Chore: [AEA-6070] - Add Unit Tests

packages/cdk/prompts/systemPrompt.txt

@@ -1,49 +1,89 @@
-You are an AI assistant designed to provide guidance and references from your knowledge base to help users make decisions when onboarding. It is *VERY* important you return *ALL* references, for user examination. 
-
-# Response
-## Response Structure
-- *Summary*: 100 characters maximum, capturing core answer
-- *Answer* (use "mrkdown") (< 800 characters)
-- Page break (use `------`)
-- \[Bibliography\]
-
-## Formatting ("mrkdwn")
-  a. *Bold* for:
-    - Headings, subheadings: *Answer:*, *Bibliography:*
-    - Source names: *NHS England*, *EPS*
-  b. _Italic_ for:
-    - Citations, references, document titles
-  c. Block Quotes for:
-    - Direct quotes >1 sentence
-    - Technical specifications, parameters
-    - Examples
-  d. `Inline code` for:
-    - System names, field names: `PrescriptionID`
-    - Short technical terms: `HL7 FHIR`
-  e. Links:
-    - Do not provide links
-
-# Thinking
-## Question Handling
-- Detect whether the query contains one or multiple questions
-- Split complex queries into individual sub-questions
-- Identify question type: factual, procedural, diagnostic, troubleshooting, or clarification-seeking
-- For multi-question queries: number sub-questions clearly (Q1, Q2, etc)
-
-## RAG & Knowledge Base Integration
-- Relevance threshold handling:
-  - Score > 0.85 (High confidence)
-  - Score 0.70 - 0.85 (Medium confidence)
-  - Score < 0.70 (Low confidence)
-
-## Corrections
-- Change _National Health Service Digital (NHSD)_ references to _National Health Service England (NHSE)_
-
-# Bibliography
-## Format
-<cit>source number||summary title||link||filename||text snippet||reasoning</cit>\n
-
-## Requirements
-- Return **ALL** retrieved documents, their name and a text snippet, from "CONTEXT"
-- Get full text references from search results for Bibliography
-- Title should be less than 50 characters
+# 1. Persona
+You are an AI assistant designed to provide guidance and references from your knowledge base to help users make decisions during onboarding.
+
+It is **VERY** important that you return **ALL** references found in the context for user examination.
+
+---
+
+# 2. THINKING PROCESS & LOGIC
+Before generating a response, adhere to these processing rules:
+
+## A. Context Verification
+Scan the retrieved context for the specific answer
+1. **No information found**: If the information is not present in the context:
+   - Do NOT formulate a general answer.
+   - Do NOT user external resources (i.e., websites, etc) to get an answer.
+   - Do NOT infer an answer from the users question.
+
+## B. Question Analysis
+1.  **Detection:** Determine if the query contains one or multiple questions.
+2.  **Decomposition:** Split complex queries into individual sub-questions.
+3.  **Classification:** Identify if the question is Factual, Procedural, Diagnostic, Troubleshooting, or Clarification-seeking.
+4.  **Multi-Question Strategy:** Number sub-questions clearly (Q1, Q2, etc).
+5.  **No Information:** If there is no information supporting an answer to the query, do not try and fill in the information
+6. **Strictness:** Do not infer information, be strict on evidence.
+
+## C. Entity Correction
+- If you encounter "National Health Service Digital (NHSD)", automatically treat and output it as **"National Health Service England (NHSE)"**.
+
+## D. RAG Confidence Scoring
+```
+Evaluate retrieved context using these relevance score thresholds:
+- `Score > 0.9`     : **Diamond** (Definitive source)
+- `Score 0.8 - 0.9` : **Gold** (Strong evidence)
+- `Score 0.7 - 0.8` : **Silver** (Partial context)
+- `Score 0.6 - 0.7` : **Bronze** (Weak relevance)
+- `Score < 0.6`     : **Scrap** (Ignore completely)
+```
+
+---
+
+# 3. OUTPUT STRUCTURE
+Construct your response in this exact order:
+
+1.  **Summary:** A concise overview (Maximum **100 characters**).
+2.  **Answer:** The core response using the specific "mrkdwn" styling defined below (Maximum **800 characters**).
+3.  **Separator:** A literal line break using `------`.
+4.  **Bibliography:** The list of all sources used.
+
+---
+
+# 4. FORMATTING RULES ("mrkdwn")
+You must use a specific variation of markdown. Follow this table strictly:
+
+| Element | Style to Use | Example |
+| :--- | :--- | :--- |
+| **Headings / Subheadings** | Bold (`*`) | `*Answer:*`, `*Bibliography:*` |
+| **Source Names** | Bold (`*`) | `*NHS England*`, `*EPS*` |
+| **Citations / Titles** | Italic (`_`) | `_Guidance Doc v1_` |
+| **Quotes (>1 sentence)** | Blockquote (`>`) | `> text` |
+| **Tech Specs / Examples** | Blockquote (`>`) | `> param: value` |
+| **System / Field Names** | Inline Code (`` ` ``) | `` `PrescriptionID` `` |
+| **Technical Terms** | Inline Code (`` ` ``) | `` `HL7 FHIR` `` |
+| **Hyperlinks** | **NONE** | Do not output any URLs. |
+
+---
+
+# 5. BIBLIOGRAPHY GENERATOR
+**Requirements:**
+- Return **ALL** retrieved documents from the context.
+- Title length must be **< 50 characters**.
+- Use the exact string format below (do not render it as a table or list).
+
+**Template:**
+```text
+<cit>source number||summary title||excerpt||relevance score||source name</cit>
+
+# 6. Example
+"""
+*Summary*
+Short summary text
+
+* Answer *
+A longer answer, going into more detail gained from the knowledge base and using critical thinking.
+
+------
+<cit>1||A document||This is the precise snippet of the pdf file which answers the question.||0.98||very_helpful_doc.pdf</cit>
+<cit>2||Another file||A 500 word text excerpt which gives some inference to the answer, but the long citation helps fill in the information for the user, so it's worth the tokens.||0.76||something_interesting.txt</cit>
+<cit>3||A useless file||This file doesn't contain anything that useful||0.05||folder/another/some_file.txt</cit>
+"""

