the dists
-uses: actions/download-artifact@v3
-with:
-name: python-package-distributions
-path: dist/
-- name: Sign the dists with Sigstore
-uses: sigstore/[email protected]
-with:
-inputs: >-
-./dist/*.tar.gz
-./dist/*.whl
- - name: Create Release
-env:
-_TOKEN: ${{ .token }}
-run: >-
-gh release create
-'${{ .ref_name }}'
---repo '${{ .repository }}'
---notes ""
- - name: Upload artifact signatures to Release
-env:
-_TOKEN: ${{ .token }}
-run: >-
-gh release upload
-'${{ .ref_name }}' dist/**
---repo '${{ .repository }}'
+- name: Download all the dists
+uses: actions/download-artifact@v3
+with:
+name: python-package-distributions
+path: dist/
+- name: Sign the dists with Sigstore
+uses: sigstore/[email protected]
+with:
+inputs: >-
+./dist/*.tar.gz
+./dist/*.whl
+ - name: Create Release
+env:
+_TOKEN: ${{ .token }}
+run: >-
+gh release create
+'${{ .ref_name }}'
+--repo '${{ .repository }}'
+--notes ""
+ - name: Upload artifact signatures to Release
+env:
+_TOKEN: ${{ .token }}
+run: >-
+gh release upload
+'${{ .ref_name }}' dist/**
+--repo '${{ .repository }}'

.gitignore

@@ -67,3 +67,9 @@ target/
 
 .DS_Store
 .venv
+
+# test
+test/.pytest_cache/
+test/log/
+
+**/allure-report

.pre-commit-config.yaml

@@ -0,0 +1,27 @@
+repos:
+- repo: https://.com/pre-commit/pre-commit-hooks
+rev: v4.5.0
+hooks:
+- id: trailing-whitespace
+- id: end-of-file-fixer
+- id: check-yaml
+
+- repo: https://.com/psf/black
+rev: 23.12.1
+hooks:
+- id: black
+args: ["-l", "120"]
+language_version: python3
+
+- repo: https://.com/myint/autoflake
+rev: v2.2.1
+hooks:
+- id: autoflake
+args: ["--in-place", "--remove-all-unused-imports"]
+exclude: "__init__.py$"
+
+- repo: https://.com/pre-commit/mirrors-prettier
+rev: "v4.0.0-alpha.8" # Use the ref you want to point at
+hooks:
+- id: prettier
+args: ["--write"]

MANIFEST.in

@@ -0,0 +1 @@
+prune test

README.md

@@ -1,11 +1,13 @@
 # TaskingAI-client
+
 The TaskingAI Python client for creating and managing AI-driven applications.
 
 For more information, see the docs at [TaskingAI Documentation](https://docs.tasking.ai/)
 
 ## Installation
 
 Install the latest released version using pip:
+
 ```shell
 pip3 install taskingai
 ```
@@ -15,19 +17,21 @@ pip3 install taskingai
 Here's how you can quickly start building and managing AI-driven applications using the TaskingAI client.
 
 ### Assistants
+
 Explore the ease of creating and customizing your own AI assistants with TaskingAI to enhance user interactions.
+
 ```python
 import taskingai
 from taskingai.assistant import *
+from taskingai.assistant.memory import AssistantNaiveMemory
 
 # Initialize your API key if you haven't already set it in the environment
 taskingai.init(api_key="YOUR_API_KEY")
 
 # Create an assistant
 assistant = create_assistant(
 model_id="YOUR_MODEL_ID",
-name="My Assistant",
-description="An assistant that understands numbers.",
+memory=AssistantNaiveMemory(),
 system_prompt_template=["You are a professional assistant."],
 )
 print(f"Assistant created: {assistant.id}")
@@ -49,7 +53,9 @@ print("Assistant deleted successfully.")
 ```
 
 ### Retrieval
+
 Leverage TaskingAI's retrieval capabilities to store, manage, and extract information, making your applications smarter and more responsive.
+
 ```python
 import taskingai
 from taskingai.retrieval import *
@@ -62,9 +68,10 @@ collection = create_collection(
 print(f"Collection created: {collection.id}")
 
 # Add a record to the collection
-record = create_text_record(
+record = create_record(
 collection_id=collection.id,
-text="Example text for machine learning."
+content="Example text for machine learning.",
+text_splitter=TokenTextSplitter(chunk_size=200, chunk_overlap=20),
 )
 print(f"Record added to collection: {record.id}")
 
@@ -88,7 +95,9 @@ print("Collection deleted.")
 ```
 
