Perfect! Now I have all the information. Let me compile a comprehensive report with all the file contents and analysis.

## COMPLETE CODEBASE EXPLORATION REPORT: ReviewLens AI

### 1. DIRECTORY STRUCTURE

```
/Users/shaynelarocque/Documents/GitHub/futuresight/
├── knowledge/
│   ├── briefbot-casestudy.md          (28.8 KB - BriefBot reference project)
│   └── take-home.md                   (4.9 KB - Assignment spec)
└── reviewlens/
    ├── app/
    │   ├── __init__.py                (Empty)
    │   ├── agent.py                   (111 lines - Conversational agent loop)
    │   ├── ingest.py                  (498 lines - CSV parsing & URL scraping)
    │   ├── main.py                    (416 lines - FastAPI application)
    │   ├── models.py                  (56 lines - Pydantic data models)
    │   ├── prompts.py                 (69 lines - System prompt builder)
    │   ├── store.py                   (106 lines - File-based session persistence)
    │   ├── tools.py                   (410 lines - MCP tools for agent)
    │   └── vectordb.py                (152 lines - ChromaDB wrapper)
    ├── static/
    │   ├── app.js                     (17 KB - Frontend JS)
    │   └── style.css                  (27 KB - Styling)
    ├── templates/
    │   ├── app.html                   (21 KB - Main app template)
    │   └── partials/
    │       └── error.html
    ├── .env.example
    ├── .gitignore
    ├── requirements.txt
    ├── README.md
    ├── render.yaml
    └── Dockerfile
```

**Total Python LOC: ~1,810 lines**

---

### 2. COMPLETE FILE CONTENTS

#### **app/prompts.py** (69 lines)
```python
"""System prompt builder for the ReviewLens agent."""

from __future__ import annotations

from .models import IngestionSummary


def build_system_prompt(summary: IngestionSummary) -> str:
    """Build the scope-guarded system prompt for the conversational agent."""

    rating_info = ""
    if summary.rating_distribution:
        dist_lines = [f"  {k}★: {v} reviews" for k, v in sorted(summary.rating_distribution.items())]
        rating_info = "\n".join(dist_lines)

    return f"""You are ReviewLens AI, a review intelligence analyst. You help users analyse customer reviews that have been ingested into your system.

## Dataset Context

- Product/Entity: {summary.product_name}
- Platform: {summary.platform}
- Total Reviews: {summary.total_reviews}
- Date Range: {summary.date_range or "Unknown"}
- Average Rating: {summary.average_rating or "Unknown"}
- Rating Distribution:
{rating_info or "  Not available"}

## CRITICAL: Scope Guard — Your #1 Rule

You MUST ONLY answer questions about the ingested review dataset described above. This is non-negotiable.

**You MUST refuse if the user asks about:**
- Reviews from other platforms (e.g., if data is from Amazon, refuse questions about Google Maps reviews)
- Competitor products or brands not mentioned in the reviews
- General knowledge, news, current events, or facts not in the dataset
- Predictions, forecasts, or speculation beyond what the data shows
- Anything requiring information you don't have from the reviews

**How to refuse:**
- Be friendly but firm: "I can only analyse the {summary.total_reviews} {summary.platform} reviews for {summary.product_name} that were uploaded. I don't have access to [what they asked about]. Would you like to explore something in this dataset instead?"
- Always suggest a relevant alternative question about the actual data.

**Before answering ANY question, verify:**
1. Can this be answered using ONLY the ingested reviews?
2. If you used the search_reviews tool, did it return relevant results?
3. Are you about to state something that comes from your general knowledge rather than the data? If so, STOP.

If your search returns no relevant results, say so honestly. Never fill gaps with general knowledge.

## Your Tools

You have access to these tools — use them to give data-grounded answers:

- **search_reviews**: Semantic search over the review database. Use this to find reviews relevant to the user's question. Always search before answering.
- **analyze_sentiment**: Extract aspects and sentiment from a set of reviews. Use for sentiment breakdowns, aspect analysis, and opinion mining.
- **generate_chart**: Create a Chart.js chart that renders inline in the chat. Use when a visual would communicate the answer better than text. Keep charts clean and focused.
- **calculate_stats**: Run aggregations, distributions, and trend analysis on the review data. Use for quantitative questions.
- **suggest_follow_ups**: Generate contextual follow-up question buttons based on what was just discussed. Call this at the end of EVERY response.

## Response Guidelines

1. **Always search first.** Before answering any question about the reviews, call search_reviews to ground your response in actual data.
2. **Cite specific reviews.** Quote or paraphrase actual review text to support your claims. Use phrases like "One reviewer noted..." or "Several reviews mention..."
3. **Use charts strategically.** A chart adds value for distributions, trends over time, and comparisons. Don't chart everything — use them when visual communication is genuinely better.
4. **Be quantitative.** When you can give numbers (counts, percentages, averages), do so. Use calculate_stats for aggregations.
5. **Always suggest follow-ups.** End every response by calling suggest_follow_ups to give the user contextual next steps.
6. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with data, suggest next steps.
"""
```

**Key Design:** Strict scope-guarding with 3-layer defense: explicit refusal instructions, retrieval validation, and architectural boundary (no tools access external data).

---

#### **app/tools.py** (410 lines)
```python
"""MCP tools for the ReviewLens agent, using create_sdk_mcp_server."""

from __future__ import annotations

import json
from collections import Counter
from typing import Any, Callable, Awaitable

from claude_agent_sdk import create_sdk_mcp_server, tool

from . import vectordb

# Type alias for the SSE emit callback
EmitFn = Callable[[str, str, str], Awaitable[None]]


def create_review_tools_server(
    session_id: str,
    emit_fn: EmitFn,
):
    """Create the MCP server with all review analysis tools.

    Like briefbot, uses closure over session_id so tools access the right data.
    """

    # ── search_reviews ───────────────────────────────────────────────

    @tool(
        name="search_reviews",
        description="Semantic search over the ingested review database. Use this to find reviews relevant to the user's question. Returns the most relevant reviews ranked by similarity.",
        input_schema={
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query — describe what you're looking for in natural language.",
                },
                "n_results": {
                    "type": "integer",
                    "description": "Number of results to return (default 10, max 25).",
                    "default": 10,
                },
                "min_rating": {
                    "type": "number",
                    "description": "Optional: only return reviews with rating >= this value.",
                },
                "max_rating": {
                    "type": "number",
                    "description": "Optional: only return reviews with rating <= this value.",
                },
            },
            "required": ["query"],
        },
    )
    async def search_reviews_tool(args: dict[str, Any]) -> dict[str, Any]:
        query = args["query"]
        n = min(args.get("n_results", 10), 25)

        where = None
        if "min_rating" in args and "max_rating" in args:
            where = {
                "$and": [
                    {"rating": {"$gte": args["min_rating"]}},
                    {"rating": {"$lte": args["max_rating"]}},
                ]
            }
        elif "min_rating" in args:
            where = {"rating": {"$gte": args["min_rating"]}}
        elif "max_rating" in args:
            where = {"rating": {"$lte": args["max_rating"]}}

        results = vectordb.search_reviews(session_id, query, n_results=n, where=where)

        await emit_fn(
            session_id,
            f"Searched reviews: \"{query}\" — {len(results)} results",
            "tool",
        )

        return {
            "content": [
                {
                    "type": "text",
                    "text": json.dumps(
                        {
                            "query": query,
                            "result_count": len(results),
                            "results": results,
                            "note": "If no results are relevant, tell the user you couldn't find matching reviews. Do NOT make up information.",
                        }
                    ),
                }
            ]
        }

    # ── analyze_sentiment ────────────────────────────────────────────

    @tool(
        name="analyze_sentiment",
        description="Analyse sentiment and extract aspects from reviews matching a query. Returns aspect-sentiment pairs and overall sentiment distribution. Use for questions about what people like/dislike, pain points, praise, etc.",
        input_schema={
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "Search query to find relevant reviews for analysis.",
                },
                "n_reviews": {
                    "type": "integer",
                    "description": "Number of reviews to analyse (default 15, max 30).",
                    "default": 15,
                },
            },
            "required": ["query"],
        },
    )
    async def analyze_sentiment_tool(args: dict[str, Any]) -> dict[str, Any]:
        query = args["query"]
        n = min(args.get("n_reviews", 15), 30)

        results = vectordb.search_reviews(session_id, query, n_results=n)

        if not results:
            return {
                "content": [
                    {
                        "type": "text",
                        "text": json.dumps({"error": "No reviews found matching query.", "results": []}),
                    }
                ]
            }

        # Provide the raw reviews to the LLM — it does the actual sentiment analysis.
        # This is more flexible and accurate than a rule-based approach.
        await emit_fn(
            session_id,
            f"Analysing sentiment: \"{query}\" — {len(results)} reviews",
            "tool",
        )

        return {
            "content": [
                {
                    "type": "text",
                    "text": json.dumps(
                        {
                            "query": query,
                            "review_count": len(results),
                            "reviews": results,
                            "instruction": (
                                "Analyse these reviews for sentiment and aspects. "
                                "For each review, identify: (1) key aspects mentioned "
                                "(e.g., 'battery life', 'customer service', 'price'), "
                                "(2) sentiment per aspect (positive/negative/neutral/mixed), "
                                "(3) overall sentiment. Then summarise the patterns across all reviews. "
                                "Only report what the reviews actually say."
                            ),
                        }
                    ),
                }
            ]
        }

    # ── generate_chart ───────────────────────────────────────────────

    @tool(
        name="generate_chart",
        description="Generate a Chart.js chart configuration that renders inline in the chat. Use for visualising rating distributions, sentiment breakdowns, trends over time, aspect comparisons, etc. The chart renders automatically — just return valid config.",
        input_schema={
            "type": "object",
            "properties": {
                "chart_type": {
                    "type": "string",
                    "enum": ["bar", "line", "pie", "doughnut"],
                    "description": "The type of chart to generate.",
                },
                "title": {
                    "type": "string",
                    "description": "Chart title displayed above the visualisation.",
                },
                "labels": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "X-axis labels (categories, dates, etc.).",
                },
                "datasets": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "label": {"type": "string"},
                            "data": {
                                "type": "array",
                                "items": {"type": "number"},
                            },
                        },
                        "required": ["label", "data"],
                    },
                    "description": "One or more data series.",
                },
            },
            "required": ["chart_type", "title", "labels", "datasets"],
        },
    )
    async def generate_chart_tool(args: dict[str, Any]) -> dict[str, Any]:
        chart_config = {
            "type": args["chart_type"],
            "title": args["title"],
            "data": {
                "labels": args["labels"],
                "datasets": args["datasets"],
            },
        }

        await emit_fn(
            session_id,
            f"Generated chart: {args['title']}",
            "tool",
        )

        return {
            "content": [
                {
                    "type": "text",
                    "text": json.dumps(
                        {
                            "chart": chart_config,
                            "instruction": "This chart will render automatically in the chat. Reference it in your text response.",
                        }
                    ),
                }
            ]
        }

    # ── calculate_stats ──────────────────────────────────────────────

    @tool(
        name="calculate_stats",
        description="Calculate aggregate statistics over the full review dataset. Use for quantitative questions: average ratings, distributions, counts by category, trends over time periods, etc.",
        input_schema={
            "type": "object",
            "properties": {
                "operation": {
                    "type": "string",
                    "enum": [
                        "rating_distribution",
                        "rating_over_time",
                        "review_volume_over_time",
                        "keyword_frequency",
                        "summary_stats",
                    ],
                    "description": "The type of statistical analysis to run.",
                },
                "keyword": {
                    "type": "string",
                    "description": "For keyword_frequency: the keyword or phrase to count.",
                },
            },
            "required": ["operation"],
        },
    )
    async def calculate_stats_tool(args: dict[str, Any]) -> dict[str, Any]:
        operation = args["operation"]
        all_reviews = vectordb.get_all_reviews(session_id)

        if not all_reviews:
            return {
                "content": [
                    {
                        "type": "text",
                        "text": json.dumps({"error": "No reviews in database."}),
                    }
                ]
            }

        result: dict[str, Any] = {"operation": operation}

        if operation == "rating_distribution":
            ratings = [r["metadata"].get("rating") for r in all_reviews if r["metadata"].get("rating") is not None]
            dist = Counter(int(round(r)) for r in ratings)
            result["distribution"] = {str(k): v for k, v in sorted(dist.items())}
            result["total_with_ratings"] = len(ratings)
            result["total_reviews"] = len(all_reviews)
            if ratings:
                result["average"] = round(sum(ratings) / len(ratings), 2)

        elif operation == "rating_over_time":
            by_month: dict[str, list[float]] = {}
            for r in all_reviews:
                date_str = r["metadata"].get("date", "")
                rating = r["metadata"].get("rating")
                if date_str and rating is not None:
                    month = date_str[:7]  # YYYY-MM
                    by_month.setdefault(month, []).append(rating)
            result["monthly_averages"] = {
                m: round(sum(v) / len(v), 2)
                for m, v in sorted(by_month.items())
            }

        elif operation == "review_volume_over_time":
            by_month: dict[str, int] = {}
            for r in all_reviews:
                date_str = r["metadata"].get("date", "")
                if date_str:
                    month = date_str[:7]
                    by_month[month] = by_month.get(month, 0) + 1
            result["monthly_volume"] = dict(sorted(by_month.items()))

        elif operation == "keyword_frequency":
            keyword = args.get("keyword", "").lower()
            if not keyword:
                return {
                    "content": [
                        {
                            "type": "text",
                            "text": json.dumps({"error": "keyword parameter required"}),
                        }
                    ]
                }
            count = sum(1 for r in all_reviews if keyword in r["text"].lower())
            result["keyword"] = keyword
            result["count"] = count
            result["percentage"] = round(count / len(all_reviews) * 100, 1)

        elif operation == "summary_stats":
            ratings = [r["metadata"].get("rating") for r in all_reviews if r["metadata"].get("rating") is not None]
            result["total_reviews"] = len(all_reviews)
            result["total_with_ratings"] = len(ratings)
            if ratings:
                result["average_rating"] = round(sum(ratings) / len(ratings), 2)
                result["min_rating"] = min(ratings)
                result["max_rating"] = max(ratings)
            dates = [r["metadata"].get("date", "") for r in all_reviews if r["metadata"].get("date")]
            if dates:
                result["earliest_review"] = min(dates)
                result["latest_review"] = max(dates)
            avg_length = sum(len(r["text"]) for r in all_reviews) / len(all_reviews)
            result["average_review_length"] = round(avg_length)

        await emit_fn(
            session_id,
            f"Calculated stats: {operation}",
            "tool",
        )

        return {
            "content": [
                {
                    "type": "text",
                    "text": json.dumps(result),
                }
            ]
        }

    # ── suggest_follow_ups ───────────────────────────────────────────

    @tool(
        name="suggest_follow_ups",
        description="Generate contextual follow-up question buttons based on the current conversation. Call this at the END of every response. The questions should be specific to what was just discussed and lead the user deeper into the data.",
        input_schema={
            "type": "object",
            "properties": {
                "questions": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "3-4 contextual follow-up questions. Make them specific and actionable.",
                    "minItems": 2,
                    "maxItems": 4,
                },
            },
            "required": ["questions"],
        },
    )
    async def suggest_follow_ups_tool(args: dict[str, Any]) -> dict[str, Any]:
        questions = args["questions"]

        await emit_fn(
            session_id,
            f"Suggested {len(questions)} follow-up questions",
            "tool",
        )

        return {
            "content": [
                {
                    "type": "text",
                    "text": json.dumps(
                        {
                            "follow_ups": questions,
                            "instruction": "These will appear as clickable buttons below your message. Do not repeat them in your text response.",
                        }
                    ),
                }
            ]
        }

    # ── Build and return the MCP server ──────────────────────────────

    return create_sdk_mcp_server(
        name="reviewlens",
        version="1.0.0",
        tools=[
            search_reviews_tool,
            analyze_sentiment_tool,
            generate_chart_tool,
            calculate_stats_tool,
            suggest_follow_ups_tool,
        ],
    )
```

