feat(deploy): Implement and deploy live Gradio API

This commit introduces a fully functional, live API for the FOT Recommender, deployed on Hugging Face Spaces using Gradio. It marks a major architectural shift from local scripts to a production-oriented, interactive web application.

This provides a frictionless "it just works" experience for reviewers, demonstrating the entire RAG pipeline from a simple web interface without requiring them to handle API keys or run code locally.

Key architectural changes and fixes include:
- **Gradio API:** A new `app.py` serves the model, handling user input and displaying synthesized, formatted recommendations.
- **Offline Artifact Generation:** The build process was refactored to pre-compute and save the `faiss_index.bin` vector database. This ensures the live application has a fast startup time and is highly efficient.
- **Secure Configuration:** All secrets (Google API Key, Demo Password) are now read from the secure Hugging Face environment, removing them from source code.
- **Deployment Stabilization:** The deployment environment was configured to use the correct Python version (`3.12`) and dependencies, and all file paths were made absolute to ensure stability on the remote server.

.gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,18 @@
+---
+title: Fot Recommender Api
+emoji: ⚡
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 5.41.0
+python_version: "3.12"
+app_file: app.py
+pinned: false
+license: mit
+short_description: POC - Freshman On-Track RAG Intervention Recommender
+---
+
+
 # Demo Application
 
 A Python project for coding exercises and development.
@@ -65,4 +80,4 @@ uv run mypy src/
 src/demo_application/    # Main package
 tests/                   # Test files
 pyproject.toml          # Project configuration
-```
+```

app.py

@@ -0,0 +1,106 @@
+import gradio as gr
+import faiss
+import os
+import numpy as np
+import sys
+from pathlib import Path
+from dotenv import load_dotenv
+
+load_dotenv()
+
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+from fot_recommender.rag_pipeline import ( # noqa: E402
+    load_knowledge_base,
+    initialize_embedding_model,
+    generate_recommendation_summary,
+)
+
+# --- Define the project root based on this script's location ---
+APP_ROOT = Path(__file__).parent
+
+# --- Define ABSOLUTE paths to the data artifacts ---
+FAISS_INDEX_PATH = APP_ROOT / "data" / "processed" / "faiss_index.bin"
+KB_PATH = APP_ROOT / "data" / "processed" / "knowledge_base_final_chunks.json"
+
+ACCESS_PASSWORD = os.environ.get("DEMO_PASSWORD", "")
+if ACCESS_PASSWORD and len(ACCESS_PASSWORD) > 10: # Check if it looks like a real key
+    print("✅ DEMO_PASSWORD secret loaded successfully from the environment.")
+else:
+    print("❌ FATAL: DEMO_PASSWORD secret was NOT FOUND in the environment.")
+ 
+FOT_GOOGLE_API_KEY = os.environ.get("FOT_GOOGLE_API_KEY", "")
+if FOT_GOOGLE_API_KEY and len(FOT_GOOGLE_API_KEY) > 10: # Check if it looks like a real key
+    print("✅ FOT_GOOGLE_API_KEY secret loaded successfully from the environment.")
+else:
+    print("❌ FATAL: FOT_GOOGLE_API_KEY secret was NOT FOUND in the environment.")
+    
+print("--- Initializing API: Loading models and data... ---")
+
+# --- Load artifacts using the new absolute paths ---
+index = faiss.read_index(str(FAISS_INDEX_PATH))
+knowledge_base_chunks = load_knowledge_base(str(KB_PATH))
+embedding_model = initialize_embedding_model()
+
+print("✅ API initialized successfully.")
+
+# --- Define the core RAG function that the API exposes ---
+
+
+def get_recommendations_api(student_narrative, persona, password):
+    """The main function that runs the RAG pipeline, protected by a password."""
+    if password != ACCESS_PASSWORD:
+        return "Authentication failed. Please check the access key."
+    if not student_narrative:
+        return "Please enter a student narrative."
+
+    # 1. RETRIEVE
+    query_embedding = np.asarray(embedding_model.encode([student_narrative])).astype(
+        "float32"
+    )
+    scores, indices = index.search(query_embedding, k=3)
+    retrieved_chunks_with_scores = [
+        (knowledge_base_chunks[i], score)
+        for i, score in zip(indices[0], scores[0])
+        if score >= 0.4
+    ]
+    if not retrieved_chunks_with_scores:
+        return "Could not find relevant interventions for this query."
+
+    # 2. GENERATE
+    synthesized_recommendation = generate_recommendation_summary(
+        retrieved_chunks=retrieved_chunks_with_scores,
+        student_narrative=student_narrative,
+        api_key=FOT_GOOGLE_API_KEY,
+        persona=persona,
+    )
+
+    # 3. Augment with evidence
+    evidence_header = "\n\n---\n\n**Evidence Base:**"
+    evidence_list = ""
+    for chunk, score in retrieved_chunks_with_scores:
+        evidence_list += f"\n- **{chunk['title']}** (Source: {chunk['source_document']}, Relevance: {score:.2f})"
+    return synthesized_recommendation + evidence_header + evidence_list
+
+
+# --- Create and launch the Gradio Interface ---
+sample_narrative = "This student is struggling to keep up with coursework, having failed one core class and earning only 2.5 credits..."
+interface = gr.Interface(
+    fn=get_recommendations_api,
+    inputs=[
+        gr.Textbox(lines=5, label="Student Narrative", value=sample_narrative),
+        gr.Radio(
+            ["teacher", "parent", "principal"],
+            label="Who is this for?",
+            value="teacher",
+        ),
+        gr.Textbox(
+            label="Access Key",
+            type="password",
+            info="Enter the access key provided for the demo.",
+        ),
+    ],
+    outputs=gr.Markdown(label="Synthesized Recommendation", show_copy_button=True),
+    title="Freshman On-Track Intervention Recommender API",
+    description="A live API demonstrating the FOT Recommender. Enter the provided access key to use.",
+    theme=gr.themes.Soft(),  # type: ignore
+).launch()

