Works again (minus most Controls)

frontend/public/docs/about.md

@@ -23,10 +23,10 @@ The backstory about Backstory...
 
 ## Some questions I've been asked
 
-Q. <ChatQuery text="Why aren't you providing this as a Platform As a Service (PaaS) application?"/>
+Q. <ChatQuery prompt="Why aren't you providing this as a Platform As a Service (PaaS) application?" tunables={{ "enable_tools": false }} />
 
 A. I could; but I don't want to store your data. I also don't want to have to be on the hook for support of this service. I like it, it's fun, but it's not what I want as my day-gig, you know? If it was, I wouldn't be looking for a job...
 
-Q. <ChatQuery text="Why can't I just ask Backstory these questions?"/>
+Q. <ChatQuery prompt="Why can't I just ask Backstory these questions?" tunables={{ "enable_tools": false }} />
 
 A. Try it. See what you find out :)

frontend/src/App.tsx

@@ -19,11 +19,12 @@ import { SxProps } from '@mui/material';
 
 
 import { ResumeBuilder } from './ResumeBuilder';
-import { Message, ChatQuery, MessageList } from './Message';
+import { Message, MessageList } from './Message';
 import { Snack, SeverityType } from './Snack';
 import { VectorVisualizer } from './VectorVisualizer';
 import { Controls } from './Controls';
 import { Conversation, ConversationHandle } from './Conversation';
+import { ChatQuery, QueryOptions } from './ChatQuery';
 import { Scrollable } from './AutoScroll';
 import { BackstoryTab } from './BackstoryTab';
 
@@ -112,9 +113,9 @@ const App = () => {
     fetchAbout();
   }, [about, setAbout])
 
-  const handleSubmitChatQuery = (query: string) => {
+  const handleSubmitChatQuery = (prompt: string, tunables?: QueryOptions) => {
-    console.log(`handleSubmitChatQuery: ${query} -- `, chatRef.current ? ' sending' : 'no handler');
+    console.log(`handleSubmitChatQuery: ${prompt} ${tunables || {}} -- `, chatRef.current ? ' sending' : 'no handler');
-    chatRef.current?.submitQuery(query);
+    chatRef.current?.submitQuery(prompt, tunables);
     setActiveTab(0);
   };
 
@@ -137,10 +138,10 @@ const App = () => {
 
     const backstoryQuestions = [
       <Box sx={{ display: "flex", flexDirection: isMobile ? "column" : "row" }}>
-        <ChatQuery text="What is James Ketrenos' work history?" submitQuery={handleSubmitChatQuery} />
+        <ChatQuery prompt="What is James Ketrenos' work history?" tunables={{ enable_tools: false }} submitQuery={handleSubmitChatQuery} />
-        <ChatQuery text="What programming languages has James used?" submitQuery={handleSubmitChatQuery} />
+        <ChatQuery prompt="What programming languages has James used?" tunables={{ enable_tools: false }} submitQuery={handleSubmitChatQuery} />
-        <ChatQuery text="What are James' professional strengths?" submitQuery={handleSubmitChatQuery} />
+        <ChatQuery prompt="What are James' professional strengths?" tunables={{ enable_tools: false }} submitQuery={handleSubmitChatQuery} />
-        <ChatQuery text="What are today's headlines on CNBC.com?" submitQuery={handleSubmitChatQuery} />
+        <ChatQuery prompt="What are today's headlines on CNBC.com?" tunables={{ enable_tools: true, enable_rag: false, enable_context: false }} submitQuery={handleSubmitChatQuery} />
       </Box>,
       <Box sx={{ p: 1 }}>
         <MuiMarkdown>

frontend/src/ChatQuery.tsx

@@ -0,0 +1,48 @@
+import Box from '@mui/material/Box';
+import Button from '@mui/material/Button';
+
+type QueryOptions = {
+  enable_rag?: boolean,
+  enable_tools?: boolean,
+  enable_context?: boolean,
+};
+
+interface ChatQueryInterface {
+  prompt: string,
+  tunables?: QueryOptions,
+  submitQuery?: (prompt: string, tunables?: QueryOptions) => void
+}
+
+const ChatQuery = (props : ChatQueryInterface) => {
+  const { prompt, submitQuery } = props;
+  let tunables = props.tunables;
+  
+  if (typeof (tunables) === "string") {
+    tunables = JSON.parse(tunables);
+  }
+  console.log(tunables);
+
+  if (submitQuery === undefined) {
+    return (<Box>{prompt}</Box>);
+  }
+  return (
+    <Button variant="outlined" sx={{
+      color: theme => theme.palette.custom.highlight, // Golden Ochre (#D4A017)
+      borderColor: theme => theme.palette.custom.highlight,
+      m: 1
+    }}
+      size="small" onClick={(e: any) => { submitQuery(prompt, tunables); }}>
+      {prompt}
+    </Button>
+  );
+}
+
+export type {
+  ChatQueryInterface,
+  QueryOptions,
+};
+
+export  {
+  ChatQuery,
+};
+

frontend/src/Controls.tsx

@@ -319,7 +319,7 @@ const Controls = ({ sessionId, setSnack, connectionBase }: ControlsParams) => {
   };
 
   return (<div className="Controls">
-    <Typography component="span" sx={{ mb: 1 }}>
+    {/* <Typography component="span" sx={{ mb: 1 }}>
       You can change the information available to the LLM by adjusting the following settings:
     </Typography>
     <Accordion>
@@ -414,7 +414,8 @@ const Controls = ({ sessionId, setSnack, connectionBase }: ControlsParams) => {
             )
           }</FormGroup>
       </AccordionActions>
-    </Accordion>
+    </Accordion> */}
+
     <Accordion>
       <AccordionSummary expandIcon={<ExpandMoreIcon />}>
         <Typography component="span">System Information</Typography>
@@ -426,8 +427,9 @@ const Controls = ({ sessionId, setSnack, connectionBase }: ControlsParams) => {
         <SystemInfoComponent systemInfo={systemInfo} />
       </AccordionActions>
     </Accordion>
-    <Button startIcon={<ResetIcon />} onClick={() => { reset(["history"], "History cleared."); }}>Delete Backstory History</Button>
+
-    <Button onClick={() => { reset(["rags", "tools", "system_prompt", "message_history_length"], "Default settings restored.") }}>Reset system prompt, tunables, and RAG to defaults</Button>
+    {/* <Button startIcon={<ResetIcon />} onClick={() => { reset(["history"], "History cleared."); }}>Delete Backstory History</Button>
+    <Button onClick={() => { reset(["rags", "tools", "system_prompt", "message_history_length"], "Default settings restored.") }}>Reset system prompt, tunables, and RAG to defaults</Button> */}
   </div>);
 }
 

frontend/src/Conversation.tsx

@@ -13,7 +13,7 @@ import { SetSnackType } from './Snack';
 import { ContextStatus } from './ContextStatus';
 import { useAutoScrollToBottom } from './AutoScroll';
 import { DeleteConfirmation } from './DeleteConfirmation';
-
+import { QueryOptions } from './ChatQuery';
 import './Conversation.css';
 
 const loadingMessage: MessageData = { "role": "status", "content": "Establishing connection with server..." };
@@ -21,7 +21,7 @@ const loadingMessage: MessageData = { "role": "status", "content": "Establishing
 type ConversationMode = 'chat' | 'job_description' | 'resume' | 'fact_check';
 
 interface ConversationHandle {
-  submitQuery: (query: string) => void;
+  submitQuery: (prompt: string, options?: QueryOptions) => void;
 }
 
 interface BackstoryMessage {
@@ -54,6 +54,7 @@ interface ConversationProps {
   setSnack: SetSnackType, // Callback to display snack popups
   defaultPrompts?: React.ReactElement[],  // Set of Elements to display after the TextField
   defaultQuery?: string,      // Default text to populate the TextField input
+  emptyPrompt?: string,       // If input is not shown and an action is taken, send this prompt
   preamble?: MessageList,     // Messages to display at start of Conversation until Action has been invoked
   hidePreamble?: boolean,     // Whether to hide the preamble after an Action has been invoked
   hideDefaultPrompts?: boolean, // Whether to hide the defaultPrompts after an Action has been invoked
@@ -67,6 +68,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
   className,
   type,
   prompt,
+  emptyPrompt,
   actionLabel,
   resetAction,
   multiline,
@@ -256,8 +258,8 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
   };
 
   useImperativeHandle(ref, () => ({
-    submitQuery: (query: string) => {
+    submitQuery: (query: string, tunables?: QueryOptions) => {
-      sendQuery(query);
+      sendQuery(query, tunables);
     }
   }));
 
@@ -303,38 +305,34 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
     }
   };
 
-  const sendQuery = async (query: string) => {
+  const sendQuery = async (request: string, options?: QueryOptions) => {
-    query = query.trim();
+    request = request.trim();
 
     // If the query was empty, a default query was provided,
     // and there is no prompt for the user, send the default query.
-    if (!query && defaultQuery && !prompt) {
+    if (!request && defaultQuery && !prompt) {
-      query = defaultQuery.trim();
+      request = defaultQuery.trim();
     }
 