**Key Design:** 5 core tools bound via closure over session_id. Each tool emits progress via SSE, returns JSON with context/instructions for the agent.

---

#### **app/agent.py** (111 lines)
```python
"""Conversational agent loop — invoked per-message, not per-session."""

from __future__ import annotations

import json
import os
from typing import Any

from claude_agent_sdk import (
    AssistantMessage,
    ClaudeAgentOptions,
    ClaudeSDKClient,
    HookMatcher,
    ResultMessage,
    TextBlock,
)

from .models import ChatMessage, IngestionSummary
from .prompts import build_system_prompt
from .tools import create_review_tools_server

DEFAULT_MODEL = "claude-sonnet-4-6"


async def handle_message(
    session_id: str,
    user_message: str,
    conversation_history: list[ChatMessage],
    summary: IngestionSummary,
    emit_fn,
) -> ChatMessage:
    """Run the agent for a single user message. Returns the assistant's response."""

    model = os.getenv("CLAUDE_MODEL", DEFAULT_MODEL)
    system_prompt = build_system_prompt(summary)

    # Build conversation context for the agent
    messages_for_context = []
    for msg in conversation_history[-20:]:  # Last 20 messages for context window
        messages_for_context.append(f"{'User' if msg.role == 'user' else 'Assistant'}: {msg.content}")

    prompt_parts = []
    if messages_for_context:
        prompt_parts.append("Previous conversation:\n" + "\n".join(messages_for_context))
    prompt_parts.append(f"User: {user_message}")
    prompt = "\n\n".join(prompt_parts)

    # Create per-request MCP server (closure over session_id)
    server = create_review_tools_server(
        session_id=session_id,
        emit_fn=emit_fn,
    )

    # Track tool outputs for charts and follow-ups
    charts: list[dict[str, Any]] = []
    follow_ups: list[str] = []

    async def post_tool_hook(input_data, tool_use_id, context):
        """Intercept tool results to extract charts and follow-ups."""
        # The tool result is in context
        try:
            result = context.get("result", {})
            content = result.get("content", [])
            for block in content:
                if block.get("type") == "text":
                    data = json.loads(block["text"])
                    if "chart" in data:
                        charts.append(data["chart"])
                    if "follow_ups" in data:
                        follow_ups.extend(data["follow_ups"])
        except (json.JSONDecodeError, KeyError, TypeError):
            pass
        return {}

    options = ClaudeAgentOptions(
        system_prompt=system_prompt,
        allowed_tools=["mcp__reviewlens__*"],
        permission_mode="bypassPermissions",
        max_turns=15,
        model=model,
        mcp_servers={"reviewlens": server},
        hooks={
            "PostToolUse": [HookMatcher(matcher=".*", hooks=[post_tool_hook])],
        },
    )

    response_text = ""

    try:
        async with ClaudeSDKClient(options=options) as client:
            await client.query(prompt)
            async for message in client.receive_response():
                if isinstance(message, AssistantMessage):
                    for block in message.content:
                        if isinstance(block, TextBlock) and block.text.strip():
                            response_text += block.text.strip() + "\n"
                elif isinstance(message, ResultMessage):
                    if message.result and not response_text.strip():
                        response_text = message.result

    except Exception as e:
        response_text = f"I encountered an error processing your question. Please try again.\n\nError: {str(e)}"
        await emit_fn(session_id, f"Agent error: {e}", "error")

    return ChatMessage(
        role="assistant",
        content=response_text.strip(),
        charts=charts,
        follow_ups=follow_ups,
    )
```

**Key Design:** Per-message agent loop (not per-session). Closure pattern captures session_id for tool context. Hooks intercept tool outputs to extract charts and follow-ups for rich UI rendering.

---

#### **app/models.py** (56 lines)
```python
"""Pydantic models for ReviewLens."""

from __future__ import annotations

from datetime import datetime
from enum import Enum
from typing import Any

from pydantic import BaseModel, Field


class Review(BaseModel):
    """A single review record."""

    id: str = ""
    text: str
    rating: float | None = None
    date: datetime | None = None
    author: str = ""
    platform: str = ""
    metadata: dict[str, Any] = Field(default_factory=dict)


class IngestionSummary(BaseModel):
    """Summary shown after ingestion."""

    total_reviews: int = 0
    date_range: str = ""
    rating_distribution: dict[str, int] = Field(default_factory=dict)
    average_rating: float | None = None
    platform: str = ""
    product_name: str = ""
    source_type: str = ""  # "csv" or "url"


class ChatMessage(BaseModel):
    """A single chat message."""

    role: str  # "user" or "assistant"
    content: str
    charts: list[dict[str, Any]] = Field(default_factory=list)
    follow_ups: list[str] = Field(default_factory=list)
    timestamp: datetime = Field(default_factory=datetime.utcnow)


class Session(BaseModel):
    """A review analysis session."""

    session_id: str
    product_name: str = ""
    platform: str = ""
    summary: IngestionSummary = Field(default_factory=IngestionSummary)
    messages: list[ChatMessage] = Field(default_factory=list)
    status: str = "pending"  # pending, ready, error
    created_at: datetime = Field(default_factory=datetime.utcnow)
```

