Caddy Opensearch

.github/workflows/automated-deployment-dev.yml

@@ -29,5 +29,5 @@ jobs:
           docker push ${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_DEV }}:${{ github.sha }}
       - name: Deploy infrastructure
         run: |
-          aws cloudformation deploy --template-file infra/template.yaml --stack-name "caddy-dev" --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=dev  MessageTableName=${{ vars.MESSAGES_TABLE_NAME_DEV }} ResponsesTableName=${{ vars.RESPONSES_TABLE_NAME_DEV }} UserTableName=${{ vars.USERS_TABLE_NAME_DEV }} OfficesTableName=${{ vars.OFFICES_TABLE_NAME_DEV }} EvaluationTableName=${{ vars.EVALUATION_TABLE_NAME_DEV }} OpensearchUrl=${{ secrets.OPENSEARCH_DEV_URL }} OpensearchIndex=${{ secrets.OPENSEARCH_INDEX }} CaddyServiceAccountSecretArn=${{ secrets.CADDY_SA_ARN_DEV }} CaddySupervisorServiceAccountSecretArn=${{ secrets.CADDY_SUPERVISOR_SA_ARN_DEV }} CaddyImage=${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_DEV }}:${{ github.sha }} CaddySupervisorGoogleCloudProject=${{ secrets.CADDY_SUPERVISOR_GOOGLE_CLOUD_PROJECT_DEV }} CaddyGoogleCloudProject=${{ secrets.CADDY_GOOGLE_CLOUD_PROJECT_DEV }} LLM=${{ vars.LLM }} DomainName=${{ secrets.CERT_DOMAIN }}
+          aws cloudformation deploy --template-file infra/template.yaml --stack-name "caddy-dev" --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=dev  MessageTableName=${{ vars.MESSAGES_TABLE_NAME_DEV }}  RoutesTableName=${{ vars.ROUTES_TABLE_NAME_DEV }} PromptsTableName=${{ vars.PROMPTS_TABLE_NAME_DEV }} ResponsesTableName=${{ vars.RESPONSES_TABLE_NAME_DEV }} UserTableName=${{ vars.USERS_TABLE_NAME_DEV }} OfficesTableName=${{ vars.OFFICES_TABLE_NAME_DEV }} EvaluationTableName=${{ vars.EVALUATION_TABLE_NAME_DEV }} OpensearchUrl=${{ secrets.OPENSEARCH_DEV_URL }} OpensearchIndex=${{ secrets.OPENSEARCH_INDEX }} CaddyServiceAccountSecretArn=${{ secrets.CADDY_SA_ARN_DEV }} CaddySupervisorServiceAccountSecretArn=${{ secrets.CADDY_SUPERVISOR_SA_ARN_DEV }} CaddyImage=${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_DEV }}:${{ github.sha }} CaddySupervisorGoogleCloudProject=${{ secrets.CADDY_SUPERVISOR_GOOGLE_CLOUD_PROJECT_DEV }} CaddyGoogleCloudProject=${{ secrets.CADDY_GOOGLE_CLOUD_PROJECT_DEV }} LLM=${{ vars.LLM }} DomainName=${{ secrets.CERT_DOMAIN }} MicrosoftAppIdSecretArn=${{ secrets.MICROSOFT_APP_ID_SECRET_ARN }} MicrosoftAppPasswordSecretArn=${{ secrets.MICROSOFT_APP_PASSWORD_SECRET_ARN }}
           aws cloudformation deploy --template-file infra/opensearch.yaml --stack-name "caddy-opensearch-dev" --region eu-west-3 --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=dev CaddyRoleArn=${{ secrets.CADDY_ROLE_ARN_DEV }}

.github/workflows/automated-deployment-prod.yml

@@ -29,5 +29,5 @@ jobs:
           docker push ${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_PROD }}:${{ github.sha }}
       - name: Deploy infrastructure
         run: |
