Fs 102/create report agent

.env.example

@@ -38,6 +38,7 @@ WS_URL=ws://localhost:8250/ws
 # llm
 ANSWER_AGENT_LLM="openai"
 INTENT_AGENT_LLM="openai"
+ESG_REPORT_AGENT_LLM="mistral"
 VALIDATOR_AGENT_LLM="openai"
 DATASTORE_AGENT_LLM="openai"
 MATHS_AGENT_LLM="openai"
@@ -52,6 +53,7 @@ DYNAMIC_KNOWLEDGE_GRAPH_LLM="openai"
 # model
 ANSWER_AGENT_MODEL="gpt-4o-mini"
 INTENT_AGENT_MODEL="gpt-4o-mini"
+ESG_REPORT_AGENT_MODEL="mistral-large-latest"
 VALIDATOR_AGENT_MODEL="gpt-4o-mini"
 DATASTORE_AGENT_MODEL="gpt-4o-mini"
 MATHS_AGENT_MODEL="gpt-4o-mini"

backend/promptfoo/create_esg_report_config.yaml

@@ -0,0 +1,110 @@
+description: "Test ESG Report Prompt"
+
+providers:
+  - id: mistral:mistral-large-latest
+    config:
+      temperature: 0
+
+prompts: file://promptfoo_test_runner.py:create_prompt
+
+tests:
+  - description: "test model prompt references all csv headers in result using valid json format"
+    vars:
+      user_prompt_template: "create-esg-report-user-prompt"
+      system_prompt_template: "create-esg-report-system-prompt"
+      user_prompt_args:
+        document_text: "Published September 2024  Carbon Reduction Plan 
+Supplier name: Amazon Web Services EU SARL (UK Branch)  (“AWS UK”)  
+Publication date: September 30, 2024 
+Commitment to Achieving Net Zero  
+AWS UK, as part of Amazon.com, Inc. (“Amazon”), is committed to achieving net -zero 
+emissions by 2040. In 2019, Amazon co -founded The Climate Pledge, a public commitment 
+to innovate, use our scale for good and go faster to address the urgency of the climate crisis 
+to reach net -zero carbon across the entire organization by 2040. Since committing to the 
+Pledge, we’ve changed how we conduct our business and the running of our operations, and 
+we’ve increased funding and implementation of new technologies and services that 
+decarbonize and help preserve the natural world, alon gside the ambitious goals outlined in 
+The Climate Pledge. We’re fully committed to our goals and our work to build a better planet.  
+Baseline Emissions Footprint  
+Base Year emissions are a record of the greenhouse gases that have been produced in the 
+past an d are the reference point against which emissions reduction can be measured.  
+Baseline Year: 2020  
+Additional Details relating to the Baseline Emissions calculations: 
+AWS UK utilized January 1, 2020 to December 31, 2020 as the baseline year for emissions 
+reporting under this Carbon Reduction Plan. Our plan includes emissions data from relevant 
+affiliate companies helping to provide AWS UK’s services to our customers. We ’ve included both 
+location -based and market -based method Scope 2 emissions in the following tables. AWS UK 
+benefits from contractual arrangements entered into by our affiliate(s) for renewable electricity 
+and/or renewable attributes that are reflected in t he market -based data set. More information 
+about our corporate carbon footprint and methodology can be found on our website . 
+Our baseline year does not include Scope 1 emissions. In 2022 we updated our methodology 
+and Scope 1 emissions are now included in total emissions for AWS UK
+
+ Published September 2024  Baseline year emissions:  
+EMISSIONS  TOTAL (tCO 2e) 
+Scope 1  0 
+Scope 2  61,346  – Location -based method  
+2,813  – Market -based method  
+Scope 3 (Included 
+Sources)  3,770  
+Total Emissions  65,116  – Location -based method  
+6,583  – Market -based method  
+Current Emissions Reporting
+Reporting Year: 202 3 (January 1, 202 3 to December 31, 202 3) 
+EMISSIONS  TOTAL (tCO 2e) 
+Scope 1  2,23 3 
+Scope 2  126,755  – Location -based method  
+0 – Market -based method  
+Scope 3 (Included 
+Sources)  13,188  
+Total Emissions  142,17 6 – Location -based method  
+15,42 1 – Market -based method  
+
+ Published September 2024  Emissions Reduction Targets
+In 2019, we set an ambitious goal to match 100% of the electricity we use with renewable 
+energy by 2030. This goal includes all data centres , logistics facilities, physical stores, and 
+corporate offices, as well as on -site charg ing points and our financially integrated subsidiaries. 
+We are proud to have achieved this goal in 2023, seven years early, with 100% of the electricity 
+consum ed by Amazon matched with renewable energy sources.  
+Amazon continue s to be transparent and share our progress  to reach net -zero carbon  in our 
+annual  Sustainability Report , which also includes details on how we  measure carbon . 
+Carbon Reduction Projects  
+Completed Carbon Reduction Initiatives  
+Amazon continues to take actions across our operations to drive carbon reduction around  the 
+world, including in the UK. As of January 202 4, Amazon’s renewable energy portfolio includes 
+243 wind and solar farms and 2 70 rooftop solar projects, totalling 513 projects  and 28 
+gigawatts of renewable energy capacity.  This includes several utility -scale renewable energy 
+projects located within the UK:  
+•In 2019, Amazon  announced our first power purchase agreement in the UK, located in
+Kintyre Peninsula, Scotland. The “Amazon Wind Farm Scotland – Beinn an Tuirc 3”
+began o perating in 2021, providing 50 megawatts  (MW)  of new renewable capacity to
+the electricity grid with expected generation of 168,000 megawatt hours (MWh) of
+clean energy annually. That’s enough to power 46,000 UK homes every year.
+•In December 2020,  Amazon a nnounced a two -phase renewable energy project located
+in South Lanarkshire, Scotland, the Kennoxhead wind farm. Kennoxhead will be the
+largest single -site onshore wind project in the UK, enabled through corporate
+procurement.  Once fully operational, Kenno xhead will produce 129 MW of renewable
+capacity and is expected to generate 439,000 MWh of clean energy annually. Phase 1
+(60 MW) began operating in 2022, and Phase 2 (69  MW) will begin operations in 2024 .
+•In 2022, Amazon announced its first project in Nor thern Ireland, a 16  MW onshore
+windfarm in Co Antrim.
+•In 2022, Amazon  also announced  a new 473 MW offshore wind farm, Moray West,
+located off the coast of Scotland . Amazon expects completion of Moray West in 2024.
+This is Amazon’s largest project in Scotland and the largest corporate renewable
+energy deal announced by any company in the UK to date.
+•In 2023, Amazon announced a new 47  MW solar farm, Warl ey located in Essex.
+This project is expected to be operational in 2024.
+
+ Published September 2024  Declaration and Sign Off 
+This Carbon Reduction Plan has been completed in accordance with PPN 06/21 and 
+associated guidance and reporting standard for Carbon Reduction Plans.  
+Emiss ions have been reported and recorded in accordance with  the published reporting 
+standard for Carbon Reduction Plans and the GHG Reporting Protocol corporate standard1 
+and uses the appropri ate Government emission conversion factors for greenhouse gas 
+company reporting2. 
+Scope 1 and Scope 2 emissions have been reported in accordance with S ECR requirements, 
+and the required subset of Scope 3 emissions have been reported in accordance with the 
+published reporting standard for Carbon Reduction Plans and the Corporate Value Chain 
+(Scope 3) Standard3. 
+This Carbon Reduction Plan has been reviewe d and signed off by the board of directors (or 
+equivalent management body)."

