Assistant Architect - File base improvements (LangChain documentation…

…: More powerful code examples) | AdversarialPromptChain Python module | Python module revisions(#55)

See changelog

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "src/gpt-crawler"]
+	path = src/gpt-crawler
+	url = https://github.com/daethyra/gpt-crawler

Custom-GPT-Uploadable_Knowledge_Base/Assistant_Architect/1-LangChain-Core_Concepts.md

@@ -1,14 +1,12 @@
 # LangChain Core Concepts 
 
 ## Introduction
-Welcome to the comprehensive guide for LangChain, LangServe, and LangSmith. These powerful tools collectively offer a robust framework for building, deploying, and managing advanced AI and language model applications. 
+Welcome to the comprehensive guide for LangChain, LangServe, and LangSmith.
 
 - **LangChain**: A versatile toolkit for creating and managing chains of language models and AI functionalities, facilitating complex tasks and interactions.
 - **LangServe**: Dedicated to server-side operations, LangServe manages the deployment and scaling of language models, ensuring efficient and reliable performance.
 - **LangSmith**: Focused on tracing, debugging, and detailed analysis, LangSmith provides the necessary tools to monitor, evaluate, and improve AI applications.
 
-This documentation aims to provide users, developers, and AI enthusiasts with a thorough understanding of each tool's capabilities, practical applications, and best practices for integration and usage. Whether you're building sophisticated AI-driven applications or seeking to enhance existing systems with cutting-edge language technologies, these documents will serve as your roadmap to mastering LangChain, LangServe, and LangSmith.
-
 ---
 
 ## Core Concepts
@@ -35,8 +33,6 @@ print(response.content)
 ```
 - **Explanation**: This code block shows how to create a simple chain that asks the AI to generate a joke based on a user-provided topic. `ChatPromptTemplate` is used to format the prompt, and `ChatOpenAI` is the model that generates the response.
 
----
-
 ### Section: Memory
 - **Objective**: To illustrate how to integrate memory into a LangChain application, enabling the chain to maintain context across interactions. This is particularly useful for applications like chatbots where retaining context from previous interactions is crucial.
 - **Example Code**:
@@ -77,8 +73,6 @@ memory.save_context({"input": "Tell me about LangChain"}, {"output": response.co
 ```
 - **Explanation**: This code demonstrates the use of `ConversationBufferMemory` to keep a record of the conversation. The `ChatPromptTemplate` is configured to include a history of messages, allowing the model to generate responses considering previous interactions. 
 
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-
 ### Section: Using Tools
 - **Objective**: To demonstrate how to integrate third-party tools into a LangChain application, thereby enhancing its capabilities. This example will specifically show how to use the `DuckDuckGoSearchRun` tool within a LangChain for web searches.
 - **Example Code**:
@@ -110,4 +104,4 @@ print(search_result)
 ```
 - **Explanation**: This example shows the use of `DuckDuckGoSearchRun` to perform web searches. The user's input is formatted into a search query using `ChatPromptTemplate`, passed through a chat model, and then processed by the search tool to retrieve information.
 
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+---

Custom-GPT-Uploadable_Knowledge_Base/Assistant_Architect/2-LangChain-Buillding_Advanced_Applications.md → Custom-GPT-Uploadable_Knowledge_Base/Assistant_Architect/2-LangChain-Advanced_Generative_Applications.md

@@ -1,11 +1,11 @@
-# LangChain/Serve/Smith Advanced Features
+# LangChain Advanced Features
 
 ## Introduction
 
 Building on the core concepts, this guide covers advanced features like embeddings, prompt management, agents, and code writing. These empower sophisticated applications.
 
 ### Section: Embedding Router
-- **Objective**: To explain and demonstrate the use of embeddings to dynamically route queries to the most relevant prompt based on semantic similarity. This advanced feature allows LangChain applications to handle a variety of inputs more intelligently.
+- **Objective**: To answer "what are routers?" and demonstrate the use of embeddings to dynamically route queries to the most relevant prompt based on semantic similarity. This advanced feature allows LangChain applications to handle a variety of inputs more intelligently.
 - **Example Code**:
 ```python
 from langchain.chat_models import ChatOpenAI   
@@ -183,4 +183,4 @@ print(code_result)
 ```
 - **Explanation**: This code block demonstrates how LangChain can be used to automatically generate Python code in response to a given problem statement. The `ChatPromptTemplate` guides the AI to focus on code generation, and the output is sanitized and executed using `PythonREPL`. This illustrates LangChain's capability in automating and assisting with coding tasks.
 
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+---