-    // If the query is empty, and a prompt was provided, do not
+    // Do not send an empty query.
-    // send an empty query.
+    if (!request) {
-    if (!query && prompt) {
       return;
     }
 
     setNoInteractions(false);
 
-    if (query) {
+    setConversation([
-      setConversation([
+      ...conversationRef.current,
-        ...conversationRef.current,
+      {
-        {
+        role: 'user',
-          role: 'user',
+        origin: type,
-          origin: type,
+        content: request,
-          content: query,
+        disableCopy: true
-          disableCopy: true
+      }
-        }
+    ]);
-      ]);
-    }
 
     // Add a small delay to ensure React has time to update the UI
     await new Promise(resolve => setTimeout(resolve, 0));
-    console.log(conversation);
 
     // Clear input
     setQuery('');
@@ -353,13 +351,25 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
       await new Promise(resolve => setTimeout(resolve, 0));
 
       // Make the fetch request with proper headers
+      let query;
+      if (options) {
+        query = {
+          options: options,
+          prompt: request.trim()
+        }
+      } else {
+        query = {
+          prompt: request.trim()
+        }
+      }
+
       const response = await fetch(connectionBase + `/api/chat/${sessionId}/${type}`, {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
           'Accept': 'application/json',
         },
-        body: JSON.stringify({ role: 'user', content: query.trim() }),
+        body: JSON.stringify(query)
       });
 
       // We'll guess that the response will be around 500 tokens...
@@ -383,14 +393,14 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
       let buffer = '';
 
       const process_line = async (line: string) => {
-        const update = JSON.parse(line);
+        let update = JSON.parse(line);
 
         switch (update.status) {
           case 'done':
             console.log('Done processing:', update);
             // Replace processing message with final result
             if (onResponse) {
-              update.message = onResponse(update);
+              update = onResponse(update);
             }
             setProcessingMessage(undefined);
             const backstoryMessage: BackstoryMessage = update;
@@ -451,6 +461,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
             await process_line(line);
           } catch (e) {
             setSnack("Error processing query", "error")
+            console.error(e);
           }
         }
       }
@@ -461,6 +472,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
           await process_line(buffer);
         } catch (e) {
           setSnack("Error processing query", "error")
+          console.error(e);
         }
       }
 
@@ -579,7 +591,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>(({
 
 export type {
   ConversationProps,
-  ConversationHandle
+  ConversationHandle,
 };
 
 export {

frontend/src/Message.tsx

@@ -76,11 +76,6 @@ interface MessageProps {
   className?: string,
 };
 
-interface ChatQueryInterface {
-  text: string,
-  submitQuery?: (text: string) => void
-}
-
 interface MessageMetaProps {
   metadata: MessageMetaData,
   messageProps: MessageProps
@@ -157,7 +152,7 @@ const MessageMeta = (props: MessageMetaProps) => {
                   <Box sx={{ fontSize: "0.75rem", display: "flex", flexDirection: "column", mt: 1, mb: 1, fontWeight: "bold" }}>
                     {tool.name}
                   </Box>
-                  <JsonView displayDataTypes={false} objectSortKeys={true} collapsed={2} value={JSON.parse(tool.content)} style={{ fontSize: "0.8rem", maxHeight: "20rem", overflow: "auto" }}>
+                  <JsonView displayDataTypes={false} objectSortKeys={true} collapsed={1} value={JSON.parse(tool.content)} style={{ fontSize: "0.8rem", maxHeight: "20rem", overflow: "auto" }}>
                     <JsonView.String
                       render={({ children, ...reset }) => {
                         if (typeof (children) === "string" && children.match("\n")) {
@@ -209,7 +204,7 @@ const MessageMeta = (props: MessageMetaProps) => {
         </Box>
       </AccordionSummary>
       <AccordionDetails>
-        <JsonView displayDataTypes={false} objectSortKeys={true} collapsed={2} value={message} style={{ fontSize: "0.8rem", maxHeight: "20rem", overflow: "auto" }}>
+        <JsonView displayDataTypes={false} objectSortKeys={true} collapsed={1} value={message} style={{ fontSize: "0.8rem", maxHeight: "20rem", overflow: "auto" }}>
           <JsonView.String
             render={({ children, ...reset }) => {
               if (typeof (children) === "string" && children.match("\n")) {
@@ -223,22 +218,6 @@ const MessageMeta = (props: MessageMetaProps) => {
   </>);
 };
 
-const ChatQuery = ({ text, submitQuery }: ChatQueryInterface) => {
-  if (submitQuery === undefined) {
-    return (<Box>{text}</Box>);
-  }
-  return (
-    <Button variant="outlined" sx={{
-      color: theme => theme.palette.custom.highlight, // Golden Ochre (#D4A017)
-      borderColor: theme => theme.palette.custom.highlight,
-      m: 1
-    }}
-      size="small" onClick={(e: any) => { submitQuery(text); }}>
-      {text}
-    </Button>
-  );
-}
-
 const Message = (props: MessageProps) => {
   const { message, submitQuery, isFullWidth, sx, className } = props;
   const [expanded, setExpanded] = useState<boolean>(false);
@@ -323,14 +302,12 @@ const Message = (props: MessageProps) => {
 export type {
   MessageProps, 
   MessageList,
-  ChatQueryInterface,
   MessageData,
   MessageRoles
 };
 
 export  {
   Message,
-  ChatQuery,
   MessageMeta
 };
 

frontend/src/ResumeBuilder.tsx

@@ -18,7 +18,7 @@ import {
 } from '@mui/icons-material';
 import { SxProps, Theme } from '@mui/material';
 
-import { ChatQuery } from './Message';
+import { ChatQuery } from './ChatQuery';
 import { MessageList, MessageData } from './Message';
 import { SetSnackType } from './Snack';
 import { Conversation } from './Conversation';
@@ -97,6 +97,12 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
     if (messages === undefined || messages.length === 0) {
       return [];
     }
+    console.log("filterJobDescriptionMessages disabled")
+    if (messages.length > 1) {
+      setHasResume(true);
+    }
+
+    return messages;
 
     let reduced = messages.filter((m, i) => {
       const keep = (m.metadata?.origin || m.origin || "no origin") === 'job_description';
@@ -135,6 +141,11 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
     if (messages === undefined || messages.length === 0) {
       return [];
     }
+    console.log("filterResumeMessages disabled")
+    if (messages.length > 3) {
+      setHasFacts(true);
+    }
+    return messages;
 
     let reduced = messages.filter((m, i) => {
       const keep = (m.metadata?.origin || m.origin || "no origin") === 'resume';
@@ -182,6 +193,9 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
     if (messages === undefined || messages.length === 0) {
       return [];
     }
+    console.log("filterFactsMessages disabled")
+    return messages;
+
     // messages.forEach((m, i) => console.log(`filterFactsMessages: ${i + 1}:`, m))
 
     const reduced = messages.filter(m => {
@@ -240,8 +254,8 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
     console.log('renderJobDescriptionView');
     const jobDescriptionQuestions = [
       <Box sx={{ display: "flex", flexDirection: small ? "column" : "row" }}>
-        <ChatQuery text="What are the key skills necessary for this position?" submitQuery={handleJobQuery} />
+        <ChatQuery prompt="What are the key skills necessary for this position?" tunables={{ enable_tools: false }} submitQuery={handleJobQuery} />
-        <ChatQuery text="How much should this position pay (accounting for inflation)?" submitQuery={handleJobQuery} />
+        <ChatQuery prompt="How much should this position pay (accounting for inflation)?" tunables={{ enable_tools: false }} submitQuery={handleJobQuery} />
       </Box>,
     ];
 
@@ -289,8 +303,8 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
   const renderResumeView = useCallback((small: boolean) => {
     const resumeQuestions = [
       <Box sx={{ display: "flex", flexDirection: small ? "column" : "row" }}>
-        <ChatQuery text="Is this resume a good fit for the provided job description?" submitQuery={handleResumeQuery} />
+        <ChatQuery prompt="Is this resume a good fit for the provided job description?" tunables={{ enable_tools: false }} submitQuery={handleResumeQuery} />
-        <ChatQuery text="Provide a more concise resume." submitQuery={handleResumeQuery} />
+        <ChatQuery prompt="Provide a more concise resume." tunables={{ enable_tools: false }} submitQuery={handleResumeQuery} />
       </Box>,
     ];
 
