Google Palm support

config_template.yaml

@@ -3,6 +3,7 @@ PINECONE_API_KEY: YOUR_PINECONE_API_KEY
 PINECONE_ENVIRONMENT: YOUR_PINECONE_ENVIRONMENT
 
 OPENAI_API_KEY: YOUR_OPEN_API_KEY
+PALM_API_KEY: YOUR_PALM_API_KEY
 
 # For locally hosted LLMs comment out the next line and uncomment the one after
 # to configure a local llm point your browser to 127.0.0.1:7860 and click on the model tab in text generation web ui.
@@ -11,6 +12,7 @@ OPENAI_API_BASE: https://api.openai.com/v1
 
 # "gpt-3.5-turbo-0301": 4032, "gpt-4-0314": 8092, "gpt-3.5-turbo": 4032, "gpt-4": 8092, "gpt-4-32k": 32768, "gpt-4-32k-0314": 32768, "llama":2048, "mpt-7b-storywriter":45000
 MODEL_NAME: "gpt-3.5-turbo-0301"
+# "gpt-3.5-turbo", , "gpt-4", "models/chat-bison-001"
 RESOURCES_SUMMARY_MODEL_NAME: "gpt-3.5-turbo"
 MAX_TOOL_TOKEN_LIMIT: 800
 MAX_MODEL_TOKEN_LIMIT: 4032 # set to 2048 for llama

gui/pages/Content/Agents/AgentCreate.js

@@ -44,12 +44,12 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
   const [goals, setGoals] = useState(['Describe the agent goals here']);
   const [instructions, setInstructions] = useState(['']);
 
-  const models = ['gpt-4', 'gpt-3.5-turbo','gpt-3.5-turbo-16k', 'gpt-4-32k']
+  const models = ['gpt-4', 'gpt-3.5-turbo','gpt-3.5-turbo-16k', 'gpt-4-32k', 'google-palm-bison-001']
   const [model, setModel] = useState(models[1]);
   const modelRef = useRef(null);
   const [modelDropdown, setModelDropdown] = useState(false);
 
-  const agentTypes = ["Don't Maintain Task Queue", "Maintain Task Queue"]
+  const agentTypes = ["Don't Maintain Task Queue", "Maintain Task Queue", "Action Based"]
   const [agentType, setAgentType] = useState(agentTypes[0]);
   const agentRef = useRef(null);
   const [agentDropdown, setAgentDropdown] = useState(false);
@@ -61,8 +61,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
 
   const [stepTime, setStepTime] = useState(500);
 
-  const rollingWindows = ["5", "10", "15", "20"]
-  const [rollingWindow, setRollingWindow] = useState(rollingWindows[1]);
   const rollingRef = useRef(null);
   const [rollingDropdown, setRollingDropdown] = useState(false);
 
@@ -128,7 +126,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
             setLocalStorageArray("agent_constraints_" + String(internalId), data.constraints, setConstraints);
             setLocalStorageValue("agent_iterations_" + String(internalId), data.max_iterations, setIterations);
             setLocalStorageValue("agent_step_time_" + String(internalId), data.iteration_interval, setStepTime);
-            setLocalStorageValue("agent_rolling_window_" + String(internalId), data.memory_window, setRollingWindow);
             setLocalStorageValue("agent_permission_" + String(internalId), data.permission_type, setPermission);
             setLocalStorageArray("agent_instructions_" + String(internalId), data.instruction, setInstructions);
             setLocalStorageValue("agent_database_" + String(internalId), data.LTM_DB, setDatabase);
@@ -225,10 +222,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
     setDatabaseDropdown(false);
   };
 
-  const handleWindowSelect = (index) => {
-    setLocalStorageValue("agent_rolling_window_" + String(internalId), rollingWindows[index], setRollingWindow);
-    setRollingDropdown(false);
-  };
 
   const handleStepChange = (event) => {
     setLocalStorageValue("agent_step_time_" + String(internalId), event.target.value, setStepTime);
@@ -246,6 +239,9 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
 
   const handleModelSelect = (index) => {
     setLocalStorageValue("agent_model_" + String(internalId), models[index], setModel);
+    if (models[index] == "google-palm-bison-001") {
+      setAgentType("Action Based")
+    }
     setModelDropdown(false);
   };
 
@@ -333,7 +329,7 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
 
   const handleAddAgent = () => {
     if(!hasAPIkey) {
-      toast.error("Your OpenAI API key is empty!", {autoClose: 1800});
+      toast.error("Your OpenAI/Palm API key is empty!", {autoClose: 1800});
       openNewTab(-3, "Settings", "Settings");
       return
     }
@@ -382,7 +378,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
       "max_iterations": maxIterations,
       "permission_type": permission_type,
       "LTM_DB": longTermMemory ? database : null,
-      "memory_window": rollingWindow
     };
 
     createAgent(agentData)
@@ -568,10 +563,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
       setAgentType(agent_type);
     }
 
