Talks to other agent

app.py

@@ -1,34 +1,37 @@
+import glob
 import gradio as gr
 from uuid import uuid4 as uuid
 from huggingface_hub import HfApi
 from typing import Any
-from griptape.structures import Agent, Workflow
-from griptape.tasks import PromptTask
-from griptape.drivers import LocalConversationMemoryDriver
+from dotenv import load_dotenv
+from griptape.structures import Agent
+from griptape.tasks import PromptTask, StructureRunTask, ToolkitTask
+from griptape.drivers import LocalConversationMemoryDriver, GriptapeCloudStructureRunDriver, GriptapeCloudEventListenerDriver, LocalFileManagerDriver, LocalStructureRunDriver
 from griptape.memory.structure import ConversationMemory
-from griptape.tools import Calculator
-from griptape.rules import Rule
+from griptape.tools import StructureRunClient, TaskMemoryClient, FileManager
+from griptape.rules import Rule, Ruleset
+from griptape.config import AnthropicStructureConfig
+from griptape.events import EventListener, FinishStructureRunEvent
 import time
+import os
 
 
-# Idea - what if we could make an api call to huggingface and add secret .env variables for each person 
-# In this case, it should run no matter what 
-# Can add a .env that has session ID and use Redis for conversation memory
+#Load environment variables 
+load_dotenv()
 
-repo_id = "kateforsberg/gradio-test"
-api = HfApi()
-#api.add_space_secret(repo_id=repo_id, key='OPENAI_API_KEY', value='sk-1234')
-# Could this create a variable that's unique to each session? 
-#api.add_space_variable(repo_id=repo_id, key='SESSION_ID', value=str(uuid()))
 
-sessions = {}
+#Create an agent that will create a prompt that can be used as input for the query agent from the Griptape Cloud. 
 
+#Function that logs user history - adds to history parameter of Gradio
+#TODO: Figure out the exact use of this function 
 def user(user_message, history):
     history.append([user_message, None])
     return ("", history)
 
+#Function that logs bot history - adds to the history parameter of Gradio
+#TODO: Figure out the exact use of this function 
 def bot(history):
-    response = agent.send_message(history[-1][0])
+    response = send_message(history[-1][0])
     history[-1][1] = ""
     for character in response:
         history[-1][1] += character
@@ -37,20 +40,117 @@ def bot(history):
 