-          aws cloudformation deploy --template-file infra/template.yaml --stack-name "caddy-prod" --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=prod  MessageTableName=${{ vars.MESSAGES_TABLE_NAME_PROD }} ResponsesTableName=${{ vars.RESPONSES_TABLE_NAME_PROD }} UserTableName=${{ vars.USERS_TABLE_NAME_PROD }} OfficesTableName=${{ vars.OFFICES_TABLE_NAME_PROD }} EvaluationTableName=${{ vars.EVALUATION_TABLE_NAME_PROD }} OpensearchUrl=${{ secrets.OPENSEARCH_PROD_URL }} OpensearchIndex=${{ secrets.OPENSEARCH_INDEX }} CaddyServiceAccountSecretArn=${{ secrets.CADDY_SA_ARN_PROD }} CaddySupervisorServiceAccountSecretArn=${{ secrets.CADDY_SUPERVISOR_SA_ARN_PROD }} CaddyImage=${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_PROD }}:${{ github.sha }} CaddySupervisorGoogleCloudProject=${{ secrets.CADDY_SUPERVISOR_GOOGLE_CLOUD_PROJECT_PROD }} CaddyGoogleCloudProject=${{ secrets.CADDY_GOOGLE_CLOUD_PROJECT_PROD }} LLM=${{ vars.LLM }} DomainName=${{ secrets.CERT_DOMAIN }}
+          aws cloudformation deploy --template-file infra/template.yaml --stack-name "caddy-prod" --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=prod  MessageTableName=${{ vars.MESSAGES_TABLE_NAME_PROD }} RoutesTableName=${{ vars.ROUTES_TABLE_NAME_PROD }} PromptsTableName=${{ vars.PROMPTS_TABLE_NAME_PROD }} ResponsesTableName=${{ vars.RESPONSES_TABLE_NAME_PROD }} UserTableName=${{ vars.USERS_TABLE_NAME_PROD }} OfficesTableName=${{ vars.OFFICES_TABLE_NAME_PROD }} EvaluationTableName=${{ vars.EVALUATION_TABLE_NAME_PROD }} OpensearchUrl=${{ secrets.OPENSEARCH_PROD_URL }} OpensearchIndex=${{ secrets.OPENSEARCH_INDEX }} CaddyServiceAccountSecretArn=${{ secrets.CADDY_SA_ARN_PROD }} CaddySupervisorServiceAccountSecretArn=${{ secrets.CADDY_SUPERVISOR_SA_ARN_PROD }} CaddyImage=${{ secrets.CADDY_ECR_ENDPOINT }}/${{ secrets.CADDY_ECR_REPO_ID_PROD }}:${{ github.sha }} CaddySupervisorGoogleCloudProject=${{ secrets.CADDY_SUPERVISOR_GOOGLE_CLOUD_PROJECT_PROD }} CaddyGoogleCloudProject=${{ secrets.CADDY_GOOGLE_CLOUD_PROJECT_PROD }} LLM=${{ vars.LLM }} DomainName=${{ secrets.CERT_DOMAIN }}
           aws cloudformation deploy --template-file infra/opensearch.yaml --stack-name "caddy-opensearch-prod" --region eu-west-3 --capabilities CAPABILITY_NAMED_IAM --parameter-overrides StageName=prod CaddyRoleArn=${{ secrets.CADDY_ROLE_ARN_PROD }}

.python-version

@@ -0,0 +1 @@
+3.12.3

README.md

@@ -49,6 +49,12 @@ To explore the connection to DynamoDB, I have attached the docker-compose file t
 $ docker compose up
 ```
 
+If you get an error with docker, note you may need to change credsStore your .docker/config.json file with:
+
+```json
+"credStore": "desktop",
+```
+
 ## Testing
 
 Running tests on platform agnostic Caddy components with pytest. Tests are stored in tests/caddy_components, and can be invoked by running the below:
@@ -95,3 +101,24 @@ For ease of deletion, you can remove all the created resources with
 ```bash
 $ aws cloudformation delete-stack --stack-name "INSERT_CUSTOM_STACK_NAME"
 ```
+
+## Local Teams Deployment   
+
+In azure (https://portal.azure.com/#home), creat an azure bot, and add app_id, app_password to the .env from configuration space.
+
+Then you need to build the container and provide a tunnel.