**Key Design:** Simple, composable Pydantic models. ChatMessage includes charts and follow-ups for rich responses.

---

#### **app/store.py** (106 lines)
```python
"""File-based session and message persistence."""

from __future__ import annotations

import json
import os
import shutil
from pathlib import Path

from .models import ChatMessage, IngestionSummary, Session

DATA_DIR = Path(os.getenv("DATA_DIR", "data"))


def _session_dir(session_id: str) -> Path:
    d = DATA_DIR / session_id
    d.mkdir(parents=True, exist_ok=True)
    return d


# ── Session lifecycle ────────────────────────────────────────────────

def save_session(session: Session) -> None:
    path = _session_dir(session.session_id) / "session.json"
    path.write_text(session.model_dump_json(indent=2))


def load_session(session_id: str) -> Session | None:
    path = _session_dir(session_id) / "session.json"
    if not path.exists():
        return None
    return Session.model_validate_json(path.read_text())


def set_status(session_id: str, status: str) -> None:
    session = load_session(session_id)
    if session:
        session.status = status
        save_session(session)


def update_summary(session_id: str, summary: IngestionSummary) -> None:
    session = load_session(session_id)
    if session:
        session.summary = summary
        session.product_name = summary.product_name
        session.platform = summary.platform
        save_session(session)


# ── Chat messages ────────────────────────────────────────────────────

def append_message(session_id: str, message: ChatMessage) -> None:
    session = load_session(session_id)
    if session:
        session.messages.append(message)
        save_session(session)


def get_messages(session_id: str) -> list[ChatMessage]:
    session = load_session(session_id)
    return session.messages if session else []


# ── Reviews (raw JSON for reference) ────────────────────────────────

def save_reviews_json(session_id: str, reviews: list[dict]) -> None:
    path = _session_dir(session_id) / "reviews.json"
    path.write_text(json.dumps(reviews, default=str, indent=2))


def load_reviews_json(session_id: str) -> list[dict]:
    path = _session_dir(session_id) / "reviews.json"
    if not path.exists():
        return []
    return json.loads(path.read_text())


# ── Session listing ──────────────────────────────────────────────────

def list_sessions() -> list[Session]:
    """Return all sessions, newest first."""
    sessions = []
    if not DATA_DIR.exists():
        return sessions
    for d in DATA_DIR.iterdir():
        if not d.is_dir():
            continue
        path = d / "session.json"
        if path.exists():
            try:
                sessions.append(Session.model_validate_json(path.read_text()))
            except Exception:
                continue
    sessions.sort(key=lambda s: s.created_at, reverse=True)
    return sessions


def delete_session(session_id: str) -> bool:
    """Delete a session and all its data. Returns True if it existed."""
    d = DATA_DIR / session_id
    if d.exists() and d.is_dir():
        shutil.rmtree(d)
        return True
    return False
```

**Key Design:** Simple file-based persistence in `data/{session_id}/`. Session.json contains full session state including chat history.

---

#### **app/main.py** (416 lines)
**[Complete file content provided above - too long to repeat here]**

**Key Endpoints:**
- `GET /` - Home page with session list
- `GET /chat/{session_id}` - Chat interface
- `POST /upload` - CSV upload ingestion
- `POST /sample` - Load sample data
- `POST /scrape` - URL scraping (Firecrawl)
- `POST /chat/{session_id}/send` - Send message (returns HTML + SSE stream)
- `GET /chat/{session_id}/stream` - SSE event stream
- `GET /api/status/{session_id}` - Status polling
- `DELETE /api/sessions/{session_id}` - Delete session

**Key Design:** SSE streaming with in-memory event queues per session. Background task for URL scraping. HTML rendering with markdown + charts inline.

---

#### **app/vectordb.py** (152 lines)
**[Complete file content provided above]**

**Key Functions:**
- `index_reviews()` - Embed reviews into ChromaDB with batching (500 at a time)
- `search_reviews()` - Semantic search with optional rating filters
- `get_all_reviews()` - Full collection retrieval for stats
- `get_review_count()` - Count reviews in collection

**Key Design:** In-process ChromaDB (no external server). Collection names hashed from session_id for safety.

---

#### **app/ingest.py** (498 lines)
**[Complete file content provided above]**

**Key Components:**

1. **CSV Parsing:**
   - Static alias-based column mapping with fallback
   - AI-powered column mapping via Claude + tool use
   - Date/rating parsers with format detection
   - Supplementary text concatenation (pros/cons → review body)
   - Unmapped columns preserved as metadata

2. **URL Scraping:**
   - Firecrawl `/v2/agent` endpoint for autonomous navigation
   - Structured output with schema validation
   - Poll-based completion checking (2s intervals, 5min timeout)
   - Multi-strategy fallback on transient errors
   - Platform auto-detection (Amazon, G2, Capterra, etc.)

3. **Summary Building:**
   - Rating distribution bucketing
   - Date range calculation
   - Average rating computation
   - Platform detection from review metadata

**Key Design:** CSV-first, URL scraping as best-effort secondary. AI column mapping for flexibility; graceful fallback to static rules.

---

### 3. BRIEFBOT REFERENCE

The `knowledge/briefbot-casestudy.md` file is a detailed case study of **BriefBot**, a prior project also by Shayne La Rocque using Claude Agent SDK. Key insights that influenced ReviewLens:

**BriefBot Design Patterns (Applied to ReviewLens):**

1. **Sandbox Over Script** - Agent gets environment (knowledge, tools, goal) rather than task checklist
2. **Closure Pattern** - Tools access session-specific data via closure over session_id
3. **MCP Server Architecture** - Custom tool definitions with Claude Agent SDK
4. **Knowledge Base** - Context documents that ground the agent
5. **Multi-Strategy Fetch Cascade** - Resilient data retrieval (GitHub API → HTTP → Agent-driven → Jina → Wayback)
6. **Self-Assessment Loops** - Agent evaluates confidence after each phase
7. **Human-in-the-Loop** - Two touchpoints: mid-run clarification questions + post-run review flags
8. **Honest Gap Flagging** - Never fabricate; explicitly surface limitations

**ReviewLens Adaptation:**
- Scope guard enforces single-dataset boundary (vs BriefBot's multi-source research)
- Tools return JSON with instructions for chart/follow-up rendering
- Per-message loop (vs BriefBot's single brief run)
- SSE streaming for real-time UI updates

---

### 4. KEY ARCHITECTURAL DECISIONS

**1. Scope Guarding (3-Layer Defense)**
   - System prompt instructions
   - Retrieval validation (search must return relevant results)
   - Architectural boundary (tools can't access external data)

**2. Per-Message Agent Loop**
   - Fresh agent invocation for each user message (not per-session)
   - Conversation history injected as context (last 20 messages)
   - Simplifies reasoning about state and tool context

**3. CSV-First Ingestion**
   - Primary path: CSV upload with AI + static fallback column mapping
   - Secondary: URL scraping via Firecrawl agent (best-effort)
   - Firecrawl agent handles pagination autonomously

**4. ChromaDB In-Process**
   - No external vector DB server dependency
   - Persistent storage: `data/chroma/`
   - Per-session collection hashed from session_id
   - Batched upserts (500 at a time) for performance

**5. SSE Streaming UI**
   - Event queue per session (in-memory deque)
   - Synchronization via asyncio.Event
   - Charts and follow-ups extracted from tool outputs via hook
   - HTML rendering with markdown + Chart.js inline

**6. File-Based Session Persistence**
   - Simple, zero-dependency storage in `data/{session_id}/`
   - Session.json contains full state (summary + chat history)
   - Reviews.json backup for debugging
   - Scales to thousands of sessions before DB needed

**7. Tool Design Pattern**
   - Closure over session_id for data isolation
   - JSON-in-JSON encoding (tool output contains instructions)
   - Post-tool-use hooks extract charts/follow-ups for UI
   - All tools emit progress via SSE

---

### 5. ASSIGNMENT CONTEXT

From `knowledge/take-home.md`, the task was to build **ReviewLens AI** for an ORM consultancy:

**Business Context:** Analysts manually spend hours reading reviews to find pain points.

**Solution:** Guardrailed Q&A interface over ingested reviews.

**Core Requirements:**
1. Ingestion (CSV or URL) + Summary
2. Scope-guarded Q&A (refuse out-of-scope questions)
3. Public deployment

**Stack Chosen:** FastAPI, Claude Agent SDK, ChromaDB, Chart.js, HTMX, Firecrawl

**Deployment:** Render (free tier) via `render.yaml`

---

### 6. FILE MANIFEST

| File | Lines | Purpose |
|------|-------|---------|
| `app/prompts.py` | 69 | System prompt with scope guard + response guidelines |
| `app/tools.py` | 410 | 5 MCP tools: search, sentiment, chart, stats, follow-ups |
| `app/agent.py` | 111 | Per-message agent loop with Claude SDK |
| `app/models.py` | 56 | Pydantic: Review, Session, ChatMessage, IngestionSummary |
| `app/store.py` | 106 | File-based session persistence |
| `app/main.py` | 416 | FastAPI endpoints: upload, scrape, chat, SSE stream |
| `app/vectordb.py` | 152 | ChromaDB wrapper: index, search, stats |
| `app/ingest.py` | 498 | CSV parsing + Firecrawl URL scraping |
| `app/__init__.py` | 0 | Package marker |
| `requirements.txt` | 12 | Dependencies (FastAPI, Claude SDK, ChromaDB, etc.) |
| `README.md` | 135 | Setup, architecture, env vars |
| `render.yaml` | 12 | Render deployment config |
| **Python Total** | **1,810** | |

---

### 7. NO "knowledge.py" FILE

There is **no `knowledge.py`** in the codebase. The knowledge base referenced in BriefBot (D3's rubric, stream definitions, SDG framework) is **not implemented in ReviewLens** because ReviewLens operates on generic review data, not structured application documents.

The closest pattern is the **IngestionSummary context** injected into the system prompt, which grounds the agent to the specific dataset.

---

This comprehensive exploration reveals a well-architected, production-ready review intelligence platform that successfully adapts BriefBot's agentic sandbox pattern to a focused, scope-guarded conversational interface.

agentId: afeaf6bc84010b97c (use SendMessage with to: 'afeaf6bc84010b97c' to continue this agent)
<usage>total_tokens: 65728
tool_uses: 28
duration_ms: 98967</usage>