@@ -298,9 +312,9 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
       return <Conversation
         ref={resumeConversationRef}
         {...{
-          actionLabel: "Fact Check",
-          multiline: true,
           type: "resume",
+          actionLabel: "Fact Check",
+          defaultQuery: "Fact check the resume.",
           resetLabel: `job description${hasFacts ? ", resume, and fact check" : hasResume ? " and resume" : ""}`,
           messageFilter: filterResumeMessages,
           onResponse: resumeResponse,
@@ -319,12 +333,12 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
           prompt: "Ask a question about this job resume...",
           resetLabel: `job description${hasFacts ? ", resume, and fact check" : hasResume ? " and resume" : ""}`,
           messageFilter: filterResumeMessages,
-          defaultPrompts: resumeQuestions,
-          resetAction: resetResume,
           onResponse: resumeResponse,
+          resetAction: resetResume,
           sessionId,
           connectionBase,
           setSnack,
+          defaultPrompts: resumeQuestions,
         }}
       />
     }
@@ -336,7 +350,7 @@ const ResumeBuilder: React.FC<ResumeBuilderProps> = ({
   const renderFactCheckView = useCallback((small: boolean) => {
     const factsQuestions = [
       <Box sx={{ display: "flex", flexDirection: small ? "column" : "row" }}>
-        <ChatQuery text="Rewrite the resume to address any discrepancies." submitQuery={handleFactsQuery} />
+        <ChatQuery prompt="Rewrite the resume to address any discrepancies." tunables={{ enable_tools: false }} submitQuery={handleFactsQuery} />
       </Box>,
     ];
 

frontend/src/StyledMarkdown.tsx

@@ -2,12 +2,12 @@ import React from 'react';
 import { MuiMarkdown } from 'mui-markdown';
 import { useTheme } from '@mui/material/styles';
 import { Link } from '@mui/material';
-import { ChatQuery } from './Message';
+import { ChatQuery, QueryOptions } from './ChatQuery';
 
 interface StyledMarkdownProps {
   className?: string,
   content: string,
-  submitQuery?: (query: string) => void,
+  submitQuery?: (prompt: string, tunables?: QueryOptions) => void,
   [key: string]: any, // For any additional props
 };
 
@@ -38,7 +38,7 @@ const StyledMarkdown: React.FC<StyledMarkdownProps> = ({ className, content, sub
     options.overrides.ChatQuery = {
       component: ChatQuery,
       props: {
-        submitQuery
+        submitQuery,
       },
     };
   }

frontend/src/VectorVisualizer.tsx

@@ -158,7 +158,7 @@ const VectorVisualizer: React.FC<VectorVisualizerProps> = (props: VectorVisualiz
   useEffect(() => {
     if (!result || !result.embeddings) return;
     if (result.embeddings.length === 0) return;
-    console.log('Result:', result);
+
     const vectors: (number[])[] = [...result.embeddings];
     const documents = [...result.documents || []];
     const metadatas = [...result.metadatas || []];

src/server.py

@@ -18,6 +18,7 @@ import math
 import warnings
 from typing import Any
 from collections import deque
+from datetime import datetime
 
 from uuid import uuid4
 
@@ -53,6 +54,7 @@ from utils import (
     tools as Tools,
     Context, Conversation, Message, 
     Agent,
+    Tunables,
     defines,
     logger,
 )
@@ -64,26 +66,8 @@ rags = [
     # { "name": "LKML", "enabled": False, "description": "Full associative data for entire LKML mailing list archive." },
 ]
 
-system_message = f"""
-Launched on {Tools.DateTime()}.
-
-When answering queries, follow these steps:
-
-- First analyze the query to determine if real-time information from the tools might be helpful
-- Even when <|context|> is provided, consider whether the tools would provide more current or comprehensive information
-- Use the provided tools whenever they would enhance your response, regardless of whether context is also available
-- When presenting weather forecasts, include relevant emojis immediately before the corresponding text. For example, for a sunny day, say \"☀️ Sunny\" or if the forecast says there will be \"rain showers, say \"🌧️ Rain showers\". Use this mapping for weather emojis: Sunny: ☀️, Cloudy: ☁️, Rainy: 🌧️, Snowy: ❄️
-- When both <|context|> and tool outputs are relevant, synthesize information from both sources to provide the most complete answer
-- Always prioritize the most up-to-date and relevant information, whether it comes from <|context|> or tools
-- If <|context|> and tool outputs contain conflicting information, prefer the tool outputs as they likely represent more current data
-- If there is information in the <|context|>, <|job_description|>, or <|context|> sections to enhance the answer, incorporate it seamlessly and refer to it as 'the latest information' or 'recent data' instead of mentioning '<|context|>' (etc.) or quoting it directly.
-- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, or <|context|> tags.
-
-Always use tools and <|context|> when possible. Be concise, and never make up information. If you do not know the answer, say so.
-"""
-
 system_message_old = f"""
-Launched on {Tools.DateTime()}.
+Launched on {datetime.now().isoformat()}.
  
 When answering queries, follow these steps:
 
@@ -98,53 +82,9 @@ When answering queries, follow these steps:
 Always use tools and <|context|> when possible. Be concise, and never make up information. If you do not know the answer, say so.
 """.strip()
 
-system_generate_resume = f"""
-Launched on {Tools.DateTime()}.
-
-You are a professional resume writer. Your task is to write a concise, polished, and tailored resume for a specific job based only on the individual's <|context|>.
-
-When answering queries, follow these steps:
-
-- You must not invent or assume any inforation not explicitly present in the <|context|>.
-- Analyze the <|job_description|> to identify skills required for the job.
-- Use the <|job_description|> provided to guide the focus, tone, and relevant skills or experience to highlight from the <|context|>. 
-- Identify and emphasize the experiences, achievements, and responsibilities from the <|context|> that best align with the <|job_description|>.
-- Only provide information from <|context|> items if it is relevant to the <|job_description|>.
-- Do not use the <|job_description|> skills unless listed in <|context|>.
-- Do not include any information unless it is provided in <|context|>.
-- Use the <|context|> to create a polished, professional resume.
-- Do not list any locations or mailing addresses in the resume.
-- If there is information in the <|context|>, <|job_description|>, <|context|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
-- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, or <|context|> tags.
-- Ensure the langauge is clear, concise, and aligned with industry standards for professional resumes.
-
-Structure the resume professionally with the following sections where applicable:
-
-* Name: Use full name
-* Professional Summary: A 2-4 sentence overview tailored to the job.
-* Skills: A bullet list of key skills derived from the work history and relevant to the job.
-* Professional Experience: A detailed list of roles, achievements, and responsibilities from <|context|> that relate to the <|job_description|>.
-* Education: Include only if available in the work history.
-* Notes: Indicate the initial draft of the resume was generated using the Backstory application.
-
-""".strip()
-
-system_fact_check = f"""
-Launched on {Tools.DateTime()}.
-
-You are a professional resume fact checker. Your task is to identify any inaccuracies in the <|resume|> based on the individual's <|context|>.
-
-If there are inaccuracies, list them in a bullet point format.
-
-When answering queries, follow these steps:
-- You must not invent or assume any information not explicitly present in the <|context|>.
-- Analyze the <|resume|> to identify any discrepancies or inaccuracies based on the <|context|>.
-- If there is information in the <|context|>, <|job_description|>, <|context|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
-- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, <|resume|>, or <|context|> tags.
-""".strip()
 
 system_fact_check_QA = f"""
-Launched on {Tools.DateTime()}.
+Launched on {datetime.now().isoformat()}.
 
 You are a professional resume fact checker. 
 
@@ -153,18 +93,6 @@ You are provided with a <|resume|> which was generated by you, the <|context|> y
 Your task is to answer questions about the <|fact_check|> you generated based on the <|resume|> and <|context>.
 """
 
-system_job_description = f"""
-Launched on {Tools.DateTime()}.
-
-You are a hiring and job placing specialist. Your task is to answers about a job description.
-
-When answering queries, follow these steps:
-- Analyze the <|job_description|> to provide insights for the asked question.
-- If any financial information is requested, be sure to account for inflation.
-- If there is information in the <|context|>, <|job_description|>, <|context|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
-- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, <|resume|>, or <|context|> tags.
-""".strip()
-
 def get_installed_ram():
     try:
         with open("/proc/meminfo", "r") as f:
@@ -440,27 +368,27 @@ class WebServer:
                     match reset_operation:
                         case "system_prompt":
                             logger.info(f"Resetting {reset_operation}")
-                            match agent_type:
+                            # match agent_type:
-                                case "chat":
+                            #     case "chat":
-                                    prompt = system_message
+                            #         prompt = system_message
-                                case "job_description":
+                            #     case "job_description":
-                                    prompt = system_generate_resume
+                            #         prompt = system_generate_resume
-                                case "resume":
+                            #     case "resume":
-                                    prompt = system_generate_resume
+                            #         prompt = system_generate_resume
-                                case "fact_check":
+                            #     case "fact_check":
-                                    prompt = system_message
+                            #         prompt = system_message
-                                case _:
+                            #     case _:
-                                    prompt = system_message
+                            #         prompt = system_message
 
-                            agent.system_prompt = prompt
+                            # agent.system_prompt = prompt
-                            response["system_prompt"] = { "system_prompt": prompt }
+                            # response["system_prompt"] = { "system_prompt": prompt }
                         case "rags":
                             logger.info(f"Resetting {reset_operation}")
                             context.rags = rags.copy()
                             response["rags"] = context.rags
                         case "tools":
                             logger.info(f"Resetting {reset_operation}")
-                            context.tools = Tools.default_tools(Tools.tools)
+                            context.tools = Tools.enabled_tools(Tools.tools)
                             response["tools"] = context.tools
                         case "history":
                             reset_map = {
@@ -579,27 +507,48 @@ class WebServer:
         @self.app.post("/api/chat/{context_id}/{agent_type}")
         async def post_chat_endpoint(context_id: str, agent_type: str, request: Request):
             logger.info(f"{request.method} {request.url.path}")
+            if not is_valid_uuid(context_id):
+                logger.warning(f"Invalid context_id: {context_id}")
+                return JSONResponse({"error": "Invalid context_id"}, status_code=400)
+
             try:
-                if not is_valid_uuid(context_id):
-                    logger.warning(f"Invalid context_id: {context_id}")
-                    return JSONResponse({"error": "Invalid context_id"}, status_code=400)
                 context = self.upsert_context(context_id)
 
                 try:
-                    data = await request.json()
                     agent = context.get_agent(agent_type)
-                    if not agent and agent_type == "job_description":
-                        logger.info(f"Agent {agent_type} not found. Returning empty history.")
-                        # Create a new agent if it doesn't exist
-                        agent = context.get_or_create_agent("job_description", system_prompt=system_generate_resume, job_description=data["content"])
                 except Exception as e:
                     logger.info(f"Attempt to create agent type: {agent_type} failed", e)
                     return JSONResponse({ "error": f"{agent_type} is not recognized", "context": context.id }, status_code=404)   
 
