Merge pull request #3 from LangChain-OpenTutorial/soo-dev

🚚 move and new setting sample folder

Author: sooyoung-wind

sample/04-Model/06-GoogleGenerativeAI.ipynb

@@ -14,6 +14,7 @@ pre {
     background-color: #027c7c;
     padding-left: 0.5em;
 }
+
 </style>
 
 # Google Generative AI
@@ -52,13 +53,11 @@ Set up the environment. You may refer to [Environment Setup](https://wikidocs.ne
 - `langchain-opentutorial` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.
 - You can checkout the [`langchain-opentutorial`](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details.
 
-
 ```python
 %%capture --no-stderr
 !pip install langchain-opentutorial
 ```
 
-
 ```python
 # Install required packages
 from langchain_opentutorial import package
@@ -73,7 +72,6 @@ package.install(
 )
 ```
 
-
 ```python
 # Set environment variables
 from langchain_opentutorial import set_env
@@ -101,7 +99,6 @@ You can alternatively set `GOOGLE_API_KEY` in `.env` file and load it.
 
 [Note] This is not necessary if you've already set `GOOGLE_API_KEY` in previous steps.
 
-
 ```python
 from dotenv import load_dotenv
 
@@ -125,7 +122,6 @@ Because the `ChatGoogleGenerativeAI` class is integrated with the LangChain fram
 
 For information about supported models, see: https://ai.google.dev/gemini-api/docs/models/gemini?hl=en
 
-
 ```python
 from langchain_google_genai import ChatGoogleGenerativeAI
 
@@ -143,7 +139,6 @@ for token in answer:
     LangChain is a framework for developing applications powered by large language models (LLMs).  It simplifies building applications by connecting LLMs to other sources of data and computation.  This enables creation of sophisticated chains of prompts and actions, going beyond single LLM calls.
 
 
-
 ```python
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_core.prompts import PromptTemplate
@@ -170,7 +165,6 @@ print(response.content)
 
 Gemini models have default safety settings that can be overridden. If you are receiving lots of "Safety Warnings" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:
 
-
 ```python
 from langchain_google_genai import (
     ChatGoogleGenerativeAI,
@@ -242,7 +236,6 @@ print(response.content)
 
 `ChatGoogleGenerativeAI` natively supports streaming and batching. Below is an example.
 
-
 ```python
 from langchain_google_genai import ChatGoogleGenerativeAI
 
@@ -289,7 +282,6 @@ for chunk in llm.stream("Can you recommend 5 travel destinations in California?"
     ---
 
 
-
 ```python
 from langchain_google_genai import ChatGoogleGenerativeAI
 
@@ -324,7 +316,6 @@ To provide an image, pass a human message with contents of type `List[dict]`, wh
 - A base64 encoded image (e.g., `data:image/png;base64,abcd124`)
 - A PIL image
 
-
 ```python
 # CASE - A pulbic image URL
 import requests
@@ -339,8 +330,7 @@ Image(content)
 
 
 
-![jpeg](output_17_0.jpg)
-
+![jpeg](sample\img\output_17_0.jpg)
 
 
 

sample/04-Model/07-Huggingface-Endpoints.ipynb

@@ -27,7 +27,7 @@ pre {
 
 ## Overview
 
-You can use the `ChatGoogleGenerativeAI` class from the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) integration package to access not only Google AI¡¯s `gemini` and `gemini-vision` models, but also other generative models.
+You can use the `ChatGoogleGenerativeAI` class from the [langchain-google-genai](https://pypi.org/project/langchain-google-genai/) integration package to access not only Google AI��s `gemini` and `gemini-vision` models, but also other generative models.
 
 ### Table of Contents
 
@@ -106,7 +106,7 @@ load_dotenv(override=True)
 
 Import the `ChatGoogleGenerativeAI` class from the `langchain_google_genai` package.
 
-The `ChatGoogleGenerativeAI` class is used to implement conversational AI systems using Google¡¯s Generative AI models. Through this class, users can interact with Google¡¯s conversational AI model. Conversations with the model take place in a chat format, and the model generates appropriate responses based on user input.
+The `ChatGoogleGenerativeAI` class is used to implement conversational AI systems using Google��s Generative AI models. Through this class, users can interact with Google��s conversational AI model. Conversations with the model take place in a chat format, and the model generates appropriate responses based on user input.
 
 Because the `ChatGoogleGenerativeAI` class is integrated with the LangChain framework, it can be used alongside other LangChain components.