+
+```bash
+$ docker compose up
+```
+
+Go to ngrok dashboard (https://dashboard.ngrok.com/) and create a domain then expose the domain endpoint for local forwarding on the desired port i.e. 80
+
+```bash
+$ ngrok http --domain=insert-static-url 80
+```
+
+If you recieve a ngrok 381 errror, this is because ngrok has created an automatic edge. Delete the edge in ngrok dashboard and retry command
+
+In azure bot  got to setting > configuration and add static-url with /microsoft-teams/chat into the messaging endpoint
+Then in channels use the open in teams button to test locally

caddy_chatbot/src/app.py

@@ -3,13 +3,15 @@
 
 from caddy_core import components as caddy
 from caddy_core.services import enrolment
+from caddy_core.utils.monitoring import logger
 
 from integrations.google_chat.structures import GoogleChat
 from integrations.google_chat.verification import (
     verify_google_chat_request,
     verify_google_chat_supervision_request,
 )
 
+from integrations.microsoft_teams.structures import MicrosoftTeams
 
 from threading import Thread
 
@@ -26,17 +28,22 @@ def google_chat_endpoint(event=Depends(verify_google_chat_request)) -> dict:
     """
     Handles inbound requests from Google Chat for Caddy
     """
+    logger.info("New Google Chat Request")
     google_chat = GoogleChat()
     user = event["user"]["email"]
     domain = user.split("@")[1]
 
     domain_enrolled, office = enrolment.check_domain_status(domain)
     if domain_enrolled is not True:
+        logger.info("Domain not enrolled")
         return google_chat.responses.DOMAIN_NOT_ENROLLED
+    logger.info("Domain is enrolled")
 
     user_enrolled, user_record = enrolment.check_user_status(user)
     if user_enrolled is not True:
+        logger.info("User is not enrolled")
         return google_chat.responses.USER_NOT_ENROLLED
+    logger.info("User is enrolled")
 
     included_in_rct = enrolment.check_rct_status(office)
     if included_in_rct is True:
@@ -247,14 +254,39 @@ def google_chat_supervision_endpoint(
 
 
 @app.post("/microsoft-teams/chat")
-def microsoft_teams_endpoint(request: Request):
-    return JSONResponse(
-        status_code=status.HTTP_200_OK, content={"text": "Request received"}
-    )
+async def microsoft_teams_endpoint(request: Request):
+    event = await request.json()
+    print("POST request received", event)
+
+    microsoft_teams = MicrosoftTeams()
+
+    match event["type"]:
+        case "message":
+            query = microsoft_teams.format_message(event)
+            caddy.temporary_teams_invoke(microsoft_teams, query, event)
+        case "invoke":
+            match event["value"]["action"]["verb"]:
+                case "proceed":
+                    # TODO Handle Proceed Route
+                    print("Adviser choice was to proceed")
+                    microsoft_teams.update_card(event)
+                    return microsoft_teams.responses.OK
+                case "redacted_query":
+                    # TODO Handle edit original query
+                    print("Adviser choice was to edit original query")
+                    redacted_card = microsoft_teams.messages.create_redacted_card(event)
+                    microsoft_teams.update_card(event, card=redacted_card)
+                    return microsoft_teams.responses.OK
+                case "approved":
+                    microsoft_teams.handle_thumbs_up(event)
+                    return microsoft_teams.responses.OK
+                case "rejected":
+                    microsoft_teams.handle_thumbs_down(event)
+                    return microsoft_teams.responses.OK
 
 
 @app.post("/microsoft-teams/supervision")
-def microsoft_teams_supervision_endpoint(request: Request):
+async def microsoft_teams_supervision_endpoint(request: Request):
     return JSONResponse(
         status_code=status.HTTP_200_OK, content={"text": "Request received"}
     )

caddy_chatbot/src/caddy_core/components.py

@@ -3,8 +3,7 @@
 from fastapi import status
 
 from langchain.prompts import PromptTemplate
-from caddy_core.utils.prompts.default_template import CADDY_PROMPT_TEMPLATE
-from caddy_core.utils.prompts.prompt import retrieve_route_specific_augmentation
+from caddy_core.utils.prompt import retrieve_route_specific_augmentation, get_prompt
 
 from time import sleep
 
@@ -18,10 +17,10 @@
 
 from caddy_core.utils.tables import (
     evaluation_table,
-    message_table,
     responses_table,
