initial commit

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

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+3.12

README.md

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+# Report for Software Engineering for AI-Enabled System
+
+## Section 1: ML Model Implementation
+
+### Task 1.1: ML Canvas Design
+
+![AI Canvas](/assets/ai-canvas.png)
+
+The AI Canvas comprises eight interconnected sections that collectively define the system's purpose and operation. The Prediction section establishes the core functionality: estimating and capturing context from each data source and mapping it into each field in the canonical data schema. This works in concert with the Judgment section, which articulates the critical trade-offs the system must evaluate, focusing on assessing the correctness of the output unified data schema and measuring the amount of knowledge potentially lost throughout the mapping process.
+
+The Action section defines how the system's outputs are translated into tangible steps, outputting the results in JSON format with correctly mapped fields. These actions lead to the Outcome section, which clarifies the ultimate value proposition: generating a unified dataset represented as a single JSON object conforming to the CanonicalRecord schema, including transformed fields such as price, area, and address.
+
+The Input Data section catalogues the available information sources: user prompts containing instructions for mapping raw property data snippets to a specified schema (CanonicalRecord) using transformation rules, including both the schema specifications and the raw data itself. Complementing this, the Training Data section defines the labeled examples powering the model: JSONL datasets where both prompts and responses are wrapped in contents arrays with clearly labeled roles ("user" and "model"), each containing parts arrays with text.
+
+The Feedback section outlines how the model will learn over time by tracking metrics like JSON Syntactic Validity and Pydantic Schema Conformance. The Intervention section establishes boundaries for human oversight, calling for expert involvement when input data sources fall outside the real estate scope. The Explanation section details the technical approaches for transparency: Traceable Prompting and Chain-of-Thought (CoT) Prompting methodologies to provide insight into the system's decision-making processes.
+
+
+### Task 1.2: Model Training Implementation
+
+I did not train the LLM model by myself but instead, I do fine-tuning on gemini-2.0-flash-lite-001 in vertex AI platform with supervised learning approach.
+
+#### Input data
+
+Here is example of training data I use to fine-tune the model:
+```json
+{"contents":[{"role":"user","parts":[{"text":"You are a data mapping assistant. Your task is to map the provided 'Raw Property Data Snippet' to the 'CanonicalRecord Schema Definition'. \n\nRULES:\n- Your entire response must be ONLY the mapped JSON object string, conforming strictly to the CanonicalRecord schema.\n- Do NOT include any explan...line\": \"2024-02-10T00:00:00Z\"}"}]},{"role":"model","parts":[{"text":"{\"canonical_record_id\": \"cre-GENERATED-UUID\", \"original_source_identifier\": \"https://www.baania.com/some-property-link-v2\", \"original_source_type\": \"scrape\", \"entity_type\": \"RealEstateListing\", \"mapping_model_version\": null, \"mapping_timestamp\": \"2024-02-10T00:00:00Z\", \"address\": {\"street_address\": \"Sukhumvit Soi 31\", \"city\": \"Ban...., Wattana. Excellent amenities including pool, gym, and 24-hour security. Built 2018.\", \"images\": [\"https://cdn.baania.com/img/condo/A1.jpg\", \"https://cdn.baania.com/img/condo/A2.jpg\"], \"contact\": {\"agency\": \"Urban Living Thailand\", \"tel\": \"+66-81-234-5678\"}, \"date_online\": \"2024-02-10T00:00:00Z\"}}"}]}]}
+```
+It is in JSONL or JSONLines format which suitable for large scale training data, these datas are combination from two sources
+1. Collected from my pipeline service
+- Combine the data output from pipeline with specific prompt to create user role and define the target canonical dataset for model role
+2. Generate with Gemini 2.5 Flash Preview 04-17 with this prompt
+- Craft prompt to more synthetic datas and cover more cases
+
+We need to do data generation because pipeline process take a lot of time to scrape data from web.
+
+Separate into 3 versions
+
+- `train-1.jsonl`: 1 samples (2207 tokens)
+- `train-2.jsonl`: 19 samples (33320 tokens) + 12 samples `evluation.jsonl`
+- `train-3.jsonl`: 25 samples (43443 tokens) + 12 samples `evluation.jsonl`
+
+#### Fine-tuning loop
+
+In Vertex AI plaform, I use tuning job to fine-tune the model. We can specify the training data and evaluation data in the tuning job.
+Those datas need to be in JSONL format.
+
+![tuning-1](assets/vertex/tuning-1.png)
+![tuning-2](assets/vertex/tuning-2.png)
+
+#### Validation methodology
+For validation, we separate into two parts
+1. Validation During Fine-Tuning 
+2. Post-Fine-Tuning Evaluation 
+
+##### Validation During Fine-Tuning
+
+During fine-tuning, if we provide evaluation data, Vertex AI will calculate the metrics for us.
+
+![validation-metrics-1](assets/vertex/model-2-metrics.png)
+![validation-metrics-2](assets/vertex/model-3-metrics.png)
+
+##### Post-Fine-Tuning Evaluation
+
+We approach two methods
+1. JSON Syntactic Validity: Parse generated json string with json.loads()
+2. Pydantic Schema Conformance: If the generated output is valid JSON, try to instantiate your CanonicalRecord Pydantic model with the parsed dictionary: CanonicalRecord(**parsed_generated_json).
+
+All models are evaluated on these settings.
+
+![evaluation](assets/model-setting.png)
+
+#### Results analysis
+
+
+
+### Task 1.4: Model Versioning and Experimentation
+
+Instead of MLFlow, Vertex AI platform provide model versioning and experimentation through Colab Enterprise for us. It also provide prompt versioning to track the changes of prompt too.
+
+![model-version](assets/vertex/model-versioning.png)
+
+### Task 1.5 + 1.6: Model Explainability + Prediction Reasoning
+
+For model explainability and prediction reasoning, we follow the `Traceable Prompting / Chain-of-Thought (CoT) Prompting` method.
+
+#### Traceable Prompting
+
+We add `Traceable Prompting` to the prompt to make the model explainable.
+
+### Task 1.7: Model Deployment as a Service
+
+Model are deployed as a service in Vertex AI platform via GCP Compute Engine. We pick `borbann-pipeline-4` as the final model to deploy according to the evaluation result.
+
+Anyway, currently we are not using this model with the pipeline service yet, so we will demonstrate it manually.
+
+## Section 2: UI-Model Interface
+
+### Task 2.1 UI design
+
+### Task 2.2: Demonstration
+
+#### UI - Model Interface Design
+#### Interface Testing and Implementation