data/processed/faiss_index.bin

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data/source_pdfs/17-quick-tips-for-your-credit-recovery-program.pdf

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data/source_pdfs/NCS_OTToolkit_2ndEd_October_2017_updated.pdf

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data/source_pdfs/Session-2-GROUP-3-NATIONAL-Early-Intervention-Strategies-v3.pdf

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data/source_pdfs/handout-strategies-address-chronic-absenteeism.pdf

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data/source_pdfs/high-quality-tutoring-evidence-based-strategy-tackle-learning-loss.pdf

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data/source_pdfs/wwc_checkconnect_050515.pdf

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notebooks/fot_recommender_poc.ipynb

@@ -74,7 +74,7 @@
     "print(\"🚀 Setting up LOCAL development environment...\")\n",
     "\n",
     "# We assume the notebook is in 'notebooks/'. The project root is one level up.\n",
-    "project_path = Path.cwd().parent \n",
+    "project_path = Path.cwd().parent\n",
     "\n",
     "# Configure Python Path to use the local 'src' directory\n",
     "src_path = project_path / \"src\"\n",
@@ -264,7 +264,7 @@
     "\n",
     "if user_query:\n",
     "    print(\"\\n🔍 Searching for interventions based on your query...\")\n",
-    "    \n",
+    "\n",
     "    # Perform a new search using the user's input\n",
     "    custom_recommendations = search_interventions(\n",
     "        query=user_query,\n",
@@ -273,10 +273,10 @@
     "        knowledge_base=knowledge_base_chunks,\n",
     "        k=3,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    # Display the new results using our helper function\n",
     "    display_recommendations(custom_recommendations)\n",
-    "    \n",
+    "\n",
     "else:\n",
     "    print(\"\\nNo query entered. Skipping custom search.\")"
    ]
@@ -324,11 +324,13 @@
     "\n",
     "if project_path_to_clean.exists():\n",
     "    print(f\"The project directory '{project_path_to_clean}' was found.\")\n",
-    "    \n",
+    "\n",
     "    # Ask for user confirmation before deleting anything\n",
-    "    response = input(\"Would you like to delete the git repository folder that was downloaded during the running of this notebook? (y/n): \")\n",
-    "    \n",
-    "    if response.lower().strip() == 'y':\n",
+    "    response = input(\n",
+    "        \"Would you like to delete the git repository folder that was downloaded during the running of this notebook? (y/n): \"\n",
+    "    )\n",
+    "\n",
+    "    if response.lower().strip() == \"y\":\n",
     "        try:\n",
     "            shutil.rmtree(project_path_to_clean)\n",
     "            print(f\"✅ Successfully deleted '{project_path_to_clean}'.\")\n",

pyproject.toml

@@ -25,6 +25,8 @@ dependencies = [
     "transformers",
     "google-generativeai",
     "python-dotenv",
+    "gradio",
+    "gradio_client",
 ]
 