     users_table,
 )
+from caddy_core.utils.monitoring import logger
 from caddy_core.services.retrieval_chain import build_chain
 from caddy_core.services import enrolment
 from caddy_core.services.evaluation import execute_optional_modules
@@ -49,6 +48,7 @@ def rct_survey_reminder(event, user_record, chat_client):
 
 
 def handle_message(caddy_message, chat_client):
+    logger.info("Running message handler")
     module_values, survey_complete = check_existing_call(caddy_message)
 
     if survey_complete is True:
@@ -107,10 +107,12 @@ def remove_role_played_responses(response: str) -> str:
     """
     adviser_index = response.find("Adviser: ")
     if adviser_index != -1:
+        logger.info("Removing role played response")
         return True, response[:adviser_index].strip()
 
     adviser_index = response.find("Advisor: ")
     if adviser_index != -1:
+        logger.info("Removing role played response")
         return True, response[:adviser_index].strip()
 
     return False, response.strip()
@@ -128,9 +130,14 @@ def format_chat_history(user_messages: List) -> List:
     """
     history_langchain_format = []
     for message in user_messages:
-        human = message["llmPrompt"]
-        ai = message["llmAnswer"]
-        history_langchain_format.append((human, ai))
+        human = message.get("llmPrompt")
+        ai = message.get("llmAnswer")
+
+        if human and ai:
+            history_langchain_format.append((human, ai))
+        elif human:
+            history_langchain_format.append((human, ""))
+
     return history_langchain_format
 
 
@@ -147,7 +154,15 @@ def get_chat_history(message: UserMessage) -> List:
     response = responses_table.query(
         KeyConditionExpression=Key("threadId").eq(message.thread_id),
     )
-    history = format_chat_history(response["Items"])
+
+    sorted_items = sorted(
+        response["Items"],
+        key=lambda x: x.get(
+            "messageReceivedTimestamp", x.get("llmPromptTimestamp", "")
+        ),
+    )
+
+    history = format_chat_history(sorted_items)
     return history
 
 
@@ -198,37 +213,37 @@ def format_chat_message(event: ProcessChatMessageEvent) -> UserMessage:
 
 
 def store_message(message: UserMessage):
-    message_table.put_item(
+    responses_table.put_item(
         Item={
+            "threadId": str(message.thread_id),
             "messageId": str(message.message_id),
             "conversationId": str(message.conversation_id),
-            "threadId": str(message.thread_id),
             "client": message.client,
             "userEmail": str(message.user_email),
-            "message": message.message,
+            "llmPrompt": message.message,
             "messageSentTimestamp": message.message_sent_timestamp,
             "messageReceivedTimestamp": str(message.message_received_timestamp),
         }
     )
 
 
 def store_response(response: LlmResponse):
-    responses_table.put_item(
-        Item={
-            "responseId": str(response.response_id),
-            "messageId": str(response.message_id),
-            "threadId": str(response.thread_id),
-            "llmPrompt": response.llm_prompt,
-            "llmAnswer": response.llm_answer,
-            "llmResponseJSon": response.llm_response_json,
-            "llmPromptTimestamp": str(response.llm_prompt_timestamp),
-            "llmResponseTimestamp": str(response.llm_response_timestamp),
-        }
+    responses_table.update_item(
+        Key={"threadId": str(response.thread_id)},
+        UpdateExpression="set responseId = :rId, llmAnswer = :la, llmResponseJSon = :lrj, llmPromptTimestamp = :lpt, llmResponseTimestamp = :lrt, route = :route, context = :context",
+        ExpressionAttributeValues={
+            ":rId": response.response_id,
+            ":la": response.llm_answer,
+            ":lrj": response.llm_response_json,
+            ":lpt": str(response.llm_prompt_timestamp),
+            ":lrt": str(response.llm_response_timestamp),
+            ":route": response.route,
+            ":context": response.context,
+        },
     )
 
 
 def store_user_thanked_timestamp(ai_answer: LlmResponse):
-    # Updating response in DynamoDB
     responses_table.update_item(
         Key={"threadId": ai_answer.thread_id},
         UpdateExpression="set userThankedTimestamp=:t",
@@ -333,12 +348,12 @@ def check_existing_call(caddy_message) -> Tuple[Dict[str, Any], bool]:
 
 def send_to_llm(caddy_query: UserMessage, chat_client):
