Merge branch 'master' into draft-llamafile-support

.gitignore

@@ -32,7 +32,6 @@ dist/
 downloads/
 eggs/
 .eggs/
-lib/
 lib64/
 parts/
 sdist/

.vscode/all-projects.code-workspace

@@ -0,0 +1,47 @@
+{
+  "folders": [
+    {
+      "name": "autogpt",
+      "path": "../autogpt"
+    },
+    {
+      "name": "benchmark",
+      "path": "../benchmark"
+    },
+    {
+      "name": "docs",
+      "path": "../docs"
+    },
+    {
+      "name": "forge",
+      "path": "../forge"
+    },
+    {
+      "name": "frontend",
+      "path": "../frontend"
+    },
+    {
+      "name": "autogpt_server",
+      "path": "../rnd/autogpt_server"
+    },
+    {
+      "name": "autogpt_builder",
+      "path": "../rnd/autogpt_builder"
+    },
+    {
+      "name": "[root]",
+      "path": ".."
+    }
+  ],
+  "settings": {},
+  "extensions": {
+    "recommendations": [
+      "charliermarsh.ruff",
+      "dart-code.flutter",
+      "ms-python.black-formatter",
+      "ms-python.vscode-pylance",
+      "prisma.prisma",
+      "qwtel.sqlite-viewer"
+    ]
+  }
+}

autogpt/autogpt/agent_factory/profile_generator.py

@@ -19,7 +19,7 @@
 
 
 class AgentProfileGeneratorConfiguration(SystemConfiguration):
-    model_classification: LanguageModelClassification = UserConfigurable(
+    llm_classification: LanguageModelClassification = UserConfigurable(
         default=LanguageModelClassification.SMART_MODEL
     )
     _example_call: object = {
@@ -148,21 +148,21 @@ class AgentProfileGenerator(PromptStrategy):
 
     def __init__(
         self,
-        model_classification: LanguageModelClassification,
+        llm_classification: LanguageModelClassification,
         system_prompt: str,
         user_prompt_template: str,
         create_agent_function: dict,
     ):
-        self._model_classification = model_classification
+        self._llm_classification = llm_classification
         self._system_prompt_message = system_prompt
         self._user_prompt_template = user_prompt_template
         self._create_agent_function = CompletionModelFunction.model_validate(
             create_agent_function
         )
 
     @property
-    def model_classification(self) -> LanguageModelClassification:
-        return self._model_classification
+    def llm_classification(self) -> LanguageModelClassification:
+        return self._llm_classification
 
     def build_prompt(self, user_objective: str = "", **kwargs) -> ChatPrompt:
         system_message = ChatMessage.system(self._system_prompt_message)

autogpt/autogpt/agents/agent.py

@@ -119,7 +119,7 @@ def __init__(
                 lambda x: self.llm_provider.count_tokens(x, self.llm.name),
                 llm_provider,
                 ActionHistoryConfiguration(
-                    model_name=app_config.fast_llm, max_tokens=self.send_token_limit
+                    llm_name=app_config.fast_llm, max_tokens=self.send_token_limit
                 ),
             )
             .run_after(WatchdogComponent)

autogpt/autogpt/agents/prompt_strategies/one_shot.py

@@ -100,7 +100,7 @@ def __init__(
         self.logger = logger
 
     @property
-    def model_classification(self) -> LanguageModelClassification:
+    def llm_classification(self) -> LanguageModelClassification:
         return LanguageModelClassification.FAST_MODEL  # FIXME: dynamic switching
 
     def build_prompt(

docs/content/forge/components/built-in-components.md

@@ -40,7 +40,7 @@ Necessary for saving and loading agent's state (preserving session).
 
 | Config variable  | Details                                | Type  | Default                            |
 | ---------------- | -------------------------------------- | ----- | ---------------------------------- |
-| `storage_path`     | Path to agent files, e.g. state        | `str` | `agents/{agent_id}/`[^1]           |
+| `storage_path`   | Path to agent files, e.g. state        | `str` | `agents/{agent_id}/`[^1]           |
 | `workspace_path` | Path to files that agent has access to | `str` | `agents/{agent_id}/workspace/`[^1] |
 