backend/src/agents/__init__.py

@@ -1,4 +1,5 @@
 from typing import List
+
 from src.utils import Config
 from src.agents.agent import Agent, agent
 from src.agents.datastore_agent import DatastoreAgent
@@ -9,6 +10,7 @@
 from src.agents.answer_agent import AnswerAgent
 from src.agents.chart_generator_agent import ChartGeneratorAgent
 from src.agents.file_agent import FileAgent
+from src.agents.esg_report_agent import ESGReportAgent
 
 
 config = Config()
@@ -26,6 +28,10 @@ def get_answer_agent() -> Agent:
     return AnswerAgent(config.answer_agent_llm, config.answer_agent_model)
 
 
+def get_esg_report_agent() -> Agent:
+    return ESGReportAgent(config.esg_agent_llm, config.esg_report_agent_model)
+
+
 def agent_details(agent) -> dict:
     return {"name": agent.name, "description": agent.description}
 

backend/src/agents/esg_report_agent.py

@@ -0,0 +1,22 @@
+from src.agents import Agent, agent
+from src.prompts import PromptEngine
+
+engine = PromptEngine()
+
+
+@agent(
+    name="ESGReportAgent",
+    description="This agent is responsible for generating an ESG report",
+    tools=[],
+)
+class ESGReportAgent(Agent):
+    async def invoke(self, utterance: str) -> str:
+        user_prompt = engine.load_prompt(
+            "create-esg-report-user-prompt",
+            document_text=utterance)
+
+        system_prompt = engine.load_prompt(
+            "create-esg-report-system-prompt",
+        )
+
+        return await self.llm.chat(self.model, system_prompt=system_prompt, user_prompt=user_prompt)