+                query = await request.json()
+                prompt = query["prompt"]
+                if not isinstance(prompt, str) or len(prompt) == 0:
+                    logger.info(f"Prompt is empty")
+                    return JSONResponse({"error": "Prompt can not be empty"}, status_code=400)
+                try:
+                    options = Tunables(**query["options"]) if "options" in query else None
+                except Exception as e:
+                    logger.info(f"Attempt to set tunables failed: {query['options']}.", e)
+                    return JSONResponse({"error": f"Invalid options: {query['options']}"}, status_code=400)
+
+                if not agent:
+                    # job_description is the only agent that is dynamically generated from a
+                    # Rest API endpoint.
+                    # - 'chat' is created on context creation. 
+                    # - 'resume' is created on actions by 'job_description'
+                    # - 'fact_check' is created on ations by 'fact_check'
+                    match agent_type:
+                        case "job_description":
+                            logger.info(f"Agent {agent_type} not found. Returning empty history.")
+                            agent = context.get_or_create_agent("job_description", job_description=prompt)
+                        case _:
+                            logger.info(f"Invalid agent creation sequence for {agent_type}. Returning error.")
+                            return JSONResponse({ "error": f"{agent_type} is not recognized", "context": context.id }, status_code=404)   
+
                 # Create a custom generator that ensures flushing
                 async def flush_generator():
                     logging.info(f"Message starting. Streaming partial results.")
-                    async for message in self.generate_response(context=context, agent=agent, content=data["content"]):
+                    async for message in self.generate_response(context=context, agent=agent, prompt=prompt, options=options):
                         if message.status != "done":
                             result = {
                                 "status": message.status,
@@ -607,11 +556,15 @@ class WebServer:
                             }
                         else:
                             logging.info(f"Message complete. Providing full response.")
-                            result = message.model_dump(mode='json')
+                            try:
+                                result = message.model_dump(by_alias=True, mode='json')
+                            except Exception as e:
+                                result = { "status": "error", "response": e }
+                                exit(1)
+                        # Convert to JSON and add newline
                         result = json.dumps(result) + "\n"
                         message.network_packets += 1
                         message.network_bytes += len(result)
-                        # Convert to JSON and add newline
                         yield result
                         # Explicitly flush after each yield
                         await asyncio.sleep(0)  # Allow the event loop to process the write
@@ -653,7 +606,7 @@ class WebServer:
                 if not agent:
                     logger.info(f"Agent {agent_type} not found. Returning empty history.")
                     return JSONResponse({ "messages": [] })
-                logger.info(f"History for {agent_type} contains {len(agent.conversation.messages)} entries.")
+                logger.info(f"History for {agent_type} contains {len(agent.conversation)} entries.")
                 return agent.conversation
             except Exception as e:
                 logger.error(f"get_history error: {str(e)}")
@@ -735,7 +688,7 @@ class WebServer:
         
         # Serialize the data to JSON and write to file
         with open(file_path, "w") as f:
-            f.write(context.model_dump_json())
+            f.write(context.model_dump_json(by_alias=True))
 
         return context_id
     
@@ -800,13 +753,12 @@ class WebServer:
         
         if os.path.exists(defines.resume_doc):
             context.user_resume = open(defines.resume_doc, "r").read()
-        context.get_or_create_agent(
+        context.get_or_create_agent(agent_type="chat")
-            agent_type="chat", 
+        #    system_prompt=system_message)
-            system_prompt=system_message)
         # context.add_agent(Resume(system_prompt = system_generate_resume))
         # context.add_agent(JobDescription(system_prompt = system_job_description))
         # context.add_agent(FactCheck(system_prompt = system_fact_check))
-        context.tools = Tools.default_tools(Tools.tools)
+        context.tools = Tools.enabled_tools(Tools.tools)
         context.rags = rags.copy()
 
         logger.info(f"{context.id} created and added to contexts.")
@@ -814,73 +766,6 @@ class WebServer:
         self.save_context(context.id)
         return context
 
-    def get_optimal_ctx_size(self, context, messages, ctx_buffer = 4096):
-        ctx = round(context + len(str(messages)) * 3 / 4)
-        return max(defines.max_context, min(2048, ctx + ctx_buffer))
-
-    # %%
-    # async def handle_tool_calls(self, message):
-    #     """
-    #     Process tool calls and yield status updates along the way.
-    #     The last yielded item will be a tuple containing (tool_result, tools_used).
-    #     """
-    #     tools_used = []
-    #     all_responses = []
-
-    #     for i, tool_call in enumerate(message["tool_calls"]):
-    #         arguments = tool_call["function"]["arguments"]
-    #         tool = tool_call["function"]["name"]
-            
-    #         # Yield status update before processing each tool
-    #         yield {"status": "processing", "message": f"Processing tool {i+1}/{len(message['tool_calls'])}: {tool}..."}
-            
-    #         # Process the tool based on its type
-    #         match tool:
-    #             case "TickerValue":
-    #                 ticker = arguments.get("ticker")
-    #                 if not ticker:
-    #                     ret = None
-    #                 else:
-    #                     ret = Tools.TickerValue(ticker)
-    #                 tools_used.append({ "tool": f"{tool}({ticker})", "result": ret})
-                
-    #             case "AnalyzeSite":
-    #                 url = arguments.get("url")
-    #                 question = arguments.get("question", "what is the summary of this content?")
-                    
-    #                 # Additional status update for long-running operations
-    #                 yield {"status": "processing", "message": f"Retrieving and summarizing content from {url}..."}
-    #                 ret = await Tools.AnalyzeSite(llm=self.llm, model=self.model, url=url, question=question)
-    #                 tools_used.append({ "tool": f"{tool}('{url}', '{question}')", "result": ret })
-                
-    #             case "DateTime":
-    #                 tz = arguments.get("timezone")
-    #                 ret = Tools.DateTime(tz)
-    #                 tools_used.append({ "tool": f"{tool}('{tz}')", "result": ret })
-                
-    #             case "WeatherForecast":
-    #                 city = arguments.get("city")
-    #                 state = arguments.get("state")
-                    
-    #                 yield {"status": "processing", "message": f"Fetching weather data for {city}, {state}..."}
-    #                 ret = Tools.WeatherForecast(city, state)
-    #                 tools_used.append({ "tool": f"{tool}('{city}', '{state}')", "result": ret })
-                
-    #             case _:
-    #                 ret = None
-            
-    #         # Build response for this tool
-    #         tool_response = {
-    #             "role": "tool",
-    #             "content": str(ret),
-    #             "name": tool_call["function"]["name"]
-    #         }
-    #         all_responses.append(tool_response)
-        
-    #     # Yield the final result as the last item
-    #     final_result = all_responses[0] if len(all_responses) == 1 else all_responses
-    #     yield (final_result, tools_used)
-
     def upsert_context(self, context_id = None) -> Context:
         """
         Upsert a context based on the provided context_id.
@@ -900,72 +785,32 @@ class WebServer:
         logger.info(f"Context {context_id} is not yet loaded.")
         return self.load_or_create_context(context_id)
         
