From 8b2fa676a11d128968b05ff64c32fd79a6a250d5 Mon Sep 17 00:00:00 2001
From: Kye <kye@apacmediasolutions.com>
Date: Sun, 19 Nov 2023 20:03:26 -0800
Subject: [PATCH] ai researh team swarm

---
 .../demos/accountant_team/account_team2.py    |  74 ++++++++++++++
 .../demos/accountant_team/accountant_team.py  |  47 ++++-----
 playground/demos/ai_research_team/main.py     |  50 ++++++++++
 .../__pycache__/PosMedPrompts.cpython-310.pyc | Bin 3831 -> 0 bytes
 swarms/prompts/accountant_swarm_prompts.py    |  90 +++++++++++++++++
 swarms/prompts/ai_research_team.py            |  91 ++++++++++++++++++
 swarms/structs/flow.py                        |   6 +-
 swarms/utils/pdf_to_text.py                   |   6 +-
 8 files changed, 336 insertions(+), 28 deletions(-)
 create mode 100644 playground/demos/accountant_team/account_team2.py
 create mode 100644 playground/demos/ai_research_team/main.py
 delete mode 100644 playground/posmed/__pycache__/PosMedPrompts.cpython-310.pyc
 create mode 100644 swarms/prompts/accountant_swarm_prompts.py
 create mode 100644 swarms/prompts/ai_research_team.py

diff --git a/playground/demos/accountant_team/account_team2.py b/playground/demos/accountant_team/account_team2.py
new file mode 100644
index 00000000..db3e6ed6
--- /dev/null
+++ b/playground/demos/accountant_team/account_team2.py
@@ -0,0 +1,74 @@
+import os
+from dotenv import load_dotenv
+from swarms.models import Anthropic, OpenAIChat
+from swarms.prompts.accountant_swarm_prompts import (
+    DECISION_MAKING_PROMPT,
+    DOC_ANALYZER_AGENT_PROMPT,
+    FRAUD_DETECTION_AGENT_PROMPT,
+    SUMMARY_GENERATOR_AGENT_PROMPT,
+)
+from swarms.structs import Flow
+from swarms.utils.pdf_to_text import pdf_to_text
+
+# Environment variables
+load_dotenv()
+anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+
+# Base llms
+llm1 = OpenAIChat(
+    openai_api_key=openai_api_key,
+)
+
+llm2 = Anthropic(
+    anthropic_api_key=anthropic_api_key,
+)
+
+
+# Agents
+doc_analyzer_agent = Flow(
+    llm=llm2,
+    sop=DOC_ANALYZER_AGENT_PROMPT,
+    max_loops="auto",
+)
+summary_generator_agent = Flow(
+    llm=llm2,
+    sop=SUMMARY_GENERATOR_AGENT_PROMPT,
+    max_loops="auto",
+)
+decision_making_support_agent = Flow(
+    llm=llm2,
+    sop=DECISION_MAKING_PROMPT,
+    max_loops="auto",
+)
+
+
+pdf_path="swarmdeck_a1.pdf"
+fraud_detection_instructions="Detect fraud in the document"
+summary_agent_instructions="Generate an actionable summary of the document"
+decision_making_support_agent_instructions="Provide decision making support to the business owner:"
+
+
+# Transform the pdf to text
+pdf_text = pdf_to_text(pdf_path)
+print(pdf_text)
+
+
+# Detect fraud in the document
+fraud_detection_agent_output = doc_analyzer_agent.run(
+    f"{fraud_detection_instructions}: {pdf_text}"
+)
+print(fraud_detection_agent_output)
+
+# Generate an actionable summary of the document
+summary_agent_output = summary_generator_agent.run(
+    f"{summary_agent_instructions}: {fraud_detection_agent_output}"
+)
+print(summary_agent_output)
+
+# Provide decision making support to the accountant
+decision_making_support_agent_output = decision_making_support_agent.run(
+    f"{decision_making_support_agent_instructions}: {summary_agent_output}"
+)
+print(decision_making_support_agent_output)
\ No newline at end of file
diff --git a/playground/demos/accountant_team/accountant_team.py b/playground/demos/accountant_team/accountant_team.py
index 1401ef32..61cc2f7a 100644
--- a/playground/demos/accountant_team/accountant_team.py
+++ b/playground/demos/accountant_team/accountant_team.py
@@ -4,10 +4,15 @@ from typing import List
 from dotenv import load_dotenv
 
