feat(recommender): Implement generative synthesis for educator-friendly output

This commit completes the core Proof-of-Concept by implementing the final Generation step of the RAG pipeline. The system now moves beyond simple document retrieval to provide a fully synthesized, actionable recommendation tailored for educators.

This fulfills the critical project requirement that the output must be clear and directly usable, not just a list of raw search results. This work completes the objectives outlined in Phase 3 of the implementation plan.

Key Changes:
- **Added Generative Synthesis**: Introduced a function in . This function leverages the Google Gemini API to process the top-k retrieved document chunks and synthesize them into a coherent, practical recommendation.
- **Created Prompt Templates**: Added a new module to store and manage persona-specific prompts. The initial implementation includes a detailed prompt for the 'teacher' persona, designed to produce professional and encouraging output.
- **Integrated into Main Application**: Refactored to call the new synthesis function. The application now prints a final, formatted recommendation, including the synthesized advice and the source documents used as evidence.
- **Updated Dependencies**: Added and to to support the new generative capabilities.

Docs:
- Updated to reflect the completion of Phase 3 tasks.

pyproject.toml

@@ -23,6 +23,8 @@ dependencies = [
     "sentence-transformers",
     "faiss-cpu",
     "transformers",
+    "google-generativeai",
+    "python-dotenv",
 ]
 
 [project.scripts]
@@ -42,4 +44,15 @@ dev = [
 where = ["src"]
 
 [tool.setuptools.package-dir]
-"" = "src"
+"" = "src"
+
+[tool.ruff.lint]
+# Add any specific rules you want to enforce here in the future.
+# For now, we will just define what to exclude.
+
+exclude = [
+    ".venv",
+    "*/__pycache__/*", # Also excludes pycache within subdirectories
+    "src/fot_intervention_recommender.egg-info",
+    "*.ipynb", # <-- THIS IS THE KEY LINE TO EXCLUDE ALL NOTEBOOKS
+]

scripts/build_knowledge_base.py

@@ -7,8 +7,9 @@ from pathlib import Path
 project_root = Path(__file__).parent.parent
 sys.path.append(str(project_root))
 
-from src.fot_recommender.config import RAW_KB_PATH, PROCESSED_DATA_DIR
-from src.fot_recommender.semantic_chunker import chunk_by_concept
+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
+
 
 def build():
     """
@@ -31,7 +32,7 @@ def build():
     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.")
 
     # 2. Process and chunk the knowledge base using the existing chunker
@@ -41,10 +42,10 @@ def build():
 
     # 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.
@@ -53,5 +54,6 @@ def build():
     print("\n✅ Success! The final knowledge base is built and ready.")
     print("You can now run the main application.")
 
+
 if __name__ == "__main__":
-    build()
+    build()

src/fot_recommender/__init__.py

@@ -1,3 +1,3 @@
-from .main import main
+from fot_recommender.main import main
 
 __all__ = ["main"]

src/fot_recommender/main.py

@@ -1,10 +1,11 @@
-from .config import PROCESSED_DATA_DIR
-from .rag_pipeline import (
+from fot_recommender.config import PROCESSED_DATA_DIR
+from fot_recommender.rag_pipeline import (
     load_knowledge_base,
     initialize_embedding_model,
     create_embeddings,
     create_vector_db,
     search_interventions,
+    generate_recommendation_summary
 )
 
 # --- Sample Student Profile from Project Description ---
@@ -68,26 +69,32 @@ def main():
         model=embedding_model,
         index=vector_db,
         knowledge_base=knowledge_base_chunks,
+        min_similarity_score=0.4,
         k=3,
     )
 
-    print("\n--- Top 3 Recommended Intervention Chunks ---")
-    for i, (chunk, score) in enumerate(top_interventions):
-        print(f"\n--- Recommendation {i + 1} (Score: {score:.4f}) ---")
-        print(f"Title: {chunk['title']}")
-        print(f"Source: {chunk['source_document']} ({chunk['fot_pages']})")
-        # To keep the output clean, we'll show the first 300 chars of the content
-        print(f"Content Snippet: {chunk['original_content'][:300]}...")
+    if not top_interventions:
+        print("Could not find relevant interventions for the student.")
+        return
 