-    def generate_rag_results(self, context, content):
+    async def generate_response(self, context : Context, agent : Agent, prompt : str, options: Tunables | None) -> AsyncGenerator[Message, None]:
-        if not self.file_watcher:
-            raise Exception("File watcher not initialized")
-        
-        results_found = False
-        
-        for rag in context.rags:
-            if rag["enabled"] and rag["name"] == "JPK": # Only support JPK rag right now...
-                yield {"status": "processing", "message": f"Checking RAG context {rag['name']}..."}
-                chroma_results = self.file_watcher.find_similar(query=content, top_k=10)
-                if chroma_results:
-                    results_found = True
-                    chroma_embedding = np.array(chroma_results["query_embedding"]).flatten()  # Ensure correct shape
-                    logger.info(f"Chroma embedding shape: {chroma_embedding.shape}")
-
-                    umap_2d = self.file_watcher.umap_model_2d.transform([chroma_embedding])[0].tolist()
-                    logger.info(f"UMAP 2D output: {umap_2d}, length: {len(umap_2d)}")  # Debug output
-
-                    umap_3d = self.file_watcher.umap_model_3d.transform([chroma_embedding])[0].tolist()
-                    logger.info(f"UMAP 3D output: {umap_3d}, length: {len(umap_3d)}")  # Debug output
-
-                    yield { 
-                        **chroma_results,
-                        "name": rag["name"],
-                        "umap_embedding_2d": umap_2d,
-                        "umap_embedding_3d": umap_3d
-                    }
-        
-        if not results_found:
-            yield {"status": "complete", "message": "No RAG context found"}
-            yield {
-                "rag": None,
-                "documents": [],
-                "embeddings": [],
-                "umap_embedding_2d": [],
-                "umap_embedding_3d": []
-            }
-        else:
-            yield {"status": "complete", "message": "RAG processing complete"}
-        
-    async def generate_response(self, context : Context, agent : Agent, content : str) -> AsyncGenerator[Message, None]:
         if not self.file_watcher:
             raise Exception("File watcher not initialized")
         
         agent_type = agent.get_agent_type()
-        logger.info(f"generate_response: type - {agent_type} prompt - {content}")
+        logger.info(f"generate_response: type - {agent_type}")
-        if agent_type == "chat":
+        message = Message(prompt=prompt, options=agent.tunables)
-            message = Message(prompt=content)
+        if options:
-            async for message in agent.prepare_message(message):
+            message.tunables = options
-                # logger.info(f"{agent_type}.prepare_message: {value.status} - {value.response}")
-                if message.status == "error":
-                    yield message
-                    return
-                if message.status != "done":
-                    yield message
-            async for message in agent.process_message(self.llm, self.model, message):
-                if message.status == "error":
-                    yield message
-                    return
-                if message.status != "done":
-                    yield message
-            logger.info(f"{agent_type}.process_message: {message.status} {f'...{message.response[-20:]}' if len(message.response) > 20 else message.response}")
-            message.status = "done"
-            yield message
-            return
 
+        async for message in agent.prepare_message(message):
+            # logger.info(f"{agent_type}.prepare_message: {value.status} - {value.response}")
+            if message.status == "error":
+                yield message
+                return
+            if message.status != "done":
+                yield message
+        async for message in agent.process_message(self.llm, self.model, message):
+            if message.status == "error":
+                yield message
+                return
+            if message.status != "done":
+                yield message
+        logger.info(f"{agent_type}.process_message: {message.status} {f'...{message.response[-20:]}' if len(message.response) > 20 else message.response}")
+        message.status = "done"
+        yield message
         return
 
         if self.processing:
@@ -1124,7 +969,7 @@ Use to the above information to respond to this prompt:
                 stuffingMessage.response = "Job description stored to use in future queries."
                 stuffingMessage.metadata["origin"] = "job_description"
                 stuffingMessage.metadata["display"] = "hide"
-                conversation.add_message(stuffingMessage)
+                conversation.add(stuffingMessage)
 
                 message.add_action("generate_resume")
 
@@ -1210,7 +1055,7 @@ Use the above <|resume|> and <|job_description|> to answer this query:
                 stuffingMessage.metadata["display"] = "hide"
                 stuffingMessage.actions = [ "fact_check" ]
                 logger.info("TODO: Switch this to use actions to keep the UI from showingit")
-                conversation.add_message(stuffingMessage)
+                conversation.add(stuffingMessage)
 
                 # For all future calls to job_description, use the system_job_description
                 logger.info("TODO: Create a system_resume_QA prompt to use for the resume agent")
@@ -1226,7 +1071,7 @@ Use the above <|resume|> and <|job_description|> to answer this query:
             case _:
                 raise Exception(f"Invalid chat agent_type: {agent_type}")
 
-        conversation.add_message(message)
+        conversation.add(message)
         # llm_history.append({"role": "user", "content": message.preamble + content})
         # user_history.append({"role": "user", "content": content, "origin": message.metadata["origin"]})
         # message.metadata["full_query"] = llm_history[-1]["content"]

src/utils/__init__.py

@@ -5,13 +5,14 @@ import importlib
 from . import defines  
 from . context import Context
 from . conversation import Conversation
-from . message import Message
+from . message import Message, Tunables
 from . rag import ChromaDBFileWatcher, start_file_watcher
 from . setup_logging import setup_logging
 from . agents import class_registry, AnyAgent, Agent, __all__ as agents_all
 
 __all__ = [
   'Agent', 
+  'Tunables',
   'Context', 
   'Conversation', 
   'Message', 

src/utils/agents/__init__.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-from typing import TypeAlias, Dict, Tuple
+from typing import TypeAlias, Dict, Tuple, Optional
 import importlib
 import pathlib
 import inspect
@@ -9,10 +9,9 @@ from .. setup_logging import setup_logging
 from .. import defines
 from . base import Agent
 
-
 logger = setup_logging(defines.logging_level)
 
-__all__ = [ "AnyAgent", "registry", "class_registry" ]
+__all__ = [ "AnyAgent", "Agent", "registry", "class_registry" ]
 
 # Type alias for Agent or any subclass
 AnyAgent: TypeAlias = Agent  # BaseModel covers Agent and subclasses

src/utils/agents/base.py

@@ -4,16 +4,15 @@ from typing import (
     Literal, get_args, List, AsyncGenerator, TYPE_CHECKING, Optional, ClassVar, Any,
     TypeAlias, Dict, Tuple
 )
-from abc import ABC
-from .. setup_logging import setup_logging
-from .. import defines
-from abc import ABC
-import logging
-from .. message import Message
-from .. tools import ( TickerValue, WeatherForecast, AnalyzeSite, DateTime, llm_tools ) # type: ignore -- dynamically added to __all__
 import json
 import time
 import inspect
+from abc import ABC
+
+from .. setup_logging import setup_logging
+from .. import defines
+from .. message import Message
+from .. tools import ( TickerValue, WeatherForecast, AnalyzeSite, DateTime, llm_tools ) # type: ignore -- dynamically added to __all__
 
 logger = setup_logging()
 
@@ -24,7 +23,12 @@ if TYPE_CHECKING:
 from .types import registry
 
 from .. conversation import Conversation
-from .. message import Message
+from .. message import Message, Tunables
+
+class LLMMessage(BaseModel):
+    role : str = Field(default="")
+    content : str = Field(default="")
+    tool_calls : Optional[List[Dict]] = Field(default={}, exclude=True)
 
 class Agent(BaseModel, ABC):
     """
@@ -35,6 +39,15 @@ class Agent(BaseModel, ABC):
     agent_type: Literal["base"] = "base"
     _agent_type: ClassVar[str] = agent_type  # Add this for registration
 
+    # Tunables (sets default for new Messages attached to this agent)
+    tunables: Tunables = Field(default_factory=Tunables)
+
+    # Agent properties
+    system_prompt: str # Mandatory
+    conversation: Conversation = Conversation()
+    context_tokens: int = 0
+    context: Optional[Context] = Field(default=None, exclude=True) # Avoid circular reference, require as param, and prevent serialization
+    
     # context_size is shared across all subclasses
     _context_size: ClassVar[int] = int(defines.max_context * 0.5)    
     @property
@@ -45,14 +58,6 @@ class Agent(BaseModel, ABC):
     def context_size(self, value: int):
         Agent._context_size = value
 
-    # Agent properties
-    system_prompt: str # Mandatory
-    conversation: Conversation = Conversation()
-    context_tokens: int = 0
-    context: Optional[Context] = Field(default=None, exclude=True) # Avoid circular reference, require as param, and prevent serialization
-    
-    _content_seed: str = PrivateAttr(default="")
-
     def set_optimal_context_size(self, llm: Any, model: str, prompt: str, ctx_buffer=2048) -> int:
         # # Get more accurate token count estimate using tiktoken or similar
         # response = llm.generate(
@@ -114,18 +119,18 @@ class Agent(BaseModel, ABC):
         """
         Prepare message with context information in message.preamble
         """
-        logging.info(f"{self.agent_type} - {inspect.stack()[1].function}")
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
 
         if not self.context:
             raise ValueError("Context is not set for this agent.")
         
         # Generate RAG content if enabled, based on the content
         rag_context = ""
-        if message.enable_rag:
+        if message.tunables.enable_rag and message.prompt:
             # Gather RAG results, yielding each result
             # as it becomes available
             for message in self.context.generate_rag_results(message):
-                logging.info(f"RAG: {message.status} - {message.response}")
+                logger.info(f"RAG: {message.status} - {message.response}")
                 if message.status == "error":
                     yield message
                     return
@@ -142,27 +147,16 @@ class Agent(BaseModel, ABC):
         if rag_context:
             message.preamble["context"] = rag_context
 
-        if self.context.user_resume:
+        if message.tunables.enable_context and self.context.user_resume:
             message.preamble["resume"] = self.context.user_resume
         
-        if message.preamble:
-            preamble_types = [f"<|{p}|>" for p in message.preamble.keys()]
-            preamble_types_AND = " and ".join(preamble_types)
-            preamble_types_OR = " or ".join(preamble_types)
-            message.preamble["rules"] = f"""\
-    - Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
-    - If there is no information in these sections, answer based on your knowledge, or use any available tools.
-    - Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
-    """
-        message.preamble["question"] = "Respond to:"
-        
         message.system_prompt = self.system_prompt
         message.status = "done"
         yield message
         return
 
