0.2.0 - conf for sandbox

.env

@@ -1,20 +1,21 @@
 # Ollama configuration
-LLM_OLLAMA_BASE_URL=http://192.168.0.140:11434
+# LLM_OLLAMA_BASE_URL=http://192.168.0.140:11434
 # LLM_OLLAMA_BASE_URL=http://192.168.0.122:11434
-# LLM_OLLAMA_BASE_URL="https://api-amer-sandbox-gbl-mdm-hub.pfizer.com/ollama"
+LLM_OLLAMA_BASE_URL="https://api-amer-sandbox-gbl-mdm-hub.pfizer.com/ollama"
 LLM_OLLAMA_MODEL=phi4-mini:latest
 # LLM_OLLAMA_MODEL=smollm:360m
 # LLM_OLLAMA_MODEL=qwen3:0.6b
 # LLM_OLLAMA_MODEL=qwen3:1.7b
+# LLM_OLLAMA_MODEL=qwen3:8b
 # Logging configuration
 LOG_LEVEL=DEBUG
 # Ollama API Key (required when using Ollama mode)
 # Langfuse configuration
-LANGFUSE_ENABLED=true
+LANGFUSE_ENABLED=false
-LANGFUSE_PUBLIC_KEY="pk-lf-17dfde63-93e2-4983-8aa7-2673d3ecaab8"
+LANGFUSE_PUBLIC_KEY="pk-lf-"
-LANGFUSE_SECRET_KEY="sk-lf-ba41a266-6fe5-4c90-a483-bec8a7aaa321"
+LANGFUSE_SECRET_KEY="sk-lf-"
 LANGFUSE_HOST="https://cloud.langfuse.com"
 # Gemini configuration
-LLM_GEMINI_API_KEY="AIzaSyDl12gxyTf2xCaTbT6OMJg0I-Rc82Ib77c"
+LLM_GEMINI_API_KEY=""
 LLM_GEMINI_MODEL="gemini-2.5-flash"
-LLM_MODE=gemini
+LLM_MODE=ollama

Dockerfile

@@ -42,11 +42,11 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
 COPY config ./config
 
 # Copy your application source code.
-COPY jira_webhook_llm.py .
+COPY main.py .
 COPY config.py .
 
 # Expose the port your application listens on.
 EXPOSE 8000
 
 # Define the command to run your application.
-CMD ["uvicorn", "jira-webhook-llm:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

config/application.yml

@@ -3,7 +3,7 @@ llm:
   # The mode to run the application in.
   # Can be 'openai' or 'ollama'.
   # This can be overridden by the LLM_MODE environment variable.
-  mode: gemini # Change mode to gemini
+  mode: ollama # Change mode to gemini
 
   # Settings for OpenAI-compatible APIs (like OpenRouter)
   openai:
@@ -26,7 +26,7 @@ llm:
     # It's HIGHLY recommended to set this via an environment variable
     # instead of saving it in this file.
     # Can be overridden by GEMINI_API_KEY
-    api_key: "AIzaSyDl12gxyTf2xCaTbT6OMJg0I-Rc82Ib77c" # Move from openai
+    api_key: "" 
 
     # Can be overridden by GEMINI_MODEL
     # model: "gemini-2.5-flash" 
@@ -38,7 +38,7 @@ llm:
   # Settings for Ollama
   ollama:
     # Can be overridden by OLLAMA_BASE_URL
-    #base_url: "http://192.168.0.140:11434"
+    # base_url: "http://192.168.0.122:11434"
     base_url: "https://api-amer-sandbox-gbl-mdm-hub.pfizer.com/ollama"
 
 
@@ -47,17 +47,18 @@ llm:
     # model: "qwen3:1.7b"
     # model: "smollm:360m"
     # model: "qwen3:0.6b"
+    # model: "qwen3:8b"
 # Langfuse configuration for observability and analytics
 langfuse:
   # Enable or disable Langfuse integration
   # Can be overridden by LANGFUSE_ENABLED environment variable
-  enabled: true
+  enabled: false
   
   # Langfuse API credentials
   # It's HIGHLY recommended to set these via environment variables
   # instead of saving them in this file
-  public_key: "pk-lf-17dfde63-93e2-4983-8aa7-2673d3ecaab8"
+  public_key: "pk-lf-"
-  secret_key: "sk-lf-ba41a266-6fe5-4c90-a483-bec8a7aaa321"
+  secret_key: "sk-lf-"
   host: "https://cloud.langfuse.com"
 
 # Processor configuration
@@ -68,7 +69,7 @@ processor:
  
  # Maximum number of retries for failed Jira analysis requests
  # Can be overridden by PROCESSOR_MAX_RETRIES environment variable
- max_retries: 5
+ max_retries: 0
  