evaluate.py

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+"""
+Demonstrate Post-Fine-Tuning Evaluation with these metrics:
+1. JSON Syntactic Validity
+2. Pydantic Schema Conformance
+"""
+
+# --- JSON Syntactic Validity ---
+# HOW: parse generated json string with json.loads()
+# METRIC: Percentage of generated outputs that are valid JSON
+# IMPORTANCE: Fundamental. If it's not valid JSON, it's useless.
+
+# ---  Pydantic Schema Conformance (CanonicalRecord Validation Rate) ---
+# HOW: If the generated output is valid JSON, try to instantiate your CanonicalRecord Pydantic model with the parsed dictionary: CanonicalRecord(**parsed_generated_json).
+# METRIC: Percentage of syntactically valid JSON outputs that also conform to the CanonicalRecord Pydantic schema (correct field names, data types, required fields present, enum values correct).
+# IMPORTANCE: Crucial for ensuring the output is usable by downstream systems. Pydantic's ValidationError will give details on why it failed.

explainability.py

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+"""
+Demonstrate Model explainability and resoning with
+Traceable Prompting / Chain-of-Thought (CoT) Prompting
+"""
+
+# Structure the prompt to include reasoning steps, or ask the model to generate
+# intermediate rationales

output.py

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+"""
+Demonstrate how to generate output in a format that can be easily parsed by downstream systems.
+"""
+
+from google import genai
+from google.genai import types
+
+# pyright: reportArgumentType=false
+
+
+def generate():
+    client = genai.Client(
+        vertexai=True,
+        project="83228855505",
+        location="us-central1",
+    )
+
+    model = "projects/83228855505/locations/us-central1/endpoints/7800363197466148864"
+    contents = [types.Content(role="user", parts=[])]
+
+    generate_content_config = types.GenerateContentConfig(
+        temperature=1,
+        top_p=0.95,
+        max_output_tokens=8192,
+        safety_settings=[
+            types.SafetySetting(category="HARM_CATEGORY_HATE_SPEECH", threshold="OFF"),
+            types.SafetySetting(
+                category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="OFF"
+            ),
+            types.SafetySetting(
+                category="HARM_CATEGORY_SEXUALLY_EXPLICIT", threshold="OFF"
+            ),
+            types.SafetySetting(category="HARM_CATEGORY_HARASSMENT", threshold="OFF"),
+        ],
+    )
+
+    for chunk in client.models.generate_content_stream(
+        model=model,
+        contents=contents,
+        config=generate_content_config,
+    ):
+        print(chunk.text, end="")
+
+
+generate()

pyproject.toml

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+[project]
+name = "data-mapping-llm"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "google-genai>=1.15.0",
+]

