First commit.

.gitignore

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+# Edit at https://www.toptal.com/developers/gitignore?templates=python,java,c++,pycharm,visualstudiocode,macos,linux,windows
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+# End of https://www.toptal.com/developers/gitignore/api/python,java,c++,pycharm,visualstudiocode,macos,linux,windows
+
+.*
+flow_modules/

AutoGPT.yaml

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+flow:
+  _target_: aiflows.AutoGPTFlowModule.AutoGPTFlow.instantiate_from_default_config
+  max_rounds: 30
+
+  ### Subflows specification
+  subflows_config:
+    Controller:
+      _target_: aiflows.ControllerExecutorFlowModule.ControllerAtomicFlow.instantiate_from_default_config
+      commands:
+        wiki_search:
+          description: "Performs a search on Wikipedia."
+          input_args: [ "search_term" ]
+        ddg_search:
+          description: "Query the search engine DuckDuckGo."
+          input_args: [ "query" ]
+        finish:
+          description: "Signal that the objective has been satisfied, and returns the answer to the user."
+          input_args: [ "answer" ]
+      human_message_prompt_template:
+        template: |2-
+          Here is the response to your last action:
+          {{observation}}
+          Here is the feedback from the user:
+          {{human_feedback}}
+        input_variables:
+          - "observation"
+          - "human_feedback"
+      input_interface_initialized:
+        - "observation"
+        - "human_feedback"
+
+    Executor:
+      _target_: flows.base_flows.BranchingFlow.instantiate_from_default_config
+      subflows_config:
+        wiki_search:
+          _target_: aiflows.ControllerExecutorFlowModule.WikiSearchAtomicFlow.instantiate_from_default_config
+        ddg_search:
+          _target_: aiflows.LCToolFlowModule.LCToolFlow.instantiate_from_default_config
+          backend:
+            _target_: langchain.tools.DuckDuckGoSearchRun

AutoGPTFlow.py

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+import sys
+from typing import Dict, Any
+
+from flows.base_flows import CircularFlow
+from flows.utils import logging
+
+logging.set_verbosity_debug()
+
+log = logging.get_logger(__name__)
+
+from flow_modules.aiflows.ControllerExecutorFlowModule import ControllerAtomicFlow
+from flow_modules.aiflows.VectorStoreFlowModule import ChromaDBFlow
+
+class AutoGPTFlow(CircularFlow):
+    def _on_reach_max_round(self):
+        self._state_update_dict({
+            "answer": "The maximum amount of rounds was reached before the model found an answer.",
+            "status": "unfinished"
+        })
+
+    @staticmethod
+    def _get_memory_key(flow_state):
+        goal = flow_state.get("goal")
+        last_command = flow_state.get("command")
+        last_command_args = flow_state.get("command_args")
+        last_observation = flow_state.get("observation")
+        last_human_feedback = flow_state.get("human_feedback")
+
+        if last_command is None:
+            return ""
+
+        assert goal is not None, goal
+        assert last_command_args is not None, last_command_args
+        assert last_observation is not None, last_observation
+
+        current_context = \
+            f"""
+            == Goal == 
+            {goal}
+            
+            == Command ==
+            {last_command}
+            == Args
+            {last_command_args}
+            == Result
+            {last_observation}
+
+            == Human Feedback ==
+            {last_human_feedback}
+            """
+
+        return current_context
+
+    @CircularFlow.input_msg_payload_builder
+    def prepare_memory_read_input(self, flow_state: Dict[str, Any], dst_flow: ChromaDBFlow) -> Dict[str, Any]:
+        """A (very) basic example implementation of how the memory retrieval could be constructed."""
+        query = self._get_memory_key(flow_state)
+
+        return {
+            "operation": "read",
+            "content": query
+        }
+
+    @CircularFlow.output_msg_payload_processor
+    def prepare_memory_read_output(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow):
+        retrieved_memories = output_payload["retrieved"][0][1:]
+        return {"memory": "\n".join(retrieved_memories)}
+
+    @CircularFlow.input_msg_payload_builder
+    def prepare_memory_write_input(self, flow_state: Dict[str, Any], dst_flow: ChromaDBFlow) -> Dict[str, Any]:
+        """An (very) example (not optimized) implementation of how the memory population could be implemented."""
+        query = self._get_memory_key(flow_state)
+
+        return {
+            "operation": "write",
+            "content": str(query)
+        }
+
+    @CircularFlow.output_msg_payload_processor
+    def detect_finish_or_continue(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow) -> Dict[
+        str, Any]:
+        command = output_payload["command"]
+        if command == "finish":
+            return {
+                "EARLY_EXIT": True,
+                "answer": output_payload["command_args"]["answer"],
+                "status": "finished"
+            }
+        else:
+            return output_payload
+
+    @CircularFlow.output_msg_payload_processor
+    def detect_finish_in_human_input(self, output_payload: Dict[str, Any], src_flow: ControllerAtomicFlow) -> Dict[
+        str, Any]:
+        human_feedback = output_payload["human_input"]
+        if human_feedback.strip().lower() == "q":
+            return {
+                "EARLY_EXIT": True,
+                "answer": "The user has chosen to exit before a final answer was generated.",
+                "status": "unfinished"
+            }
+
+        return {"human_feedback": human_feedback}