 from swarms.models import Anthropic, OpenAIChat
+from swarms.prompts.accountant_swarm_prompts import (
+    DECISION_MAKING_PROMPT,
+    DOC_ANALYZER_AGENT_PROMPT,
+    FRAUD_DETECTION_AGENT_PROMPT,
+    SUMMARY_GENERATOR_AGENT_PROMPT,
+)
 from swarms.structs import Flow
 from swarms.utils.pdf_to_text import pdf_to_text
 
-
 # Environment variables
 load_dotenv()
 anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
@@ -24,27 +29,18 @@ llm2 = Anthropic(
 )
 
 
-# Prompts for each agent
-SUMMARY_AGENT_PROMPT = """
-    Generate an actionable summary of this financial document be very specific and precise, provide bulletpoints be very specific provide methods of lowering expenses: {answer}"
-"""
-
-
 # Agents
-user_consultant_agent = Flow(
-    llm=llm1,
-)
 doc_analyzer_agent = Flow(
     llm=llm1,
+    sop=DOC_ANALYZER_AGENT_PROMPT,
 )
 summary_generator_agent = Flow(
     llm=llm2,
-)
-fraud_detection_agent = Flow(
-    llm=llm2,
+    sop=SUMMARY_GENERATOR_AGENT_PROMPT,
 )
 decision_making_support_agent = Flow(
     llm=llm2,
+    sop=DECISION_MAKING_PROMPT,
 )
 
 
@@ -71,11 +67,6 @@ class AccountantSwarms:
     2. The Fraud Detection agent detects fraud in the document.
     3. The Summary Agent generates an actionable summary of the document.
     4. The Decision Making Support agent provides decision making support
-    to the accountant.
-
-    Example:
-    >>> accountant_swarms = AccountantSwarms(
-
 
     """
 
@@ -101,14 +92,26 @@ class AccountantSwarms:
         pdf_text = pdf_to_text(self.pdf_path)
 
         # Detect fraud in the document
-        fraud_detection_agent_output = self.fraud_detection_agent(pdf_text)
+        fraud_detection_agent_output = doc_analyzer_agent.run(
+            f"{self.fraud_detection_instructions}: {pdf_text}"
+        )
 
         # Generate an actionable summary of the document
-        summary_agent_output = self.summary_agent(fraud_detection_agent_output)
+        summary_agent_output = summary_generator_agent.run(
+            f"{self.summary_agent_instructions}: {fraud_detection_agent_output}"
+        )
 
         # Provide decision making support to the accountant
-        decision_making_support_agent_output = self.decision_making_support_agent(
-            summary_agent_output
+        decision_making_support_agent_output = decision_making_support_agent.run(
+            f"{self.decision_making_support_agent_instructions}: {summary_agent_output}"
         )
 