  # Initial delay in seconds before the first retry attempt (exponential backoff)
  # Can be overridden by PROCESSOR_INITIAL_RETRY_DELAY_SECONDS environment variable

docker-compose.yml

@@ -8,7 +8,7 @@ services:
 
   # Service for your FastAPI application
   jira-webhook-llm:
-    image: artifactory.pfizer.com/mdmhub-docker-dev/mdmtools/ollama/jira-webhook-llm:0.1.8
+    image: artifactory.pfizer.com/mdmhub-docker-dev/mdmtools/ollama/jira-webhook-llm:0.2.0
     ports:
       - "8000:8000"
     environment:
@@ -30,4 +30,4 @@ services:
     # Command to run your FastAPI application using Uvicorn
     # --host 0.0.0.0 is crucial for the app to be accessible from outside the container
     # --reload is good for development; remove for production
-    command: uvicorn jira-webhook-llm:app --host 0.0.0.0 --port 8000
+    command: uvicorn main:app --host 0.0.0.0 --port 8000

llm/chains.py

@@ -32,7 +32,6 @@ if settings.llm.mode == 'openai':
     llm = ChatOpenAI(
         model=settings.llm.openai_model if settings.llm.openai_model else "", # Ensure model is str
         temperature=0.7,
-        max_tokens=2000,
         api_key=settings.llm.openai_api_key, # type: ignore # Suppress Pylance error due to SecretStr type mismatch
         base_url=settings.llm.openai_api_base_url
     )
@@ -52,7 +51,8 @@ elif settings.llm.mode == 'ollama':
         
         llm = OllamaLLM(
             model=settings.llm.ollama_model,
-            base_url=base_url
+            base_url=base_url,
+            num_ctx=32000
             # Removed streaming, timeout, max_retries as they are not valid parameters for OllamaLLM
         )
         
@@ -92,7 +92,6 @@ elif settings.llm.mode == 'gemini': # New: Add Gemini initialization
         llm = ChatGoogleGenerativeAI(
             model=settings.llm.gemini_model,
             temperature=0.7,
-            max_tokens=2000,
             google_api_key=settings.llm.gemini_api_key
         )
         
@@ -131,7 +130,7 @@ if llm is None:
 llm_runnable: Runnable = llm # type: ignore
 
 # Set up Output Parser for structured JSON
-parser = JsonOutputParser(pydantic_object=AnalysisFlags)
+parser = JsonOutputParser()
 
 # Load prompt template from file
 def load_prompt_template(version="v1.2.0"):
@@ -222,8 +221,8 @@ def validate_response(response: Union[dict, str], issue_key: str = "N/A") -> boo
             AnalysisFlags.model_validate(response)
             return True
         except Exception as e:
-            logger.error(f"[{issue_key}] Pydantic validation error: {e}. Invalid response: {response}")
+            logger.warning(f"[{issue_key}] Pydantic validation failed: {e}. Continuing with raw response: {response}")
-            return False
+            return True # Allow processing even if validation fails
     except Exception as e:
         logger.error(f"[{issue_key}] Unexpected error during response validation: {e}. Response: {response}")
         return False

llm/jira_analysis_v1.2.0.txt

@@ -1,60 +1,58 @@
 SYSTEM:
-You are a precise AI assistant that analyzes Jira tickets and outputs a JSON object.
+You are an expert AI assistant that analyzes Jira tickets and outputs a concise summary in a valid JSON format.
-Your task is to analyze the provided Jira ticket data and generate a JSON object based on the rules below.
+Your output MUST be a single JSON object and nothing else.
-Your output MUST be ONLY the JSON object, with no additional text or explanations.
 
 ## JSON Output Schema
 {format_instructions}
 
 ## Field-by-Field Instructions
 
-### `hasMultipleEscalations` (boolean)
+1.  **`issueCategory` (string)**
-- Set to `true` ONLY if the user has made multiple requests for help from the "MDM HUB team" without getting a response.
+    *   Classify the core problem. Choose ONE: "technical_issue", "data_request", "access_problem", "general_question", "other".
-- A normal back-and-forth conversation is NOT an escalation.
 