packages/cdk/prompts/userPrompt.txt

@@ -1,6 +1,4 @@
-# QUERY
-{{user_query}}
+<user_query>{{user_query}}<user_query>
 
 # CONTEXT
-## Results $search_results$
-## LIST ALL RESULTS IN TABLE
+<search_results>$search_results$<search_results>

packages/cdk/resources/BedrockPromptResources.ts

@@ -20,14 +20,12 @@ export class BedrockPromptResources extends Construct {
   constructor(scope: Construct, id: string, props: BedrockPromptResourcesProps) {
     super(scope, id)
 
-    // Nova Pro is recommended for text generation tasks requiring high accuracy and complex understanding.
-    const novaProModel = BedrockFoundationModel.AMAZON_NOVA_PRO_V1
-    // Nova Lite is recommended for tasks
-    const novaLiteModel = BedrockFoundationModel.AMAZON_NOVA_LITE_V1
+    const ragModel = new BedrockFoundationModel("meta.llama3-70b-instruct-v1:0")
+    const reformulationModel = BedrockFoundationModel.AMAZON_NOVA_LITE_V1
 
     const queryReformulationPromptVariant = PromptVariant.text({
       variantName: "default",
-      model: novaLiteModel,
+      model: reformulationModel,
       promptVariables: ["topic"],
       promptText: props.settings.reformulationPrompt.text
     })
@@ -41,7 +39,7 @@ export class BedrockPromptResources extends Construct {
 
     const ragResponsePromptVariant = PromptVariant.chat({
       variantName: "default",
-      model: novaProModel,
+      model: ragModel,
       promptVariables: ["query", "search_results"],
       system: props.settings.systemPrompt.text,
       messages: [props.settings.userPrompt]
@@ -59,8 +57,8 @@ export class BedrockPromptResources extends Construct {
     })
 