-    print("-" * 50)
-    print("\n✅ PHASE 2 (RAG Pipeline Implementation) is complete!")
-    print(
-        "The system can now retrieve relevant interventions based on a student narrative."
-    )
-    print(
-        "\nNext step: Phase 3 - System Integration & Testing (Formatting the final output for educators)."
+    # --- 4. Generate Synthesized Recommendation (for 'teacher' persona) ---
+    synthesized_recommendation = generate_recommendation_summary(
+        top_interventions, student_query, persona="teacher"
     )
 
+    # --- 5. Display Final Output ---
+    print("\n" + "="*50)
+    print("      FINAL SYNTHESIZED RECOMMENDATION FOR EDUCATOR")
+    print("="*50 + "\n")
+    print(synthesized_recommendation)
+    
+    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("\n\n✅ Full RAG process complete!")
+
 
 if __name__ == "__main__":
     main()

src/fot_recommender/prompts.py

@@ -0,0 +1,37 @@
+PROMPT_TEMPLATES = {
+    "teacher": """
+You are an expert educational strategist AI, a supportive co-pilot for high school teachers. Your tone is professional, encouraging, and practical.
+
+**Student Profile:**
+{student_narrative}
+
+**Evidence-Based Interventions:**
+Based on the student's profile, the following intervention strategies have been identified from our knowledge base:
+
+--- BEGIN CONTEXT ---
+{context}
+--- END CONTEXT ---
+
+**Your Task:**
+Synthesize the provided context to write a concise, actionable recommendation for the student's teacher.
+
+**Instructions:**
+1.  Start with a brief summary of the student's key challenges.
+2.  Recommend 2-3 concrete, actionable strategies derived *only* from the provided context.
+3.  For each strategy, briefly explain *why* it is relevant to this student, citing the core ideas from the sources.
+4.  Do not invent information. Ground your entire response in the provided context.
+5.  Format the output clearly using Markdown for readability.
+""",
+    "parent": """
+# THIS IS A PLACEHOLDER PROMPT. A detailed prompt would be developed next.
+# Synthesize the context into simple, non-jargon language for a parent.
+# Student: {student_narrative}
+# Context: {context}
+""",
+    "principal": """
+# THIS IS A PLACEHOLDER PROMPT. A detailed prompt would be developed next.
+# Synthesize the context into a strategic overview for a principal.
+# Student: {student_narrative}
+# Context: {context}
+""",
+}

src/fot_recommender/rag_pipeline.py

@@ -1,8 +1,12 @@
 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
 
 
 def load_knowledge_base(path: str) -> List[Dict[str, Any]]:
@@ -72,6 +76,7 @@ def search_interventions(
     index: faiss.Index,
     knowledge_base: List[Dict[str, Any]],
     k: int = 3,
+    min_similarity_score: float = 0.4,
 ) -> List[Tuple[Dict[str, Any], float]]:
     """
     Performs a semantic search to find the most relevant interventions.
@@ -92,5 +97,53 @@ def search_interventions(
         if i != -1:  # FAISS returns -1 for no result
             results.append((knowledge_base[i], score))
 
-    print(f"Found {len(results)} relevant interventions.")
-    return results
+    filtered_results = [
+        (chunk, score) for chunk, score in results if score >= min_similarity_score
+    ]
+
+    print(f"Found {len(filtered_results)} relevant interventions.")
+    return filtered_results
+
+
+def generate_recommendation_summary(
+    retrieved_chunks: List[Tuple[Dict[str, Any], float]],
+    student_narrative: 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:
+        return f"ERROR: Persona '{persona}' is not a valid choice."
+
+    # 1. Prepare the context from retrieved chunks for the prompt
+    context = ""
+    for i, (chunk, _) in enumerate(retrieved_chunks):
+        context += f"--- Intervention Chunk {i + 1} ---\n"
+        context += f"Title: {chunk['title']}\n"
+        context += f"Content: {chunk['original_content']}\n"
+        context += f"(Source Document: {chunk['source_document']})\n\n"
+
+    # 2. Select and format the persona-specific prompt
+    prompt_template = PROMPT_TEMPLATES[persona]
+    prompt = prompt_template.format(
+        student_narrative=student_narrative, context=context
+    )
+
+    # 3. Call the Gemini API
+    try:
+        print(f"\nSynthesizing recommendation for persona: '{persona}' using Gemini...")
+        model = genai.GenerativeModel("gemini-1.5-flash-latest")  # type: ignore
+        response = model.generate_content(prompt)
+        print("Synthesis complete.")
+        return response.text
+    except Exception as e:
+        return f"An error occurred while calling the Gemini API: {e}"