-### `customerSentiment` (string: "neutral", "frustrated", "calm")
+2.  **`area` (string)**
-- Set to `"frustrated"` if the user mentions blockers, deadlines, or uses urgent language (e.g., "urgent", "asap", "blocked").
+    *   Classify the technical domain. Choose the BEST fit from the following options:
-- Set to `"calm"` if the language is polite and patient.
+    *   `"Direct Channel"`
-- Set to `"neutral"` otherwise.
+    *   `"Streaming Channel"`
+    *   `"Java Batch Channel"`
+    *   `"ETL Batch Channel"`
+    *   `"DCR Service"`
+    *   `"API Gateway"`
+    *   `"Callback Service"`
+    *   `"Publisher"`
+    *   `"Reconciliation"`
+    *   `"Snowflake"`
+    *   `"Authentication"`
+    *   `"Other"`
 
-### `issueCategory` (string: "technical_issue", "data_request", "access_problem", "general_question", "other")
+3.  **`customerSentiment` (string)**
-- `"technical_issue"`: Errors, bugs, system failures, API problems.
+    *   Analyze the user's tone.
-- `"data_request"`: Asking for data exports, reports, or information retrieval.
+    *   `"frustrated"`: User mentions blockers, deadlines, or uses urgent language ("ASAP", "urgent", "blocked").
-- `"access_problem"`: User cannot log in, has permission issues.
+    *   `"neutral"`: Default, non-emotional tone.
-- `"general_question"`: "How do I..." or other general inquiries.
-- `"other"`: If it doesn't fit any other category.
 
-### `area` (string)
+4.  **`isEscalated` (boolean)**
-- Classify the ticket into ONE of the following areas based on keywords:
+    *   Set to `true` if the user explicitly states they are escalating, mentions previous unanswered requests, or if the tone is highly frustrated and urgent.
-- `"Direct Channel"`: "REST API", "API Gateway", "Create/Update HCP/HCO"
+    *   Set to `false` otherwise.
-- `"Streaming Channel"`: "Kafka", "SQS", "Reltio events", "Snowflake"
+
-- `"Java Batch Channel"`: "Batch", "File loader", "Airflow"
+5.  **`oneSentenceSummary` (string)**
-- `"ETL Batch Channel"`: "ETL", "Informatica"
+    *   A single paragraph in concise English that summarizes the discussion.
-- `"DCR Service"`: "DCR", "PforceRx", "OneKey", "Veeva"
-- `"API Gateway"`: "Kong", "authentication", "routing"
-- `"Callback Service"`: "Callback", "HCO names", "ranking"
-- `"Publisher"`: "Publisher", "routing rules"
-- `"Reconciliation"`: "Reconciliation", "sync"
-- `"Snowflake"`: "Snowflake", "Data Mart", "SQL"
-- `"Authentication"`: "PingFederate", "OAuth2", "Key-Auth"
-- `"Other"`: If it doesn't fit any other category.
 
 ## Example
 
 ### Input:
 - Summary: "DCR Rejected by OneKey"
-- Description: "Our DCR for PforceRx was rejected by OneKey. Can the MDM HUB team investigate?"
+- Description: "Our DCR for PforceRx was rejected by OneKey. Can the MDM HUB team investigate? This is blocking our weekly report."
 - Comment: ""
 
 ### Output:
 ```json
 {{
-  "Hasmultipleescalations": false,
+  "issueCategory": "technical_issue",
-  "CustomerSentiment": "neutral",
+  "area": "Application Service",
-  "IssueCategory": "technical_issue",
+  "customerSentiment": "frustrated",
-  "Area": "DCR Service"
+  "isEscalated": false,
+  "oneSentenceSummary": "A DCR for PforceRx was rejected by OneKey, which is blocking a weekly report."
 }}
-```
 
 USER:
 Analyze the following Jira ticket:

llm/models.py

@@ -57,11 +57,13 @@ class JiraWebhookPayload(BaseModel):
     updated: Optional[str] = None
 
 class AnalysisFlags(BaseModel):
-    hasMultipleEscalations: bool = Field(alias="Hasmultipleescalations", description="Is there evidence of multiple escalation attempts?")
+    model_config = ConfigDict(alias_generator=lambda x: ''.join(word.capitalize() if i > 0 else word for i, word in enumerate(x.split('_'))), populate_by_name=True)
-    customerSentiment: Optional[CustomerSentiment] = Field(alias="CustomerSentiment", description="Overall customer sentiment (e.g., 'neutral', 'frustrated', 'calm').")
+    
-    # New: Add category and area fields
+    issueCategory: IssueCategory = Field(..., description="The primary category of the Jira ticket.")
-    issueCategory: IssueCategory = Field(alias="IssueCategory", description="The primary category of the Jira ticket.")
+    area: Area = Field(..., description="The technical area of the MDM HUB related to the issue.")
-    area: Area = Field(alias="Area", description="The technical area of the MDM HUB related to the issue.")
+    customerSentiment: Optional[CustomerSentiment] = Field(..., description="Overall customer sentiment (e.g., 'neutral', 'frustrated', 'calm').")
+    isEscalated: bool = Field(..., description="Is there evidence of multiple escalation attempts?")
+    oneSentenceSummary: str = Field(..., description="A single paragraph in concise English that summarizes the discussion.")
 
 
 class JiraAnalysisResponse(BaseModel):