-    const agent_rolling_window = localStorage.getItem("agent_rolling_window_" + String(internalId));
-    if(agent_rolling_window) {
-      setRollingWindow(agent_rolling_window);
-    }
 
     const agent_database = localStorage.getItem("agent_database_" + String(internalId));
     if(agent_database) {
@@ -798,21 +789,6 @@ export default function AgentCreate({sendAgentData, selectedProjectId, fetchAgen
                 <label className={styles.form_label}>Time between steps (in milliseconds)</label>
                 <input className="input_medium" type="number" value={stepTime} onChange={handleStepChange}/>
               </div>
-              <div style={{marginTop: '15px'}}>
-                <label className={styles.form_label}>Short term memory - Rolling window</label>
-                <div className="dropdown_container_search" style={{width:'100%'}}>
-                  <div className="custom_select_container" onClick={() => setRollingDropdown(!rollingDropdown)} style={{width:'100%'}}>
-                    {rollingWindow} messages<Image width={20} height={21} src={!rollingDropdown ? '/images/dropdown_down.svg' : '/images/dropdown_up.svg'} alt="expand-icon"/>
-                  </div>
-                  <div>
-                    {rollingDropdown && <div className="custom_select_options" ref={rollingRef} style={{width:'100%'}}>
-                      {rollingWindows.map((window, index) => (<div key={index} className="custom_select_option" onClick={() => handleWindowSelect(index)} style={{padding:'12px 14px',maxWidth:'100%'}}>
-                        {window}
-                      </div>))}
-                    </div>}
-                  </div>
-                </div>
-              </div>
               {/*<div style={{marginTop: '15px'}}>*/}
               {/*  <div style={{display:'flex'}}>*/}
               {/*    <input className="checkbox" type="checkbox" checked={longTermMemory} onChange={() => setLocalStorageValue("has_LTM_" + String(internalId), !longTermMemory, setLongTermMemory)} />*/}

gui/pages/Content/Agents/AgentWorkspace.js

@@ -225,7 +225,7 @@ export default function AgentWorkspace({agentId, selectedView}) {
             <div style={{marginLeft:'7px'}}>
               <button onClick={() => setLeftPanel('activity_feed')} className={styles.tab_button} style={leftPanel === 'activity_feed' ? {background:'#454254'} : {background:'transparent'}}>Activity Feed</button>
             </div>
-            {agentDetails && agentDetails.agent_type === 'Maintain Task Queue' && <div style={{marginLeft:'7px'}}>
+            {agentDetails && (agentDetails.agent_type === 'Maintain Task Queue' || agentDetails.agent_type == "Action Based")  && <div style={{marginLeft:'7px'}}>
               <button onClick={() => setLeftPanel('agent_type')} className={styles.tab_button} style={leftPanel === 'agent_type' ? {background:'#454254'} : {background:'transparent'}}>Task Queue</button>
             </div>}
           </div>

gui/pages/Dashboard/Settings/Settings.js

@@ -1,14 +1,19 @@
-import React, {useState, useEffect} from 'react';
+import React, {useState, useEffect, useRef} from 'react';
 import {ToastContainer, toast} from 'react-toastify';
 import 'react-toastify/dist/ReactToastify.css';
 import agentStyles from "@/pages/Content/Agents/Agents.module.css";
 import {getOrganisationConfig, updateOrganisationConfig} from "@/pages/api/DashboardService";
 import {EventBus} from "@/utils/eventBus";
-import {removeTab} from "@/utils/utils";
+import {removeTab, setLocalStorageValue} from "@/utils/utils";
+import Image from "next/image";
 
 export default function Settings({organisationId}) {
-  const [openAIKey, setKey] = useState('');
+  const [modelApiKey, setKey] = useState('');
   const [temperature, setTemperature] = useState(0.5);
+  const [sourceDropdown, setSourceDropdown] = useState(false);
+  const [source, setSource] = useState('OpenAi');
+  const sourceRef = useRef(null);
+  const sources = ['OpenAi', 'Google Palm']
 
   function getKey(key) {
     getOrganisationConfig(organisationId, key)
@@ -20,8 +25,30 @@ export default function Settings({organisationId}) {
         });
   }
 