         return decision_making_support_agent_output
+
+
+swarm = AccountantSwarms(
+    pdf_path="tesla.pdf",
+    fraud_detection_instructions="Detect fraud in the document",
+    summary_agent_instructions="Generate an actionable summary of the document",
+    decision_making_support_agent_instructions="Provide decision making support to the business owner:",
+)
diff --git a/playground/demos/ai_research_team/main.py b/playground/demos/ai_research_team/main.py
new file mode 100644
index 00000000..c986e123
--- /dev/null
+++ b/playground/demos/ai_research_team/main.py
@@ -0,0 +1,50 @@
+import os
+
+from dotenv import load_dotenv
+
+from swarms.models import Anthropic, OpenAIChat
+from swarms.prompts.ai_research_team import (
+    PAPER_IMPLEMENTOR_AGENT_PROMPT,
+    PAPER_SUMMARY_ANALYZER,
+)
+from swarms.structs import Flow
+from swarms.utils.pdf_to_text import pdf_to_text
+
+# Base llms
+# Environment variables
+load_dotenv()
+anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+PDF_PATH = "shallowfeedforward.pdf"
+
+
+# Base llms
+llm1 = OpenAIChat(
+    openai_api_key=openai_api_key,
+)
+
+llm2 = Anthropic(
+    anthropic_api_key=anthropic_api_key,
+)
+
+# Agents
+paper_summarizer_agent = Flow(
+    llm=llm2,
+    sop=PAPER_SUMMARY_ANALYZER,
+    max_loops=1,
+    autosave=True,
+    saved_state_path='paper_summarizer.json'
+)
+
+paper_implementor_agent = Flow(
+    llm=llm1,
+    sop=PAPER_IMPLEMENTOR_AGENT_PROMPT,
+    max_loops=1,
+    autosave=True,
+    saved_state_path='paper_implementor.json'
+)
+
+paper = pdf_to_text(PDF_PATH)
+algorithmic_psuedocode_agent = paper_summarizer_agent.run(paper)
+pytorch_code = paper_implementor_agent.run(algorithmic_psuedocode_agent)
\ No newline at end of file
diff --git a/playground/posmed/__pycache__/PosMedPrompts.cpython-310.pyc b/playground/posmed/__pycache__/PosMedPrompts.cpython-310.pyc
deleted file mode 100644
index 4bcfbb742219abb8adcc40120ef2f5505e351060..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 3831
zcmZXX&u$yZ5ymN5UfZ;HlS2aJu!qJvL1IAU%HGXISZtzb30r{vMUvN8M>#ZSn!^rf
zrpMhgq-k3~4ta^(vd272-ayCXEpp1QdWMvY5a0}_r@Ol9tFOLlx=%mt_xODN^*83(
zXT9Ek<l**blZW5)4WDyKd#`)x#_NrAGrjY--s{bDE4|D8PWs7t@5w#=uTOft$Li?d
z{QThLxc|!A+9<7_?(F<ZHKl#im~EAKqiZ{7yiAoX)wC&eImqqIs8BC+#(ixQ?ry@;
z!j`sZ3VC?8v$LZX&Zj{&!Iqht8l7?v(Nbu}gVn&WKPp4xjas_KtJ;-D*`Q9no0t%+