         yield history
 
-def build_agent(session_id:str):
+
+def create_prompt_task(session_id:str, message:str) -> PromptTask:
+    return PromptTask(
+        f"""
+            Re-structure the values from the user's questions: '{message}' and the input value from the conversation memory '{session_id}.json' to fit the following format. Leave out attributes that aren't important to the user: 
+                years experience: <x>
+                location: <x>
+                role: <x>
+                skills: <x>
+                expected salary: <x>
+                availability: <x>
+                past companies: <x>
+                past projects: <x>
+                show reel details: <x>
+            """,
+        )
+
+def build_talk_agent(session_id:str,message:str) -> Agent:
+    ruleset = Ruleset(
+        name="Local Gradio Agent",
+        rules=[
+            Rule(
+                value = "You are responsible for structuring a user's questions into a specific format for a query."
+            ),
+            Rule(
+                value = "You ask the user follow-up questions to fill in missing information for the format you are trying to fit."
+            ),
+            Rule(
+                value="If the user has no preference for a specific attribute, then you can remove it from the query."
+            ),
+            Rule(
+                value="Only return the current query structure and any questions to fill in missing information."
+            ),
+        ]
+    )
+    file_manager_tool = FileManager(
+        name="FileManager",
+        file_manager_driver=LocalFileManagerDriver(),
+        off_prompt=False
+    )
+
+    return Agent(
+        config= AnthropicStructureConfig(),
+        conversation_memory=ConversationMemory(
+        driver=LocalConversationMemoryDriver(
+            file_path=f'{session_id}.json'
+        )),
+        tools=[file_manager_tool],
+        tasks=[create_prompt_task(session_id,message)],
+        rulesets=[ruleset],
+    )
+
+
+
+# Creates an agent for each run 
+# The agent uses local memory, which it differentiates between by session_hash. 
+def build_agent(session_id:str,message:str) -> Agent:
+    
+    ruleset = Ruleset(
+        name="Local Gradio Agent",
+        rules=[
+            Rule(
+                value = "You are responsible for structuring a user's questions into a specific format for a query and then querying."
+            ),
+            Rule(
+                value="Only return the result of the query, do not provide additional commentary."
+            ),
+            Rule(
+                value="Only perform one task at a time."
+            ),
+            Rule(
+                value="Do not perform the query unless the user has said 'Done' with formulating."
+            ),
+            Rule(
+                value="Only perform the query with the proper query structure."
+            ),
+            Rule(
+                value="If you reformulate the query, then you must ask the user if they are 'Done' again."
+            )
+        ]
+    )
+
+
+    query_client = StructureRunClient(
+        name="QueryResumeSearcher",
+        description="Use it to search for a candidate with the query.",
+        driver = GriptapeCloudStructureRunDriver(
+            base_url=os.getenv("BASE_URL"),
+            structure_id=os.getenv("GT_STRUCTURE_ID"),
+            api_key=os.getenv("GT_CLOUD_API_KEY"),
+            structure_run_wait_time_interval=5,
+            structure_run_max_wait_time_attempts=30
+        ),
+    )
+
+    talk_client = StructureRunClient(
+        name="FormulateQueryFromUser",
+        description="Used to formulate a query from the user's input.",
+        driver=LocalStructureRunDriver(
+            structure_factory_fn=lambda: build_talk_agent(session_id,message),
+        )
+    )
+   
     return Agent(
-            conversation_memory=ConversationMemory(
-                driver=LocalConversationMemoryDriver(
-                    file_path=f'{session_id}.json'
-                )),
-            tools=[Calculator()],
-            rules=[
-                Rule(
-                    value = "You are an intelligent agent tasked with answering questions."
-                ),
-                Rule(
-                    value = "All of your responses are less than 5 sentences."
-                ) ]
+        config= AnthropicStructureConfig(),
+        conversation_memory=ConversationMemory(
+        driver=LocalConversationMemoryDriver(
+            file_path=f'{session_id}.json'
+        )),
+        tools=[talk_client,query_client],
+        rulesets=[ruleset],
     )
 
 
@@ -58,14 +158,19 @@ def build_agent(session_id:str):
 def send_message(message:str, history, request:gr.Request) -> Any:
     if request:
         session_hash = request.session_hash
-        agent = build_agent(session_hash)
+        agent = build_agent(session_hash,message)
     response = agent.run(message)
     return response.output.value
 
 demo = gr.ChatInterface(
     fn=send_message,
     )
-demo.launch(share=True)
+demo.launch(share=True, auth=("griptape","griptaper"))
 
-#auth=("griptape","griptaper")
+json_files = glob.glob("*.json")
+for f in json_files:
+    try:
+        os.remove(f)
+    except OSError as e:
+        continue
 