 [project.scripts]
@@ -52,7 +54,7 @@ where = ["src"]
 
 exclude = [
     ".venv",
-    "*/__pycache__/*", # Also excludes pycache within subdirectories
+    "*/__pycache__/*", 
     "src/fot_intervention_recommender.egg-info",
-    "*.ipynb", # <-- THIS IS THE KEY LINE TO EXCLUDE ALL NOTEBOOKS
-]
+    "*.ipynb",
+]

requirements.txt

@@ -1,141 +1,326 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml --output-file requirements.txt
+aiofiles==24.1.0
+    # via gradio
 annotated-types==0.7.0
+    # via pydantic
 anyio==4.10.0
-appnope==0.1.4
-argon2-cffi==25.1.0
-argon2-cffi-bindings==25.1.0
-arrow==1.3.0
-asttokens==3.0.0
-async-lru==2.0.5
-attrs==25.3.0
-babel==2.17.0
-beautifulsoup4==4.13.4
-black==25.1.0
-bleach==6.2.0
+    # via
+    #   gradio
+    #   httpx
+    #   starlette
+brotli==1.1.0
+    # via gradio
+cachetools==5.5.2
+    # via google-auth
 certifi==2025.8.3
-cffi==1.17.1
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
 charset-normalizer==3.4.2
+    # via requests
 click==8.2.1
-comm==0.2.3
-debugpy==1.8.15
-decorator==5.2.1
-defusedxml==0.7.1
-executing==2.2.0
+    # via
+    #   typer
+    #   uvicorn
 faiss-cpu==1.11.0.post1
-fastjsonschema==2.21.1
+    # via fot-intervention-recommender (pyproject.toml)
+fastapi==0.116.1
+    # via gradio
+ffmpy==0.6.1
+    # via gradio
 filelock==3.18.0
--e file:///Users/charlesfeinn/Developer/job_applications/fot-intervention-recommender
-fqdn==1.5.1
+    # via
+    #   huggingface-hub
+    #   torch
+    #   transformers
 fsspec==2025.7.0
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+google-ai-generativelanguage==0.6.15
+    # via google-generativeai
+google-api-core==2.25.1
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-python-client
+    #   google-generativeai
+google-api-python-client==2.178.0
+    # via google-generativeai
+google-auth==2.40.3
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-core
+    #   google-api-python-client
+    #   google-auth-httplib2
+    #   google-generativeai
+google-auth-httplib2==0.2.0
+    # via google-api-python-client
+google-generativeai==0.8.5
+    # via fot-intervention-recommender (pyproject.toml)
+googleapis-common-protos==1.70.0
+    # via
+    #   google-api-core
+    #   grpcio-status
+gradio==5.41.0
+    # via fot-intervention-recommender (pyproject.toml)
+gradio-client==1.11.0
+    # via
+    #   fot-intervention-recommender (pyproject.toml)
+    #   gradio
 greenlet==3.2.3
+    # via sqlalchemy
+groovy==0.1.2
+    # via gradio
+grpcio==1.74.0
+    # via
+    #   google-api-core
+    #   grpcio-status
+grpcio-status==1.71.2
+    # via google-api-core
 h11==0.16.0
+    # via
+    #   httpcore
+    #   uvicorn
 hf-xet==1.1.5
+    # via huggingface-hub
 httpcore==1.0.9
+    # via httpx
+httplib2==0.22.0
+    # via
+    #   google-api-python-client
+    #   google-auth-httplib2
 httpx==0.28.1
+    # via
+    #   gradio
+    #   gradio-client
+    #   langsmith
+    #   safehttpx