src/fot_recommender/semantic_chunker.py

@@ -1,6 +1,7 @@
 import collections
 from typing import List, Dict, Any
 
+
 def _serialize_table_to_markdown(table_data: List[Dict[str, Any]]) -> str:
     """
     Converts a list of dictionaries (representing a table) into a Markdown string.
@@ -11,28 +12,31 @@ def _serialize_table_to_markdown(table_data: List[Dict[str, Any]]) -> str:
 
     # Extract headers from the first row
     headers = table_data[0].keys()
-    
+
     # Create Markdown header row and separator row
     md_header = "| " + " | ".join(headers) + " |"
-    md_separator = "|-" + "-|-".join(['-' * len(h) for h in headers]) + "-|" # Basic separator
+    md_separator = (
+        "|-" + "-|-".join(["-" * len(h) for h in headers]) + "-|"
+    )  # Basic separator
 
     # Create Markdown data rows
     md_rows = [md_header, md_separator]
     for row_dict in table_data:
         # Ensure all keys from headers are present, even if with None values
-        row_values = [str(row_dict.get(header, '')) for header in headers]
+        row_values = [str(row_dict.get(header, "")) for header in headers]
         md_rows.append("| " + " | ".join(row_values) + " |")
-    
+
     return "\n".join(md_rows)
 
+
 def chunk_by_concept(raw_knowledge_base: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
     """
-    Groups items from the raw knowledge base by a composite key of 
+    Groups items from the raw knowledge base by a composite key of
     (source_document, concept) to create high-quality, coherent semantic chunks.
     Includes table data serialization for embedding.
 
     Args:
-        raw_knowledge_base: A list of dictionaries, where each dict represents a page 
+        raw_knowledge_base: A list of dictionaries, where each dict represents a page
                             or a piece of content from the source documents.
 
     Returns:
@@ -40,39 +44,43 @@ def chunk_by_concept(raw_knowledge_base: List[Dict[str, Any]]) -> List[Dict[str,
     """
     grouped_by_source_and_concept = collections.defaultdict(list)
     for item in raw_knowledge_base:
-        composite_key = (item['source_document'], item['concept'])
+        composite_key = (item["source_document"], item["concept"])
         grouped_by_source_and_concept[composite_key].append(item)
 
     final_chunks = []
     for (source_doc, concept), items in grouped_by_source_and_concept.items():
-        items.sort(key=lambda x: x.get('absolute_page', 0))
+        items.sort(key=lambda x: x.get("absolute_page", 0))
 
         # Collect all content and any table data
         all_content_parts = []
         for item in items:
-            if item.get('content'):
-                all_content_parts.append(item['content'])
-            if item.get('table_data'):
+            if item.get("content"):
+                all_content_parts.append(item["content"])
+            if item.get("table_data"):
                 # Serialize table data to Markdown and add it as a content part
-                table_md = _serialize_table_to_markdown(item['table_data'])
-                if table_md: # Only add if serialization produced something
+                table_md = _serialize_table_to_markdown(item["table_data"])
+                if table_md:  # Only add if serialization produced something
                     all_content_parts.append(f"\nExample Table:\n{table_md}")
-        
+
         combined_content = "\n\n".join(all_content_parts).strip()
-        
-        pages = sorted(list(set(item['absolute_page'] for item in items if 'absolute_page' in item)))
+
+        pages = sorted(
+            list(
+                set(item["absolute_page"] for item in items if "absolute_page" in item)
+            )
+        )
         page_str = f"Pages: {', '.join(map(str, pages))}" if pages else "N/A"
-        
+
         # Prepend title to content for embedding
         content_for_embedding = f"Title: {concept}. Content: {combined_content}"
-        
+
         final_chunk = {
             "title": concept,
             "source_document": source_doc,
             "fot_pages": page_str,
             "content_for_embedding": content_for_embedding,
-            "original_content": combined_content # Keep original for potential display
+            "original_content": combined_content,  # Keep original for potential display
         }
         final_chunks.append(final_chunk)
-        
-    return final_chunks
+
+    return final_chunks