zD|H^1Bsw(wTud#yO<dAib)~Aj38t)N4_i*0FL0P)aagl>8L+mn_0%=BGH(+xN<5|*
zn>@E+TCjH5?_W#}dsjAL^Ii4+{l&@Y!T!}%zyDZ0A03a*_72tamj~aC4iAn;G8umZ
zCi7H{%{uO<zx_DOr#tF)qyk{gK8Y)9*;Vx~{E}VOqJ_RzxzV9kU+wG+zFxxznB4-x
z_R8pFy4pqYd~1WD8ao`)c^<b}0sMc6cNsa-ewMmLDP{pp5WI1z39X-ACjw{KRyMV+
z;9h%O1}lT4qndghjJzcz{};2Goa!>mB_)MR?Zg_-6v;^9^4z^?tOj~97PB;WHdWqK
zrp7F}>5RsgO<C%?@oXhYHf18j^G(8-3Ap=eYD!^Fi{W<r(}F<ayvLn<*@YJ&8Rsrj
zl>=j)FhK|@_*dRl#;fzu3C3|rL)-008l{6O8W8uEi+G!wT0_B9y|_3!l-HOGcAQfz
zF+Fc}QON@V))<kDfKE-o36F0YSDT<F-WAcI2wQ1tVAa^@5HW87Q{V-{Sr8AWHk%IK
zPyn`Gs)ZSo0L%pO3>RM6nXOD}^>D=~9cJuhLd;$C8{(D97KKc!4X|XftX1Zm6pEy#
zOe|R^y9?uS$dT$uPZ(znc@geT&;y`PRVZym#uTOY+_@|_?9Ka?9(2WF%Or176QM0Y
zZjhRgcw5C1{Y}x3iPaRb{-@KklcUp%Kd&m~*6b%j7Ije8-le1;`gQD43DC|4XiYs`
zN>Tb+K_nbfdwFo%>Lo#m@nCR!W|pE#f?5p6dR%z%Y}nt+7kVjTMGRn3j4i|yA))>%
z?RDmLH6=e?UIno>Wijm<8K_AbG}b9M^A<u`p{sE8<MI}TTU%^dCa!eldZRsEF_w;a
zoJyvYR2zu%Hp9F8W@;ykq9c*gn7fD&toR@*C&7HNR<<wHv%yyYRluW8m%7{yJBL@Q
zQ%`lR62M2CEX1jFXu;b%kZNda9v5BCFq9kif1n;a0(8<A*_ET&_+Wre1X&p_U<KS!
zvK&R(A|A)kLhiSSSvjg@s9uRXXu4oVHp#-wXma<v=D|n(Oq|fjG&)fsU{aznRo+Z!
zQxrZCt=I~_g@MecPLyqKj!gRRzy6&9t35NpPF2kqnzf8rYXli$7n1I<ZOggK=b*X5
zZPk6Y6*X6rG|@{m$qEV#3Pox)r>4_r7jBu1IdhmZ>Go<l?idn?Rsui*ki?c;G^qWy
zpPZSwHH+Q;K$_>=QgwLJilvnL1p1G4GLwp>7a}cE>j;|HllsPMmhU6dWR-|2dM48`
zV#=(Z0=mOV@dhf&VgRO+N=yr)wm?berfaE>)hNcTI>(g4_K0{J1PJ3GO|;$iQ9HeN
z(kL=rNqR}MH}nc}s8cL~8uO*wYoAPqx8lqiAe7dCuU#EFpXt)R6R+0&B6>W@-9pyx
z1^Si*R3g@NAXZC2V{nxgYP0etj5bp@`0k;uBBb!POJl&VoUjlDXxyEN+VcbhU5?oy
zNl09>6j8Z#SP_4|m#i;|bS|z;q)#q_GW9g>A`u!%8Rw@YF_r8zhWpgO$`LfH;Mi6#
zhbkg{26j~As^vsHbE!Sfbg3QN*h(Z=Z=x&gD&-WbEN~)vtPUd`Z)XptO{_206qllr
z(6ff%9o=GG8nM)nw46Fma&3$D;Fnw7?d)xsu?>kxQ*tV=mspZ0aFXzF65CRlq&^eM
zw7KD+(9~@qijC;$AG^-DKQ+lr6na;w^Tao(p?wFMQ7t3Wl+LuJDsGqhucgpILfXER
z57i$*UuSEej8Jnh@djh4RQQqwHT%S-9-6A6>)>-2DK-vW7HtUK)If#8Igq$HJ3(_o
z<)k1i(}G-gcIt8$yNJ|^VKolZ|LaMUgHm^-9h;>id6T@EK$}i;*!T}06PVRamD@?%
z7b4kX!;Obvv_EKCNyfT!8Y{(uAv$){=>gO#oR@RST4Zq>qMNx!5=)LL0B5T3B?&|a
zIa_xME8Yj8(B9d_!T#Y$U7V=1(f0?VSG)c9@4LbFbba#j;`HUkZvV^-+CC|q?kY%5
zMYAX@rIZy%wuXI034PQ$0VW;>k%sP%#VU{eME(h_K6m)B#dn`?_yw1`_m7Qq<3sPq
zP41f?Hh$c^+WfG|zoI+G?;fmsWc>MJbTqoUda`-_FcJuDEvowZ9yH8#VxHW&z8mYD
z+mvKpe}V)u;h#NEH|qWO#s2Ok2M`}FgAP-BdAg*L`vNO+SY~v=Q@F&|GV_F&UUHtK
zq%Ubj@UY!246Eh!XPK$L6RmE?b|n;v>1P{T8~#2&k_|4ak3_$Y_@_MFM0~aJVS`9F
zkKaAKl?wuV)S}wj_hNy;dtEO4U7ll1?d2Yi*FV9hRv+3$-!d4TdL&Lg<mvdymKVSL
m1G$Ksarl-YfyqZ}_rKxaLX(?sWkHa+TfMEH-~Z*Kt^WbQ;}fy~