AutoGPTFlow.yaml

@@ -0,0 +1,137 @@
+name: "AutoGPTFlow"
+description: "An example implementation of AutoGPT with Flows."
+max_rounds: 30
+early_exit_key: "EARLY_EXIT"
+
+### Information used by the default get_interface_description implementation
+input_interface:
+  - "goal"
+output_interface:
+  - "answer"
+  - "status"
+
+### Subflows specification
+subflows_config:
+  Controller:
+    _target_: aiflows.ControllerExecutorFlowModule.ControllerAtomicFlow.instantiate_from_default_config
+    finish:
+      description: "Signal that the objective has been satisfied, and returns the answer to the user."
+      input_args: ["answer"]
+# E.g.,
+#    commands:
+#      wiki_search:
+#        description: "Performs a search on Wikipedia."
+#        input_args: ["search_term"]
+#      ddg_search:
+#        description: "Query the search engine DuckDuckGo."
+#        input_args: ["query"]
+    human_message_prompt_template:
+      template: |2
+        Potentially relevant information retrieved from your memory:
+        {{memory}}
+        =================
+        Here is the response to your last action:
+        {{observation}}
+        Here is the feedback from the user:
+        {{human_feedback}}
+      input_variables:
+        - "observation"
+        - "human_feedback"
+        - "memory"
+    input_interface_initialized:
+      - "observation"
+      - "human_feedback"
+      - "memory"
+
+  Executor:
+    _target_: flows.base_flows.BranchingFlow.instantiate_from_default_config
+# E.g.,
+#    subflows_config:
+#      wiki_search:
+#        _target_: .WikiSearchAtomicFlow.instantiate_from_default_config
+#      ddg_search:
+#        _target_: flows.application_flows.LCToolFlowModule.LCToolFlow.instantiate_from_default_config
+#        backend:
+#          _target_: langchain.tools.DuckDuckGoSearchRun
+
+  HumanFeedback:
+    _target_: aiflows.HumanStandardInputFlowModule.HumanStandardInputFlow.instantiate_from_default_config
+    request_multi_line_input_flag: False
+    query_message_prompt_template:
+      template: |2-
+        Please provide feedback on the last step. To quit type: "q".
+        
+        Relevant information:
+        == Goal == 
+        {{goal}}
+        
+        == Last Command ==
+        {{command}}
+        
+        == Args
+        {{command_args}}
+        
+        == Result
+        {{observation}}
+      input_variables:
+        - "goal"
+        - "command"
+        - "command_args"
+        - "observation"
+    input_interface_initialized:
+      - "goal"
+      - "command"
+      - "command_args"
+      - "observation"
+
+  Memory:
+    _target_: aiflows.VectorStoreFlowModule.ChromaDBFlow.instantiate_from_default_config
+    n_results: 2
+
+topology:
+  - goal: "Retrieve relevant information from memory."
+    input_interface:
+      _target_: AutoGPTFlow.prepare_memory_read_input # An interface defined as a function in the Flow class
+    flow: Memory
+    output_interface:
+      _target_: AutoGPTFlow.prepare_memory_read_output
+    reset: false
+
+  - goal: "Select the next action and prepare the input for the executor."
+    input_interface:
+      _target_: flows.interfaces.KeyInterface
+      additional_transformations:
+        - _target_: flows.data_transformations.KeyMatchInput
+    flow: Controller
+    output_interface:
+      _target_: AutoGPTFlow.detect_finish_or_continue # An interface defined as a function in the Flow class
+    reset: false
+
+  - goal: "Execute the action specified by the Controller."
+    input_interface:
+      _target_: flows.interfaces.KeyInterface
+      keys_to_rename:
+        command: branch
+        command_args: branch_input_data
+      keys_to_select: ["branch", "branch_input_data"]
+    flow: Executor
+    output_interface:
+      _target_: flows.interfaces.KeyInterface
+      keys_to_rename:
+        branch_output_data: observation
+      keys_to_select: ["observation"]
+    reset: false
+
+  - goal: "Ask the user for feedback."
+    input_interface:
+      _target_: flows.interfaces.KeyInterface
+    flow: HumanFeedback
+    output_interface:
+      _target_: AutoGPTFlow.detect_finish_in_human_input
+    reset: false
+
+  - goal: "Write relevant information to memory"
+    input_interface:
+      _target_: AutoGPTFlow.prepare_memory_write_input  # An interface defined as a function in the Flow class
+    flow: Memory
+    reset: false

