Update docs quick overview

docs/DOCS_BUILDING.md

@@ -35,6 +35,9 @@ python create_api_rst.py
 
 # Build the docs
 make html
+
+## Alternatively, to rebuild the docs on changes with live-reload in the browser
+sphinx-autobuild docs _build/html
 ```
 
 Project Docs Structure

docs/intro/quick_overview.md

@@ -1,55 +1,59 @@
 # GPT-Engineer Documentation
-
-GPT-Engineer is a project that uses GPT-4 to automate the process of software engineering. It includes several Python scripts that interact with the GPT-4 model to generate code, clarify requirements, generate specifications, and more.
+GPT-Engineer is a project that uses LLMs (such as GPT-4) to automate the process of software engineering. It includes several Python scripts that interact with the LLM to generate code, clarify requirements, generate specifications, and more.
 
 <br>
 
 ## Core Components
-### 1. AI Class (`gpt_engineer/ai.py`)
-The AI class is the main interface to the GPT-4 model. It provides methods to start a conversation with the model, continue an existing conversation, and format system and user messages.
+
+(ai_class)=
+### 1. AI Class (`gpt_engineer/core/ai.py`)
+The `AI` class is the main interface to the LLM. It provides methods to start a conversation with the model, continue an existing conversation, and format system and user messages.
 
 <br>
 
-### 2. Chat to Files (`gpt_engineer/chat_to_files.py`)
-This module contains two main functions:
+(agent_class)=
+### 2. Agent Class (`gpt_engineer/applications/cli/cli_agent.py`)
+The `Agent` class is responsible for managing the lifecycle of code generation and improvement. Its main functions are:
 
-`parse_chat(chat)`: This function takes a chat conversation and extracts all the code blocks and preceding filenames. It returns a list of tuples, where each tuple contains a filename and the corresponding code block.
+- `init(self, prompt)`: Generates a new piece of code using the AI based on the provided prompt. It also generates a entrypoint file based on the generated code.
 
-`to_files_and_memory(chat, dbs)`: This function takes the chat and the DBs as arguments. DBs contains the workspace and memory path. The function first saves the entire chat as a text file in the memory path. Then it calls the to_files function to write each file to the workspace.
+- `improve(self, files_dict, prompt)`: Improves an existing piece of code using the AI class based on the provided prompt and files dictionary.
 
-`to_files(chat, db)`: This function takes the chat and workspace DB as arguments. It calls the parse_chat function to parse the chat and get the files. Each file is then saved to the workspace.
+<br>
+
+(files_dictionary_class)=
+### 3. Files Dictionary Class (`gpt_engineer/core/files_dict.py`)
+The `Files Dictionary` class extends the standard dictionary to enforce string keys and values, representing filenames and their corresponding code content. It provides a method to format its contents for chat-based interaction with the `AI` class.
 
 <br>
 
-### 3. DB Class (`gpt_engineer/db.py`)
-The DB class represents a simple database that stores its data as files in a directory. It provides methods to check if a key (filename) exists in the database, get the value (file content) associated with a key, and set the value associated with a key.
+### 4. Chat to Files (`gpt_engineer/core/chat_to_files.py`)
+This module provides utilities to handle and process chat content, including parsing chat messages to retrieve code blocks, storing these blocks in the [`Files Dictionary`](files_dictionary_class), and overwriting the files based on new chat messages. The module contains four main functions:
 
-The DBs class is a dataclass that contains instances of the DB class for different types of data (memory, logs, input, workspace, and preprompts).
+- `chat_to_files_dict(chat)`: This function takes a chat conversation and extracts all the code blocks and preceding filenames. It returns an instance of [`Files Dictionary`](files_dictionary_class) representing filenames and their corresponding code content.
 
-<br>
+- `parse_edits(chat)`: This function parses edits from a chat and returns them as a list of `Edit` class objects.
 
-### 4. Main Script (`gpt_engineer/main.py`)
-The main script uses the `Typer` library to create a command-line interface. It sets up the AI model and the databases, and then runs a series of steps based on the provided configuration.
+- `apply_edits(edits, files_dict)`: This function takes a list of Edit objects and applies each edit to the code object. It handles the creation of new files and the modification of existing files when required.
+
+- `overwrite_code_with_edits(chat, files_dict)`: This function takes a chat string, and employs the `parse_edits` function to parse it for edits, before applying the edits to the relevant code object via the `apply_edits` function.
 
 <br>
 
-### 5. Steps (`gpt_engineer/steps.py`)
-This module defines a series of steps that can be run in the main script. Each step is a function that takes an instance of the AI class and an instance of the DBs class, and returns a list of messages.
+(steps)=
+### 5. Steps (`gpt_engineer/core/default/steps.py`)
+This module defines a series of steps that can be run by the agent.
+The main steps are:
+
+- `gen_code(ai, prompt, memory, preprompts_holder)`: Generate new code based on the specification.
+
+- `gen_entrypoint(ai, files_dict, memory, preprompts_holder)`: Generate an entrypoint file code based on the generated code.
+
+- `execute_entrypoint(ai, execution_env, files_dict, preprompts_holder)`: Uses the entrypoint file to run the generated code inside the execution environment.
+
+- `improve(ai, prompt, files_dict, memory, preprompts_holder)`: Improves an existing codebase based on the provided specifications.
 
 <br>
 
-The steps include:
-
-`simple_gen(ai, dbs)`: Run the AI on the main prompt and save the results. <br>
-`clarify(ai, dbs)`: Ask the user if they want to clarify anything and save the results to the workspace. <br>
-`gen_spec(ai, dbs)`: Generate a spec from the main prompt + clarifications and save the results to the workspace. <br>
-`respec(ai, dbs)`: Ask the AI to reiterate the specification and save the results to the workspace. <br>
-`gen_unit_tests(ai, dbs)`: Generate unit tests based on the specification. <br>
-`gen_clarified_code(ai, dbs)`: Generate code based on the main prompt and clarifications. <br>
-`gen_code(ai, dbs)`: Generate code based on the specification and unit tests. <br>
-`execute_entrypoint(ai, dbs)`: Execute the entrypoint script in the workspace. <br>
-`gen_entrypoint(ai, dbs)`: Generate an entrypoint script based on the code in the workspace. <br>
-`use_feedback(ai, dbs)`: Ask the AI to generate code based on feedback. <br>
-`fix_code(ai, dbs)`: Ask the AI to fix any errors in the code. <br>
-
-The steps are grouped into different configurations (default, benchmark, simple, tdd, tdd+, clarify, respec, execute_only, use_feedback), which can be selected when running the main script.
+### 6. Main Script (`gpt_engineer/applications/cli/main.py`)
+The main script is the is the entry point of the application and uses the `Typer` library to create a command-line interface. It sets up instances of an [`AI`](ai_class), a [`Files Dictionary`](files_dictionary_class), a `BaseMemory`, a `BaseExecutionEnv` and an [`Agent`](agent_class) that runs a series of [steps](steps) based on the provided configuration.