     1→"""FastAPI application for ReviewLens AI."""
     2→
     3→from __future__ import annotations
     4→
     5→import asyncio
     6→import html as html_module
     7→import json
     8→import os
     9→import uuid
    10→from collections import deque
    11→from datetime import datetime
    12→from pathlib import Path
    13→from typing import Any
    14→
    15→import markdown
    16→from dotenv import load_dotenv
    17→from fastapi import FastAPI, File, Form, Request, UploadFile
    18→from fastapi.responses import HTMLResponse, JSONResponse
    19→from fastapi.staticfiles import StaticFiles
    20→from fastapi.templating import Jinja2Templates
    21→from sse_starlette.sse import EventSourceResponse
    22→
    23→from . import store, vectordb
    24→from .agent import handle_message
    25→from .ingest import build_summary, parse_csv, scrape_url
    26→from .models import ChatMessage, IngestionSummary, Session
    27→
    28→load_dotenv()
    29→
    30→BASE_DIR = Path(__file__).resolve().parent.parent
    31→app = FastAPI(title="ReviewLens AI")
    32→app.mount("/static", StaticFiles(directory=str(BASE_DIR / "static")), name="static")
    33→templates = Jinja2Templates(directory=str(BASE_DIR / "templates"))
    34→
    35→# ── In-memory SSE event queues (per-session) ────────────────────────
    36→_event_queues: dict[str, deque[dict[str, str]]] = {}
    37→_response_events: dict[str, asyncio.Event] = {}
    38→
    39→
    40→def _get_queue(session_id: str) -> deque[dict[str, str]]:
    41→    if session_id not in _event_queues:
    42→        _event_queues[session_id] = deque()
    43→    return _event_queues[session_id]
    44→
    45→
    46→def _get_response_event(session_id: str) -> asyncio.Event:
    47→    if session_id not in _response_events:
    48→        _response_events[session_id] = asyncio.Event()
    49→    return _response_events[session_id]
    50→
    51→
    52→async def _emit(session_id: str, message: str, level: str = "info") -> None:
    53→    _get_queue(session_id).append({"event": level, "data": message})
    54→
    55→
    56→# ── Health check ─────────────────────────────────────────────────────
    57→
    58→@app.get("/health")
    59→async def health():
    60→    return {"status": "ok"}
    61→
    62→
    63→# ── Sample data discovery ────────────────────────────────────────────
    64→
    65→SAMPLE_DIR = BASE_DIR / "sample-data"
    66→
    67→
    68→def _list_sample_files() -> list[dict[str, str]]:
    69→    """List available sample CSVs with human-readable labels."""
    70→    if not SAMPLE_DIR.exists():
    71→        return []
    72→    files = []
    73→    for f in sorted(SAMPLE_DIR.glob("*.csv")):
    74→        label = f.stem.replace("_", " ").replace("-", " ").title()
    75→        files.append({"filename": f.name, "label": label})
    76→    return files
    77→
    78→
    79→# ── App shell (home + chat share the same layout) ───────────────────
    80→
    81→def _shell_context(request: Request, session=None):
    82→    """Build template context for the app shell."""
    83→    sessions = store.list_sessions()
    84→    ctx = {
    85→        "request": request,
    86→        "sessions": sessions,
    87→        "session": session,
    88→        "summary": session.summary if session else None,
    89→        "messages": session.messages if session else [],
    90→        "active_id": session.session_id if session else None,
    91→        "sample_files": _list_sample_files(),
    92→    }
    93→    return ctx
    94→
    95→
    96→@app.get("/", response_class=HTMLResponse)
    97→async def index(request: Request):
    98→    return templates.TemplateResponse("app.html", _shell_context(request))
    99→
   100→
   101→@app.get("/chat/{session_id}", response_class=HTMLResponse)
   102→async def chat_page(request: Request, session_id: str):
   103→    session = store.load_session(session_id)
   104→    if not session:
   105→        return HTMLResponse("<h1>Session not found</h1>", status_code=404)
   106→    return templates.TemplateResponse("app.html", _shell_context(request, session))
   107→
   108→
   109→# ── CSV Upload ───────────────────────────────────────────────────────
   110→
   111→@app.post("/upload")
   112→async def upload_csv(
   113→    request: Request,
   114→    file: UploadFile = File(...),
   115→    product_name: str = Form(""),
   116→    platform: str = Form(""),
   117→):
   118→    session_id = str(uuid.uuid4())
   119→
   120→    content = await file.read()
   121→    try:
   122→        reviews = await parse_csv(content, platform=platform, product_name=product_name)
   123→    except Exception as e:
   124→        return templates.TemplateResponse(
   125→            "partials/error.html",
   126→            {"request": request, "error": f"Failed to parse CSV: {e}"},
   127→            status_code=400,
   128→        )
   129→
   130→    if not reviews:
   131→        return templates.TemplateResponse(
   132→            "partials/error.html",
   133→            {"request": request, "error": "No reviews found in the CSV. Make sure it has a text/review column."},
   134→            status_code=400,
   135→        )
   136→
   137→    summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
   138→
   139→    session = Session(
   140→        session_id=session_id,
   141→        product_name=summary.product_name,
   142→        platform=summary.platform,
   143→        summary=summary,
   144→        status="indexing",
   145→    )
   146→    store.save_session(session)
   147→    store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
   148→
   149→    indexed = vectordb.index_reviews(session_id, reviews)
   150→    summary.total_reviews = indexed
   151→    store.update_summary(session_id, summary)
   152→    store.set_status(session_id, "ready")
   153→
   154→    return HTMLResponse(
   155→        status_code=200,
   156→        content=f'<script>window.location.href="/chat/{session_id}";</script>',
   157→        headers={"HX-Redirect": f"/chat/{session_id}"},
   158→    )
   159→
   160→
   161→# ── Sample Data ──────────────────────────────────────────────────────
   162→
   163→@app.post("/sample")
   164→async def load_sample(
   165→    request: Request,
   166→    filename: str = Form(...),
   167→):
   168→    # Sanitise: only allow filenames that exist in sample-data/
   169→    path = SAMPLE_DIR / filename
   170→    if not path.exists() or not path.suffix == ".csv" or ".." in filename:
   171→        return templates.TemplateResponse(
   172→            "partials/error.html",
   173→            {"request": request, "error": "Sample file not found."},
   174→            status_code=400,
   175→        )
   176→
   177→    content = path.read_bytes()
   178→    # Derive product name and platform from the filename
   179→    stem = path.stem.replace("_", " ").replace("-", " ")
   180→    product_name = stem.title()
   181→    platform = ""
   182→    for plat in ("Amazon", "Google Maps", "G2", "Capterra", "Trustpilot", "Yelp"):
   183→        if plat.lower().replace(" ", "_") in path.stem.lower() or plat.lower().replace(" ", "") in path.stem.lower():
   184→            platform = plat
   185→            break
   186→
   187→    reviews = await parse_csv(content, platform=platform, product_name=product_name)
   188→
   189→    if not reviews:
   190→        return templates.TemplateResponse(
   191→            "partials/error.html",
   192→            {"request": request, "error": "Could not parse reviews from this sample file."},
   193→            status_code=400,
   194→        )
   195→
   196→    session_id = str(uuid.uuid4())
   197→    summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
   198→
   199→    session = Session(
   200→        session_id=session_id,
   201→        product_name=summary.product_name,
   202→        platform=summary.platform,
   203→        summary=summary,
   204→        status="indexing",
   205→    )
   206→    store.save_session(session)
   207→    store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
   208→
   209→    indexed = vectordb.index_reviews(session_id, reviews)
   210→    summary.total_reviews = indexed
   211→    store.update_summary(session_id, summary)
   212→    store.set_status(session_id, "ready")
   213→
   214→    return HTMLResponse(
   215→        status_code=200,
   216→        content=f'<script>window.location.href="/chat/{session_id}";</script>',
   217→        headers={"HX-Redirect": f"/chat/{session_id}"},
   218→    )
   219→
   220→
   221→# ── URL Scrape ───────────────────────────────────────────────────────
   222→
   223→@app.post("/scrape")
   224→async def scrape(
   225→    request: Request,
   226→    url: str = Form(...),
   227→    product_name: str = Form(""),
   228→    platform: str = Form(""),
   229→):
   230→    session_id = str(uuid.uuid4())
   231→
   232→    # Create session immediately with "scraping" status so user gets feedback
   233→    session = Session(
   234→        session_id=session_id,
   235→        product_name=product_name or "Unknown Product",
   236→        platform=platform or "Web",
   237→        status="scraping",
   238→    )
   239→    store.save_session(session)
   240→
   241→    # Run Firecrawl agent in background — it can take minutes
   242→    asyncio.create_task(_run_scrape(session_id, url, product_name, platform))
   243→
   244→    # Redirect to chat page which shows a progress view
   245→    return HTMLResponse(
   246→        status_code=200,
   247→        content=f'<script>window.location.href="/chat/{session_id}";</script>',
   248→        headers={"HX-Redirect": f"/chat/{session_id}"},
   249→    )
   250→
   251→
   252→async def _run_scrape(session_id: str, url: str, product_name: str, platform: str):
   253→    """Background task: scrape URL, index reviews, update session status."""
   254→    try:
   255→        reviews = await scrape_url(url, platform=platform)
   256→
   257→        if not reviews:
   258→            store.set_status(session_id, "error")
   259→            await _emit(session_id, "No reviews could be extracted from that URL.", "error")
   260→            return
   261→
   262→        summary = build_summary(reviews, source_type="url", product_name=product_name, platform=platform)
   263→        store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
   264→        indexed = vectordb.index_reviews(session_id, reviews)
   265→        summary.total_reviews = indexed
   266→        store.update_summary(session_id, summary)
   267→        store.set_status(session_id, "ready")
   268→        await _emit(session_id, f"Scraping complete — {indexed} reviews indexed.", "info")
   269→
   270→    except Exception as e:
   271→        store.set_status(session_id, "error")
   272→        await _emit(session_id, f"Scraping failed: {e}", "error")
   273→
   274→
   275→# ── Session status polling (for scraping progress) ───────────────────
   276→
   277→@app.get("/api/status/{session_id}")
   278→async def get_status(session_id: str):
   279→    session = store.load_session(session_id)
   280→    if not session:
   281→        return JSONResponse({"status": "not_found"}, status_code=404)
   282→    return JSONResponse({"status": session.status})
   283→
   284→
   285→# ── Archive (delete) session ──────────────────────────────────────────
   286→
   287→@app.delete("/api/sessions/{session_id}")
   288→async def delete_session(session_id: str):
   289→    deleted = store.delete_session(session_id)
   290→    if not deleted:
   291→        return JSONResponse({"error": "not found"}, status_code=404)
   292→    return JSONResponse({"ok": True})
   293→
   294→
   295→# ── Send chat message ───────────────────────────────────────────────
   296→
   297→@app.post("/chat/{session_id}/send")
   298→async def send_message(
   299→    request: Request,
   300→    session_id: str,
   301→    message: str = Form(...),
   302→):
   303→    session = store.load_session(session_id)
   304→    if not session:
   305→        return JSONResponse({"error": "Session not found"}, status_code=404)
   306→
   307→    user_msg = ChatMessage(role="user", content=message)
   308→    store.append_message(session_id, user_msg)
   309→
   310→    event = _get_response_event(session_id)
   311→    event.clear()
   312→
   313→    user_html = _render_message(user_msg)
   314→    asyncio.create_task(_run_agent_and_respond(session_id, message, session))
   315→
   316→    thinking_html = (
   317→        '<div id="thinking-indicator" class="message assistant-message thinking">'
   318→        '<div class="message-content">'
   319→        '<div class="thinking-dots"><span></span><span></span><span></span></div>'
   320→        '</div></div>'
   321→    )
   322→    return HTMLResponse(user_html + thinking_html)
   323→
   324→
   325→async def _run_agent_and_respond(session_id: str, message: str, session: Session):
   326→    try:
   327→        history = store.get_messages(session_id)
   328→
   329→        assistant_msg = await handle_message(
   330→            session_id=session_id,
   331→            user_message=message,
   332→            conversation_history=history[:-1],
   333→            summary=session.summary,
   334→            emit_fn=_emit,
   335→        )
   336→
   337→        store.append_message(session_id, assistant_msg)
   338→        html = _render_message(assistant_msg)
   339→        _get_queue(session_id).append({"event": "message", "data": html})
   340→
   341→    except Exception as e:
   342→        error_msg = ChatMessage(
   343→            role="assistant",
   344→            content=f"Sorry, I encountered an error: {e}",
   345→        )
   346→        store.append_message(session_id, error_msg)
   347→        html = _render_message(error_msg)
   348→        _get_queue(session_id).append({"event": "message", "data": html})
   349→
   350→    _get_response_event(session_id).set()
   351→
   352→
   353→# ── SSE stream ───────────────────────────────────────────────────────
   354→
   355→@app.get("/chat/{session_id}/stream")
   356→async def chat_stream(session_id: str):
   357→    async def event_generator():
   358→        queue = _get_queue(session_id)
   359→        event = _get_response_event(session_id)
   360→
   361→        while True:
   362→            while queue:
   363→                item = queue.popleft()
   364→                yield {"event": item["event"], "data": item["data"]}
   365→                if item["event"] == "message":
   366→                    yield {"event": "done", "data": ""}
   367→                    return
   368→
   369→            if event.is_set() and not queue:
   370→                yield {"event": "done", "data": ""}
   371→                return
   372→
   373→            await asyncio.sleep(0.15)
   374→
   375→    return EventSourceResponse(event_generator())
   376→
   377→
   378→# ── HTML rendering helpers ───────────────────────────────────────────
   379→
   380→def _render_message(msg: ChatMessage) -> str:
   381→    role_class = "user-message" if msg.role == "user" else "assistant-message"
   382→    escaped = html_module.escape(msg.content)
   383→
   384→    if msg.role == "assistant":
   385→        content_html = markdown.markdown(
   386→            msg.content,
   387→            extensions=["tables", "fenced_code"],
   388→        )
   389→    else:
   390→        content_html = f"<p>{escaped}</p>"
   391→
   392→    parts = [f'<div class="message {role_class}">']
   393→    parts.append(f'<div class="message-content">{content_html}</div>')
   394→
   395→    for i, chart in enumerate(msg.charts):
   396→        chart_id = f"chart-{uuid.uuid4().hex[:8]}"
   397→        parts.append(
   398→            f'<div class="chart-container">'
   399→            f'<canvas id="{chart_id}"></canvas>'
   400→            f'<script>renderChart("{chart_id}", {json.dumps(chart)});</script>'
   401→            f'</div>'
   402→        )
   403→
   404→    if msg.follow_ups:
   405→        parts.append('<div class="follow-ups">')
   406→        for q in msg.follow_ups:
   407→            escaped_q = html_module.escape(q)
   408→            parts.append(
   409→                f'<button class="follow-up-btn" onclick="sendFollowUp(this)" '
   410→                f'data-question="{escaped_q}">{escaped_q}</button>'
   411→            )
   412→        parts.append('</div>')
   413→
   414→    parts.append('</div>')
   415→    return "\n".join(parts)
   416→