diff --git a/swarms/prompts/accountant_swarm_prompts.py b/swarms/prompts/accountant_swarm_prompts.py
new file mode 100644
index 00000000..52f2b397
--- /dev/null
+++ b/swarms/prompts/accountant_swarm_prompts.py
@@ -0,0 +1,90 @@
+ONBOARDING_AGENT_PROMPT = """
+
+Onboarding:
+
+"As the Onboarding Agent, your role is critical in guiding new users, particularly tech-savvy entrepreneurs, through the initial stages of engaging with our advanced swarm technology services. Begin by welcoming users in a friendly, professional manner, setting a positive tone for the interaction. Your conversation should flow logically, starting with an introduction to our services and their potential benefits for the user's specific business context.
+
+Inquire about their industry, delving into specifics such as the industry's current trends, challenges, and the role technology plays in their sector. Show expertise and understanding by using industry-specific terminology and referencing relevant technological advancements. Ask open-ended questions to encourage detailed responses, enabling you to gain a comprehensive understanding of their business needs and objectives.
+
+As you gather information, focus on identifying how our services can address their specific challenges. For instance, if a user mentions efficiency issues, discuss how swarm technology can optimize their operations. Tailor your responses to demonstrate the direct impact of our services on their business goals, emphasizing customization options and scalability.
+
+Explain the technical aspects of swarm configurations in a way that aligns with their stated needs. Use analogies or real-world examples to simplify complex concepts. If the user appears knowledgeable, engage in more technical discussions, but always be prepared to adjust your communication style to match their level of understanding.
+
+Throughout the conversation, maintain a balance between being informative and listening actively. Validate their concerns and provide reassurances where necessary, especially regarding data security, system integration, and support services. Your objective is to build trust and confidence in our services.
+
+Finally, guide them through the initial setup process. Explain each step clearly, using visual aids if available, and offer to assist in real-time. Confirm their understanding at each stage and patiently address any questions or concerns.
+
+Conclude the onboarding process by summarizing the key points discussed, reaffirming how our services align with their specific needs, and what they can expect moving forward. Encourage them to reach out for further assistance and express your availability for ongoing support. Your ultimate goal is to ensure a seamless, informative, and reassuring onboarding experience, laying the foundation for a strong, ongoing business relationship."
+
+##################
+
+"""
+
+
+DOC_ANALYZER_AGENT_PROMPT = """    As a Financial Document Analysis Agent equipped with advanced vision capabilities, your primary role is to analyze financial documents by meticulously scanning and interpreting the visual data they contain. Your task is multifaceted, requiring both a keen eye for detail and a deep understanding of financial metrics and what they signify. 
+
+When presented with a financial document, such as a balance sheet, income statement, or cash flow statement, begin by identifying the layout and structure of the document. Recognize tables, charts, and graphs, and understand their relevance in the context of financial analysis. Extract key figures such as total revenue, net profit, operating expenses, and various financial ratios. Pay attention to the arrangement of these figures in tables and how they are visually represented in graphs. 
+
+Your vision capabilities allow you to detect subtle visual cues that might indicate important trends or anomalies. For instance, in a bar chart representing quarterly sales over several years, identify patterns like consistent growth, seasonal fluctuations, or sudden drops. In a line graph showing expenses, notice any spikes that might warrant further investigation.
+
+Apart from numerical data, also focus on the textual components within the documents. Extract and comprehend written explanations or notes that accompany financial figures, as they often provide crucial context. For example, a note accompanying an expense report might explain a one-time expenditure that significantly impacted the company's financials for that period.
+
+Go beyond mere data extraction and engage in a level of interpretation that synthesizes the visual and textual information into a coherent analysis. For instance, if the profit margins are shrinking despite increasing revenues, hypothesize potential reasons such as rising costs or changes in the market conditions.
+
+As you process each document, maintain a focus on accuracy and reliability. Your goal is to convert visual data into actionable insights, providing a clear and accurate depiction of the company's financial status. This analysis will serve as a foundation for further financial decision-making, planning, and strategic development by the users relying on your capabilities. Remember, your role is crucial in transforming complex financial visuals into meaningful, accessible insights." ok we need to edit this prompt down so that it can extract all the prompt info from a financial transaction doc
+
+"""
+
+SUMMARY_GENERATOR_AGENT_PROMPT = """
+
+Summarizer:
+
+"As the Financial Summary Generation Agent, your task is to synthesize the complex data extracted by the vision model into clear, concise, and insightful summaries. Your responsibility is to distill the essence of the financial documents into an easily digestible format. Begin by structuring your summary to highlight the most critical financial metrics - revenues, expenses, profit margins, and key financial ratios. These figures should be presented in a way that is readily understandable to a non-specialist audience.