 ### Tools
+
 Utilize TaskingAI's tools to create actions that enable your assistant to interact with external APIs and services, enriching the user experience.
+
 ```python
 import taskingai
 from taskingai.tool import *
@@ -121,8 +130,3 @@ print("Action deleted.")
 ## Contributing
 
 We welcome contributions of all kinds. Please read our [Contributing Guidelines](./CONTRIBUTING.md) for more information on how to get started.
-
-## Security
-
-For any security concerns or issues, please reach out to us directly at [email protected].
-

examples/assistant/chat_with_assistant.ipynb

@@ -91,7 +91,7 @@
 " }\n",
 "}\n",
 "actions: List[Action] = taskingai.tool.bulk_create_actions(\n",
-" schema=NUMBERS_API_SCHEMA,\n",
+" openapi_schema=NUMBERS_API_SCHEMA,\n",
 " authentication=ActionAuthentication(\n",
 " type=ActionAuthenticationType.NONE,\n",
 " )\n",
@@ -250,14 +250,15 @@
 "messages = taskingai.assistant.list_messages(\n",
 " assistant_id=assistant.assistant_id,\n",
 " chat_id=chat.chat_id,\n",
+" order=\"asc\"\n",
 ")\n",
 "for message in messages:\n",
 " print(f\"{message.role}: {message.content.text}\")"
 ],
 "metadata": {
 "collapsed": false
 },
-"id": "34bae46ba56752bb"
+"id": "e94e3adb0d15373b"
 },
 {
 "cell_type": "code",
@@ -278,19 +279,11 @@
 "cell_type": "code",
 "execution_count": null,
 "outputs": [],
-"source": [
-"# list messages\n",
-"messages = taskingai.assistant.list_messages(\n",
-" assistant_id=assistant.assistant_id,\n",
-" chat_id=chat.chat_id,\n",
-")\n",
-"for message in messages:\n",
-" print(f\"{message.role}: {message.content.text}\")"
-],
+"source": [],
 "metadata": {
 "collapsed": false
 },
-"id": "e94e3adb0d15373b"
+"id": "9cfed1128acbdd30"
 }
 ],
 "metadata": {

examples/crud/retrieval_crud.ipynb

@@ -10,7 +10,6 @@
 "outputs": [],
 "source": [
 "import taskingai\n",
-"import time\n",
 "# Load TaskingAI API Key from environment variable"
 ]
 },
@@ -29,7 +28,7 @@
 "execution_count": null,
 "outputs": [],
 "source": [
-"from taskingai.retrieval import Collection, Record\n",
+"from taskingai.retrieval import Collection, Record, TokenTextSplitter\n",
 "\n",
 "# choose an available text_embedding model from your project\n",
 "embedding_model_id = \"YOUR_MODEL_ID\""
@@ -49,6 +48,19 @@
 },
 "id": "a6874f1ff8ec5a9c"
 },
+{
+"cell_type": "code",
+"execution_count": null,
+"outputs": [],
+"source": [
+"collections = taskingai.retrieval.list_collections()\n",
+"print(collections)"
+],
+"metadata": {
+"collapsed": false
+},
+"id": "81ec82280d5c8c64"
+},
 {
 "cell_type": "code",
 "execution_count": null,
@@ -146,25 +158,14 @@
 },
 "id": "1b7688a3cf40c241"
 },
-{
-"cell_type": "markdown",
-"source": [],
-"metadata": {
-"collapsed": false
-},
-"id": "dbc3aafe16758b4c"
-},
 {
 "cell_type": "code",
 "execution_count": null,
 "outputs": [],
 "source": [
 "# create a new collection\n",
 "collection: Collection = create_collection()\n",
-"print(collection)\n",
-"\n",
-"# wait for the collection creation to finish\n",
-"time.sleep(3)"
+"print(collection)"
 ],
 "metadata": {
 "collapsed": false
@@ -177,9 +178,10 @@
 "outputs": [],
 "source": [
 "# create a new text record\n",
-"record: Record = taskingai.retrieval.create_text_record(\n",
+"record: Record = taskingai.retrieval.create_record(\n",
 " collection_id=collection.collection_id,\n",
-" text=\"Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. The term \\\"machine learning\\\" was coined by Arthur Samuel in 1959. In other words, machine learning enables a system to automatically learn and improve from experience without being explicitly programmed. This is achieved by feeding the system massive amounts of data, which it uses to learn patterns and make inferences. There are three main types of machine learning: 1. Supervised Learning: This is where the model is given labeled training data and the goal of learning is to generalize from the training data to unseen situations in a principled way. 2. Unsupervised Learning: This involves training on a dataset without explicit labels. The goal might be to discover inherent groupings or patterns within the data. 3. Reinforcement Learning: In this type, an agent learns to perform actions based on reward/penalty feedback to achieve a goal. It's commonly used in robotics, gaming, and navigation. Deep learning, a subset of machine learning, uses neural networks with many layers (\\\"deep\\\" structures) and has been responsible for many recent breakthroughs in AI, including speech recognition, image recognition, and natural language processing. It's important to note that machine learning is a rapidly developing field, with new techniques and applications emerging regularly.\"\n",
+" content=\"Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms that allow computers to learn from and make decisions or predictions based on data. The term \\\"machine learning\\\" was coined by Arthur Samuel in 1959. In other words, machine learning enables a system to automatically learn and improve from experience without being explicitly programmed. This is achieved by feeding the system massive amounts of data, which it uses to learn patterns and make inferences. There are three main types of machine learning: 1. Supervised Learning: This is where the model is given labeled training data and the goal of learning is to generalize from the training data to unseen situations in a principled way. 2. Unsupervised Learning: This involves training on a dataset without explicit labels. The goal might be to discover inherent groupings or patterns within the data. 3. Reinforcement Learning: In this type, an agent learns to perform actions based on reward/penalty feedback to achieve a goal. It's commonly used in robotics, gaming, and navigation. Deep learning, a subset of machine learning, uses neural networks with many layers (\\\"deep\\\" structures) and has been responsible for many recent breakthroughs in AI, including speech recognition, image recognition, and natural language processing. It's important to note that machine learning is a rapidly developing field, with new techniques and applications emerging regularly.\",\n",
+" text_splitter=TokenTextSplitter(chunk_size=200, chunk_overlap=20)\n",
 ")\n",
 "print(f\"created record: {record.record_id} for collection: {collection.collection_id}\\n\")"
 ],