     1→https://github.com/shaynelarocque/briefbot
     2→Replacing the research pile with a briefing agent - Case Study - Shayne La Rocque
     3→HomeWorksHistoryConnect
     4→Back to works
     5→Replacing the research pile with a briefing agent
     6→
     7→Turning a 4 hour research scramble into an AI-native workflow
     8→
     9→I had wanted to build this for District 3 since the commercial AI wave kicked off in mid-2022. The admissions workflow was exactly the kind of process that made me think "this wouldn't exist like this if built today," but I never had the right opening to pursue it. Wealthsimple's AI Builder prompt finally gave me one.
    10→My RoleDesigner & Developer
    11→StackClaude Agent SDK, FastAPI, HTMX
    12→Built ForWealthsimple AI Builder Application
    13→Cost Per Brief~$2.50 CAD
    14→Time Per Brief~18 minutes (vs. 3–4 hours manual)
    15→The Bottleneck
    16→
    17→District 3 is a publicly funded startup incubator at Concordia University in Montreal, processing roughly 200 to 250 applications per month. When an application lands, an operations team member spends one to four hours on background research: scouring LinkedIn, checking founder websites, mapping the competitive landscape, looking for patents and press. They write hasty bullet-point notes and assign a program stream. Stream leads walk into panel meetings and spend their first ten minutes just orienting themselves.
    18→
    19→The research is the bottleneck. It's tedious and inconsistent. Two people reviewing the same application can produce dramatically different notes. The quality of the research dictates the quality of the panel's decision, yet it's treated as rote admin work.
    20→
    21→This is the kind of process that evolved before modern AI. It wouldn't exist like this if built today.
    22→Before
    23→
    24→    Application sits for days
    25→    1–4 hours of manual research
    26→    Hasty, inconsistent bullet-point notes
    27→    Stream leads walk in cold
    28→    Quality depends on who's on shift
    29→
    30→After
    31→
    32→    Application triggers agent immediately
    33→    ~18 minutes, ~$2.50 CAD in API costs
    34→    9-section cited brief with risk flags
    35→    Stream leads arrive informed
    36→    Consistent quality, every time
    37→
    38→What I Built
    39→
    40→I built an AI system that eliminates the research bottleneck. When a new application is submitted, an agent autonomously generates a research brief: founder profiles with verified sources, competitive analysis, SDG alignment assessment, stream classification with reasoning, a scored evaluation rubric, risk flags, and interview questions for both operations staff and panelists, all cited to real URLs or application fields.
    41→
    42→The human can now open an application that's already deeply researched. Operations shifts from doing research to reviewing research. Stream leads arrive at panel meetings with context instead of spending their first ten minutes catching up.
    43→The Brief: Nine Sections, All Cited
    44→
    45→Every brief the agent produces contains nine sections, each with mandatory citations to URLs, application fields, or knowledge base documents:
    46→Section	Purpose
    47→Synthesis	What the startup does, overall confidence score, and a plain-language recommendation
    48→Founder Profiles	Per-founder background research with credibility signals and gaps
    49→SDG Coherence	Assessment of whether claimed UN Sustainable Development Goals actually match the work
    50→Competitive Context	Comparable ventures, market landscape, and differentiation analysis
    51→Evaluation Scorecard	Each rubric criterion scored with justification and confidence level
    52→Stream Classification	Best-fit D3 program stream and stage with reasoning
    53→Key Risks	Red flags, gaps, and concerns ranked by severity
    54→Questions for Ops	Gap-based questions for the operations team to investigate before interview
    55→Questions for Panelists	Evaluation-based questions to probe during the interview itself
    56→Design Philosophy: Sandbox, Not Script
    57→
    58→Version 1 of this system was a scripted pipeline, a chain of prompts with predetermined steps and retry logic. It broke constantly. If a LinkedIn page was down or a founder didn't have a website, the whole chain derailed. Wrong output was worse than no output, because it created more work for the ops team to correct.
    59→
    60→For version 2, I replaced choreography with agentic design. The agent isn't given a task list. It's given an environment: knowledge to reference, tools to use, a goal to achieve, and guidelines for quality. Within that environment, the agent decides what to research, in what order, and how deep to go.
    61→Knowledge
    62→
    63→A /knowledge folder the agent reads at the start of every run, containing D3's mandate, evaluation rubric, stream definitions, and SDG framework. This is what makes it D3's agent, not a generic research bot.
    64→Tools
    65→
    66→Thirteen tools the agent can call in any order, from web research with multi-strategy fallback to self-assessment checkpoints, brief section emitters, human review flags, mid-run human input requests, and working memory for notes and research plans.
    67→Goal
    68→
    69→Produce nine brief sections, all cited. The agent knows what "done" looks like but has full autonomy over how to get there.
    70→Guidelines
    71→
    72→A quality bar, not step-by-step instructions. Every factual claim needs a citation. Confidence thresholds determine whether to proceed, retry, or flag for human review. And critically: zero revisions is a sign of a first-pass report.
    73→How the Agent Works
    74→
    75→The agent operates in a loop of research, assessment, and output. It loads D3's knowledge base, reads the application, creates a research plan, then executes that plan phase by phase. After each phase, it self-assesses its confidence and decides whether to proceed, retry with different sources, or flag for human review.
    76→
    77→Brief sections are emitted incrementally as the agent completes them, not batched at the end. This means the brief builds in real time, and observers can watch the research unfold through a live server-sent event stream.
    78→The Backtracking Loop
    79→
    80→The key design insight. After each self-assessment, the agent reviews all previously emitted sections and asks: "Does anything I've already published need updating in light of what I just learned?" If the answer is yes, it revises the section and logs the reason. This is how the system produces an honest, non-linear brief rather than a first-pass report.
    81→One Real Run
    82→
    83→The useful artifact is not hidden chain-of-thought. It's the audit trail: what the agent tried, what failed, when it asked for help, and what brief it ultimately produced.
    84→live agent log
    85→04:01:10[INFO]Starting briefing agent...
    86→04:01:15[INFO]I'll begin researching the Training Club application now. Let me start by reading the reference library and creating a research plan.
    87→04:01:15[INFO]Starting research on Training Club application. First step: reading reference library to ground research in D3's actual criteria.
    88→04:01:17[INFO]Knowledge library: 4 files available
    89→04:01:20[INFO]Read knowledge file: d3-evaluation-rubric (7183 chars)
    90→04:01:20[INFO]Read knowledge file: d3-streams (5444 chars)
    91→04:01:20[INFO]Read knowledge file: sdgs (2046 chars)
    92→04:01:42[INFO]Research plan saved
    93→04:01:47[INFO]Plan created. Key flags: John Rockwell has no Canadian status (need to assess eligibility implications), both founders are part-time, SDG alignment looks questionable. Starting with website and founder research in parallel.
    94→04:01:49[INFO]Now let me research the website and both founders in parallel:
    95→04:01:49[INFO]Fetching: https://trainingclub.app
    96→04:01:58[ERROR]All fetch strategies failed for https://trainingclub.app: [Errno 8] nodename nor servname provided, or not known
    97→04:01:58[INFO]Fetching: https://www.linkedin.com/in/shaynelarocque/
    98→04:01:59[ERROR]All fetch strategies failed for https://www.linkedin.com/in/shaynelarocque/: Client error '429 Request denied' for url 'https://www.linkedin.com/in/shaynelarocque/' For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
    99→04:01:59[INFO]Fetching: https://shaynelarocque.com
   100→04:02:00[SUCCESS]Fetched https://shaynelarocque.com — 3936 chars
   101→04:02:00[INFO]Fetching: https://www.linkedin.com/in/j-rockwell/
   102→04:02:01[SUCCESS]Fetched https://www.linkedin.com/in/j-rockwell/ — 15050 chars [login_wall]
   103→04:02:01[INFO]Fetching: https://jrockwell.dev
   104→04:02:01[SUCCESS]Fetched https://jrockwell.dev — 1109 chars
   105→04:02:27[WARNING]🚨 CRITICAL FINDINGS: 1. trainingclub.app website is DOWN — DNS resolution failure, no Wayback archive 2. Shayne La Rocque's website schema.org data shows he WORKS FOR District 3 (the incubator being applied to!) 3. John Rockwell is based in Las Vegas, NV — confirms no Canadian status 4. John's LinkedIn activity mentions "recently wrapped up my work with District 3" Both founders have D3 connections. Investigating further.
   106→04:02:35[INFO]Note saved: critical_findings_round1 (1217 chars)
   107→04:02:37[INFO]Let me dig deeper into Shayne's background and the D3 connection, plus explore more of the founders' online presence:
   108→04:02:38[INFO]Fetching: https://www.shaynelarocque.com/works
   109→04:02:38[SUCCESS]Fetched https://www.shaynelarocque.com/works — 3365 chars
   110→04:02:39[INFO]Fetching: https://www.shaynelarocque.com/history
   111→04:02:39[SUCCESS]Fetched https://www.shaynelarocque.com/history — 4436 chars
   112→04:02:39[INFO]Fetching [GitHub API]: https://github.com/shaynelarocque
   113→04:02:39[SUCCESS]Fetched https://github.com/shaynelarocque — 617 chars [github_api]
   114→04:02:39[INFO]Fetching [GitHub API]: https://github.com/j-rockwell
   115→04:02:40[SUCCESS]Fetched https://github.com/j-rockwell — 405 chars [github_api]
   116→04:03:18[WARNING]🔍 MAJOR DISCOVERY CONFIRMED: Shayne La Rocque is a CURRENT D3 EMPLOYEE (UX & Systems Designer, 2023-2026). He has a district3.co email. His portfolio showcases D3 work projects. John Rockwell also recently worked with D3. Both founders have direct D3 organizational ties. Requesting human input on how to treat this.
   117→04:03:27[WARNING]REQUESTING HUMAN INPUT: Shayne La Rocque appears to be a CURRENT District 3 employee (UX & Systems Designer, 2023–2026, with a @district3.co email). His portfolio includes D3 projects like "Rebuilding District 3's site and design system." John Rockwell's LinkedIn activity also mentions "recently wrapped up my work with District 3." Should I treat this as a conflict of interest / potential disqualifier, as neutral context (D3 staff may be eligible for incubator services), or as something else? This significantly affects how I frame the brief.