+  function getSource(key) {
+    getOrganisationConfig(organisationId, key)
+        .then((response) => {
+          setSource(response.data.value);
+        })
+        .catch((error) => {
+          console.error('Error fetching project:', error);
+        });
+  }
+
   useEffect(() => {
     getKey("model_api_key");
+    getSource("model_source");
+
+    function handleClickOutside(event) {
+      if (sourceRef.current && !sourceRef.current.contains(event.target)) {
+        setSourceDropdown(false)
+      }
+    }
+
+    document.addEventListener('mousedown', handleClickOutside);
+    return () => {
+      document.removeEventListener('mousedown', handleClickOutside);
+    };
   }, [organisationId]);
 
   function updateKey(key, value) {
@@ -37,21 +64,27 @@ export default function Settings({organisationId}) {
         });
   }
 
-  const handleOpenAIKey = (event) => {
+  const handleModelApiKey = (event) => {
     setKey(event.target.value);
   };
 
+  const handleSourceSelect = (index) => {
+    setSource(sources[index]);
+    setSourceDropdown(false);
+  };
+
   const preventDefault = (e) => {
     e.stopPropagation();
   };
 
   const saveSettings = () => {
-    if (openAIKey === null || openAIKey.replace(/\s/g, '') === '') {
+    if (modelApiKey === null || modelApiKey.replace(/\s/g, '') === '') {
       toast.error("API key is empty", {autoClose: 1800});
       return
     }
 
-    updateKey("model_api_key", openAIKey);
+    updateKey("model_api_key", modelApiKey);
+    updateKey("model_source", source);
   };
 
   const handleTemperatureChange = (event) => {
@@ -66,8 +99,23 @@ export default function Settings({organisationId}) {
           <div className={agentStyles.page_title}>Settings</div>
         </div>
         <div>
-          <label className={agentStyles.form_label}>Open-AI API Key</label>
-          <input placeholder="Enter your Open-AI API key" className="input_medium" type="password" value={openAIKey} onChange={handleOpenAIKey}/>
+            <label className={agentStyles.form_label}>Model Source</label>
+            <div className="dropdown_container_search" style={{width:'100%'}}>
+                <div className="custom_select_container" onClick={() => setSourceDropdown(!sourceDropdown)} style={{width:'100%'}}>
+                  {source}<Image width={20} height={21} src={!sourceDropdown ? '/images/dropdown_down.svg' : '/images/dropdown_up.svg'} alt="expand-icon"/>
+                </div>
+              <div>
+                {sourceDropdown && <div className="custom_select_options" ref={sourceRef} style={{width:'100%'}}>
+                  {sources.map((source, index) => (<div key={index} className="custom_select_option" onClick={() => handleSourceSelect(index)} style={{padding:'12px 14px',maxWidth:'100%'}}>
+                    {source}
+                  </div>))}
+                </div>}
+              </div>
+            </div>
+        </div><br/>
+        <div>
+          <label className={agentStyles.form_label}>Open-AI/Palm API Key</label>
+          <input placeholder="Enter your Open-AI/Palm API key" className="input_medium" type="password" value={modelApiKey} onChange={handleModelApiKey}/>
         </div>
         {/*<div style={{marginTop:'15px'}}>*/}
         {/*  <label className={agentStyles.form_label}>Temperature</label>*/}

main.py

@@ -262,6 +262,49 @@ def build_task_based_agents():
         workflow_step4.next_step_id = workflow_step3.id
         session.commit()
 
+    def build_action_based_agents():
+        agent_workflow = session.query(AgentWorkflow).filter(AgentWorkflow.name == "Action Based").first()
+        if agent_workflow is None:
+            agent_workflow = AgentWorkflow(name="Action Based", description="Action Based")
+            session.add(agent_workflow)
+            session.commit()
+
+        output = AgentPromptBuilder.start_task_based()
+
+        workflow_step1 = session.query(AgentWorkflowStep).filter(AgentWorkflowStep.unique_id == "ab1").first()
+        if workflow_step1 is None:
+            workflow_step1 = AgentWorkflowStep(unique_id="ab1",
+                                               prompt=output["prompt"], variables=str(output["variables"]),
+                                               step_type="TRIGGER",
+                                               agent_workflow_id=agent_workflow.id, next_step_id=-1,
+                                               output_type="tasks")
+            session.add(workflow_step1)
+        else:
+            workflow_step1.prompt = output["prompt"]
+            workflow_step1.variables = str(output["variables"])
+            workflow_step1.output_type = "tasks"
+            session.commit()
+
+        workflow_step2 = session.query(AgentWorkflowStep).filter(AgentWorkflowStep.unique_id == "ab2").first()
+        output = AgentPromptBuilder.analyse_task()
+        if workflow_step2 is None:
+            workflow_step2 = AgentWorkflowStep(unique_id="ab2",
+                                               prompt=output["prompt"], variables=str(output["variables"]),
+                                               step_type="NORMAL",
+                                               agent_workflow_id=agent_workflow.id, next_step_id=-1,
+                                               output_type="tools")
+            session.add(workflow_step2)
+        else:
+            workflow_step2.prompt = output["prompt"]
+            workflow_step2.variables = str(output["variables"])
+            workflow_step2.output_type = "tools"
+            session.commit()
+
+        session.commit()
+        workflow_step1.next_step_id = workflow_step2.id
+        workflow_step2.next_step_id = workflow_step2.id
+        session.commit()
+
     def check_toolkit_registration():
         organizations = session.query(Organisation).all()
         for organization in organizations:
@@ -270,6 +313,7 @@ def check_toolkit_registration():
 