 [^1] This option is set dynamically during component construction as opposed to by default inside the configuration model, `{agent_id}` is replaced with the agent's unique identifier.
@@ -84,7 +84,7 @@ Keeps track of agent's actions and their outcomes. Provides their summary to the
 
 | Config variable        | Details                                                 | Type        | Default            |
 | ---------------------- | ------------------------------------------------------- | ----------- | ------------------ |
-| `model_name`           | Name of the llm model used to compress the history      | `ModelName` | `"gpt-3.5-turbo"`  |
+| `llm_name`             | Name of the llm model used to compress the history      | `ModelName` | `"gpt-3.5-turbo"`  |
 | `max_tokens`           | Maximum number of tokens to use for the history summary | `int`       | `1024`             |
 | `spacy_language_model` | Language model used for summary chunking using spacy    | `str`       | `"en_core_web_sm"` |
 | `full_message_count`   | Number of cycles to include unsummarized in the prompt  | `int`       | `4`                |
@@ -178,7 +178,7 @@ Allows agent to read websites using Selenium.
 
 | Config variable               | Details                                     | Type                                          | Default                                                                                                                      |
 | ----------------------------- | ------------------------------------------- | --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| `model_name`                  | Name of the llm model used to read websites | `ModelName`                                   | `"gpt-3.5-turbo"`                                                                                                            |
+| `llm_name`                    | Name of the llm model used to read websites | `ModelName`                                   | `"gpt-3.5-turbo"`                                                                                                            |
 | `web_browser`                 | Web browser used by Selenium                | `"chrome" \| "firefox" \| "safari" \| "edge"` | `"chrome"`                                                                                                                   |
 | `headless`                    | Run browser in headless mode                | `bool`                                        | `True`                                                                                                                       |
 | `user_agent`                  | User agent used by the browser              | `str`                                         | `"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"` |

