Merge pull request #60 from morph-data/add/package-install-poetry-uv

Added sample install command of uv and poetry

Author: KT83

docs/en/advanced/llm.mdx

@@ -51,9 +51,19 @@ Morph offers a self-hosting feature for LLM provided by [Ollama](https://ollama.
 
 	<Note>
 		You need to install the `langchain-ollama` package to use in Python.
-		```bash Shell
-		pip install langchain-ollama
-		```
+
+		<CodeGroup>
+			```bash pip
+			pip install langchain-ollama
+			```
+			```bash poetry
+			poetry add langchain-ollama
+			```
+			```bash uv
+			uv add langchain-ollama
+			```
+		</CodeGroup>
+
 	</Note>
 	<CodeGroup>
 ```python Python

docs/en/develop/tutorials/chat_sidebyside.mdx

@@ -8,6 +8,21 @@ In this tutorial, you will create an application that automatically generates Py
 
 ## Prerequisites
 
+Please install pageckages in advance by the following command.
+
+<CodeGroup>
+    ```bash pip
+    pip install openai plotly
+    ```
+    ```bash poetry
+    poetry add openai plotly
+    ```
+    ```bash uv
+    uv add openai plotly
+    ```
+</CodeGroup>
+
+
 Obtain the OpenAI API key in advance from the OpenAI dashboard and save it in an `.env` file.
 
 ```shell .env
@@ -30,16 +45,17 @@ The `text_to_plotly` function takes a user prompt and a DataFrame as arguments t
 Additionally, the `side-by-side` layout of the `<LLM />` component allows the HTML passed as the second argument to the `create_chunk` function to be displayed next to the chat UI, enabling real-time display.
 
 ```python
+import json
 import pandas as pd
 import os
 import morph
+from openai import OpenAI
 from morph import MorphGlobalContext
 from morph_lib.stream import create_chunk
-from morph_lib.ai.openai.code_generation import text_to_plotly
 
 @morph.func
 def text_to_plotly_app(context: MorphGlobalContext):
-    data = pd.DataFrame({
+    df = pd.DataFrame({
         "city": [
             "Los Angeles", "San Francisco", "San Diego", "Sacramento",
             "Houston", "Dallas", "Austin", "San Antonio",
@@ -64,18 +80,86 @@ def text_to_plotly_app(context: MorphGlobalContext):
     })
     prompt = context.vars["prompt"]
 
-    result = text_to_plotly(
-        prompt=prompt,
-        api_key=os.getenv("OPENAI_API_KEY"),
-        df=data,
+    data = df.head(5).to_markdown()
+    messages = [
+        {
+            "role": "system",
+            "content": """You are a great data analyst using python.
+You can generate python code based on user prompt and you can use given python libraries.
+Please generate python code using plotly or matplotlib for visualization and pandas based on the user's question.
+"""},
+        {
+            "role": "system",
+            "content": f"""Generate Python code for visualization using pandas, plotly and you should follow user's question.
+Make sure to write `import pandas as pd` at the beginning of the code otherwise the code must throw errors.
+
+## Rules:
+1. You must generate Python code using plotly for visualization and the code should return plotly object.
+2. You must use the given data for visualization.
+3. You must use following code block and use additional packages if needed but do not change original import statements.
+4. Function name is `main` and it should accept a pandas DataFrame as an argument.
+
+This is the template you should follow:
+
+---
+import pandas as pd # DO NOT FORGET TO IMPORT PANDAS
+
+def main(df: pd.DataFrame):
+    # Your code here
+    return plotly object
+---
+
+## Importable libraries:
+- pandas
+- plotly
+
+But you may have other packages installed in the environment. So you can use other packages if needed.
+
+## Data:
+Given sample data is below. You need to use this data as data schema only.
+DO NOT write contents of this data to the code. The df is always passed as an argument to the main function.
+
+{data}
+""",
+        },
+        {
+            "role": "user",
+            "content": prompt,
+        },
+    ]
+    code = ""
+
+    openai_model = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+    functions = {
+        "name": "generate_python_code",
+        "description": "Generate python code using plotly for visualization based on user prompt",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "code": {
+                    "type": "string",
+                    "description": "The python code using plotly generated based on the prompt",
+                }
+            },
+            "required": ["code"],
+        },
+    }
+    response = openai_model.chat.completions.create(
         model="gpt-4o",
+        messages=messages,
+        functions=[functions],
     )
-    fig = result.content
+    code = json.loads(response.choices[0].message.function_call.arguments)["code"]
+
+    code_ = compile(code, "<string>", "exec")
+    namespace = {}
+    exec(code_, namespace)
+    func = namespace["main"]
 
-    yield create_chunk(f"Here is python code generated.\n")
-    yield create_chunk(f"```python\n{result.code}\n```\n", fig.to_html())
-    return result
+    fig = func(df)
 
+    yield create_chunk(f"""Successfully, a chart was created!
+""", fig.to_html())
 ```
     </Tab>
     <Tab title="2. MDX(pages)">