   118→Evidence:(1) Shayne's personal site schema.org data lists worksFor as District 3; (2) His /history page shows D3 employment from 2022 to present (2026); (3) ZoomInfo shows his email as s***@district3.co; (4) His /works page shows D3 project case studies; (5) His application lists current role as "Senior Product Designer (full-time)" without naming D3; (6) John Rockwell's LinkedIn activity mentions "recently wrapped up my work with District 3." The application does not disclose this D3 relationship anywhere.
   119→Answered:Shayne left D3 in January, and John is not an employee. that is shaynes psost on his linkedin.
   120→04:06:20[SUCCESS]Human input received: Shayne La Rocque appears to be a CURRENT District 3 employee (UX & Systems Designer, 2023–2026, with a @district3.co email). His portfolio includes D3 projects like "Rebuilding District 3's site and design system." John Rockwell's LinkedIn activity also mentions "recently wrapped up my work with District 3." Should I treat this as a conflict of interest / potential disqualifier, as neutral context (D3 staff may be eligible for incubator services), or as something else? This significantly affects how I frame the brief.
   121→Shayne left D3 in January, and John is not an employee. that is shaynes psost on his linkedin.
   122→04:06:34[INFO]Human clarification: Shayne LEFT D3 in January 2026 (former employee, not current). The LinkedIn post about "wrapping up work with D3" was Shayne's post, liked by John — John was never a D3 employee. Shayne's website hasn't been updated yet. This is important context (former D3 staffer applying) but not a conflict of interest. Continuing research.
   123→04:06:43[INFO]Note saved: d3_connection_clarified (770 chars)
   124→04:06:47[INFO]Now let me continue research — check the Training Club GitHub repo, search for the app/product online, and begin competitive analysis:
   125→04:06:47[INFO]Fetching [GitHub API]: https://github.com/j-rockwell/tc2
   126→04:06:48[SUCCESS]Fetched https://github.com/j-rockwell/tc2 — 449 chars [github_api]
   127→04:07:39[INFO]GitHub repo "tc2" for Training Club: created Jul 2025, last pushed Aug 2025 — no activity in 6+ months. Python/FastAPI backend with Swift iOS frontend. Competitive landscape has several social fitness apps (GymRats, Motion, Fitness Pact, Workout Buddy, Fito, Squaddy). Now searching for app store listings and name collision with Nike Training Club.
   128→04:08:01[INFO]Fetching: https://www.fitbudd.com/post/best-app-for-fitness-challenges-guide
   129→04:08:01[SUCCESS]Fetched https://www.fitbudd.com/post/best-app-for-fitness-challenges-guide — 15050 chars [login_wall]
   130→[LOGIN WALL DETECTED] This page requires authentication. Only publicly visible content (if any) is shown below.
   131→=== EXTRACTED METADATA ===
   132→Page Title: Best Fitness Apps for Groups: Social Workouts and Challenges
   133→Meta Description: Discover why fitness apps for groups are trending. Learn how a fitness challenge app builds community and accountability for better results.
   134→OG Description: Discover why fitness apps for groups are trending. Learn how a fitness challenge app builds community and accountability for better results." name="description"/><meta content="Best Fitness Apps for Groups: So
   135→04:09:21[INFO]Key competitive finding: "Training Club" name has MAJOR collision with Nike Training Club (one of world's largest fitness apps). Also found "The Training Club" already on App Store. John Rockwell confirmed as Senior Software Engineer at Wesco, Las Vegas, with strong technical background. Now fetching his resume and doing self-assessment of founder research phase.
   136→04:09:24[INFO]Fetching: https://jrockwell.dev/about
   137→04:09:25[ERROR]All fetch strategies failed for https://jrockwell.dev/about: Client error '404 Not Found' for url 'https://jrockwell.dev/about' For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
   138→04:09:25[INFO]Fetching: https://jrockwell.dev/John-Rockwell-Resume.pdf
   139→04:09:34[ERROR]All fetch strategies failed for https://jrockwell.dev/John-Rockwell-Resume.pdf: Client error '404 Not Found' for url 'https://jrockwell.dev/John-Rockwell-Resume.pdf' For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/404
   140→04:09:38[INFO]Both pages on John's site return 404. Let me now do my self-assessment for the founder research phase and start emitting sections:
   141→04:09:43[SUCCESS]Self-assessment [Founder Research]: confidence=0.78, action=proceed
   142→Strong data on both founders from multiple sources. Shayne: personal site with full work history and portfolio, schema.org data, GitHub, ZoomInfo, web search results, and human observer confirmation of D3 departure. John: LinkedIn public profile with activity, projects, certifications, and education; GitHub profile with TC repo; web search results providing employment history at Wesco and Credit One. Gaps: LinkedIn login walls prevented full profile access for both; John's personal site is broken (404s); Shayne's LinkedIn was rate-limited. However, the composite picture from multiple sources is reasonably complete.
   143→04:10:02[INFO]Note saved: founder_research_complete (2340 chars)
   144→04:10:06[INFO]Now let me emit the founder profiles section and start the competitive context:
   145→04:10:37[SUCCESS]Brief section completed: founder_profiles
   146→04:10:41[INFO]✅ Founder profiles emitted. Now building competitive context and SDG coherence sections. Also noting: the Training Club project appears dormant — last code push Aug 2025, project marked as ended Sep 2025 on LinkedIn, and the website doesn't exist.
   147→04:10:44[INFO]Now let me save competitive research notes and emit that section, along with SDG coherence:
   148→04:10:56[INFO]Note saved: competitive_landscape (1997 chars)
   149→04:11:19[SUCCESS]Brief section completed: competitive_context
   150→04:11:36[SUCCESS]Brief section completed: sdg_coherence
   151→04:11:42[SUCCESS]Self-assessment [Competitive Context & SDG Coherence]: confidence=0.82, action=proceed
   152→Competitive landscape is well-documented from multiple sources (App Store search, web search, industry articles). The Nike Training Club name collision is a clear, verifiable finding. SDG assessment is straightforward — Goal 3 is obviously relevant, Goals 1 and 10 have no substantiation in the application. The competitive analysis correctly identifies the synchronous vs. asynchronous distinction as Training Club's genuine differentiator while noting that Workout Buddy already has real-time features. One gap: I didn't do deep-dive on pricing models or funding of competitors, but this isn't critical for a D3 admissions brief.
   153→04:11:47[INFO]✅ Competitive context and SDG coherence sections emitted. Now working on stream classification, scorecard, key risks, and questions. Reviewing previously emitted sections for consistency as I go.
   154→04:11:50[INFO]Now let me emit the stream classification, scorecard, key risks, and questions sections:
   155→04:12:05[SUCCESS]Brief section completed: stream_classification
   156→04:12:29[SUCCESS]Brief section completed: scorecard
   157→04:12:49[SUCCESS]Brief section completed: key_risks
   158→04:13:02[SUCCESS]Brief section completed: questions_ops
   159→04:13:20[SUCCESS]Brief section completed: questions_panelists
   160→04:13:29[SUCCESS]Self-assessment [Scorecard, Key Risks, and Questions]: confidence=0.85, action=proceed
   161→The scorecard, risks, and questions are all well-grounded in cited evidence from multiple sources. Every risk is backed by specific URLs and data points. The questions are probing and directly address the identified gaps. The scoring aligns with the rubric criteria. One area where I could improve: I should double-check whether the founder profiles section needs updating given anything I learned during scorecard/risk writing. The experience claim discrepancy is covered in both founder profiles and key risks — this is consistent. The dormancy concern is threaded through multiple sections. I'm satisfied with internal consistency.
   162→04:13:32[INFO]Now let me write the synthesis — the overall summary and recommendation — and then do a final review pass:
   163→04:13:53[SUCCESS]Brief section completed: synthesis
   164→04:13:57[INFO]All 9 brief sections emitted. Now performing final review pass — checking for internal contradictions, missed propagation of findings across sections, and any needed revisions.
   165→04:14:00[INFO]Now let me do the final review pass. Let me re-read my notes and check for inconsistencies:
   166→04:14:39[INFO]Final review pass: checking all 9 sections for internal consistency. Verifying that dormancy findings, naming collision, D3 connection, experience discrepancy, and eligibility concerns are consistently threaded across all relevant sections.
   167→04:14:42[INFO]Let me flag the key items that need human investigation and do my final self-assessment:
   168→04:14:47[WARNING]HUMAN REVIEW [MEDIUM] [founder_profiles]: Shayne La Rocque is a former D3 employee (left January 2026, ~2 months before application). Application does not disclose this relationship. D3 may or may not have a policy governing former staff applications — this needs verification to ensure process integrity.
   169→04:14:52[WARNING]HUMAN REVIEW [HIGH] [key_risks]: The claimed "working prototype" cannot be verified through any external source. Website (trainingclub.app) has DNS failure, GitHub repo dormant since Aug 2025, LinkedIn lists project as ended Sep 2025, no App Store listing exists, and Training Club is absent from Shayne's design portfolio.
   170→04:15:25[SUCCESS]Self-assessment [Final Review Pass]: confidence=0.82, action=proceed
   171→04:15:25[SUCCESS]Agent completed briefing.
   172→
   173→This is the literal run log: fetch failures, escalations, self-assessments, emitted sections, and final review flags.
   174→
   175→Nine cited sections, ranked flags, and a cleaner starting point for the ops team and panelists.
   176→Download full brief PDFDownload raw log
   177→Research Resilience: The Fetch Cascade
   178→
   179→When the agent needs to research a URL, it doesn't just fetch and hope. It runs through a five-strategy cascade:
   180→1
   181→GitHub API
   182→
   183→For github.com URLs, returns structured profile data: repos, stars, languages, bio
   184→2
   185→Direct HTTP Fetch
   186→
   187→With browser headers, HTML extraction, meta tags, Next.js SSR/RSC data extraction, and structured navigation links
   188→3
   189→Agent-Driven Exploration
   190→
   191→The tool returns navigation links from the page; the agent decides which sub-pages are relevant to its research question and fetches those
   192→4
   193→Jina Reader
   194→
   195→Headless browser rendering for JavaScript-heavy SPAs that don't serve content in initial HTML
   196→5
   197→Wayback Machine
   198→
   199→CDX API lookup for dead or blocked sites, fetches most recent archived snapshot
   200→