     query = caddy_query.message
-    
+
     domain = caddy_query.user_email.split("@")[1]
 
     chat_history = get_chat_history(caddy_query)
 
-    route_specific_augmentation = retrieve_route_specific_augmentation(query)
+    route_specific_augmentation, route = retrieve_route_specific_augmentation(query)
 
     day_date_time = datetime.now(timezone("Europe/London")).strftime(
         "%A %d %B %Y %H:%M"
@@ -348,7 +363,7 @@ def send_to_llm(caddy_query: UserMessage, chat_client):
     office_regions = enrolment.get_office_coverage(office)
 
     CADDY_PROMPT = PromptTemplate(
-        template=CADDY_PROMPT_TEMPLATE,
+        template=get_prompt("CORE_PROMPT"),
         input_variables=["context", "question"],
         partial_variables={
             "route_specific_augmentation": route_specific_augmentation,
@@ -486,6 +501,8 @@ def send_to_llm(caddy_query: UserMessage, chat_client):
         llm_prompt_timestamp=ai_prompt_timestamp,
         llm_response_json=json.dumps(response_card),
         llm_response_timestamp=ai_response_timestamp,
+        route=route or "no_route",
+        context=[doc_without_embeddings(doc) for doc in caddy_response["context"]],
     )
 
     store_response(llm_response)
@@ -535,8 +552,13 @@ def send_to_llm(caddy_query: UserMessage, chat_client):
     store_approver_received_timestamp(supervision_event)
 
 
+def doc_without_embeddings(doc):
+    doc_dict = doc.dict()
+    doc_dict.pop("state", None)
+    return doc_dict
+
+
 def store_approver_received_timestamp(event: SupervisionEvent):
-    # Updating response in DynamoDB
     responses_table.update_item(
         Key={"threadId": event.thread_id},
         UpdateExpression="set approverReceivedTimestamp=:t",
@@ -545,12 +567,12 @@ def store_approver_received_timestamp(event: SupervisionEvent):
     )
 
 
-def store_approver_event(approval_event: ApprovalEvent):
-    # Updating response in DynamoDB
+def store_approver_event(thread_id: str, approval_event: ApprovalEvent):
     responses_table.update_item(
-        Key={"threadId": str(approval_event.thread_id)},
-        UpdateExpression="set approverEmail=:email, approved=:approved, approvalTimestamp=:atime, userResponseTimestamp=:utime, supervisorMessage=:sMessage",
+        Key={"threadId": thread_id},
+        UpdateExpression="set responseId=:rId, approverEmail=:email, approved=:approved, approvalTimestamp=:atime, userResponseTimestamp=:utime, supervisorMessage=:sMessage",
         ExpressionAttributeValues={
+            ":rId": approval_event.response_id,
             ":email": approval_event.approver_email,
             ":approved": approval_event.approved,
             ":atime": str(approval_event.approval_timestamp),
@@ -559,3 +581,42 @@ def store_approver_event(approval_event: ApprovalEvent):
         },
         ReturnValues="UPDATED_NEW",
     )
+
+
+def temporary_teams_invoke(chat_client, query, event):
+    """
+    Temporary solution for Teams integration
+    """
+    route_specific_augmentation, _ = retrieve_route_specific_augmentation(query)
+
+    day_date_time = datetime.now(timezone("Europe/London")).strftime(
+        "%A %d %B %Y %H:%M"
+    )
+
+    office_regions = ["England"]
+
+    CADDY_PROMPT = PromptTemplate(
+        template=get_prompt("CORE_PROMPT"),
+        input_variables=["context", "question"],
+        partial_variables={
+            "route_specific_augmentation": route_specific_augmentation,
+            "day_date_time": day_date_time,
+            "office_regions": office_regions,
+        },
+    )
+
+    chain, ai_prompt_timestamp = build_chain(CADDY_PROMPT)
+
+    caddy_response = chain.invoke(
+        {
+            "input": query,
+            "chat_history": [],
+        }
+    )
+
+    _, caddy_response["answer"] = remove_role_played_responses(caddy_response["answer"])
+
+    chat_client.send_adviser_card(
+        event,
+        card=chat_client.messages.generate_response_card(caddy_response["answer"]),
+    )

caddy_chatbot/src/caddy_core/models.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, List, Any
 from datetime import datetime
 import uuid
 import pydantic
@@ -35,6 +35,8 @@ class LlmResponse(pydantic.BaseModel):
     llm_response_json: Union[pydantic.Json, None] = None
     llm_prompt_timestamp: datetime
     llm_response_timestamp: datetime
+    route: str
+    context: Union[List[Any], None]
 
 
 class SupervisionEvent(pydantic.BaseModel):