Merge pull request #379 from KulwantSinghYadav/blog_writer_human_in_t…

…he_loop

Add human in the loop for blog writer demo

demo/blog_writer/agent_config.yml

@@ -26,7 +26,6 @@ google_search:
     role_description: Act as a question answering agent
     task: Question answering
     llm: ${llm}
-    include_metadata: true
     config: ${agent_config}
 
 citation_validation:  # The tool used to validate and add citation to the answer
@@ -49,3 +48,11 @@ qa_agent:
         # - ${doc_search}
     validations:
         - ${citation_validation}
+
+#configurations for the user agent to bring human in the loop
+user:
+    _target_: sherpa_ai.agents.user.UserAgent
+    shared_memory: ${shared_memory}
+    name: Human Expert
+    description: Review the paragraph provided by qa agent.
+    user_id: 1

demo/blog_writer/main.py

@@ -3,7 +3,7 @@
 
 from hydra.utils import instantiate
 from omegaconf import OmegaConf
-from sherpa_ai.agents import QAAgent
+from sherpa_ai.agents import QAAgent, UserAgent
 from sherpa_ai.events import EventType
 
 from outliner import Outliner
@@ -32,13 +32,37 @@ def get_qa_agent_from_config_file(
     return qa_agent
 
 
+
+def get_user_agent_from_config_file(
+    config_path: str,
+) -> UserAgent:
+    """
+    Create a UserAgent from a config file.
+
+    Args:
+        config_path: Path to the config file
+
+    Returns:
+        UserAgent: A UserAgent instance
+    """
+
+    config = OmegaConf.load(config_path)
+
+    agent_config = instantiate(config.agent_config)
+    user: UserAgent = instantiate(config.user)
+
+    return user
+
+
+
 if __name__ == "__main__":
     parser = ArgumentParser()
     parser.add_argument("--config", type=str, default="agent_config.yaml")
     parser.add_argument("--transcript", type=str, default="transcript.txt")
     args = parser.parse_args()
 
     writer_agent = get_qa_agent_from_config_file(args.config)
+    reviewer_agent = get_user_agent_from_config_file(args.config)
 
     outliner = Outliner(args.transcript)
     blueprint = outliner.full_transcript2outline_json(verbose=True)
@@ -52,8 +76,8 @@ def get_qa_agent_from_config_file(
     with open("blueprint.json", "w") as f:
         f.write(pure_json_str)
 
-    # with open("blueprint_manual.json", "r") as f:
-    #     pure_json_str = f.read()
+    #with open("blueprint.json", "r") as f:
+    #    pure_json_str = f.read()
 
     parsed_json = json.loads(pure_json_str)
 
@@ -67,6 +91,25 @@ def get_qa_agent_from_config_file(
         for evidence in evidences:
             writer_agent.shared_memory.add(EventType.task, "human", evidence)
             result = writer_agent.run()
+
+
+            reviewer_input= "\n" + "Please review the paragraph generated below. Type 'yes', 'y' or simply press Enter \
+                if everything looks good. Else provide feedback on how you would like the paragraph modified." \
+                + "\n\n" + result
+            reviewer_agent.shared_memory.add(EventType.task, "human", reviewer_input)
+
+            decision = reviewer_agent.run()
+            decision_event= reviewer_agent.shared_memory.get_by_type(EventType.result)
+            decision_content=decision_event[-1].content
+
+            if decision_content == []:
+                break
+            #
+            if decision_content.lower() in ["yes", "y", ""]:
+                pass
+            else:
+                writer_agent.shared_memory.add(EventType.task, "human", decision_content)
+                result = writer_agent.run()
             # writer_agent.belief = Belief()
             blog += f"{result}\n"
 

demo/blog_writer/outliner.py

@@ -3,17 +3,15 @@
 
 import tiktoken
 from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-)
+from langchain.prompts.chat import (ChatPromptTemplate,
+                                    HumanMessagePromptTemplate,
+                                    SystemMessagePromptTemplate)
 from langchain.text_splitter import MarkdownTextSplitter
 
 
 class Outliner:
     def __init__(self, transcript_file) -> None:
-        with open(transcript_file, "r") as f:
+        with open(transcript_file, "r", encoding="utf-8") as f:
             self.raw_transcript = f.read()
         # instantiate chat model
         self.chat = ChatOpenAI(