app
data
Dockerfile
README.md
render.yaml
requirements.txt
sample-data
static
templates

{
  "type": "tool_reference",
  "tool_name": "TaskCreate"
}

Task #1 created successfully: Create knowledge directory with ORM domain files

Task #2 created successfully: Create app/knowledge.py module

Task #3 created successfully: Update models.py — add report_findings to Session

Task #4 created successfully: Update store.py — add report findings helpers

Task #5 created successfully: Restructure prompts.py into sandbox quadrants

Task #6 created successfully: Add 5 new tools to tools.py

Task #7 created successfully: Fix conversation context passing in agent.py

Task #8 created successfully: Update main.py — add report endpoint, wire knowledge

{
  "type": "tool_reference",
  "tool_name": "TaskUpdate"
}

Updated task #1 status

Updated task #2 status

Updated task #4 status

Updated task #3 status

(Bash completed with no output)

File created successfully at: /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/knowledge/review-analysis-framework.md

File created successfully at: /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/knowledge/analysis-patterns.md

File created successfully at: /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/knowledge/report-structure.md

File created successfully at: /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/knowledge.py

     1→"""Pydantic models for ReviewLens."""
     2→
     3→from __future__ import annotations
     4→
     5→from datetime import datetime
     6→from enum import Enum
     7→from typing import Any
     8→
     9→from pydantic import BaseModel, Field
    10→
    11→
    12→class Review(BaseModel):
    13→    """A single review record."""
    14→
    15→    id: str = ""
    16→    text: str
    17→    rating: float | None = None
    18→    date: datetime | None = None
    19→    author: str = ""
    20→    platform: str = ""
    21→    metadata: dict[str, Any] = Field(default_factory=dict)
    22→
    23→
    24→class IngestionSummary(BaseModel):
    25→    """Summary shown after ingestion."""
    26→
    27→    total_reviews: int = 0
    28→    date_range: str = ""
    29→    rating_distribution: dict[str, int] = Field(default_factory=dict)
    30→    average_rating: float | None = None
    31→    platform: str = ""
    32→    product_name: str = ""
    33→    source_type: str = ""  # "csv" or "url"
    34→
    35→
    36→class ChatMessage(BaseModel):
    37→    """A single chat message."""
    38→
    39→    role: str  # "user" or "assistant"
    40→    content: str
    41→    charts: list[dict[str, Any]] = Field(default_factory=list)
    42→    follow_ups: list[str] = Field(default_factory=list)
    43→    timestamp: datetime = Field(default_factory=datetime.utcnow)
    44→
    45→
    46→class Session(BaseModel):
    47→    """A review analysis session."""
    48→
    49→    session_id: str
    50→    product_name: str = ""
    51→    platform: str = ""
    52→    summary: IngestionSummary = Field(default_factory=IngestionSummary)
    53→    messages: list[ChatMessage] = Field(default_factory=list)
    54→    status: str = "pending"  # pending, ready, error
    55→    created_at: datetime = Field(default_factory=datetime.utcnow)
    56→

     1→"""File-based session and message persistence."""
     2→
     3→from __future__ import annotations
     4→
     5→import json
     6→import os
     7→import shutil
     8→from pathlib import Path
     9→
    10→from .models import ChatMessage, IngestionSummary, Session
    11→
    12→DATA_DIR = Path(os.getenv("DATA_DIR", "data"))
    13→
    14→
    15→def _session_dir(session_id: str) -> Path:
    16→    d = DATA_DIR / session_id
    17→    d.mkdir(parents=True, exist_ok=True)
    18→    return d
    19→
    20→
    21→# ── Session lifecycle ────────────────────────────────────────────────
    22→
    23→def save_session(session: Session) -> None:
    24→    path = _session_dir(session.session_id) / "session.json"
    25→    path.write_text(session.model_dump_json(indent=2))
    26→
    27→
    28→def load_session(session_id: str) -> Session | None:
    29→    path = _session_dir(session_id) / "session.json"
    30→    if not path.exists():
    31→        return None
    32→    return Session.model_validate_json(path.read_text())
    33→
    34→
    35→def set_status(session_id: str, status: str) -> None:
    36→    session = load_session(session_id)
    37→    if session:
    38→        session.status = status
    39→        save_session(session)
    40→
    41→
    42→def update_summary(session_id: str, summary: IngestionSummary) -> None:
    43→    session = load_session(session_id)
    44→    if session:
    45→        session.summary = summary
    46→        session.product_name = summary.product_name
    47→        session.platform = summary.platform
    48→        save_session(session)
    49→
    50→
    51→# ── Chat messages ────────────────────────────────────────────────────
    52→
    53→def append_message(session_id: str, message: ChatMessage) -> None:
    54→    session = load_session(session_id)
    55→    if session:
    56→        session.messages.append(message)
    57→        save_session(session)
    58→
    59→
    60→def get_messages(session_id: str) -> list[ChatMessage]:
    61→    session = load_session(session_id)
    62→    return session.messages if session else []
    63→
    64→
    65→# ── Reviews (raw JSON for reference) ────────────────────────────────
    66→
    67→def save_reviews_json(session_id: str, reviews: list[dict]) -> None:
    68→    path = _session_dir(session_id) / "reviews.json"
    69→    path.write_text(json.dumps(reviews, default=str, indent=2))
    70→
    71→
    72→def load_reviews_json(session_id: str) -> list[dict]:
    73→    path = _session_dir(session_id) / "reviews.json"
    74→    if not path.exists():
    75→        return []
    76→    return json.loads(path.read_text())
    77→
    78→
    79→# ── Session listing ──────────────────────────────────────────────────
    80→
    81→def list_sessions() -> list[Session]:
    82→    """Return all sessions, newest first."""
    83→    sessions = []
    84→    if not DATA_DIR.exists():
    85→        return sessions
    86→    for d in DATA_DIR.iterdir():
    87→        if not d.is_dir():
    88→            continue
    89→        path = d / "session.json"
    90→        if path.exists():
    91→            try:
    92→                sessions.append(Session.model_validate_json(path.read_text()))
    93→            except Exception:
    94→                continue
    95→    sessions.sort(key=lambda s: s.created_at, reverse=True)
    96→    return sessions
    97→
    98→
    99→def delete_session(session_id: str) -> bool:
   100→    """Delete a session and all its data. Returns True if it existed."""
   101→    d = DATA_DIR / session_id
   102→    if d.exists() and d.is_dir():
   103→        shutil.rmtree(d)
   104→        return True
   105→    return False
   106→

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/models.py has been updated successfully.

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/store.py has been updated successfully.

Updated task #1 status

Updated task #2 status

Updated task #3 status

Updated task #4 status

Updated task #5 status

Updated task #6 status

Updated task #7 status

     1→"""System prompt builder for the ReviewLens agent."""
     2→
     3→from __future__ import annotations
     4→
     5→from .models import IngestionSummary
     6→
     7→
     8→def build_system_prompt(summary: IngestionSummary) -> str:
     9→    """Build the scope-guarded system prompt for the conversational agent."""
    10→
    11→    rating_info = ""
    12→    if summary.rating_distribution:
    13→        dist_lines = [f"  {k}★: {v} reviews" for k, v in sorted(summary.rating_distribution.items())]
    14→        rating_info = "\n".join(dist_lines)
    15→
    16→    return f"""You are ReviewLens AI, a review intelligence analyst. You help users analyse customer reviews that have been ingested into your system.
    17→
    18→## Dataset Context
    19→
    20→- Product/Entity: {summary.product_name}
    21→- Platform: {summary.platform}
    22→- Total Reviews: {summary.total_reviews}
    23→- Date Range: {summary.date_range or "Unknown"}
    24→- Average Rating: {summary.average_rating or "Unknown"}
    25→- Rating Distribution:
    26→{rating_info or "  Not available"}
    27→
    28→## CRITICAL: Scope Guard — Your #1 Rule
    29→
    30→You MUST ONLY answer questions about the ingested review dataset described above. This is non-negotiable.
    31→
    32→**You MUST refuse if the user asks about:**
    33→- Reviews from other platforms (e.g., if data is from Amazon, refuse questions about Google Maps reviews)
    34→- Competitor products or brands not mentioned in the reviews
    35→- General knowledge, news, current events, or facts not in the dataset
    36→- Predictions, forecasts, or speculation beyond what the data shows
    37→- Anything requiring information you don't have from the reviews
    38→
    39→**How to refuse:**
    40→- Be friendly but firm: "I can only analyse the {summary.total_reviews} {summary.platform} reviews for {summary.product_name} that were uploaded. I don't have access to [what they asked about]. Would you like to explore something in this dataset instead?"
    41→- Always suggest a relevant alternative question about the actual data.
    42→
    43→**Before answering ANY question, verify:**
    44→1. Can this be answered using ONLY the ingested reviews?
    45→2. If you used the search_reviews tool, did it return relevant results?
    46→3. Are you about to state something that comes from your general knowledge rather than the data? If so, STOP.
    47→
    48→If your search returns no relevant results, say so honestly. Never fill gaps with general knowledge.
    49→
    50→## Your Tools
    51→
    52→You have access to these tools — use them to give data-grounded answers:
    53→
    54→- **search_reviews**: Semantic search over the review database. Use this to find reviews relevant to the user's question. Always search before answering.
    55→- **analyze_sentiment**: Extract aspects and sentiment from a set of reviews. Use for sentiment breakdowns, aspect analysis, and opinion mining.
    56→- **generate_chart**: Create a Chart.js chart that renders inline in the chat. Use when a visual would communicate the answer better than text. Keep charts clean and focused.
    57→- **calculate_stats**: Run aggregations, distributions, and trend analysis on the review data. Use for quantitative questions.
    58→- **suggest_follow_ups**: Generate contextual follow-up question buttons based on what was just discussed. Call this at the end of EVERY response.
    59→
    60→## Response Guidelines
    61→
    62→1. **Always search first.** Before answering any question about the reviews, call search_reviews to ground your response in actual data.
    63→2. **Cite specific reviews.** Quote or paraphrase actual review text to support your claims. Use phrases like "One reviewer noted..." or "Several reviews mention..."
    64→3. **Use charts strategically.** A chart adds value for distributions, trends over time, and comparisons. Don't chart everything — use them when visual communication is genuinely better.
    65→4. **Be quantitative.** When you can give numbers (counts, percentages, averages), do so. Use calculate_stats for aggregations.
    66→5. **Always suggest follow-ups.** End every response by calling suggest_follow_ups to give the user contextual next steps.
    67→6. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with data, suggest next steps.
    68→"""
    69→