-    async def process_tool_calls(self, llm: Any, model: str, message: Message, tool_message: Any, messages: List[Any]) -> AsyncGenerator[Message, None]:
+    async def process_tool_calls(self, llm: Any, model: str, message: Message, tool_message: Any, messages: List[LLMMessage]) -> AsyncGenerator[Message, None]:
-        logging.info(f"{self.agent_type} - {inspect.stack()[1].function}")
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
 
         if not self.context:
             raise ValueError("Context is not set for this agent.")
@@ -170,7 +164,6 @@ class Agent(BaseModel, ABC):
             raise ValueError("tools field not initialized")
 
         tool_metadata = message.metadata["tools"]
-        tool_metadata["messages"] = messages
         tool_metadata["tool_calls"] = []
 
         message.status = "tooling"
@@ -182,7 +175,7 @@ class Agent(BaseModel, ABC):
             # Yield status update before processing each tool
             message.response = f"Processing tool {i+1}/{len(tool_message.tool_calls)}: {tool}..."
             yield message
-            logging.info(f"LLM - {message.response}")
+            logger.info(f"LLM - {message.response}")
             
             # Process the tool based on its type
             match tool:
@@ -231,17 +224,17 @@ class Agent(BaseModel, ABC):
                 yield message
                 return
 
-            message_dict = {
+            message_dict = LLMMessage(
-            "role": tool_message.get("role", "assistant"),
+                role=tool_message.get("role", "assistant"),
-            "content": tool_message.get("content", ""),
+                content=tool_message.get("content", ""),
-            "tool_calls": [ {
+                tool_calls=[ {
-                "function": {
+                    "function": {
-                    "name": tc["function"]["name"], 
+                        "name": tc["function"]["name"], 
-                    "arguments": tc["function"]["arguments"]
+                        "arguments": tc["function"]["arguments"]
-                }
+                    }
-                } for tc in tool_message.tool_calls
+                    } for tc in tool_message.tool_calls
-            ]
+                ]
-            }
+            )
 
             messages.append(message_dict)
             messages.extend(tool_metadata["tool_calls"])
@@ -262,7 +255,7 @@ class Agent(BaseModel, ABC):
                 # "temperature": 0.5,
             }
         ):
-            # logging.info(f"LLM::Tools: {'done' if response.done else 'processing'} - {response.message}")
+            # logger.info(f"LLM::Tools: {'done' if response.done else 'processing'} - {response.message}")
             message.status = "streaming"
             message.response += response.message.content
             if not response.done:
@@ -281,24 +274,25 @@ class Agent(BaseModel, ABC):
         return
             
     async def generate_llm_response(self, llm: Any, model: str, message: Message) -> AsyncGenerator[Message, None]:
-        logging.info(f"{self.agent_type} - {inspect.stack()[1].function}")
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
 
         if not self.context:
             raise ValueError("Context is not set for this agent.")
 
-        messages = [ { "role": "system", "content": message.system_prompt } ]
+        # Create a pruned down message list based purely on the prompt and responses, 
+        # discarding the full preamble generated by prepare_message
+        messages: List[LLMMessage] = [ LLMMessage(role="system", content=message.system_prompt) ]
         messages.extend([
-        item for m in self.conversation.messages
+            item for m in self.conversation
-        for item in [
+            for item in [
-            {"role": "user", "content": m.prompt.strip()},
+                LLMMessage(role="user", content=m.prompt.strip()),
-            {"role": "assistant", "content": m.response.strip()}
+                LLMMessage(role="assistant", content=m.response.strip())
-        ]
+            ]
         ])
-        messages.append({
+        # Only the actual user query is provided with the full context message
-            "role": "user",
+        messages.append(LLMMessage(role="user", content=message.context_prompt.strip()))
-            "content": message.context_prompt.strip(),
+
-        })
+        #message.metadata["messages"] = messages
-        message.metadata["messages"] = messages
         message.metadata["options"]={
             "seed": 8911,
             "num_ctx": self.context_size,
@@ -307,7 +301,7 @@ class Agent(BaseModel, ABC):
 
         message.metadata["timers"] = {}
 
-        use_tools = message.enable_tools and len(self.context.tools) > 0
+        use_tools = message.tunables.enable_tools and len(self.context.tools) > 0
         message.metadata["tools"] = {
             "available": llm_tools(self.context.tools),
             "used": False
@@ -319,37 +313,38 @@ class Agent(BaseModel, ABC):
             message.response = f"Performing tool analysis step 1/2..."
             yield message
 
-        logging.info("Checking for LLM tool usage")
+            logger.info("Checking for LLM tool usage")
-        start_time = time.perf_counter()
+            start_time = time.perf_counter()
-        # Tools are enabled and available, so query the LLM with a short token target to see if it will
+            # Tools are enabled and available, so query the LLM with a short token target to see if it will
-        # use the tools
+            # use the tools
-        tool_metadata["messages"] = [{ "role": "system", "content": self.system_prompt}, {"role": "user", "content": message.prompt}]
+            tool_metadata["messages"] = [{ "role": "system", "content": self.system_prompt}, {"role": "user", "content": message.prompt}]
-        response = llm.chat(
+            response = llm.chat(
-            model=model,
+                model=model,
-            messages=tool_metadata["messages"],
+                messages=tool_metadata["messages"],
-            tools=tool_metadata["available"],
+                tools=tool_metadata["available"],
-            options={
+                options={
-                **message.metadata["options"],
+                    **message.metadata["options"],
-                #"num_predict": 1024, # "Low" token limit to cut off after tool call
+                    #"num_predict": 1024, # "Low" token limit to cut off after tool call
-            },
+                },
-            stream=False  # No need to stream the probe
+                stream=False  # No need to stream the probe
-        )
+            )
-        end_time = time.perf_counter()
+            end_time = time.perf_counter()
-        message.metadata["timers"]["tool_check"] = f"{(end_time - start_time):.4f}"
+            message.metadata["timers"]["tool_check"] = f"{(end_time - start_time):.4f}"
-        if not response.message.tool_calls:
+            if not response.message.tool_calls:
-            logging.info("LLM indicates tools will not be used")
+                logger.info("LLM indicates tools will not be used")
-            # The LLM will not use tools, so disable use_tools so we can stream the full response
+                # The LLM will not use tools, so disable use_tools so we can stream the full response
-            use_tools = False
+                use_tools = False
+            else:
+                tool_metadata["attempted"] = response.message.tool_calls
 
         if use_tools:
-            logging.info("LLM indicates tools will be used")
+            logger.info("LLM indicates tools will be used")
 
             # Tools are enabled and available and the LLM indicated it will use them
-            tool_metadata["attempted"] = response.message.tool_calls
             message.response = f"Performing tool analysis step 2/2 (tool use suspected)..."
             yield message
 
-            logging.info(f"Performing LLM call with tools")
+            logger.info(f"Performing LLM call with tools")
             start_time = time.perf_counter()
             response = llm.chat(
                 model=model,
@@ -383,13 +378,15 @@ class Agent(BaseModel, ABC):
                 message.status = "done"
                 return
             
-            logging.info("LLM indicated tools will be used, and then they weren't")
+            logger.info("LLM indicated tools will be used, and then they weren't")
             message.response = response.message.content
             message.status = "done"
             yield message
             return
 
         # not use_tools
+        message.status = "thinking"
+        message.response = f"Generating response..."
         yield message
         # Reset the response for streaming
         message.response = ""
@@ -428,13 +425,13 @@ class Agent(BaseModel, ABC):
         return
     
     async def process_message(self, llm: Any, model: str, message:Message) -> AsyncGenerator[Message, None]:
-        logging.info(f"{self.agent_type} - {inspect.stack()[1].function}")
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
 
         if not self.context:
             raise ValueError("Context is not set for this agent.")
             
         if self.context.processing:
-            logging.info("TODO: Implement delay queing; busy for same agent, otherwise return queue size and estimated wait time")
+            logger.info("TODO: Implement delay queing; busy for same agent, otherwise return queue size and estimated wait time")
             message.status = "error"
             message.response = "Busy processing another request."
             yield message
@@ -460,7 +457,7 @@ class Agent(BaseModel, ABC):
         yield message
 
         async for message in self.generate_llm_response(llm, model, message):
-            # logging.info(f"LLM: {message.status} - {f'...{message.response[-20:]}' if len(message.response) > 20 else message.response}")
+            # logger.info(f"LLM: {message.status} - {f'...{message.response[-20:]}' if len(message.response) > 20 else message.response}")
             if message.status == "error":
                 yield message
                 self.context.processing = False
@@ -469,7 +466,7 @@ class Agent(BaseModel, ABC):
         