     build_single_step_agent()
     build_task_based_agents()
+    build_action_based_agents()
     if env != "PROD":
         check_toolkit_registration()
     session.close()

requirements.txt

@@ -150,4 +150,5 @@ pypdf==3.11.0
 python-pptx==0.6.21
 Pillow==9.5.0
 EbookLib==0.18
-html2text==2020.1.16
+html2text==2020.1.16
+google-generativeai==0.1.0

superagi/agent/agent_prompt_builder.py

@@ -35,7 +35,7 @@ def add_tools_to_prompt(cls, tools: List[BaseTool], add_finish: bool = True) ->
             'people know you have finished your objectives"'
         )
         finish_string = (
-            f"{len(tools) + 1}. {FINISH_NAME}: "
+            f"{len(tools) + 1}. \"{FINISH_NAME}\": "
             f"{finish_description}, args: {finish_args}"
         )
         if add_finish:
@@ -63,13 +63,12 @@ def get_super_agi_single_prompt(cls):
         response_format = {
             "thoughts": {
                 "text": "thought",
-                "reasoning": "reasoning",
+                "reasoning": "short reasoning",
                 "plan": "- short bulleted\n- list that conveys\n- long-term plan",
                 "criticism": "constructive self-criticism",
                 "speak": "thoughts summary to say to user",
             },
-            "tool": {"name": "tool name/task name", "description": "tool or task description",
-                     "args": {"arg name": "value"}}
+            "tool": {"name": "tool name/task name", "args": {"arg name": "arg value(escape in case of string)"}}
         }
         formatted_response_format = json.dumps(response_format, indent=4)
 

superagi/agent/output_parser.py

@@ -27,7 +27,7 @@ def parse(self, text: str) -> AgentGPTAction:
 class AgentOutputParser(BaseOutputParser):
     def parse(self, text: str) -> AgentGPTAction:
         try:
-            # logger.info(text)
+            logger.info(text)
             text = JsonCleaner.check_and_clean_json(text)
             parsed = json5.loads(text)
         except json.JSONDecodeError:
@@ -55,6 +55,8 @@ def parse(self, text: str) -> AgentGPTAction:
 
             logger.info(format_prefix_green + "Action : " + format_suffix_green)
             # print(format_prefix_yellow + "Args: "+ format_suffix_yellow + parsed["tool"]["args"] + "\n")
+            if "tool" not in parsed:
+                raise Exception("No tool found in the response..")
             if parsed["tool"] is None or not parsed["tool"]:
                 return AgentGPTAction(name="", args="")
             if "name" in parsed["tool"]:

superagi/agent/prompts/analyse_task.txt

@@ -18,9 +18,9 @@ TOOLS:
 RESPONSE FORMAT:
 {
     "thoughts": {
-        "reasoning": "reasoning"
+        "reasoning": "short reasoning"
     },
-    "tool": {"name": "tool name", "args": {"arg name": "string value"}}
+    "tool": {"name": "tool name", "args": {"arg name": "arg value(escape in case of string)"}}
 }
 
 Your answer must be something that JSON.parse() can read, and nothing else.

superagi/agent/prompts/initialize_tasks.txt

@@ -5,7 +5,7 @@ GOALS:
 
 {task_instructions}
 
-Construct a sequence of actions, not exceeding 3 steps, to achieve this goal.
+Construct a sequence of actions, not exceeding 4 steps, to achieve this goal.
 
 Submit your response as a formatted ARRAY of strings, suitable for utilization with JSON.parse().
 

superagi/agent/prompts/superagi.txt

@@ -16,13 +16,13 @@ TOOLS:
 PERFORMANCE EVALUATION:
 1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
 2. Use instruction to decide the flow of execution and decide the next steps for achieving the task.
-2. Constructively self-criticize your big-picture behavior constantly.
-3. Reflect on past decisions and strategies to refine your approach.
-4. Every tool has a cost, so be smart and efficient.
-5. Aim to complete tasks in the least number of steps.
+3. Constructively self-criticize your big-picture behavior constantly.
+4. Reflect on past decisions and strategies to refine your approach.
+5. Every tool has a cost, so be smart and efficient.
 
 I should only respond in JSON format as described below.
 Response Format:
 {response_format}
 
+
 Ensure the response can be parsed by Python json.loads.