schemas/canonical.py

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+from pydantic import BaseModel, Field, HttpUrl, field_validator
+from typing import Optional, List, Dict, Any, Literal
+from datetime import datetime, timezone
+from uuid import uuid4
+
+
+class Address(BaseModel):
+    street_address: Optional[str] = Field(None, description="Street name and number")
+    city: Optional[str] = Field(None, description="City name")
+    state_province: Optional[str] = Field(
+        None, description="State or province abbreviation/name"
+    )
+    postal_code: Optional[str] = Field(None, description="Zip or postal code")
+    country: Optional[str] = Field(
+        "USA", description="Country code or name"
+    )  # Example default
+
+
+class PropertyFeatures(BaseModel):
+    bedrooms: Optional[int] = Field(None, description="Number of bedrooms")
+    bathrooms: Optional[float] = Field(
+        None, description="Number of bathrooms (float for half baths)"
+    )
+    area_sqft: Optional[float] = Field(None, description="Total area in square feet")
+    lot_size_sqft: Optional[float] = Field(None, description="Lot size in square feet")
+    year_built: Optional[int] = Field(None, description="Year the property was built")
+    property_type: Optional[str] = Field(
+        None,
+        description="e.g., Single Family House, Condo, Townhouse, Land, Multi-Family",
+    )
+    has_pool: Optional[bool] = None
+    has_garage: Optional[bool] = None
+    stories: Optional[int] = None
+
+
+class ListingDetails(BaseModel):
+    price: Optional[float] = Field(None, description="Listing price")
+    currency: Optional[str] = Field("USD", description="Currency code")
+    listing_status: Optional[
+        Literal["For Sale", "For Rent", "Sold", "Pending", "Off Market", "Unknown"]
+    ] = Field("Unknown", description="Current status of the listing")
+    listing_type: Optional[Literal["Sale", "Rent"]] = Field(
+        None, description="Whether the property is for sale or rent"
+    )
+    listed_date: Optional[datetime] = Field(
+        None, description="Date the property was listed (UTC)"
+    )
+    last_updated_date: Optional[datetime] = Field(
+        None, description="Date the listing was last updated (UTC)"
+    )
+    listing_url: Optional[HttpUrl] = Field(
+        None, description="URL of the original listing"
+    )
+    mls_id: Optional[str] = Field(
+        None, description="Multiple Listing Service ID, if available"
+    )
+
+
+class AgentContact(BaseModel):
+    name: Optional[str] = Field(None, description="Listing agent or contact name")
+    phone: Optional[str] = Field(None, description="Contact phone number")
+    email: Optional[str] = Field(None, description="Contact email address")
+    brokerage_name: Optional[str] = Field(
+        None, description="Real estate brokerage name"
+    )
+
+
+class CanonicalRecord(BaseModel):
+    """
+    Represents a unified Real Estate Listing record after mapping.
+    Target schema for the ML mapping model.
+    """
+
+    # --- Core Identifier & Provenance ---
+    canonical_record_id: str = Field(
+        default_factory=lambda: f"cre-{uuid4()}",
+        description="Unique identifier for this canonical record.",
+        examples=[f"cre-{uuid4()}"],
+    )
+    original_source_identifier: str = Field(
+        ...,
+        description="Identifier of the original source (e.g., URL, filename + row index).",
+    )
+    original_source_type: str = Field(
+        ...,
+        description="Type of the original source adapter ('api', 'file', 'scrape').",
+    )
+    entity_type: Literal["RealEstateListing", "NewsArticle", "Other"] = Field(
+        "Other", description="Classification of the source entity."
+    )
+    mapping_model_version: Optional[str] = Field(
+        None, description="Version identifier of the ML model used for mapping."
+    )
+    mapping_timestamp: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        description="Timestamp (UTC) when the mapping was performed.",
