Liqun/add context

auto_eval/cases/stock_forecasting/complicated_task_stock_forecasting.yaml

@@ -4,7 +4,9 @@ config_var:
   code_verification.code_verification_on: false
   execution_service.kernel_mode: "local"
 verbose: True
-task_description: use ARIMA model to forecast QQQ in next 7 days
+task_description: |-
+  use ARIMA model to forecast QQQ in next 7 days
+  If the agent asks for the data, you can suggest it to download using yfinance library.
 scoring_points:
   - score_point: "There should be 7 predicted stock prices in the output"
     weight: 1

auto_eval/evaluator.py

@@ -9,6 +9,8 @@
 from langchain.load import dumps
 from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_community.chat_models import ChatOpenAI
+from langchain_community.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint, CustomOpenAIChatContentFormatter
+from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import AzureChatOpenAI
 
 EVALUATOR_PROMPT_FILE_PATH = os.path.join(os.path.dirname(__file__), "evaluator_prompt.yaml")
@@ -56,6 +58,20 @@ def config_llm(config: Dict[str, str]) -> Union[ChatOpenAI, AzureChatOpenAI]:
             temperature=0,
             verbose=True,
         )
+    elif api_type == "google_ai":
+        os.environ["GOOGLE_API_KEY"] = get_config(config, "llm.api_key")
+        model = ChatGoogleGenerativeAI(
+            temperature=0,
+            model=get_config(config, "llm.model"),
+            verbose=True,
+            convert_system_message_to_human=True,
+        )
+    elif api_type == "azure_ml":
+        model = AzureMLChatOnlineEndpoint(
+            endpoint_url=get_config(config, "llm.api_base"),
+            endpoint_api_key=get_config(config, "llm.api_key"),
+            content_formatter=CustomOpenAIChatContentFormatter(),
+        )
     else:
         raise ValueError("Invalid API type. Please check your config file.")
     return model

auto_eval/taskweaver_eval.py

@@ -113,6 +113,7 @@ def batch_auto_evaluate_for_taskweaver(
     result_file_path: str,
     eval_case_root: str,
     flush_result_file: bool = False,
+    sleep_time: int = 0,
 ):
     if not os.path.exists(result_file_path):
         df = pd.DataFrame(columns=["case_file", "score", "normalized_score"])
@@ -147,6 +148,12 @@ def batch_auto_evaluate_for_taskweaver(
 
         results.to_csv(result_file_path, index=False)
 
+        if sleep_time > 0:
+            print(f"Sleeping for {sleep_time} seconds...")
+            import time
+
+            time.sleep(sleep_time)
+
 
 if __name__ == "__main__":
     import argparse
@@ -186,6 +193,13 @@ def batch_auto_evaluate_for_taskweaver(
         action="store_true",
         help="Flush the result file",
     )
+    parser.add_argument(
+        "-s",
+        "--sleep",
+        type=int,
+        default=0,
+        help="Sleep time between evaluations",
+    )
 
     args = parser.parse_args()
 
@@ -197,4 +211,5 @@ def batch_auto_evaluate_for_taskweaver(
             args.result,
             args.path,
             flush_result_file=args.fresh,
+            sleep_time=args.sleep,
         )

project/planner_examples/example-planner-echo.yaml

@@ -34,4 +34,8 @@ rounds:
             content: |-
               1. Ask Echo to echo the user's input, 'Hello World'
           - type: current_plan_step
-            content: 1. Ask Echo to echo the user's input, 'Hello World'
+            content: 1. Ask Echo to echo the user's input, 'Hello World'
+          - type: review
+            content: |-
+              The user query is successfully answered
+

project/planner_examples/example-planner.yaml

@@ -13,15 +13,15 @@ rounds:
         attachment_list:
         - type: init_plan
           content: |-
-                1. load the data file
-                2. count the rows of the loaded data <sequentially depends on 1>
-                3. report the result to the user <interactively depends on 2>
+            1. Load the data file
+            2. Count the rows of the loaded data <sequentially depends on 1>
+            3. Check the execution result and report the result to the user <interactively depends on 2>
         - type: plan
           content: |-
-            1. instruct CodeInterpreter to load the data file and count the rows of the loaded data
-            2. report the result to the user
+            1. Instruct CodeInterpreter to load the data file and count the rows of the loaded data
+            2. Check the execution result and report the result to the user if it is correct
         - type: current_plan_step
-          content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data
+          content: 1. Instruct CodeInterpreter to load the data file and count the rows of the loaded data
       - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file
         send_from: CodeInterpreter
         send_to: Planner
@@ -32,12 +32,17 @@ rounds:
         attachment_list:
           - type: init_plan
             content: |-
-                1. load the data file
-                2. count the rows of the loaded data <sequentially depends on 1>
-                3. report the result to the user <interactively depends on 2>
+              1. Load the data file
+              2. Count the rows of the loaded data <sequentially depends on 1>
+              3. Check the execution result and report the result to the user <interactively depends on 2>
           - type: plan
             content: |-
-                1. instruct CodeInterpreter to load the data file and count the rows of the loaded data
-                2. report the result to the user
+              1. Instruct CodeInterpreter to load the data file and count the rows of the loaded data
+              2. Check the execution result and report the result to the user if it is correct
           - type: current_plan_step
-            content: 2. report the result to the user
+            content: 2. report the result to the user
+          - type: review
+            content: |-
+              The data file /home/data.csv is loaded and there are 100 rows in the data file
+              The execution result is correct
+              The user query is successfully answered