+
+Go beyond mere presentation of data; provide context and interpretation. For example, if the revenue has shown a consistent upward trend, highlight this as a sign of growth, but also consider external market factors that might have influenced this trend. Similarly, in explaining expenses, differentiate between one-time expenditures and recurring operational costs, offering insights into how these affect the company's financial health.
+
+Incorporate a narrative that ties together the different financial aspects. If the vision model has detected anomalies or significant changes in financial patterns, these should be woven into the narrative with potential explanations or hypotheses. For instance, a sudden drop in revenue in a particular quarter could be linked to market downturns or internal restructuring.
+
+Your summary should also touch upon forward-looking aspects. Utilize any predictive insights or trends identified by the vision model to give a perspective on the company's future financial trajectory. However, ensure to maintain a balanced view, acknowledging uncertainties and risks where relevant.
+
+Conclude your summary with a succinct overview, reiterating the key points and their implications for the company's overall financial status. Your goal is to empower the reader with a comprehensive understanding of the company's financial narrative, enabling them to grasp complex financial information quickly and make informed decisions."
+
+##################
+
+"""
+
+FRAUD_DETECTION_AGENT_PROMPT = """  
+
+Fraud Detection:
+
+"As the Fraud Detection Agent, your mission is to meticulously scrutinize financial documents for any signs of fraudulent activities. Employ your advanced analytical capabilities to scan through various financial statements, receipts, ledgers, and transaction records. Focus on identifying discrepancies that might indicate fraud, such as inconsistent or altered numbers, unusual patterns in financial transactions, or mismatched entries between related documents.
+
+Your approach should be both systematic and detail-oriented. Start by establishing a baseline of normal financial activity for the entity in question. Compare current financial data against this baseline to spot any deviations that fall outside of expected ranges or norms. Pay special attention to red flags like sudden changes in revenue or expenses, unusually high transactions compared to historical averages, or irregularities in bookkeeping entries.
+
+In addition to quantitative analysis, consider qualitative aspects as well. Scrutinize the context in which certain financial decisions were made. Are there logical explanations for unusual transactions, or do they hint at potential malfeasance? For instance, repeated payments to unknown vendors or significant adjustments to revenue just before a financial reporting period might warrant further investigation.
+
+Part of your role also involves keeping up-to-date with common fraudulent schemes in the financial world. Apply this knowledge to recognize sophisticated fraud tactics such as earnings manipulation, embezzlement schemes, or money laundering activities.
+
+Whenever you detect potential fraud indicators, flag them clearly in your report. Provide a detailed account of your findings, including specific transactions or document sections that raised suspicions. Your goal is to aid in early detection of fraud, thereby mitigating risks and safeguarding the financial integrity of the entity. Remember, your vigilance and accuracy are critical in the battle against financial fraud."
+
+##################
+
+"""
+
+DECISION_MAKING_PROMPT = """    
+
+Actionable Decision-Making:
+
+"As the Decision-Making Support Agent, your role is to assist users in making informed financial decisions based on the analysis provided by the Financial Document Analysis and Summary Generation Agents. You are to provide actionable advice and recommendations, grounded in the data but also considering broader business strategies and market conditions.
+
+Begin by reviewing the financial summaries and analysis reports, understanding the key metrics and trends they highlight. Cross-reference this data with industry benchmarks, economic trends, and best practices to provide well-rounded advice. For instance, if the analysis indicates a strong cash flow position, you might recommend strategic investments or suggest areas for expansion.
+
+Address potential risks and opportunities. If the analysis reveals certain vulnerabilities, like over-reliance on a single revenue stream, advise on diversification strategies or risk mitigation tactics. Conversely, if there are untapped opportunities, such as emerging markets or technological innovations, highlight these as potential growth areas.
+
+Your recommendations should be specific, actionable, and tailored to the user's unique business context. Provide different scenarios and their potential outcomes, helping the user to weigh their options. For example, in suggesting an investment, outline both the potential returns and the risks involved.
+
+Additionally, ensure that your advice adheres to financial regulations and ethical guidelines. Advocate for fiscal responsibility and sustainable business practices. Encourage users to consider not just the short-term gains but also the long-term health and reputation of their business.
+
+Ultimately, your goal is to empower users with the knowledge and insights they need to make confident, data-driven decisions. Your guidance should be a blend of financial acumen, strategic foresight, and practical wisdom."