+    )
+
+    # --- Real Estate Specific Fields ---
+    address: Optional[Address] = Field(
+        default=None, description="Structured address details."
+    )
+    features: Optional[PropertyFeatures] = Field(
+        default=None, description="Details about the property itself."
+    )
+    listing: Optional[ListingDetails] = Field(
+        default=None, description="Information about the listing status and price."
+    )
+    agent: Optional[AgentContact] = Field(
+        default=None, description="Listing agent or contact information."
+    )
+    description: Optional[str] = Field(
+        None, description="Textual description from the listing."
+    )
+    image_urls: Optional[List[HttpUrl]] = Field(
+        default=None, description="List of URLs for property images."
+    )
+
+    # --- Common Fields ---
+    raw_source_data: Optional[Dict[str, Any]] = Field(  # Changed name for clarity
+        default=None, description="Original source data record (JSON representation)."
+    )
+
+    @field_validator("listing", "features", "address", "agent")
+    def check_fields_for_real_estate(cls, v, info):
+        if info.data.get("entity_type") == "RealEstateListing" and v is None:
+            # NOTE: Depending on strictness, might raise ValueError or just allow it
+            # print(f"Warning: RealEstateListing has None for {info.field_name}")
+            pass
+        return v
+
+    class Config:
+        # Example for documentation
+        schema_extra = {
+            "example": {
+                "canonical_record_id": f"cre-{uuid4()}",
+                "original_source_identifier": "https://some.realestate.site/listing/123",
+                "original_source_type": "scrape",
+                "entity_type": "RealEstateListing",
+                "mapping_model_version": "realestate-mapper-v1.0",
+                "mapping_timestamp": "2025-04-29T12:00:00Z",
+                "address": {
+                    "street_address": "123 Main St",
+                    "city": "Anytown",
+                    "state_province": "CA",
+                    "postal_code": "90210",
+                    "country": "USA",
+                },
+                "features": {
+                    "bedrooms": 3,
+                    "bathrooms": 2.5,
+                    "area_sqft": 1850.0,
+                    "lot_size_sqft": 5500.0,
+                    "year_built": 1995,
+                    "property_type": "Single Family House",
+                    "has_pool": True,
+                    "has_garage": True,
+                    "stories": 2,
+                },
+                "listing": {
+                    "price": 750000.0,
+                    "currency": "USD",
+                    "listing_status": "For Sale",
+                    "listing_type": "Sale",
+                    "listed_date": "2025-04-15T00:00:00Z",
+                    "last_updated_date": "2025-04-28T00:00:00Z",
+                    "listing_url": "https://some.realestate.site/listing/123",
+                    "mls_id": "MLS123456",
+                },
+                "agent": {
+                    "name": "Jane Doe",
+                    "phone": "555-123-4567",
+                    "email": "jane.doe@email.com",
+                    "brokerage_name": "Best Realty",
+                },
+                "description": "Beautiful 3 bed, 2.5 bath home in a great neighborhood. Recently updated kitchen, spacious backyard with pool.",
+                "image_urls": [
+                    "https://images.site/123/1.jpg",
+                    "https://images.site/123/2.jpg",
+                ],
+                "raw_source_data": {
+                    "title": "Charming Home For Sale",
+                    "price_str": "$750,000",
+                    "sqft": "1,850",
+                    "...": "...",
+                },
+            }
+        }

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