 huggingface-hub==0.34.3
+    # via
+    #   gradio
+    #   gradio-client
+    #   sentence-transformers
+    #   tokenizers
+    #   transformers
 idna==3.10
-iniconfig==2.1.0
-ipykernel==6.30.1
-ipython==9.4.0
-ipython-pygments-lexers==1.1.1
-isoduration==20.11.0
-jedi==0.19.2
+    # via
+    #   anyio
+    #   httpx
+    #   requests
 jinja2==3.1.6
+    # via
+    #   gradio
+    #   torch
 joblib==1.5.1
-json5==0.12.0
+    # via scikit-learn
 jsonpatch==1.33
+    # via langchain-core
 jsonpointer==3.0.0
-jsonschema==4.25.0
-jsonschema-specifications==2025.4.1
-jupyter-client==8.6.3
-jupyter-core==5.8.1
-jupyter-events==0.12.0
-jupyter-lsp==2.2.6
-jupyter-server==2.16.0
-jupyter-server-terminals==0.5.3
-jupyterlab==4.4.5
-jupyterlab-pygments==0.3.0
-jupyterlab-server==2.27.3
+    # via jsonpatch
 langchain==0.3.27
+    # via fot-intervention-recommender (pyproject.toml)
 langchain-core==0.3.72
+    # via
+    #   langchain
+    #   langchain-text-splitters
 langchain-text-splitters==0.3.9
+    # via langchain
 langsmith==0.4.10
-lark==1.2.2
+    # via
+    #   langchain
+    #   langchain-core
+markdown-it-py==3.0.0
+    # via rich
 markupsafe==3.0.2
-matplotlib-inline==0.1.7
-mistune==3.1.3
+    # via
+    #   gradio
+    #   jinja2
+mdurl==0.1.2
+    # via markdown-it-py
 mpmath==1.3.0
-mypy==1.17.1
-mypy-extensions==1.1.0
-nbclient==0.10.2
-nbconvert==7.16.6
-nbformat==5.10.4
-nest-asyncio==1.6.0
+    # via sympy
 networkx==3.5
-notebook==7.4.5
-notebook-shim==0.2.4
+    # via torch
 numpy==1.26.4
+    # via
+    #   fot-intervention-recommender (pyproject.toml)
+    #   faiss-cpu
+    #   gradio
+    #   pandas
+    #   scikit-learn
+    #   scipy
+    #   transformers
 orjson==3.11.1
-overrides==7.7.0
+    # via
+    #   gradio
+    #   langsmith
 packaging==25.0
-pandocfilters==1.5.1
-parso==0.8.4
-pathspec==0.12.1
-pexpect==4.9.0
+    # via
+    #   faiss-cpu
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   langchain-core
+    #   langsmith
+    #   transformers
+pandas==2.3.1
+    # via gradio
 pillow==11.3.0
-platformdirs==4.3.8
-pluggy==1.6.0
-prometheus-client==0.22.1
-prompt-toolkit==3.0.51
-psutil==7.0.0
-ptyprocess==0.7.0
-pure-eval==0.2.3
-pycparser==2.22
+    # via
+    #   gradio
+    #   sentence-transformers
+proto-plus==1.26.1
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-core
+protobuf==5.29.5
+    # via
+    #   google-ai-generativelanguage
+    #   google-api-core
+    #   google-generativeai
+    #   googleapis-common-protos
+    #   grpcio-status
+    #   proto-plus
+pyasn1==0.6.1
+    # via
+    #   pyasn1-modules
+    #   rsa
+pyasn1-modules==0.4.2
+    # via google-auth
 pydantic==2.11.7
+    # via
+    #   fastapi
+    #   google-generativeai
+    #   gradio
+    #   langchain
+    #   langchain-core
+    #   langsmith
 pydantic-core==2.33.2
+    # via pydantic
+pydub==0.25.1
+    # via gradio
 pygments==2.19.2
-pytest==8.4.1
+    # via rich
+pyparsing==3.2.3
+    # via httplib2