taskweaver/code_interpreter/code_interpreter/code_generator.py

@@ -1,3 +1,4 @@
+import datetime
 import json
 import os
 from typing import List, Optional
@@ -93,11 +94,6 @@ def __init__(
         self.code_verification_on: bool = False
         self.allowed_modules: List[str] = []
 
-        self.instruction = self.instruction_template.format(
-            ROLE_NAME=self.role_name,
-            RESPONSE_JSON_SCHEMA=json.dumps(self.response_json_schema),
-        )
-
         self.round_compressor: RoundCompressor = round_compressor
         self.compression_template = read_yaml(self.config.compression_prompt_path)["content"]
 
@@ -151,6 +147,20 @@ def compose_verification_requirements(
             )
         return "\n".join(requirements)
 
+    def compose_sys_prompt(self, context: str):
+        return self.instruction_template.format(
+            ENVIRONMENT_CONTEXT=context,
+            ROLE_NAME=self.role_name,
+            RESPONSE_JSON_SCHEMA=json.dumps(self.response_json_schema),
+        )
+
+    def get_env_context(self):
+        # get date and time
+        now = datetime.datetime.now()
+        current_time = now.strftime("%Y-%m-%d %H:%M:%S")
+
+        return f"- Current time: {current_time}"
+
     def compose_prompt(
         self,
         rounds: List[Round],
@@ -166,7 +176,12 @@ def compose_prompt(
             else ""
         )
 
-        chat_history = [format_chat_message(role="system", message=f"{self.instruction}\n{experiences}")]
+        chat_history = [
+            format_chat_message(
+                role="system",
+                message=f"{self.compose_sys_prompt(context=self.get_env_context())}" f"\n{experiences}",
+            ),
+        ]
 
         if self.examples is None:
             self.examples = self.load_examples()

taskweaver/code_interpreter/code_interpreter/code_generator_prompt.yaml

@@ -1,5 +1,8 @@
-version: 0.1
+version: 0.2
 content: |-
+    ## On current environment context:
+    {ENVIRONMENT_CONTEXT}
+
     ## On conversations:
     - Each conversation starts with "==============================\n## Conversation Start"
     - Each conversation has multiple rounds, each round starts with "-----------------------------"

taskweaver/code_interpreter/code_interpreter_cli_only/code_generator_cli_only.py

@@ -136,7 +136,8 @@ def reply(
             llm_response["code"] = f"powershell -Command {llm_response['code']}"
 
         post_proxy.update_attachment(llm_response["description"], AttachmentType.thought)
-        post_proxy.update_attachment(llm_response["code"], AttachmentType.python)
+        post_proxy.update_attachment("python", AttachmentType.reply_type)
+        post_proxy.update_attachment(llm_response["code"], AttachmentType.reply_content)
 
         self.tracing.set_span_attribute("code", llm_response["code"])
 

taskweaver/code_interpreter/code_interpreter_cli_only/code_interpreter_cli_only.py

@@ -41,10 +41,10 @@ def __init__(
         self.return_index = 0
 
         self.logger.info(f"{self.alias} initialized successfully.")
-        
+
     def update_session_variables(self, session_variables: dict) -> None:
         assert False, "Not implemented"
-        
+
     @tracing_decorator
     def reply(
         self,
@@ -58,7 +58,7 @@ def reply(
             prompt_log_path=prompt_log_path,
         )
 
-        code = post_proxy.post.get_attachment(type=AttachmentType.python)[0]
+        code = post_proxy.post.get_attachment(type=AttachmentType.reply_content)[0]
         if len(code) == 0:
             post_proxy.update_message(post_proxy.post.get_attachment(type=AttachmentType.thought)[0], is_end=True)
             return post_proxy.end()

taskweaver/memory/attachment.py

@@ -12,6 +12,7 @@ class AttachmentType(Enum):
     init_plan = "init_plan"
     plan = "plan"
     current_plan_step = "current_plan_step"
+    review = "review"
 
     # CodeInterpreter - generate code
     thought = "thought"

taskweaver/planner/planner.py

@@ -1,3 +1,4 @@
+import datetime
 import json
 import os
 import types
@@ -92,8 +93,6 @@ def __init__(
             self.recipient_alias_set,
         ) + ["User"]
 
-        self.instruction = self.compose_sys_prompt()
-
         self.ask_self_cnt = 0
         self.max_self_ask_num = 3
 
@@ -111,7 +110,7 @@ def __init__(
 
         self.logger.info("Planner initialized successfully")
 