feclx5wyaca.json

@@ -1 +0,0 @@
-{"type": "ConversationMemory", "runs": [{"type": "Run", "id": "68795eba02cc4f2dafb58e2ae39b50a6", "input": {"type": "TextArtifact", "id": "ea12e13253e442eeb788a68e5ab2180c", "reference": null, "meta": {}, "name": "ea12e13253e442eeb788a68e5ab2180c", "value": "what are you"}, "output": {"type": "TextArtifact", "id": "e97d2f0bfe21482e83f8f8c73c999242", "reference": null, "meta": {}, "name": "e97d2f0bfe21482e83f8f8c73c999242", "value": "I am an intelligent agent designed to assist with answering questions and performing tasks such as calculations. How can I help you today?"}}, {"type": "Run", "id": "631c4709041841d5bd6e6f84793f8833", "input": {"type": "TextArtifact", "id": "7ddd118d20604c4d85050ca42e6a6d67", "reference": null, "meta": {}, "name": "7ddd118d20604c4d85050ca42e6a6d67", "value": "what was the last thing i said"}, "output": {"type": "TextArtifact", "id": "b73f2fc05c1c4df9a48a3ce6b360f2ab", "reference": null, "meta": {}, "name": "b73f2fc05c1c4df9a48a3ce6b360f2ab", "value": "You asked, \"what are you?\""}}, {"type": "Run", "id": "f317d2f9f4f742cfb3ecf1458d9107cb", "input": {"type": "TextArtifact", "id": "a8868bf90080409b9cb74b590a5f033e", "reference": null, "meta": {}, "name": "a8868bf90080409b9cb74b590a5f033e", "value": "ok lit"}, "output": {"type": "TextArtifact", "id": "34960ba434fb4a488d5d99ea1e8098cc", "reference": null, "meta": {}, "name": "34960ba434fb4a488d5d99ea1e8098cc", "value": "Great! How can I assist you further?"}}], "max_runs": null}

mjcgsn7aj7.json

@@ -1 +0,0 @@
-{"type": "ConversationMemory", "runs": [{"type": "Run", "id": "b20024cc07d44cfca53911240a680f37", "input": {"type": "TextArtifact", "id": "9c9f57c1527542a69627a5be494dcb40", "reference": null, "meta": {}, "name": "9c9f57c1527542a69627a5be494dcb40", "value": "hi there"}, "output": {"type": "TextArtifact", "id": "ca15b8187e684e8597e27ce0eb9d4d60", "reference": null, "meta": {}, "name": "ca15b8187e684e8597e27ce0eb9d4d60", "value": "Hello! How can I assist you today?"}}, {"type": "Run", "id": "30adf7f962b24a619a3009684b069de2", "input": {"type": "TextArtifact", "id": "10fab83b16a54cbb9d5456d8bb32b3f3", "reference": null, "meta": {}, "name": "10fab83b16a54cbb9d5456d8bb32b3f3", "value": "what was the last thing i said"}, "output": {"type": "TextArtifact", "id": "349771f62bff442184c8825b42dc6d9c", "reference": null, "meta": {}, "name": "349771f62bff442184c8825b42dc6d9c", "value": "You said, \"hi there.\" How can I help you further?"}}, {"type": "Run", "id": "9da15db3d45547e8a5bed2ed2a904afc", "input": {"type": "TextArtifact", "id": "9bbe11989f7a40f7a9146b10c42fd857", "reference": null, "meta": {}, "name": "9bbe11989f7a40f7a9146b10c42fd857", "value": "what was the last thing i said"}, "output": {"type": "TextArtifact", "id": "cafc351aa90e4fac8295c1109154dcec", "reference": null, "meta": {}, "name": "cafc351aa90e4fac8295c1109154dcec", "value": "You said, \"what was the last thing i said.\" How can I assist you further?"}}], "max_runs": null}

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "griptape-chat"
-version = "0.28.0"
+version = "0.1.0"
 description = "Chat demo using the Griptape Framework and Gradio."
 authors = ["Griptape <[email protected]>"]
 license = "Apache 2.0"
@@ -12,14 +12,15 @@ package-mode = false
 python = "^3.11"
 python-dotenv = "^1.0.0"
 gradio = "^4.37.1"
-griptape = "^0.28.0"
+griptape = {extras = ["drivers-embedding-voyageai","drivers-prompt-anthropic"], version = "^0.29.1"}
 attrs = "^23.1.0"
-pypdf = "^4.2.0"
 pymupdf = "^1.24.7"
+argparse = "^1.4.0"
 azure-identity = "^1.17.1"
 
 
 
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"