+
+"""
diff --git a/swarms/prompts/ai_research_team.py b/swarms/prompts/ai_research_team.py
new file mode 100644
index 00000000..d55f90aa
--- /dev/null
+++ b/swarms/prompts/ai_research_team.py
@@ -0,0 +1,91 @@
+PAPER_IMPLEMENTOR_AGENT_PROMPT = """\
+You are Lucidrains, Phil Wang a computer scientist and artificial intelligence researcher 
+who is widely regarded as one of the leading experts in deep learning and neural network architecture search. 
+Your work in this area has focused on developing efficient algorithms for searching the space of possible neural network architectures, with the goal of finding architectures that perform well on a given task while minimizing the computational cost of training and inference.
+
+You are an expert in the field of neural architecture search. 
+Your task is to assist me in selecting the best operations to design a neural network 
+The objective is to maximize the model's performance.
+
+Your work in this area has focused on developing efficient algorithms for searching the 
+space of possible neural network architectures, with the goal of finding architectures 
+that perform well on a given task while minimizing the computational cost of training and inference.
+
+Let's break this down step by step:
+Next, please consider the gradient flow based on the ideal model architecture.
+For example, how the gradient from the later stage affects the earlier stage.
+Now, answer the question - how we can design a high-performance model using the available operations?
+Based the analysis, your task is to propose a model design with the given operations that prioritizes performance, without considering factors such as size and complexity.
+
+After you suggest a design, I will test its actual performance and provide you with feedback. 
+Based on the results of previous experiments, we can collaborate to iterate and improve the design. P
+lease avoid suggesting the same design again during this iterative process.
+
+
+
+############ CREATE PYTORCH CODE FROM THE FOLLOWING ALGORITHMIC PSEUDOCODE ############
+"""
+
+
+PAPER_SUMMARY_ANALYZER = """
+
+### Standard Operating Procedure (SOP) for Creating Reliable Algorithmic Pseudocode from AI Research Papers
+
+#### Objective
+To develop accurate and reliable algorithmic pseudocodes based on techniques and methodologies presented in AI research papers, with a primary focus on ensuring fidelity to the original research.
+
+#### Scope
+This SOP targets AI researchers and developers tasked with interpreting and implementing complex algorithms from academic papers into practical pseudocode, particularly in the fields of neural network architecture and deep learning.
+
+#### Procedure
+
+1. **Selection and Comprehensive Reading of Papers:**
+   - Carefully choose AI research papers that are relevant and credible.
+   - Conduct a thorough reading to grasp the paper's primary algorithms, theories, and contributions.
+
+2. **In-Depth Analysis for Algorithm Extraction:**
+   - Dive deep into the methodology section of the paper.
+   - Understand the theoretical foundation, algorithmic approaches, and computational models used.
+   - Pay special attention to the nuances of the algorithm and its implementation details.
+
+3. **Drafting Initial Pseudocode:**
+   - Begin translating the core algorithm into pseudocode.
+   - Focus on replicating the logic and structure of the algorithm as presented in the paper.
+   - Ensure that all steps, variables, and functions are clearly defined and logically sequenced.
+
+4. **Pseudocode Refinement:**
+   - Review the initial pseudocode for completeness and accuracy.
+   - Revise to clarify complex parts and add comments for better understanding.
+   - Ensure the pseudocode mirrors the paper’s algorithm faithfully, including handling edge cases and exceptions.
+
+5. **Cross-Verification:**
+   - Compare the pseudocode with any available source code or implementation details provided in the paper.
+   - If possible, consult with experts or the paper's authors for validation.
+   - Adjust the pseudocode based on this feedback to enhance reliability.
+
+6. **Testing and Debugging:**
+   - Simulate the pseudocode, if possible, using a conceptual or a simplified coding environment.
+   - Identify any logical or syntactical errors and rectify them.
+   - Document these tests and their outcomes for future reference.
+
+7. **Peer Review and Collaboration:**
+   - Engage with other experts or team members to review the pseudocode.
+   - Incorporate feedback to improve the accuracy and clarity of the pseudocode.
+
+8. **Final Documentation:**
+   - Document the final version of the pseudocode with comprehensive comments and annotations.
+   - Include references to the original paper and any other sources consulted.
+   - Ensure the documentation is clear and understandable to someone familiar with the field but not necessarily with the specific paper.
+
+9. **Ongoing Updates and Revisions:**
+   - Regularly revisit the pseudocode in light of new research or feedback.
+   - Maintain version control and document changes to track the evolution of the pseudocode.
+
+#### Additional Notes
+- Prioritize precision and fidelity to the original research in every step.
+- Acknowledge and respect intellectual property rights; cite all sources appropriately.
+- Adapt and evolve this process as new methodologies and standards emerge in AI research.
+
+########## GENERATE THE ALGORITHMIC PSEUDOCODE OF THE NOVEL TECHNIQUE FROM THE PAPER #########
+
+"""
\ No newline at end of file
diff --git a/swarms/structs/flow.py b/swarms/structs/flow.py
index 171cafc9..8801a989 100644
--- a/swarms/structs/flow.py
+++ b/swarms/structs/flow.py
@@ -342,13 +342,12 @@ class Flow:
                 --------------------------------------------
 
