chore: Merge for release

.github/workflows/ci-macos.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: macos-latest-large
     strategy:
       matrix:
-        python-version: [ "3.9", "3.10", "3.11", "3.12" ]
+        python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
 
     steps:
       - uses: actions/checkout@v4

.github/workflows/ci-windows.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: windows-latest
     strategy:
       matrix:
-        python-version: [ "3.9", "3.10", "3.11", "3.12" ]
+        python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
 
     steps:
       - uses: actions/checkout@v4

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.9", "3.10", "3.11", "3.12" ]
+        python-version: [ "3.9", "3.10", "3.11", "3.12", "3.13" ]
 
     steps:
       - uses: actions/checkout@v4

CONTRIBUTING.md

@@ -25,7 +25,7 @@ Pull requests should be done on the `dev` branch. When the release is finalised,
 
 Whether you're interested in contributing to the repository, creating a fork, or just improving your understanding of Writer Framework, these are the suggested steps for setting up a development environment.
 
-- You can install the package in editable mode using `poetry install`
+- You can install the package in editable mode using `poetry install  --with build`
 - Enable the virtual environment with `poetry shell`
 - Install all the dev dependencies with `alfred install.dev`
 - Run Writer Framework on port 5000. For example, `writer edit apps/hello --port 5000`.

alfred/install.py

@@ -5,9 +5,9 @@
 def install_dev():
     alfred.run("poetry install --with build")
     alfred.run("npm ci")
-    alfred.invoke_command("npm.codegen")
+    alfred.run("npm run build")
 
 @alfred.command("install.ci", help="install ci dependencies and generate code", hidden=True)
 def install_ci():
     alfred.run("npm ci")
-    alfred.invoke_command("npm.codegen")
+    alfred.run("npm run build")

docs/framework/ai-module.mdx

@@ -217,7 +217,7 @@ Function tools require the following properties:
 
 When a conversation involves a tool (either a graph or a function), Framework automatically handles the requests from LLM to use the tools during interactions. If the tool needs multiple steps (for example, querying data and processing it), Framework will handle those steps recursively, calling functions as needed until the final result is returned.
 
-By default, to prevent endless recursion, Framework will only handle 3 consecutive tool calls. You can expand it in case it doesn't suit your case – both `complete()` and `stream_complete()` accept a `max_tool_depth` parameter, which configures the maximum allowed recursion depth:
+By default, to prevent endless recursion, Framework will only handle 5 consecutive tool calls. You can expand it in case it doesn't suit your case – both `complete()` and `stream_complete()` accept a `max_tool_depth` parameter, which configures the maximum allowed recursion depth:
 
 ```python
 response = conversation.complete(tools=tool, max_tool_depth=7)
@@ -340,3 +340,84 @@ for chunk in stream_ask(
 ```
 </CodeGroup>
 
+## Using the `Tools` class
+
+<Note>
+This document outlines the use of `Tools` in the Writer Framework; for more thorough documentation, check out (this guide)[https://dev.writer.com/api-guides/tools].
+</Note>
+
+The `writer.ai.tools` instance provides access to Writer SDK `tools` resources, such as text splitting, medical content comprehension, and PDF parsing. Below is a guide on how to use each method.
+
+### Splitting Content
+
+The `split` method divides text into chunks based on a selected strategy.
+
+```python
+from writer.ai import tools
+
+content = \
+    """
+    This is a long piece of text that needs to be split into smaller parts.
+    Lorem ipsum dolor sit amet...
+    """
+chunks = tools.split(content, strategy="llm_split")
+print(chunks)
+```
+
+**Parameters**:
+- `content` (str): The text to be split.
+- `strategy` (str): The splitting strategy (`llm_split`, `fast_split`, or `hybrid_split`).
+
+**Returns**:
+A list of text chunks.
+
+### Medical Content Comprehension
+
+The `comprehend_medical` method processes medical text and extracts relevant entities based on a specified response type.
+
+```python
+from writer.ai import tools
+
+medical_text = "Patient shows symptoms of hypertension and diabetes."
+entities = tools.comprehend_medical(medical_text, response_type="Entities")
+print(entities)
+```
+
+**Parameters**:
+- `content` (str): The medical text to process.
+- `response_type` (str): The type of medical response (`Entities`, `RxNorm`, `ICD-10-CM`, or `SNOMED CT`).
+
+**Returns**:
+A list of extracted medical entities.
+
+### PDF Parsing
+
+The `parse_pdf` method extracts text content from a PDF file. The file can be referenced by its ID, or provided as a `File` object.
+
+<CodeGroup>
+```python file_id
+from writer.ai import tools
+
+file_id = "example-file-id"
+parsed_content = tools.parse_pdf(file_id, format="text")
+print(parsed_content)
+```
+```python upload_file
+from writer.ai import tools
+
+file_object = upload_file(
+    data=pdf_content,
+    type="application/pdf",
+    name="uploaded_file.pdf"
+)
+parsed_content = tools.parse_pdf(file_object, format="text")
+print(parsed_content)
+```
+</CodeGroup>
+
+**Parameters**:
+- `file_id_or_file` (str or File): The file to parse (by ID or as an object).
+- `format` (str): The format of the extracted content (`text` or `markdown`).
+
+**Returns**:
+The text content of the PDF.