docs/content/server/new_nodes.md

@@ -0,0 +1,172 @@
+# Contributing to AutoGPT Agent Server: Creating Nodes!
+
+This guide will walk you through the process of creating a new node (also called a block) for the AutoGPT Agent Server. We'll use the GetWikipediaSummary node as an example.
+
+## Understanding Nodes
+
+Nodes are reusable components that can be connected to form a graph representing an agent's behavior. Each node has inputs, outputs, and a specific function it performs.
+
+## Creating a New Node
+
+To create a new node, follow these steps:
+
+1. **Create a new Python file** in the `autogpt_server/blocks` directory. Name it descriptively and use snake_case. For example: `get_wikipedia_summary.py`.
+
+2. **Import necessary modules and create a class that inherits from `Block`**. Make sure to include all necessary imports for your node. Every node should contain:
+
+   ```python
+   from autogpt_server.data.block import Block, BlockSchema, BlockOutput
+   ```
+
+   Example for the Wikipedia summary node:
+
+   ```python
+   import requests
+   from autogpt_server.data.block import Block, BlockSchema, BlockOutput
+
+   class GetWikipediaSummary(Block):
+       # Node implementation will go here
+   ```
+
+3. **Define the input and output schemas** using `BlockSchema`. These schemas specify the data structure that the node expects to receive (input) and produce (output).
+
+   - The input schema defines the structure of the data the node will process. Each field in the schema represents a required piece of input data.
+   - The output schema defines the structure of the data the node will return after processing. Each field in the schema represents a piece of output data.
+
+   Example:
+
+   ```python
+   class Input(BlockSchema):
+       topic: str  # The topic to get the Wikipedia summary for
+
+   class Output(BlockSchema):
+       summary: str  # The summary of the topic from Wikipedia
+   ```
+
+4. **Implement the `__init__` method**:
+
+   ```python
+   def __init__(self):
+       super().__init__(
+           id="h5e7f8g9-1b2c-3d4e-5f6g-7h8i9j0k1l2m",  # Unique ID for the node
+           input_schema=GetWikipediaSummary.Input,  # Assign input schema
+           output_schema=GetWikipediaSummary.Output,  # Assign output schema
+
+           # Provide sample input and output for testing the node
+
+           test_input={"topic": "Artificial Intelligence"},
+           test_output={"summary": "Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals."},
+       )
+   ```
+
+   - `id`: A unique identifier for the node.
+   - `input_schema` and `output_schema`: Define the structure of the input and output data.
+   - `test_input` and `test_output`: Provide sample input and output data for testing the node.
+
+5. **Implement the `run` method**, which contains the main logic of the node:
+
+   ```python
+   def run(self, input_data: Input) -> BlockOutput:
+       try:
+           # Make the request to Wikipedia API
+           response = requests.get(f"https://en.wikipedia.org/api/rest_v1/page/summary/{input_data.topic}")
+           response.raise_for_status()
+           summary_data = response.json()
+
+           # Output the summary
+           yield "summary", summary_data['extract']
+
+       except requests.exceptions.HTTPError as http_err:
+           raise ValueError(f"HTTP error occurred: {http_err}")
+       except requests.RequestException as e:
+           raise ValueError(f"Request to Wikipedia API failed: {e}")
+       except KeyError as e:
+           raise ValueError(f"Error processing Wikipedia data: {e}")
+   ```
+
+   - **Try block**: Contains the main logic to fetch and process the Wikipedia summary.
+   - **API request**: Send a GET request to the Wikipedia API.
+   - **Error handling**: Handle various exceptions that might occur during the API request and data processing.
+   - **Yield**: Use `yield` to output the results.
+
+6. **Register the new node** by adding it to the `__init__.py` file in the `autogpt_server/blocks` directory. This step makes your new node available to the rest of the server.
+
+   - Open the `__init__.py` file in the `autogpt_server/blocks` directory.
+   - Add an import statement for your new node at the top of the file.
+   - Add the new node to the `AVAILABLE_BLOCKS` and `__all__` lists.
+
+   Example:
+
+   ```python
+   from autogpt_server.blocks import sample, reddit, text, ai, wikipedia, discord, get_wikipedia_summary  # Import your new node
+   from autogpt_server.data.block import Block
+
+   AVAILABLE_BLOCKS = {
+       block.id: block
+       for block in [v() for v in Block.__subclasses__()]
+   }
+
+   __all__ = ["ai", "sample", "reddit", "text", "AVAILABLE_BLOCKS", "wikipedia", "discord", "get_wikipedia_summary"]
+   ```
+
+   - The import statement ensures your new node is included in the module.
+   - The `AVAILABLE_BLOCKS` dictionary includes all blocks by their ID.
+   - The `__all__` list specifies all public objects that the module exports.
+
+### Full Code example
+
+Here is the complete implementation of the `GetWikipediaSummary` nodes:
+
+```python
+import requests
+from autogpt_server.data.block import Block, BlockSchema, BlockOutput
+
+class GetWikipediaSummary(Block):
+    # Define the input schema with the required field 'topic'
+    class Input(BlockSchema):
+        topic: str  # The topic to get the Wikipedia summary for
+
+    # Define the output schema with the field 'summary'
+    class Output(BlockSchema):
+        summary: str  # The summary of the topic from Wikipedia
+
+    def __init__(self):
+        super().__init__(
+            id="h5e7f8g9-1b2c-3d4e-5f6g-7h8i9j0k1l2m",  # Unique ID for the node
+            input_schema=GetWikipediaSummary.Input,  # Assign input schema
+            output_schema=GetWikipediaSummary.Output,  # Assign output schema
+
+            # Provide sample input and output for testing the node
+
+            test_input={"topic": "Artificial Intelligence"},
+            test_output={"summary": "Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals."},
+        )
+
+    def run(self, input_data: Input) -> BlockOutput:
+        try:
+            # Make the request to Wikipedia API
+            response = requests.get(f"https://en.wikipedia.org/api/rest_v1/page/summary/{input_data.topic}")
+            response.raise_for_status()
+            summary_data = response.json()
+
+            # Output the summary
+            yield "summary", summary_data['extract']
+
+        except requests.exceptions.HTTPError as http_err:
+            raise ValueError(f"HTTP error occurred: {http_err}")
+        except requests.RequestException as e:
+            raise ValueError(f"Request to Wikipedia API failed: {e}")
+        except KeyError as e:
+            raise ValueError(f"Error processing Wikipedia data: {e}")
+```
+
+## Key Points to Remember
+
+- **Unique ID**: Give your node a unique ID in the `__init__` method.
+- **Input and Output Schemas**: Define clear input and output schemas.
+- **Error Handling**: Implement error handling in the `run` method.
+- **Output Results**: Use `yield` to output results in the `run` method.
+- **Register the Node**: Add your new node to the `__init__.py` file to make it available to the server.
+- **Testing**: Provide test input and output in the `__init__` method for automatic testing.
+
+By following these steps, you can create new nodes that extend the functionality of the AutoGPT Agent Server.