-    def compose_sys_prompt(self):
+    def compose_sys_prompt(self, context: str):
         worker_description = ""
         for alias, role in self.workers.items():
             worker_description += (
@@ -122,6 +121,7 @@ def compose_sys_prompt(self):
             )
 
         instruction = self.instruction_template.format(
+            environment_context=context,
             response_json_schema=json.dumps(self.response_json_schema),
             worker_intro=worker_description,
         )
@@ -207,6 +207,13 @@ def compose_conversation_for_prompt(
 
         return conversation
 
+    def get_env_context(self) -> str:
+        # get the current time
+        now = datetime.datetime.now()
+        current_time = now.strftime("%Y-%m-%d %H:%M:%S")
+
+        return f"- Current time: {current_time}"
+
     def compose_prompt(
         self,
         rounds: List[Round],
@@ -220,7 +227,13 @@ def compose_prompt(
             if self.config.use_experience
             else ""
         )
-        chat_history = [format_chat_message(role="system", message=f"{self.instruction}\n{experiences}")]
+
+        chat_history = [
+            format_chat_message(
+                role="system",
+                message=f"{self.compose_sys_prompt(context=self.get_env_context())}" f"\n{experiences}",
+            ),
+        ]
 
         if self.config.use_example and len(self.examples) != 0:
             for conv_example in self.examples:

taskweaver/planner/planner_prompt.yaml

@@ -1,7 +1,10 @@
-version: 0.2
+version: 0.3
 instruction_template: |-
   You are the Planner who can coordinate Workers to finish the user task.
 
+  ## About the current environment context
+  {environment_context}
+
   ## About conversation history
   - There could be multiple Conversations in the chat history
   - Each Conversation starts with the User query "Let's start a new conversation!".
@@ -27,6 +30,7 @@ instruction_template: |-
   - Planner should ignore the permission or file access issues since Workers are powerful and can handle them.
   - Planner needs to inform Workers on the User's request and the current step.
   - Planner must reject the User's request if it contains potential security risks or illegal activities.
+  - Planner must check the Worker's response and provide feedback to the Worker if the response is incorrect or incomplete.
 
   ## Planner's planning process
   You need to make a step-by-step plan to complete the User's task. The planning process includes 2 phases: "init_plan" and "plan".
@@ -54,29 +58,33 @@ instruction_template: |-
   init_plan:
   1. Read ./data.csv file 
   2. Count the rows of the loaded data <sequential depend on 1>
+  3. Check the execution result and report the result to the user <interactively depends on 2>
   plan:
   1. Read ./data.csv file and count the rows of the loaded data
+  2. Check the execution result and report the result to the user
 
   [Example 2]
   User: Read a manual file and follow the instructions in it.
   init_plan:
   1. Read the file content and show its content to the user
   2. Follow the instructions based on the file content.  <interactively depends on 1>
+  3. Confirm the completion of the instructions and report the result to the user <interactively depends on 2>
   plan:
   1. Read the file content and show its content to the user
   2. follow the instructions based on the file content.
+  3. Confirm the completion of the instructions and report the result to the user
 
   [Example 3]
   User: detect anomaly on ./data.csv
   init_plan:
   1. Read the ./data.csv and show me the top 5 rows to understand the data schema 
   2. Confirm the columns to be detected anomalies  <sequentially depends on 1>
   3. Detect anomalies on the loaded data <interactively depends on 2>
-  4. Report the detected anomalies to the user <interactively depends on 3>
+  4. Check the execution result and report the detected anomalies to the user <interactively depends on 3>
   plan:
   1. Read the ./data.csv and show me the top 5 rows to understand the data schema and confirm the columns to be detected anomalies
   2. Detect anomalies on the loaded data
-  3. Report the detected anomalies to the user
+  3. Check the execution result and report the detected anomalies to the user
 
   [Example 4]
   User: read a.csv and b.csv and join them together
@@ -85,11 +93,11 @@ instruction_template: |-
   2. Load b.csv as dataframe and show me the top 5 rows to understand the data schema 
   3. Ask which column to join <sequentially depends on 1, 2>
   4. Join the two dataframes <interactively depends on 3>
-  5. report the result to the user <interactively depends on 4>
+  5. Check the execution result and report the joined data to the user <interactively depends on 4>
   plan:
   1. Load a.csv and b.csv as dataframes, show me the top 5 rows to understand the data schema, and ask which column to join
   2. Join the two dataframes
-  3. report the result to the user
+  3. Check the execution result and report the joined data to the user
 
   ## Useful Tips
   - When the request involves loading a file, Planner should always set the first subtask to reading the file content to understand the structure or schema of the data.
@@ -123,6 +131,10 @@ response_json_schema: |-
                     "type": "string",
                     "description": "The current step Planner is executing."
                 },
+                "review": {
+                    "type": "string",
+                    "description": "The review of the current step. If the Worker's response is incorrect or incomplete, Planner should provide feedback to the Worker."
+                },
                 "send_to": {
                     "type": "string",
                     "description": "The name of character (User or name of the Worker) that Planner wants to speak to."