         # Done processing, add message to conversation
         message.status = "done"
-        self.conversation.add_message(message)
+        self.conversation.add(message)
         self.context.processing = False
         return
 

src/utils/agents/chat.py

@@ -1,9 +1,30 @@
 from __future__ import annotations
-from typing import Literal, AsyncGenerator, ClassVar, Optional
+from typing import Literal, AsyncGenerator, ClassVar, Optional, Any
-import logging
+from datetime import datetime
+import inspect
+
 from . base import Agent, registry
 from .. message import Message
-import inspect
+from .. setup_logging import setup_logging
+logger = setup_logging()
+
+system_message = f"""
+Launched on {datetime.now().isoformat()}.
+
+When answering queries, follow these steps:
+
+- First analyze the query to determine if real-time information from the tools might be helpful
+- Even when <|context|> or <|resume|> is provided, consider whether the tools would provide more current or comprehensive information
+- Use the provided tools whenever they would enhance your response, regardless of whether context is also available
+- When presenting weather forecasts, include relevant emojis immediately before the corresponding text. For example, for a sunny day, say \"☀️ Sunny\" or if the forecast says there will be \"rain showers, say \"🌧️ Rain showers\". Use this mapping for weather emojis: Sunny: ☀️, Cloudy: ☁️, Rainy: 🌧️, Snowy: ❄️
+- When any combination of <|context|>, <|resume|> and tool outputs are relevant, synthesize information from all sources to provide the most complete answer
+- Always prioritize the most up-to-date and relevant information, whether it comes from <|context|>, <|resume|> or tools
+- If <|context|> and tool outputs contain conflicting information, prefer the tool outputs as they likely represent more current data
+- If there is information in the <|context|> or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it as 'the latest information' or 'recent data' instead of mentioning '<|context|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|> or <|resume|>.
+
+Always use tools, <|resume|>, and <|context|> when possible. Be concise, and never make up information. If you do not know the answer, say so.
+"""
 
 class Chat(Agent):
   """
@@ -12,5 +33,27 @@ class Chat(Agent):
   agent_type: Literal["chat"] = "chat" # type: ignore
   _agent_type: ClassVar[str] = agent_type  # Add this for registration
 
+  system_prompt: str = system_message
+
+  async def prepare_message(self, message:Message) -> AsyncGenerator[Message, None]:
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
+    
+    async for message in super().prepare_message(message):
+      if message.status != "done":
+        yield message
+
+    if message.preamble:
+      preamble_types = [f"<|{p}|>" for p in message.preamble.keys()]
+      preamble_types_AND = " and ".join(preamble_types)
+      preamble_types_OR = " or ".join(preamble_types)
+      message.preamble["rules"] = f"""\
+- Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
+- If there is no information in these sections, answer based on your knowledge, or use any available tools.
+- Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
+"""
+    message.preamble["question"] = "Respond to:"
+
 # Register the base agent
 registry.register(Chat._agent_type, Chat)

src/utils/agents/fact_check.py

@@ -1,12 +1,32 @@
+from __future__ import annotations
 from pydantic import model_validator # type: ignore
-from typing import Literal, ClassVar, Optional
+from typing import Literal, ClassVar, Optional, Any, AsyncGenerator, List # NOTE: You must import Optional for late binding to work
-from .base import Agent, registry
+from datetime import datetime
+import inspect
+
+from . base import Agent, registry
+from .. conversation import Conversation
+from .. message import Message
+from .. setup_logging import setup_logging
+logger = setup_logging()
+
+system_fact_check = f"""
+Launched on {datetime.now().isoformat()}.
+
+You are a professional resume fact checker. Your task is answer any questions about items identified in the <|discrepancies|>.
+The <|discrepancies|> indicate inaccuracies or unsupported claims in the <|generated-resume|> based on content from the <|resume|> and <|context|>.
+
+When answering queries, follow these steps:
+- If there is information in the <|context|> or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|context|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|generated-resume|>, or <|resume|> tags.
+""".strip()
 
 class FactCheck(Agent):
   agent_type: Literal["fact_check"] = "fact_check" # type: ignore
   _agent_type: ClassVar[str] = agent_type  # Add this for registration
 
-  facts: str = ""
+  system_prompt:str = system_fact_check
+  facts: str
 
   @model_validator(mode="after")
   def validate_facts(self):
@@ -14,5 +34,36 @@ class FactCheck(Agent):
       raise ValueError("Facts cannot be empty")
     return self
 
+  async def prepare_message(self, message:Message) -> AsyncGenerator[Message, None]:
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
+
+    resume_agent = self.context.get_agent("resume")
+    if not resume_agent:
+      raise ValueError("resume agent does not exist")
+
+    message.enable_tools = False
+
+    async for message in super().prepare_message(message):
+      if message.status != "done":
+        yield message
+
+    message.preamble["generated-resume"] = resume_agent.resume
+    message.preamble["discrepancies"] = self.facts
+    
+    preamble_types = [f"<|{p}|>" for p in message.preamble.keys()]
+    preamble_types_AND = " and ".join(preamble_types)
+    preamble_types_OR = " or ".join(preamble_types)
+    message.preamble["rules"] = f"""\
+- Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
+- If there is no information in these sections, answer based on your knowledge, or use any available tools.
+- Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
+"""
+    message.preamble["question"] = "Respond to:"
+
+    yield message
+    return
+
 # Register the base agent
 registry.register(FactCheck._agent_type, FactCheck)

src/utils/agents/job_description.py

@@ -1,15 +1,63 @@
+from __future__ import annotations
 from pydantic import model_validator # type: ignore
-from typing import Literal, ClassVar, Optional
+from typing import Literal, ClassVar, Optional, Any, AsyncGenerator, List # NOTE: You must import Optional for late binding to work
-from .base import Agent, registry
+from datetime import datetime
+import inspect
+
+from . base import Agent, registry
 from .. conversation import Conversation
 from .. message import Message
-from abc import ABC
+from .. setup_logging import setup_logging
+logger = setup_logging()
+
+system_generate_resume = f"""
+Launched on {datetime.now().isoformat()}.
+
+You are a professional resume writer. Your task is to write a concise, polished, and tailored resume for a specific job based only on the individual's <|context|>.
+
+When answering queries, follow these steps:
+
+- You must not invent or assume any inforation not explicitly present in the <|context|>.
+- Analyze the <|job_description|> to identify skills required for the job.
+- Use the <|job_description|> provided to guide the focus, tone, and relevant skills or experience to highlight from the <|context|>. 
+- Identify and emphasize the experiences, achievements, and responsibilities from the <|context|> that best align with the <|job_description|>.
+- Only provide information from <|context|> items if it is relevant to the <|job_description|>.
+- Do not use the <|job_description|> skills unless listed in <|context|>.
+- Do not include any information unless it is provided in <|context|>.
+- Use the <|context|> to create a polished, professional resume.
+- Do not list any locations or mailing addresses in the resume.
+- If there is information in the <|context|>, <|job_description|>, <|context|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, or <|context|> tags.
+- Ensure the langauge is clear, concise, and aligned with industry standards for professional resumes.
+
+Structure the resume professionally with the following sections where applicable:
+
+* Name: Use full name
+* Professional Summary: A 2-4 sentence overview tailored to the job.
+* Skills: A bullet list of key skills derived from the work history and relevant to the job.
+* Professional Experience: A detailed list of roles, achievements, and responsibilities from <|context|> that relate to the <|job_description|>.
+* Education: Include only if available in the work history.
+* Notes: Indicate the initial draft of the resume was generated using the Backstory application.
+""".strip()
+
+system_job_description = f"""
+Launched on {datetime.now().isoformat()}.
+
+You are a hiring and job placing specialist. Your task is to answers about a job description.
+
+When answering queries, follow these steps:
+- Analyze the <|job_description|> to provide insights for the asked question.
+- If any financial information is requested, be sure to account for inflation.
+- If there is information in the <|context|>, <|job_description|>, <|context|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, <|resume|>, or <|context|> tags.
+""".strip()
 
 class JobDescription(Agent):
   agent_type: Literal["job_description"] = "job_description" # type: ignore
   _agent_type: ClassVar[str] = agent_type  # Add this for registration
 
-  job_description: str = ""
+  system_prompt: str = system_generate_resume
+  job_description: str
 
   @model_validator(mode="after")
   def validate_job_description(self):
@@ -17,5 +65,63 @@ class JobDescription(Agent):
       raise ValueError("Job description cannot be empty")
     return self
 
+  async def prepare_message(self, message:Message) -> AsyncGenerator[Message, None]:
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
+    
+    async for message in super().prepare_message(message):
+      if message.status != "done":
+        yield message
+
+    # Always add the job description, user resume, and question
+    message.preamble["job_description"] = self.job_description
+    message.preamble["resume"] = self.context.user_resume
+
+    preamble_types = [f"<|{p}|>" for p in message.preamble.keys()]
+    preamble_types_AND = " and ".join(preamble_types)
+    preamble_types_OR = " or ".join(preamble_types)
+    message.preamble["rules"] = f"""\
+- Answer the question based on the information provided in the {preamble_types_AND} sections by incorporating it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
+- If there is no information in these sections, answer based on your knowledge, or use any available tools.
+- Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
+"""
+
+    resume_agent = self.context.get_agent(agent_type="resume")
+    if resume_agent:
+      message.preamble["question"] = "Respond to:"
+    else:
+      message.preamble["question"] = "Generate a resume given the <|resume|> and <|job_description|>."
+
+    yield message
+    return
+
+  async def process_message(self, llm: Any, model: str, message:Message) -> AsyncGenerator[Message, None]:
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
+    
+    async for message in super().process_message(llm, model, message):
+      if message.status != "done":
+        yield message
+
+    resume_agent = self.context.get_agent(agent_type="resume")
+    if not resume_agent:
+      # Switch agent from "Create Resume from Job Desription" mode
+      # to "Answer Questions about Job Description"
+      self.system_prompt = system_job_description
+
+      # Instantiate the "resume" agent, and seed (or reset) its conversation
+      # with this message.
+      resume_agent = self.context.get_or_create_agent(agent_type="resume", resume=message.response)
+      first_resume_message = message.copy()
+      first_resume_message.prompt = "Generate a resume for the job description."
+      resume_agent.conversation.add(first_resume_message)
+      message.response = "Resume generated."
+
+    # Return the final message
+    yield message
+    return
+
 # Register the base agent
 registry.register(JobDescription._agent_type, JobDescription)