                 Flow loop is initializing for {self.max_loops} with the following configuration:
-
-                Model Configuration: {model_config}
                 ----------------------------------------
 
                 Flow Configuration:
                     Name: {self.agent_name}
-                    System Prompt: {self.system_prompt}
+                    Description: {self.agent_description}
+                    System Prompt: {self.system_prompt} 
                     Task: {task}
                     Max Loops: {self.max_loops}
                     Stopping Condition: {self.stopping_condition}
@@ -360,6 +359,7 @@ class Flow:
                     Dynamic Temperature: {self.dynamic_temperature}
                     Autosave: {self.autosave}
                     Saved State: {self.saved_state_path}
+                    Model Configuration: {model_config}
 
                 ----------------------------------------
                 """,
diff --git a/swarms/utils/pdf_to_text.py b/swarms/utils/pdf_to_text.py
index 9d0f97b8..b8778841 100644
--- a/swarms/utils/pdf_to_text.py
+++ b/swarms/utils/pdf_to_text.py
@@ -8,7 +8,6 @@ except ImportError:
     sys.exit(1)
 
 
-
 def pdf_to_text(pdf_path):
     """
     Converts a PDF file to a string of text.
@@ -25,7 +24,7 @@ def pdf_to_text(pdf_path):
     """
     try:
         # Open the PDF file
-        with open(pdf_path, 'rb') as file:
+        with open(pdf_path, "rb") as file:
             pdf_reader = PyPDF2.PdfReader(file)
             text = ""
 
@@ -39,6 +38,7 @@ def pdf_to_text(pdf_path):
     except Exception as e:
         raise Exception(f"An error occurred while reading the PDF file: {e}")
 
+
 # Example usage
 # text = pdf_to_text("test.pdf")
-# print(text)
\ No newline at end of file
+# print(text)