     // expose model IDs for use in Lambda environment variables
-    this.ragModelId = novaProModel.modelId
-    this.queryReformulationModelId = novaLiteModel.modelId
+    this.ragModelId = ragModel.modelId
+    this.queryReformulationModelId = reformulationModel.modelId
 
     this.queryReformulationPrompt = queryReformulationPrompt
     this.ragResponsePrompt = ragPrompt

packages/slackBotFunction/app/services/bedrock.py

@@ -42,8 +42,10 @@ def query_bedrock(user_query: str, session_id: str = None) -> RetrieveAndGenerat
             "type": "KNOWLEDGE_BASE",
             "knowledgeBaseConfiguration": {
                 "knowledgeBaseId": config.KNOWLEDGEBASE_ID,
-                "modelArn": config.RAG_MODEL_ID,
-                "retrievalConfiguration": {"vectorSearchConfiguration": {"numberOfResults": 5}},
+                "modelArn": prompt_template.get("model_id", config.RAG_MODEL_ID),
+                "retrievalConfiguration": {
+                    "vectorSearchConfiguration": {"numberOfResults": 5, "overrideSearchType": "SEMANTIC"}
+                },
                 "generationConfiguration": {
                     "guardrailConfiguration": {
                         "guardrailId": config.GUARD_RAIL_ID,
@@ -58,16 +60,6 @@ def query_bedrock(user_query: str, session_id: str = None) -> RetrieveAndGenerat
                         }
                     },
                 },
-                "orchestrationConfiguration": {
-                    "inferenceConfig": {
-                        "textInferenceConfig": {
-                            **inference_config,
-                            "stopSequences": [
-                                "Human:",
-                            ],
-                        }
-                    },
-                },
             },
         },
     }
@@ -87,6 +79,7 @@ def query_bedrock(user_query: str, session_id: str = None) -> RetrieveAndGenerat
     else:
         logger.info("Starting new conversation")
 
+    logger.debug("Retrieve and Generate", extra={"params": request_params})
     response = client.retrieve_and_generate(**request_params)
     logger.info(
         "Got Bedrock response",
@@ -100,10 +93,8 @@ def invoke_model(prompt: str, model_id: str, client: BedrockRuntimeClient, infer
         modelId=model_id,
         body=json.dumps(
             {
-                "anthropic_version": "bedrock-2023-05-31",
                 "temperature": inference_config["temperature"],
                 "top_p": inference_config["topP"],
-                "top_k": 50,
                 "max_tokens": inference_config["maxTokens"],
                 "messages": [{"role": "user", "content": prompt}],
             }

packages/slackBotFunction/app/services/prompt_loader.py

@@ -92,23 +92,28 @@ def load_prompt(prompt_name: str, prompt_version: str = None) -> dict:
 
         logger.info(
             f"Loading prompt {prompt_name}' (ID: {prompt_id})",
-            extra={"prompt_name": prompt_name, "prompt_id": prompt_id, "prompt_version": prompt_version},
+            extra={"prompt_version": prompt_version},
         )
 
         if is_explicit_version:
             response = client.get_prompt(promptIdentifier=prompt_id, promptVersion=selected_version)
         else:
             response = client.get_prompt(promptIdentifier=prompt_id)
 
+        logger.info("Prompt Found", extra={"prompt": response})
+
+        variant = response["variants"][0]
+
         # Extract and render the prompt template
-        template_config = response["variants"][0]["templateConfiguration"]
+        template_config = variant["templateConfiguration"]
         prompt_text = _render_prompt(template_config)
         actual_version = response.get("version", "DRAFT")
 
         # Extract inference configuration with defaults
         default_inference = {"temperature": 0, "topP": 1, "maxTokens": 1500}
-        raw_inference = response["variants"][0].get("inferenceConfiguration", {})
-        raw_text_config = raw_inference.get("textInferenceConfiguration", {})
+        model_id = variant.get("modelId", "")
+        raw_inference = variant.get("inferenceConfiguration", {})
+        raw_text_config = raw_inference.get("text", {})
         inference_config = {**default_inference, **raw_text_config}
 
         logger.info(
@@ -117,10 +122,11 @@ def load_prompt(prompt_name: str, prompt_version: str = None) -> dict:
                 "prompt_name": prompt_name,
                 "prompt_id": prompt_id,
                 "version_used": actual_version,
+                "model_id": model_id,
                 **inference_config,
             },
         )
-        return {"prompt_text": prompt_text, "inference_config": inference_config}
+        return {"prompt_text": prompt_text, "model_id": model_id, "inference_config": inference_config}
 
     except ClientError as e:
         error_code = e.response.get("Error", {}).get("Code", "Unknown")