backend/src/prompts/templates/create-esg-report-system-prompt.j2

@@ -0,0 +1,39 @@
+The user will provide a report from a company. Your goal is to analyse the document and respond answering the following questions in the format described below:
+
+Basic:
+
+1. What is the name of the company that this document refers to?
+2. What year or years does the information refer too?
+3. Summarise in one sentence what the document is about?
+
+ESG (Environment, Social, Governance:
+1. Which aspects of ESG does this document primarily discuss, respond with a percentage of each topic covered by the document.
+2. What aspects of ESG are not discussed in the document?
+
+Environmental:
+
+1. What environmental goals does this document describe?
+2. What beneficial environmental claims does the company make?
+3. What potential environment greenwashing can you identify that should be fact checked?
+4. What environmental regulations, standards or certifications can you identify in the document?
+
+Social:
+
+1. What social goals does this document describe?
+2. What beneficial societal claims does the company make?
+3. What potential societal greenwashing can you identify that should be fact checked?
+4. What societal regulations, standards or certifications can you identify in the document?
+
+Governance:
+
+1. What governance goals does this document describe?
+2. What beneficial governance claims does the company make?
+3. What potential governance greenwashing can you identify that should be fact checked?
+4. What governance regulations, standards or certifications can you identify in the document?
+
+Conclusion:
+
+1. What is your conclusion about the claims and potential greenwashing in this document?
+2. What are your recommended next steps to verify any of the claims in this document?
+
+The report should be formatted as markdown.

backend/src/prompts/templates/create-esg-report-user-prompt.j2

@@ -0,0 +1,3 @@
+Generate an ESG report using the following document:
+
+{{ document_text }}

backend/src/utils/config.py

@@ -20,6 +20,7 @@ def __init__(self):
         self.neo4j_password = None
         self.answer_agent_llm = None
         self.intent_agent_llm = None
+        self.esg_agent_llm = None
         self.validator_agent_llm = None
         self.datastore_agent_llm = None
         self.maths_agent_llm = None
@@ -32,6 +33,7 @@ def __init__(self):
         self.validator_agent_model = None
         self.intent_agent_model = None
         self.answer_agent_model = None
+        self.esg_report_agent_model = None
         self.datastore_agent_model = None
         self.chart_generator_model = None
         self.web_agent_model = None
@@ -61,6 +63,7 @@ def load_env(self):
             self.files_directory = os.getenv("FILES_DIRECTORY", default_files_directory)
             self.answer_agent_llm = os.getenv("ANSWER_AGENT_LLM")
             self.intent_agent_llm = os.getenv("INTENT_AGENT_LLM")
+            self.esg_agent_llm = os.getenv("ESG_REPORT_AGENT_LLM")
             self.validator_agent_llm = os.getenv("VALIDATOR_AGENT_LLM")
             self.datastore_agent_llm = os.getenv("DATASTORE_AGENT_LLM")
             self.chart_generator_llm = os.getenv("CHART_GENERATOR_LLM")
@@ -72,6 +75,7 @@ def load_env(self):
             self.dynamic_knowledge_graph_llm = os.getenv("DYNAMIC_KNOWLEDGE_GRAPH_LLM")
             self.answer_agent_model = os.getenv("ANSWER_AGENT_MODEL")
             self.intent_agent_model = os.getenv("INTENT_AGENT_MODEL")
+            self.esg_report_agent_model = os.getenv("ESG_REPORT_AGENT_MODEL")
             self.validator_agent_model = os.getenv("VALIDATOR_AGENT_MODEL")
             self.datastore_agent_model = os.getenv("DATASTORE_AGENT_MODEL")
             self.web_agent_model = os.getenv("WEB_AGENT_MODEL")

backend/tests/agents/esg_report_agent_test.py

@@ -0,0 +1,19 @@
+import pytest
+
+from src.agents.esg_report_agent import ESGReportAgent
+from src.llm.factory import get_llm
+
+mock_model = "mockmodel"
+mock_llm = get_llm("mockllm")
+
+@pytest.mark.asyncio
+async def test_invoke_calls_llm(mocker):
+    esg_agent = ESGReportAgent(llm_name="mockllm", model=mock_model)
+    mock_response = "A Test Report"
+
+    mock_llm.chat = mocker.AsyncMock(return_value=mock_response)
+
+    response = await esg_agent.invoke("Test Document")
+
+    assert response == mock_response
+