src/utils/agents/resume.py

@@ -1,12 +1,47 @@
+from __future__ import annotations
 from pydantic import model_validator # type: ignore
-from typing import Literal, Optional, ClassVar
+from typing import Literal, ClassVar, Optional, Any, AsyncGenerator, List # NOTE: You must import Optional for late binding to work
-from .base import Agent, registry
+from datetime import datetime
+import inspect
+
+from . base import Agent, registry
+from .. message import Message
+from .. setup_logging import setup_logging
+logger = setup_logging()
+
+system_fact_check = f"""
+Launched on {datetime.now().isoformat()}.
+
+You are a professional resume fact checker. Your task is to identify any inaccuracies in the <|generated-resume|> based on the individual's <|context|> and <|resume|>.
+
+If there are inaccuracies, list them in a bullet point format.
+
+When answering queries, follow these steps:
+- Analyze the <|generated-resume|> to identify any discrepancies or inaccuracies which are not supported by the <|context|> and <|resume|>.
+- If there is information in the <|context|> or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|context|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|generated-resume|>, or <|resume|> tags.
+
+Do not generate a revised resume.
+""".strip()
+
+system_resume = f"""
+Launched on {datetime.now().isoformat()}.
+
+You are a hiring and job placing specialist. Your task is to answers about a resume and work history as it relates to a potential job.
+
+When answering queries, follow these steps:
+- Analyze the <|job_description|> and <|generated-resume|> to provide insights for the asked question.
+- If any financial information is requested, be sure to account for inflation.
+- If there is information in the <|context|>, <|job_description|>, <|generated-resume|>, or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it using natural language instead of mentioning '<|job_description|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|>, <|job_description|>, <|resume|>, or <|context|> tags.
+""".strip()
 
 class Resume(Agent):
   agent_type: Literal["resume"] = "resume"  # type: ignore
   _agent_type: ClassVar[str] = agent_type  # Add this for registration
 
-  resume: str = ""
+  system_prompt:str = system_fact_check
+  resume: str
 
   @model_validator(mode="after")
   def validate_resume(self):
@@ -14,13 +49,65 @@ class Resume(Agent):
       raise ValueError("Resume content cannot be empty")
     return self
 
-  def get_resume(self) -> str:
+  async def prepare_message(self, message:Message) -> AsyncGenerator[Message, None]:
-    """Get the resume content."""
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
-    return self.resume
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
     
-  def set_resume(self, resume: str) -> None:
+    async for message in super().prepare_message(message):
-    """Set the resume content."""
+      if message.status != "done":
-    self.resume = resume
+        yield message
+
+    message.preamble["generated-resume"] = self.resume
+    job_description_agent = self.context.get_agent("job_description")
+    if not job_description_agent:
+      raise ValueError("job_description agent does not exist")
+    
+    message.preamble["job_description"] = job_description_agent.job_description
+
+    preamble_types = [f"<|{p}|>" for p in message.preamble.keys()]
+    preamble_types_AND = " and ".join(preamble_types)
+    preamble_types_OR = " or ".join(preamble_types)
+    message.preamble["rules"] = f"""\
+- Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
+- If there is no information in these sections, answer based on your knowledge, or use any available tools.
+- Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
+"""
+    fact_check_agent = self.context.get_agent(agent_type="fact_check")
+    if fact_check_agent:
+      message.preamble["question"] = "Respond to:"
+    else:
+      message.preamble["question"] = f"Fact check the <|generated-resume|> based on the <|resume|>{' and <|context|>' if 'context' in message.preamble else ''}."
+
+    yield message
+    return
+
+  async def process_message(self, llm: Any, model: str, message:Message) -> AsyncGenerator[Message, None]:
+    logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+    if not self.context:
+      raise ValueError("Context is not set for this agent.")
+    
+    async for message in super().process_message(llm, model, message):
+      if message.status != "done":
+        yield message
+
+    fact_check_agent = self.context.get_agent(agent_type="fact_check")
+    if not fact_check_agent:
+      # Switch agent from "Fact Check Generated Resume" mode
+      # to "Answer Questions about Generated Resume"
+      self.system_prompt = system_resume
+
+      # Instantiate the "resume" agent, and seed (or reset) its conversation
+      # with this message.
+      fact_check_agent = self.context.get_or_create_agent(agent_type="fact_check", facts=message.response)
+      first_fact_check_message = message.copy()
+      first_fact_check_message.prompt = "Fact check the generated resume."
+      fact_check_agent.conversation.add(first_fact_check_message)
+      message.response = "Resume fact checked."
+
+    # Return the final message
+    yield message
+    return
 
 # Register the base agent
 registry.register(Resume._agent_type, Resume)

src/utils/context.py

@@ -6,13 +6,12 @@ from typing_extensions import Annotated, Union
 import numpy as np # type: ignore
 import logging
 from uuid import uuid4
-import re
 
-from .message import Message
+from . message import Message, Tunables
-from .rag import ChromaDBFileWatcher
+from . rag import ChromaDBFileWatcher
 from . import defines
 from . import tools as Tools
-from .agents import AnyAgent
+from . agents import AnyAgent
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -30,7 +29,7 @@ class Context(BaseModel):
     user_resume: Optional[str] = None
     user_job_description: Optional[str] = None
     user_facts: Optional[str] = None
-    tools: List[dict] = Tools.default_tools(Tools.tools)
+    tools: List[dict] = Tools.enabled_tools(Tools.tools)
     rags: List[dict] = []
     message_history_length: int = 5
     # Class managed fields

src/utils/conversation.py

@@ -1,22 +1,41 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, PrivateAttr # type: ignore
 from typing import List
 from .message import Message
 
 class Conversation(BaseModel):
-    messages: List[Message] = []
+    Conversation_messages: List[Message] = Field(default=[], alias="messages")
 
-    def add_message(self, message: Message | List[Message]) -> None:
+    def __len__(self):
+        return len(self.Conversation_messages)
+
+    def __iter__(self):
+        return iter(self.Conversation_messages)
+
+    def reset(self):
+       self.Conversation_messages = []
+
+    @property
+    def messages(self):
+        """Return a copy of messages to prevent modification of the internal list."""
+        raise AttributeError("Cannot directly get messages. Use Conversation.add() or .reset()")
+    
+    @messages.setter
+    def messages(self, value):
+        """Control how messages can be set, or prevent setting altogether."""
+        raise AttributeError("Cannot directly set messages. Use Conversation.add() or .reset()")
+    
+    def add(self, message: Message | List[Message]) -> None:
         """Add a Message(s) to the conversation."""
         if isinstance(message, Message):
-          self.messages.append(message)
+          self.Conversation_messages.append(message)
         else:
-          self.messages.extend(message)
+          self.Conversation_messages.extend(message)
 
     def get_summary(self) -> str:
         """Return a summary of the conversation."""
-        if not self.messages:
+        if not self.Conversation_messages:
             return "Conversation is empty."
         summary = f"Conversation:\n"
-        for i, message in enumerate(self.messages, 1):
+        for i, message in enumerate(self.Conversation_messages, 1):
             summary += f"\nMessage {i}:\n{message.get_summary()}\n"
         return summary

src/utils/message.py

@@ -1,14 +1,18 @@
-from pydantic import BaseModel  # type: ignore
+from pydantic import BaseModel, Field  # type: ignore
 from typing import Dict, List, Optional, Any
 from datetime import datetime, timezone
 
+class Tunables(BaseModel):
+    enable_rag : bool = Field(default=True)     # Enable RAG collection chromadb matching
+    enable_tools : bool = Field(default=True)   # Enable LLM to use tools
+    enable_context : bool = Field(default=True) # Add <|context|> field to message
+
 class Message(BaseModel):
     # Required
     prompt: str                     # Query to be answered
 
     # Tunables
-    enable_rag: bool = True
+    tunables: Tunables = Field(default_factory=Tunables)
-    enable_tools: bool = True
     
     # Generated while processing message
     status: str = ""                # Status of the message
@@ -16,14 +20,14 @@ class Message(BaseModel):
     system_prompt: str = ""         # System prompt provided to the LLM
     context_prompt: str = ""        # Full content of the message (preamble + prompt)
     response: str = ""              # LLM response to the preamble + query
-    metadata: dict[str, Any] = {
+    metadata: Dict[str, Any] = Field(default_factory=lambda: {
-        "rag": List[dict[str, Any]],
+        "rag": [],
         "eval_count": 0,
         "eval_duration": 0,
         "prompt_eval_count": 0,
         "prompt_eval_duration": 0,
         "context_size": 0,
-    }
+    })
     network_packets: int = 0        # Total number of streaming packets
     network_bytes: int = 0          # Total bytes sent while streaming packets
     actions: List[str] = []         # Other session modifying actions performed while processing the message