0.2.0 - conf for sandbox

2025-07-22 18:24:02 +02:00 · 2025-07-22 18:24:02 +02:00 · 6a57d91b7e
commit 6a57d91b7e
parent 9e698e40f9
7 changed files with 66 additions and 65 deletions
--- a/.env
+++ b/.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_PUBLIC_KEY="pk-lf-17dfde63-93e2-4983-8aa7-2673d3ecaab8"
-LANGFUSE_SECRET_KEY="sk-lf-ba41a266-6fe5-4c90-a483-bec8a7aaa321"
+LANGFUSE_ENABLED=false
+LANGFUSE_PUBLIC_KEY="pk-lf-"
+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
--- a/4
+++ b/4
@ -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"]
--- a/config/application.yml
+++ b/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"
-  secret_key: "sk-lf-ba41a266-6fe5-4c90-a483-bec8a7aaa321"
+  public_key: "pk-lf-"
+  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
--- a/docker-compose.yml
+++ b/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
--- a/llm/chains.py
+++ b/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}")
-            return False
+            logger.warning(f"[{issue_key}] Pydantic validation failed: {e}. Continuing with raw response: {response}")
+            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
--- a/llm/jira_analysis_v1.2.0.txt
+++ b/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.
-Your task is to analyze the provided Jira ticket data and generate a JSON object based on the rules below.
-Your output MUST be ONLY the JSON object, with no additional text or explanations.
+You are an expert AI assistant that analyzes Jira tickets and outputs a concise summary in a valid JSON format.
+Your output MUST be a single JSON object and nothing else.

 ## JSON Output Schema
 {format_instructions}

 ## Field-by-Field Instructions

-### `hasMultipleEscalations` (boolean)
- Set to `true` ONLY if the user has made multiple requests for help from the "MDM HUB team" without getting a response.
- A normal back-and-forth conversation is NOT an escalation.
+1.  **`issueCategory` (string)**
+    *   Classify the core problem. Choose ONE: "technical_issue", "data_request", "access_problem", "general_question", "other".

-### `customerSentiment` (string: "neutral", "frustrated", "calm")
- Set to `"frustrated"` if the user mentions blockers, deadlines, or uses urgent language (e.g., "urgent", "asap", "blocked").
- Set to `"calm"` if the language is polite and patient.
- Set to `"neutral"` otherwise.
+2.  **`area` (string)**
+    *   Classify the technical domain. Choose the BEST fit from the following options:
+    *   `"Direct Channel"`
+    *   `"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")
- `"technical_issue"`: Errors, bugs, system failures, API problems.
- `"data_request"`: Asking for data exports, reports, or information retrieval.
- `"access_problem"`: User cannot log in, has permission issues.
- `"general_question"`: "How do I..." or other general inquiries.
- `"other"`: If it doesn't fit any other category.
+3.  **`customerSentiment` (string)**
+    *   Analyze the user's tone.
+    *   `"frustrated"`: User mentions blockers, deadlines, or uses urgent language ("ASAP", "urgent", "blocked").
+    *   `"neutral"`: Default, non-emotional tone.

-### `area` (string)
- Classify the ticket into ONE of the following areas based on keywords:
- `"Direct Channel"`: "REST API", "API Gateway", "Create/Update HCP/HCO"
- `"Streaming Channel"`: "Kafka", "SQS", "Reltio events", "Snowflake"
- `"Java Batch Channel"`: "Batch", "File loader", "Airflow"
- `"ETL Batch Channel"`: "ETL", "Informatica"
- `"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.
+4.  **`isEscalated` (boolean)**
+    *   Set to `true` if the user explicitly states they are escalating, mentions previous unanswered requests, or if the tone is highly frustrated and urgent.
+    *   Set to `false` otherwise.
+
+5.  **`oneSentenceSummary` (string)**
+    *   A single paragraph in concise English that summarizes the discussion.

 ## 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,
-  "CustomerSentiment": "neutral",
-  "IssueCategory": "technical_issue",
-  "Area": "DCR Service"
+  "issueCategory": "technical_issue",
+  "area": "Application Service",
+  "customerSentiment": "frustrated",
+  "isEscalated": false,
+  "oneSentenceSummary": "A DCR for PforceRx was rejected by OneKey, which is blocking a weekly report."
 }}
-```

 USER:
 Analyze the following Jira ticket:
--- a/llm/models.py
+++ b/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?")
-    customerSentiment: Optional[CustomerSentiment] = Field(alias="CustomerSentiment", description="Overall customer sentiment (e.g., 'neutral', 'frustrated', 'calm').")
-    # New: Add category and area fields
-    issueCategory: IssueCategory = Field(alias="IssueCategory", description="The primary category of the Jira ticket.")
-    area: Area = Field(alias="Area", description="The technical area of the MDM HUB related to the issue.")
+    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)
+    
+    issueCategory: IssueCategory = Field(..., description="The primary category of the Jira ticket.")
+    area: Area = Field(..., 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):