 python-dateutil==2.9.0.post0
-python-json-logger==3.3.0
+    # via pandas
+python-dotenv==1.1.1
+    # via fot-intervention-recommender (pyproject.toml)
+python-multipart==0.0.20
+    # via gradio
+pytz==2025.2
+    # via pandas
 pyyaml==6.0.2
-pyzmq==27.0.1
-referencing==0.36.2
+    # via
+    #   gradio
+    #   huggingface-hub
+    #   langchain
+    #   langchain-core
+    #   transformers
 regex==2025.7.34
+    # via transformers
 requests==2.32.4
+    # via
+    #   google-api-core
+    #   huggingface-hub
+    #   langchain
+    #   langsmith
+    #   requests-toolbelt
+    #   transformers
 requests-toolbelt==1.0.0
-rfc3339-validator==0.1.4
-rfc3986-validator==0.1.1
-rfc3987-syntax==1.1.0
-rpds-py==0.26.0
+    # via langsmith
+rich==14.1.0
+    # via typer
+rsa==4.9.1
+    # via google-auth
 ruff==0.12.7
+    # via gradio
+safehttpx==0.1.6
+    # via gradio
 safetensors==0.5.3
+    # via transformers
 scikit-learn==1.7.1
+    # via sentence-transformers
 scipy==1.16.1
-send2trash==1.8.3
+    # via
+    #   scikit-learn
+    #   sentence-transformers
+semantic-version==2.10.0
+    # via gradio
 sentence-transformers==5.0.0
+    # via fot-intervention-recommender (pyproject.toml)
 setuptools==80.9.0
+    # via fot-intervention-recommender (pyproject.toml)
+shellingham==1.5.4
+    # via typer
 six==1.17.0
+    # via python-dateutil
 sniffio==1.3.1
-soupsieve==2.7
+    # via anyio
 sqlalchemy==2.0.42
-stack-data==0.6.3
+    # via langchain
+starlette==0.47.2
+    # via
+    #   fastapi
+    #   gradio
 sympy==1.14.0
+    # via torch
 tenacity==9.1.2
-terminado==0.18.1
+    # via langchain-core
 threadpoolctl==3.6.0
-tinycss2==1.4.0
+    # via scikit-learn
 tokenizers==0.21.4
+    # via transformers
+tomlkit==0.13.3
+    # via gradio
 torch==2.2.2
-tornado==6.5.1
+    # via
+    #   fot-intervention-recommender (pyproject.toml)
+    #   sentence-transformers
 tqdm==4.67.1
-traitlets==5.14.3
+    # via
+    #   google-generativeai
+    #   huggingface-hub
+    #   sentence-transformers
+    #   transformers
 transformers==4.54.1
-types-python-dateutil==2.9.0.20250708
+    # via
+    #   fot-intervention-recommender (pyproject.toml)
+    #   sentence-transformers
+typer==0.16.0
+    # via gradio
 typing-extensions==4.14.1
+    # via
+    #   anyio
+    #   fastapi
+    #   google-generativeai
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   langchain-core
+    #   pydantic
+    #   pydantic-core
+    #   sentence-transformers
+    #   sqlalchemy
+    #   starlette
+    #   torch
+    #   typer
+    #   typing-inspection
 typing-inspection==0.4.1
-uri-template==1.3.0
+    # via pydantic
+tzdata==2025.2
+    # via pandas
+uritemplate==4.2.0
+    # via google-api-python-client
 urllib3==2.5.0
-wcwidth==0.2.13
-webcolors==24.11.1
-webencodings==0.5.1
-websocket-client==1.8.0
+    # via requests
+uvicorn==0.35.0
+    # via gradio
+websockets==15.0.1
+    # via gradio-client
 zstandard==0.23.0
+    # via langsmith

