update utility

docs/guide.md

@@ -54,7 +54,6 @@ Agentic Coding should be a collaboration between Human System Design and Agent I
       - *Output*: a vector of 3072 floats
       - *Necessity:* Used by the second node to embed text
 
-
 4. **Node Design**: Plan how each node will read and write data, and use utility functions.
    - Start with the shared data design
       - For simple systems, use an in-memory dictionary.
@@ -66,7 +65,6 @@ Agentic Coding should be a collaboration between Human System Design and Agent I
      - `exec`: Call the embedding utility function
      - `post`: Write "embedding" to the shared store
 
-
 5. **Implementation**: Implement the initial nodes and flows based on the design.
    - 🎉 If you’ve reached this step, humans have finished the design. Now *Agentic Coding* begins!
    - **“Keep it simple, stupid!”** Avoid complex features and full-scale type checking.

docs/index.md

@@ -25,7 +25,7 @@ We model the LLM workflow as a **Graph + Shared Store**:
 - [Flow](./core_abstraction/flow.md) connects nodes through **Actions** (labeled edges).
 - [Shared Store](./core_abstraction/communication.md) enables communication between nodes within flows.
 - [Batch](./core_abstraction/batch.md) nodes/flows allow for data-intensive tasks.
-- [(Advanced) Async](./core_abstraction/async.md) nodes/flows allow waiting for asynchronous tasks.
+- [Async](./core_abstraction/async.md) nodes/flows allow waiting for asynchronous tasks.
 - [(Advanced) Parallel](./core_abstraction/parallel.md) nodes/flows handle I/O-bound tasks.
 
 <div align="center">
@@ -49,15 +49,20 @@ From there, it’s easy to implement popular design patterns:
 
 ## Utility Function
 
-We provide utility functions not in *codes*, but in *docs*:
+We **DON'T** provide built-in utility functions. Instead, please implement your own. I believe it is a **bad practice** to provide vendor-specific implementations in a general framework:
+
+- **Vendor APIs change frequently**. Hardcoding them makes maintenance a nightmare.
+- You may need **flexibility** to switch vendors, use fine-tuned models, or deploy local LLMs.
+- You may need **optimizations** like prompt caching, request batching, or response streaming.
+
+However, we provide example implementations for commonly used utility functions.
 
 - [LLM Wrapper](./utility_function/llm.md)
-- [Tool](./utility_function/tool.md)
+- [Viz and Debug](./utility_function/viz.md)
-- [(Optional) Viz and Debug](./utility_function/viz.md)
+- [Web Search](./utility_function/websearch.md)
-- [(Optional) Web Search](./utility_function/websearch.md)
+- [Chunking](./utility_function/chunking.md)
-- [(Optional) Chunking](./utility_function/chunking.md)
+- [Embedding](./utility_function/embedding.md)
-- [(Optional) Embedding](./utility_function/embedding.md)
+- [Vector Databases](./utility_function/vector.md)
-- [(Optional) Vector Databases](./utility_function/vector.md)
+- [Text-to-Speech](./utility_function/text_to_speech.md)
-- [(Optional) Text-to-Speech](./utility_function/text_to_speech.md)
 
 ## Ready to build your Apps? [Learn Agentic Coding!](./guide.md)

docs/utility_function/chunking.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Text Chunking"
 parent: "Utility Function"
-nav_order: 5
+nav_order: 4
 ---
 
 # Text Chunking

docs/utility_function/embedding.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Embedding"
 parent: "Utility Function"
-nav_order: 6
+nav_order: 5
 ---
 
 # Embedding

docs/utility_function/llm.md

@@ -7,7 +7,7 @@ nav_order: 1
 
 # LLM Wrappers
 
-We **don't** provide built-in LLM wrappers. Instead, please implement your own or check out libraries like [litellm](https://github.com/BerriAI/litellm). 
+Check out libraries like [litellm](https://github.com/BerriAI/litellm). 
 Here, we provide some minimal example implementations:
 