docs/mkdocs.yml

@@ -40,6 +40,9 @@ nav:
       - Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/frontend/README.md
 
   - Docs: docs/index.md
+
+  - Server: 
+      - Build your own Node: server/new_nodes.md
 
   # - Challenges:
   #     - Introduction: challenges/introduction.md

forge/forge/components/README.md

@@ -116,7 +116,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
         "github_username": null
     },
     "ActionHistoryConfiguration": {
-        "model_name": "gpt-3.5-turbo",
+        "llm_name": "gpt-3.5-turbo",
         "max_tokens": 1024,
         "spacy_language_model": "en_core_web_sm"
     },
@@ -129,7 +129,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
         "duckduckgo_max_attempts": 3
     },
     "WebSeleniumConfiguration": {
-        "model_name": "gpt-3.5-turbo",
+        "llm_name": "gpt-3.5-turbo",
         "web_browser": "chrome",
         "headless": true,
         "user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",

forge/forge/components/action_history/action_history.py

@@ -16,7 +16,7 @@
 
 
 class ActionHistoryConfiguration(BaseModel):
-    model_name: ModelName = OpenAIModelName.GPT3
+    llm_name: ModelName = OpenAIModelName.GPT3
     """Name of the llm model used to compress the history"""
     max_tokens: int = 1024
     """Maximum number of tokens to use up with generated history messages"""
@@ -97,7 +97,7 @@ def after_parse(self, result: AnyProposal) -> None:
     async def after_execute(self, result: ActionResult) -> None:
         self.event_history.register_result(result)
         await self.event_history.handle_compression(
-            self.llm_provider, self.config.model_name, self.config.spacy_language_model
+            self.llm_provider, self.config.llm_name, self.config.spacy_language_model
         )
 
     @staticmethod

forge/forge/components/web/selenium.py

@@ -55,7 +55,7 @@ class BrowsingError(CommandExecutionError):
 
 
 class WebSeleniumConfiguration(BaseModel):
-    model_name: ModelName = OpenAIModelName.GPT3
+    llm_name: ModelName = OpenAIModelName.GPT3
     """Name of the llm model used to read websites"""
     web_browser: Literal["chrome", "firefox", "safari", "edge"] = "chrome"
     """Web browser used by Selenium"""
@@ -164,7 +164,7 @@ async def read_webpage(
             elif get_raw_content:
                 if (
                     output_tokens := self.llm_provider.count_tokens(
-                        text, self.config.model_name
+                        text, self.config.llm_name
                     )
                 ) > MAX_RAW_CONTENT_LENGTH:
                     oversize_factor = round(output_tokens / MAX_RAW_CONTENT_LENGTH, 1)
@@ -382,7 +382,7 @@ async def summarize_webpage(
                 text,
                 topics_of_interest=topics_of_interest,
                 llm_provider=self.llm_provider,
-                model_name=self.config.model_name,
+                model_name=self.config.llm_name,
                 spacy_model=self.config.browse_spacy_language_model,
             )
             return "\n".join(f"* {i}" for i in information)
@@ -391,7 +391,7 @@ async def summarize_webpage(
                 text,
                 question=question,
                 llm_provider=self.llm_provider,
-                model_name=self.config.model_name,
+                model_name=self.config.llm_name,
                 spacy_model=self.config.browse_spacy_language_model,
             )
             return result

forge/forge/llm/prompting/base.py

@@ -10,7 +10,7 @@
 class PromptStrategy(abc.ABC):
     @property
     @abc.abstractmethod
-    def model_classification(self) -> LanguageModelClassification:
+    def llm_classification(self) -> LanguageModelClassification:
         ...
 
     @abc.abstractmethod