scripts/build_knowledge_base.py

@@ -1,58 +1,58 @@
 import json
 import sys
+import faiss
+import numpy as np
 from pathlib import Path
 
-# This allows the script to find and import modules from your 'src' directory
-# by adding the project's root folder to the list of paths Python searches.
 project_root = Path(__file__).parent.parent
 sys.path.append(str(project_root))
 
-from src.fot_recommender.config import RAW_KB_PATH, PROCESSED_DATA_DIR  # noqa: E402
-from src.fot_recommender.semantic_chunker import chunk_by_concept  # noqa: E402
+from src.fot_recommender.config import PROCESSED_DATA_DIR, RAW_KB_PATH # noqa: E402
+from src.fot_recommender.semantic_chunker import chunk_by_concept # noqa: E402
+from src.fot_recommender.rag_pipeline import (  # noqa: E402
+    initialize_embedding_model,
+    create_embeddings,
+)
 
 
 def build():
     """
-    This script performs the knowledge base build process.
-    1. Loads the raw, manually curated knowledge base.
-    2. Uses the semantic chunker to group entries by concept.
-    3. Saves the final, consolidated chunks to the file that the main
-       RAG pipeline expects ('knowledge_base_final_chunks.json').
+    Builds the entire knowledge base artifact set needed by the application:
+    1.  The processed, semantically chunked JSON file.
+    2.  The Facebook AI Similarity Search (FAISS) vector index file (`faiss_index.bin`).
     """
-    print("--- Building Final Knowledge Base ---")
+    print("--- Building Final Knowledge Base and FAISS Index ---")
 
-    # Define the path for the output file
+    # --- Create Final Chunks ---
     final_chunks_path = PROCESSED_DATA_DIR / "knowledge_base_final_chunks.json"
-
-    # 1. Load the raw knowledge base file
     print(f"Loading raw knowledge base from: {RAW_KB_PATH}")
-    try:
-        with open(RAW_KB_PATH, "r", encoding="utf-8") as f:
-            raw_kb = json.load(f)
-    except FileNotFoundError:
-        print(f"ERROR: Raw knowledge base file not found at {RAW_KB_PATH}. Halting.")
-        return
-
-    print(f"Loaded {len(raw_kb)} raw entries.")
+    with open(RAW_KB_PATH, "r", encoding="utf-8") as f:
+        raw_kb = json.load(f)
 
-    # 2. Process and chunk the knowledge base using the existing chunker
-    print("Applying semantic chunking to consolidate related content...")
     final_chunks = chunk_by_concept(raw_kb)
-    print(f"Created {len(final_chunks)} final semantic chunks.")
-
-    # 3. Save the final chunked file
-    print(f"Saving final chunked knowledge base to: {final_chunks_path}")
-
-    # Ensure the 'processed' directory exists before trying to write to it
     PROCESSED_DATA_DIR.mkdir(parents=True, exist_ok=True)
-
     with open(final_chunks_path, "w", encoding="utf-8") as f:
-        # We use indent=4 to make the final JSON file human-readable,
-        # which is extremely helpful for debugging and verification.
         json.dump(final_chunks, f, indent=4)
+    print(f"✅ Saved {len(final_chunks)} semantic chunks to {final_chunks_path}")
+
+    # --- Create and Save FAISS Index ---
+    faiss_index_path = PROCESSED_DATA_DIR / "faiss_index.bin"
+    print("\n--- Creating FAISS Index ---")
+
+    model = initialize_embedding_model()
+    embeddings = create_embeddings(final_chunks, model)
+
+    # Explicitly set dtype for FAISS
+    embeddings = np.asarray(embeddings).astype("float32")
+
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatIP(dimension)
+    index.add(embeddings)  # type: ignore
+
+    faiss.write_index(index, str(faiss_index_path))
+    print(f"✅ Saved FAISS index with {index.ntotal} vectors to {faiss_index_path}")
 
-    print("\n✅ Success! The final knowledge base is built and ready.")
-    print("You can now run the main application.")
+    print("\n🎉 Success! All artifacts are built and ready for the application.")
 
 
 if __name__ == "__main__":

src/fot_recommender/main.py

@@ -1,3 +1,7 @@
+import os
+
+from dotenv.main import load_dotenv
+
 from fot_recommender.config import PROCESSED_DATA_DIR
 from fot_recommender.rag_pipeline import (
     load_knowledge_base,
@@ -5,7 +9,7 @@ from fot_recommender.rag_pipeline import (
     create_embeddings,
     create_vector_db,
     search_interventions,
-    generate_recommendation_summary
+    generate_recommendation_summary,
 )
 
 # --- Sample Student Profile from Project Description ---
@@ -36,9 +40,9 @@ def main():
     4. Sets up a FAISS vector database.
     5. Tests the retrieval system with the sample student profile.
     """
-    print("--- FOT Intervention Recommender: Phase 2 ---")
+    print("--- FOT Intervention Recommender ---")
 