README.md

@@ -1,3 +1,26 @@
 ---
 license: mit
 ---
+ToDo:
+
+## Description
+
+< Flow description >
+
+## Configuration parameters
+
+< Name 1 > (< Type 1 >): < Description 1 >. Required parameter.
+
+< Name 2 > (< Type 2 >): < Description 2 >. Default value is: < value 2 >
+
+## Input interface
+
+< Name 1 > (< Type 1 >): < Description 1 >. 
+
+(Note that the interface might depend on the state of the Flow.)
+
+## Output interface
+
+< Name 1 > (< Type 1 >): < Description 1 >. 
+
+(Note that the interface might depend on the state of the Flow.)

__init__.py

@@ -0,0 +1,15 @@
+# ~~~ Specify the dependencies ~~~
+dependencies = [
+    {"url": "aiflows/ControllerExecutorFlowModule",
+     "revision": "ba2c90bb9b0539af39ccad90c98a2ba1f9a22c91"},
+    {"url": "aiflows/HumanStandardInputFlowModule",
+     "revision": "890e92da1fefbae642fd84296e31bca7f61ea710"},
+    {"url": "aiflows/VectorStoreFlowModule",
+     "revision": "bfb6e70d581f81ad130af4554e824127aebf1497"},
+]
+from flows import flow_verse
+
+flow_verse.sync_dependencies(dependencies)
+# ~~~
+
+from .AutoGPTFlow import AutoGPTFlow

pip_requirements.txt

@@ -0,0 +1 @@
+duckduckgo-search==3.9.2

run.py

@@ -0,0 +1,71 @@
+import os
+
+import hydra
+
+import flows
+from flows.flow_launchers import FlowLauncher, ApiInfo
+from flows.utils.general_helpers import read_yaml_file
+
+from flows import logging
+from flows.flow_cache import CACHING_PARAMETERS, clear_cache
+
+CACHING_PARAMETERS.do_caching = False  # Set to True in order to disable caching
+# clear_cache() # Uncomment this line to clear the cache
+
+logging.set_verbosity_debug()
+
+dependencies = [
+    {"url": "aiflows/AutoGPTFlowModule", "revision": os.getcwd()},
+    {"url": "aiflows/LCToolFlowModule", "revision": "46dd24ecc3dc4f4f0191e57c202cc7d20e8e7782"},
+]
+from flows import flow_verse
+flow_verse.sync_dependencies(dependencies)
+
+if __name__ == "__main__":
+    # ~~~ Set the API information ~~~
+    # OpenAI backend
+    # api_information = ApiInfo("openai", os.getenv("OPENAI_API_KEY"))
+    # Azure backend
+    api_information = ApiInfo("azure", os.getenv("AZURE_OPENAI_KEY"), os.getenv("AZURE_OPENAI_ENDPOINT"))
+
+    root_dir = "."
+    cfg_path = os.path.join(root_dir, "AutoGPT.yaml")
+    cfg = read_yaml_file(cfg_path)
+
+    # ~~~ Instantiate the Flow ~~~
+    flow_with_interfaces = {
+        "flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
+        "input_interface": (
+            None
+            if getattr(cfg, "input_interface", None) is None
+            else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
+        ),
+        "output_interface": (
+            None
+            if getattr(cfg, "output_interface", None) is None
+            else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
+        ),
+    }
+
+    # ~~~ Get the data ~~~
+    # data = {"id": 0, "goal": "Answer the following question: What is the population of Canada?"}  # Uses wikipedia
+    # data = {"id": 0, "goal": "Answer the following question: Who was the NBA champion in 2023?"}  # Uses duckduckgo
+    data = {"id": 0, "goal": "Answer the following question: What is the date of birth of Michael Jordan?"}
+    # At first, we retrieve information about Michael Jordan the basketball player
+    # If we provide feedback, only in the first round, that we are not interested in the basketball player,
+    #   but the statistician, and skip the feedback in the next rounds, we get the correct answer
+
+    # ~~~ Run inference ~~~
+    path_to_output_file = None
+    # path_to_output_file = "output.jsonl"  # Uncomment this line to save the output to disk
+
+    _, outputs = FlowLauncher.launch(
+        flow_with_interfaces=flow_with_interfaces,
+        data=data,
+        path_to_output_file=path_to_output_file,
+        api_information=api_information,
+    )
+
+    # ~~~ Print the output ~~~
+    flow_output_data = outputs[0]
+    print(flow_output_data)