 1. OpenAI
@@ -141,8 +141,3 @@ def call_llm(prompt):
     return response
 ```
 
-## Why Not Provide Built-in LLM Wrappers?
-I believe it is a **bad practice** to provide LLM-specific implementations in a general framework:
-- **LLM APIs change frequently**. Hardcoding them makes maintenance a nightmare.
-- You may need **flexibility** to switch vendors, use fine-tuned models, or deploy local LLMs.
-- You may need **optimizations** like prompt caching, request batching, or response streaming.

docs/utility_function/text_to_speech.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Text-to-Speech"
 parent: "Utility Function"
-nav_order: 8
+nav_order: 7
 ---
 
 # Text-to-Speech

docs/utility_function/tool.md

@@ -1,199 +0,0 @@
----
-layout: default
-title: "Tool"
-parent: "Utility Function"
-nav_order: 2
----
-
-# Tool
-
-Similar to LLM wrappers, we **don't** provide built-in tools. Here, we recommend some *minimal* (and incomplete) implementations of commonly used tools. These examples can serve as a starting point for your own tooling.
-
----
-
-## 1. Embedding Calls
-
-```python
-def get_embedding(text):
-    from openai import OpenAI
-    client = OpenAI(api_key="YOUR_API_KEY_HERE")
-    r = client.embeddings.create(
-        model="text-embedding-ada-002",
-        input=text
-    )
-    return r.data[0].embedding
-
-get_embedding("What's the meaning of life?")
-```
-
----
-
-## 2. Vector Database (Faiss)
-
-```python
-import faiss
-import numpy as np
-
-def create_index(embeddings):
-    dim = len(embeddings[0])
-    index = faiss.IndexFlatL2(dim)
-    index.add(np.array(embeddings).astype('float32'))
-    return index
-
-def search_index(index, query_embedding, top_k=5):
-    D, I = index.search(
-        np.array([query_embedding]).astype('float32'), 
-        top_k
-    )
-    return I, D
-
-index = create_index(embeddings)
-search_index(index, query_embedding)
-```
-
----
-
-## 3. Local Database
-
-```python
-import sqlite3
-
-def execute_sql(query):
-    conn = sqlite3.connect("mydb.db")
-    cursor = conn.cursor()
-    cursor.execute(query)
-    result = cursor.fetchall()
-    conn.commit()
-    conn.close()
-    return result
-```
-
-> ⚠️ Beware of SQL injection risk
-{: .warning }
-
----
-
-## 4. Python Function Execution
-
-```python
-def run_code(code_str):
-    env = {}
-    exec(code_str, env)
-    return env
-
-run_code("print('Hello, world!')")
-```
-
-> ⚠️ exec() is dangerous with untrusted input
-{: .warning }
-
-
----
-
-## 5. PDF Extraction
-
-If your PDFs are text-based, use PyMuPDF:
-
-```python
-import fitz  # PyMuPDF
-
-def extract_text(pdf_path):
-    doc = fitz.open(pdf_path)
-    text = ""
-    for page in doc:
-        text += page.get_text()
-    doc.close()
-    return text
-
-extract_text("document.pdf")
-```
-
-For image-based PDFs (e.g., scanned), OCR is needed. A easy and fast option is using an LLM with vision capabilities:
-
-```python
-from openai import OpenAI
-import base64
-
-def call_llm_vision(prompt, image_data):
-    client = OpenAI(api_key="YOUR_API_KEY_HERE")
-    img_base64 = base64.b64encode(image_data).decode('utf-8')
-    
-    response = client.chat.completions.create(
-        model="gpt-4o",
-        messages=[{
-            "role": "user",
-            "content": [
-                {"type": "text", "text": prompt},
-                {"type": "image_url", 
-                 "image_url": {"url": f"data:image/png;base64,{img_base64}"}}
-            ]
-        }]
-    )
-    
-    return response.choices[0].message.content
-
-pdf_document = fitz.open("document.pdf")
-page_num = 0
-page = pdf_document[page_num]
-pix = page.get_pixmap()
-img_data = pix.tobytes("png")
-
-call_llm_vision("Extract text from this image", img_data)
-```
-
----
-
-## 6. Web Crawling
-
-```python
-def crawl_web(url):
-    import requests
-    from bs4 import BeautifulSoup
-    html = requests.get(url).text
-    soup = BeautifulSoup(html, "html.parser")
-    return soup.title.string, soup.get_text()
-```
-
----
-
-
-## 7. Audio Transcription (OpenAI Whisper)
-
-```python
-def transcribe_audio(file_path):
-    import openai
-    audio_file = open(file_path, "rb")
-    transcript = openai.Audio.transcribe("whisper-1", audio_file)
-    return transcript["text"]
-```
-
----
-
-## 8. Text-to-Speech (TTS)
-
-```python
-def text_to_speech(text):
-    import pyttsx3
-    engine = pyttsx3.init()
-    engine.say(text)
-    engine.runAndWait()
-```
-
----
-
-## 9. Sending Email
-
-```python
-def send_email(to_address, subject, body, from_address, password):
-    import smtplib
-    from email.mime.text import MIMEText
-
-    msg = MIMEText(body)
-    msg["Subject"] = subject
-    msg["From"] = from_address
-    msg["To"] = to_address
-
-    with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
-        server.login(from_address, password)
-        server.sendmail(from_address, [to_address], msg.as_string())
-```

docs/utility_function/vector.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Vector Databases"
 parent: "Utility Function"
-nav_order: 7
+nav_order: 6
 ---
 
 # Vector Databases

docs/utility_function/viz.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Viz and Debug"
 parent: "Utility Function"
-nav_order: 3
+nav_order: 2
 ---
 
 # Visualization and Debugging

docs/utility_function/websearch.md

@@ -2,7 +2,7 @@
 layout: default
 title: "Web Search"
 parent: "Utility Function"
-nav_order: 4
+nav_order: 3
 ---
 # Web Search