src/fot_recommender/utils.py

@@ -1,5 +1,6 @@
 from typing import List, Dict, Any, Tuple
 
+
 def display_recommendations(results: List[Tuple[Dict[str, Any], float]]):
     """
     A helper function to neatly print the results of a semantic search.
@@ -18,9 +19,9 @@ def display_recommendations(results: List[Tuple[Dict[str, Any], float]]):
         print(f"\n--- Recommendation {i + 1} (Similarity Score: {score:.4f}) ---")
         print(f"  Title: {chunk['title']}")
         print(f"  Source: {chunk['source_document']} ({chunk['fot_pages']})")
-        
+
         # Indent the content for better readability
-        content = chunk['original_content']
+        content = chunk["original_content"]
         indented_content = "\n  ".join(content.splitlines())
-        print(f"  \n  Content Snippet:\n  \"{indented_content[:500]}...\"")
+        print(f'  \n  Content Snippet:\n  "{indented_content[:500]}..."')
         print("-" * 50)

uv.lock

@@ -3,7 +3,8 @@ revision = 2
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14'",
-    "python_full_version < '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
 ]
 
 [[package]]
@@ -80,7 +81,8 @@ name = "argon2-cffi-bindings"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
 ]
 dependencies = [
     { name = "cffi", marker = "python_full_version < '3.14'" },
@@ -212,6 +214,15 @@ css = [
     { name = "tinycss2" },
 ]
 
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/81/3747dad6b14fa2cf53fcf10548cf5aea6913e96fab41a3c198676f8948a5/cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4", size = 28380, upload-time = "2025-02-20T21:01:19.524Z" }
+wheels = [
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+]
+
 [[package]]
 name = "certifi"
 version = "2025.8.3"
@@ -415,8 +426,10 @@ version = "0.1.0"
 source = { editable = "." }
 dependencies = [
     { name = "faiss-cpu" },
+    { name = "google-generativeai" },
     { name = "langchain" },
     { name = "numpy" },
+    { name = "python-dotenv" },
     { name = "sentence-transformers" },
     { name = "setuptools" },
     { name = "torch" },
@@ -437,12 +450,14 @@ dev = [
 requires-dist = [
     { name = "black", marker = "extra == 'dev'", specifier = ">=25.1.0" },
     { name = "faiss-cpu" },
+    { name = "google-generativeai" },
     { name = "jupyterlab", marker = "extra == 'dev'", specifier = ">=4.0" },
     { name = "langchain" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.16.1" },
     { name = "notebook", marker = "extra == 'dev'", specifier = ">=7.0" },
     { name = "numpy", specifier = "<2.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.4.1" },
+    { name = "python-dotenv" },
     { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.12.2" },
     { name = "sentence-transformers" },
     { name = "setuptools", specifier = ">=80.9.0" },
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 ]
 
+[[package]]
+name = "google-ai-generativelanguage"
+version = "0.6.15"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-api-core", extra = ["grpc"] },
+    { name = "google-auth" },
+    { name = "proto-plus" },
+    { name = "protobuf" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/11/d1/48fe5d7a43d278e9f6b5ada810b0a3530bbeac7ed7fcbcd366f932f05316/google_ai_generativelanguage-0.6.15.tar.gz", hash = "sha256:8f6d9dc4c12b065fe2d0289026171acea5183ebf2d0b11cefe12f3821e159ec3", size = 1375443, upload-time = "2025-01-13T21:50:47.459Z" }
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+]
+
+[[package]]
+name = "google-api-core"
+version = "2.25.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "googleapis-common-protos" },
+    { name = "proto-plus" },
+    { name = "protobuf" },
+    { name = "requests" },
+]
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+]
+
+[package.optional-dependencies]
+grpc = [
+    { name = "grpcio" },
+    { name = "grpcio-status" },
+]
+
+[[package]]
+name = "google-api-python-client"
+version = "2.178.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-api-core" },
+    { name = "google-auth" },
+    { name = "google-auth-httplib2" },
+    { name = "httplib2" },
+    { name = "uritemplate" },
+]
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+]
+
+[[package]]
+name = "google-auth"
+version = "2.40.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cachetools" },
+    { name = "pyasn1-modules" },
+    { name = "rsa" },
+]
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