-    # --- Load the final knowledge base created in Phase 1 ---
+    # --- Load the final knowledge base ---
     final_chunks_path = PROCESSED_DATA_DIR / "knowledge_base_final_chunks.json"
     knowledge_base_chunks = load_knowledge_base(str(final_chunks_path))
 
@@ -49,18 +53,18 @@ def main():
     print(f"Successfully loaded {len(knowledge_base_chunks)} processed chunks.")
     print("-" * 50)
 
-    # --- Phase 2.1: Vector Embedding Setup ---
+    # --- Vector Embedding Setup ---
     embedding_model = initialize_embedding_model()
 
-    # --- Phase 2.2: Create Embeddings for Knowledge Base ---
+    # --- Create Embeddings for Knowledge Base ---
     embeddings = create_embeddings(knowledge_base_chunks, embedding_model)
 
-    # --- Phase 2.3: Set up FAISS Vector Database ---
+    # --- Set up FAISS Vector Database ---
     vector_db = create_vector_db(embeddings)
 
     print("-" * 50)
 
-    # --- Phase 2.4: Test Retrieval with Sample Student Profile ---
+    # --- Test Retrieval with Sample Student Profile ---
     student_query = sample_student_profile["narrative_summary_for_embedding"]
 
     # Find the top 3 most relevant interventions
@@ -78,21 +82,31 @@ def main():
         return
 
     # --- 4. Generate Synthesized Recommendation (for 'teacher' persona) ---
+    load_dotenv()
+    api_key = os.getenv("FOT_GOOGLE_API_KEY")
+    if not api_key:
+        return "ERROR: FOT_GOOGLE_API_KEY not found. Please create a .env file and add your key."
+
     synthesized_recommendation = generate_recommendation_summary(
-        top_interventions, student_query, persona="teacher"
+        top_interventions, 
+        student_query, 
+        api_key=api_key,
+        persona="teacher"
     )
 
     # --- 5. Display Final Output ---
-    print("\n" + "="*50)
+    print("\n" + "=" * 50)
     print("      FINAL SYNTHESIZED RECOMMENDATION FOR EDUCATOR")
-    print("="*50 + "\n")
+    print("=" * 50 + "\n")
     print(synthesized_recommendation)
-    
-    print("\n" + "-"*50)
+
+    print("\n" + "-" * 50)
     print("Evidence retrieved from the following sources:")
     for chunk, score in top_interventions:
-        print(f"- {chunk['title']} (Source: {chunk['source_document']}, Relevance: {score:.2f})")
-    
+        print(
+            f"- {chunk['title']} (Source: {chunk['source_document']}, Relevance: {score:.2f})"
+        )
+
     print("\n\n✅ Full RAG process complete!")
 
 

src/fot_recommender/rag_pipeline.py

@@ -2,8 +2,7 @@ import faiss  # type: ignore
 import json
 import numpy as np
 import google.generativeai as genai
-import os
-from dotenv import load_dotenv
+
 from sentence_transformers import SentenceTransformer
 from typing import List, Dict, Any, Tuple
 from fot_recommender.prompts import PROMPT_TEMPLATES
@@ -108,17 +107,13 @@ def search_interventions(
 def generate_recommendation_summary(
     retrieved_chunks: List[Tuple[Dict[str, Any], float]],
     student_narrative: str,
+    api_key: str,
     persona: str = "teacher",
 ) -> str:
     """
     Generates a synthesized recommendation using the Google Gemini API,
     tailored to a specific persona.
     """
-    load_dotenv()
-    api_key = os.getenv("GOOGLE_API_KEY")
-    if not api_key:
-        return "ERROR: GOOGLE_API_KEY not found. Please create a .env file and add your key."
-
     genai.configure(api_key=api_key)  # type: ignore
 
     if persona not in PROMPT_TEMPLATES:

uv.lock

@@ -7,6 +7,15 @@ resolution-markers = [
     "python_full_version < '3.13'",
 ]
 
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+wheels = [
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+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -151,6 +160,62 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
 ]
 
+[[package]]
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