docs/en/develop/tutorials/chatbot.mdx

@@ -9,6 +9,20 @@ In this tutorial, you will create a chat app using Morph's LLM components and Op
 
 ## Prerequisites
 
+Please install pageckages in advance by the following command.
+
+<CodeGroup>
+    ```bash pip
+    pip install openai
+    ```
+    ```bash poetry
+    poetry add langchain-ollama
+    ```
+    ```bash uv
+    uv add langchain-ollama
+    ```
+</CodeGroup>
+
 Obtain the OpenAI API key in advance from the OpenAI dashboard and save it in an `.env` file.
 
 ```shell .env

docs/en/develop/tutorials/plotly.mdx

@@ -6,6 +6,22 @@ icon: "chart-bar"
 Morph allows you to customize and create dynamic dashboard applications with input forms.
 In this tutorial, you will create an application that filters and displays Plotly charts based on values received from input forms.
 
+## Prerequisites
+
+Please install pageckages in advance by the following command.
+
+<CodeGroup>
+```bash pip
+pip install plotly
+```
+```bash poetry
+poetry add plotly
+```
+```bash uv
+uv add plotly
+  ```
+</CodeGroup>
+
 ## Output
 
 ![plotly](/assets/images/docs/tutorial/filter_plotly.png)

docs/en/develop/tutorials/pygwalker.mdx

@@ -10,9 +10,17 @@ In this tutorial, we will introduce how to embed a rich dashboard created with P
 
 Please install PyGWalker in advance by the following command.
 
-```bash
-pip install pygwalker
-```
+<CodeGroup>
+    ```bash pip
+    pip install pygwalker
+    ```
+    ```bash poetry
+    poetry add pygwalker
+    ```
+    ```bash uv
+    uv add pygwalker
+    ```
+</CodeGroup>
 
 ## Output
 

docs/en/quickstart/building-app.mdx

@@ -25,9 +25,17 @@ As your first AI app using Morph, we will create a chat app using Langchain.
   </Step>
   <Step title="Install Packages">
     To use Langchain, run the following command to install the package.
-    ```bash
+    <CodeGroup>
+    ```bash pip
     pip install langchain langchain-openai
     ```
+    ```bash poetry
+    poetry add langchain langchain-openai
+    ```
+    ```bash uv
+    uv add langchain langchain-openai
+    ```
+    </CodeGroup>
   </Step>
   <Step title="Create chat.py">
     Create a `chat.py` file in the `src/python` directory.

docs/ja/advanced/llm.mdx

@@ -52,10 +52,18 @@ Morphでは[Ollama](https://ollama.com "Ollama")によるLLMのセルフホス
 
     <Note>
 		Pythonで使用する場合は`langchain-ollama`パッケージをインストールする必要があります。
-		```bash Shell
+		<CodeGroup>
+		```bash pip
 		pip install langchain-ollama
 		```
-	</Note>
+		```bash poetry
+		poetry add langchain-ollama
+		```
+		```bash uv
+		uv add langchain-ollama
+		```
+		</CodeGroup>
+    </Note>
     <CodeGroup>
     ```python Python
     from langchain_ollama import ChatOllama