     1→"""MCP tools for the ReviewLens agent, using create_sdk_mcp_server."""
     2→
     3→from __future__ import annotations
     4→
     5→import json
     6→from collections import Counter
     7→from typing import Any, Callable, Awaitable
     8→
     9→from claude_agent_sdk import create_sdk_mcp_server, tool
    10→
    11→from . import vectordb
    12→
    13→# Type alias for the SSE emit callback
    14→EmitFn = Callable[[str, str, str], Awaitable[None]]
    15→
    16→
    17→def create_review_tools_server(
    18→    session_id: str,
    19→    emit_fn: EmitFn,
    20→):
    21→    """Create the MCP server with all review analysis tools.
    22→
    23→    Like briefbot, uses closure over session_id so tools access the right data.
    24→    """
    25→
    26→    # ── search_reviews ───────────────────────────────────────────────
    27→
    28→    @tool(
    29→        name="search_reviews",
    30→        description="Semantic search over the ingested review database. Use this to find reviews relevant to the user's question. Returns the most relevant reviews ranked by similarity.",
    31→        input_schema={
    32→            "type": "object",
    33→            "properties": {
    34→                "query": {
    35→                    "type": "string",
    36→                    "description": "The search query — describe what you're looking for in natural language.",
    37→                },
    38→                "n_results": {
    39→                    "type": "integer",
    40→                    "description": "Number of results to return (default 10, max 25).",
    41→                    "default": 10,
    42→                },
    43→                "min_rating": {
    44→                    "type": "number",
    45→                    "description": "Optional: only return reviews with rating >= this value.",
    46→                },
    47→                "max_rating": {
    48→                    "type": "number",
    49→                    "description": "Optional: only return reviews with rating <= this value.",
    50→                },
    51→            },
    52→            "required": ["query"],
    53→        },
    54→    )
    55→    async def search_reviews_tool(args: dict[str, Any]) -> dict[str, Any]:
    56→        query = args["query"]
    57→        n = min(args.get("n_results", 10), 25)
    58→
    59→        where = None
    60→        if "min_rating" in args and "max_rating" in args:
    61→            where = {
    62→                "$and": [
    63→                    {"rating": {"$gte": args["min_rating"]}},
    64→                    {"rating": {"$lte": args["max_rating"]}},
    65→                ]
    66→            }
    67→        elif "min_rating" in args:
    68→            where = {"rating": {"$gte": args["min_rating"]}}
    69→        elif "max_rating" in args:
    70→            where = {"rating": {"$lte": args["max_rating"]}}
    71→
    72→        results = vectordb.search_reviews(session_id, query, n_results=n, where=where)
    73→
    74→        await emit_fn(
    75→            session_id,
    76→            f"Searched reviews: \"{query}\" — {len(results)} results",
    77→            "tool",
    78→        )
    79→
    80→        return {
    81→            "content": [
    82→                {
    83→                    "type": "text",
    84→                    "text": json.dumps(
    85→                        {
    86→                            "query": query,
    87→                            "result_count": len(results),
    88→                            "results": results,
    89→                            "note": "If no results are relevant, tell the user you couldn't find matching reviews. Do NOT make up information.",
    90→                        }
    91→                    ),
    92→                }
    93→            ]
    94→        }
    95→
    96→    # ── analyze_sentiment ────────────────────────────────────────────
    97→
    98→    @tool(
    99→        name="analyze_sentiment",
   100→        description="Analyse sentiment and extract aspects from reviews matching a query. Returns aspect-sentiment pairs and overall sentiment distribution. Use for questions about what people like/dislike, pain points, praise, etc.",
   101→        input_schema={
   102→            "type": "object",
   103→            "properties": {
   104→                "query": {
   105→                    "type": "string",
   106→                    "description": "Search query to find relevant reviews for analysis.",
   107→                },
   108→                "n_reviews": {
   109→                    "type": "integer",
   110→                    "description": "Number of reviews to analyse (default 15, max 30).",
   111→                    "default": 15,
   112→                },
   113→            },
   114→            "required": ["query"],
   115→        },
   116→    )
   117→    async def analyze_sentiment_tool(args: dict[str, Any]) -> dict[str, Any]:
   118→        query = args["query"]
   119→        n = min(args.get("n_reviews", 15), 30)
   120→
   121→        results = vectordb.search_reviews(session_id, query, n_results=n)
   122→
   123→        if not results:
   124→            return {
   125→                "content": [
   126→                    {
   127→                        "type": "text",
   128→                        "text": json.dumps({"error": "No reviews found matching query.", "results": []}),
   129→                    }
   130→                ]
   131→            }
   132→
   133→        # Provide the raw reviews to the LLM — it does the actual sentiment analysis.
   134→        # This is more flexible and accurate than a rule-based approach.
   135→        await emit_fn(
   136→            session_id,
   137→            f"Analysing sentiment: \"{query}\" — {len(results)} reviews",
   138→            "tool",
   139→        )
   140→
   141→        return {
   142→            "content": [
   143→                {
   144→                    "type": "text",
   145→                    "text": json.dumps(
   146→                        {
   147→                            "query": query,
   148→                            "review_count": len(results),
   149→                            "reviews": results,
   150→                            "instruction": (
   151→                                "Analyse these reviews for sentiment and aspects. "
   152→                                "For each review, identify: (1) key aspects mentioned "
   153→                                "(e.g., 'battery life', 'customer service', 'price'), "
   154→                                "(2) sentiment per aspect (positive/negative/neutral/mixed), "
   155→                                "(3) overall sentiment. Then summarise the patterns across all reviews. "
   156→                                "Only report what the reviews actually say."
   157→                            ),
   158→                        }
   159→                    ),
   160→                }
   161→            ]
   162→        }
   163→
   164→    # ── generate_chart ───────────────────────────────────────────────
   165→
   166→    @tool(
   167→        name="generate_chart",
   168→        description="Generate a Chart.js chart configuration that renders inline in the chat. Use for visualising rating distributions, sentiment breakdowns, trends over time, aspect comparisons, etc. The chart renders automatically — just return valid config.",
   169→        input_schema={
   170→            "type": "object",
   171→            "properties": {
   172→                "chart_type": {
   173→                    "type": "string",
   174→                    "enum": ["bar", "line", "pie", "doughnut"],
   175→                    "description": "The type of chart to generate.",
   176→                },
   177→                "title": {
   178→                    "type": "string",
   179→                    "description": "Chart title displayed above the visualisation.",
   180→                },
   181→                "labels": {
   182→                    "type": "array",
   183→                    "items": {"type": "string"},
   184→                    "description": "X-axis labels (categories, dates, etc.).",
   185→                },
   186→                "datasets": {
   187→                    "type": "array",
   188→                    "items": {
   189→                        "type": "object",
   190→                        "properties": {
   191→                            "label": {"type": "string"},
   192→                            "data": {
   193→                                "type": "array",
   194→                                "items": {"type": "number"},
   195→                            },
   196→                        },
   197→                        "required": ["label", "data"],
   198→                    },
   199→                    "description": "One or more data series.",
   200→                },
   201→            },
   202→            "required": ["chart_type", "title", "labels", "datasets"],
   203→        },
   204→    )
   205→    async def generate_chart_tool(args: dict[str, Any]) -> dict[str, Any]:
   206→        chart_config = {
   207→            "type": args["chart_type"],
   208→            "title": args["title"],
   209→            "data": {
   210→                "labels": args["labels"],
   211→                "datasets": args["datasets"],
   212→            },
   213→        }
   214→
   215→        await emit_fn(
   216→            session_id,
   217→            f"Generated chart: {args['title']}",
   218→            "tool",
   219→        )
   220→
   221→        return {
   222→            "content": [
   223→                {
   224→                    "type": "text",
   225→                    "text": json.dumps(
   226→                        {
   227→                            "chart": chart_config,
   228→                            "instruction": "This chart will render automatically in the chat. Reference it in your text response.",
   229→                        }
   230→                    ),
   231→                }
   232→            ]
   233→        }
   234→
   235→    # ── calculate_stats ──────────────────────────────────────────────
   236→
   237→    @tool(
   238→        name="calculate_stats",
   239→        description="Calculate aggregate statistics over the full review dataset. Use for quantitative questions: average ratings, distributions, counts by category, trends over time periods, etc.",
   240→        input_schema={
   241→            "type": "object",
   242→            "properties": {
   243→                "operation": {
   244→                    "type": "string",
   245→                    "enum": [
   246→                        "rating_distribution",
   247→                        "rating_over_time",
   248→                        "review_volume_over_time",
   249→                        "keyword_frequency",
   250→                        "summary_stats",
   251→                    ],
   252→                    "description": "The type of statistical analysis to run.",
   253→                },
   254→                "keyword": {
   255→                    "type": "string",
   256→                    "description": "For keyword_frequency: the keyword or phrase to count.",
   257→                },
   258→            },
   259→            "required": ["operation"],
   260→        },
   261→    )
   262→    async def calculate_stats_tool(args: dict[str, Any]) -> dict[str, Any]:
   263→        operation = args["operation"]
   264→        all_reviews = vectordb.get_all_reviews(session_id)
   265→
   266→        if not all_reviews:
   267→            return {
   268→                "content": [
   269→                    {
   270→                        "type": "text",
   271→                        "text": json.dumps({"error": "No reviews in database."}),
   272→                    }
   273→                ]
   274→            }
   275→
   276→        result: dict[str, Any] = {"operation": operation}
   277→
   278→        if operation == "rating_distribution":
   279→            ratings = [r["metadata"].get("rating") for r in all_reviews if r["metadata"].get("rating") is not None]
   280→            dist = Counter(int(round(r)) for r in ratings)
   281→            result["distribution"] = {str(k): v for k, v in sorted(dist.items())}
   282→            result["total_with_ratings"] = len(ratings)
   283→            result["total_reviews"] = len(all_reviews)
   284→            if ratings:
   285→                result["average"] = round(sum(ratings) / len(ratings), 2)
   286→
   287→        elif operation == "rating_over_time":
   288→            by_month: dict[str, list[float]] = {}
   289→            for r in all_reviews:
   290→                date_str = r["metadata"].get("date", "")
   291→                rating = r["metadata"].get("rating")
   292→                if date_str and rating is not None:
   293→                    month = date_str[:7]  # YYYY-MM
   294→                    by_month.setdefault(month, []).append(rating)
   295→            result["monthly_averages"] = {
   296→                m: round(sum(v) / len(v), 2)
   297→                for m, v in sorted(by_month.items())
   298→            }
   299→
   300→        elif operation == "review_volume_over_time":
   301→            by_month: dict[str, int] = {}
   302→            for r in all_reviews:
   303→                date_str = r["metadata"].get("date", "")
   304→                if date_str:
   305→                    month = date_str[:7]
   306→                    by_month[month] = by_month.get(month, 0) + 1
   307→            result["monthly_volume"] = dict(sorted(by_month.items()))
   308→
   309→        elif operation == "keyword_frequency":
   310→            keyword = args.get("keyword", "").lower()
   311→            if not keyword:
   312→                return {
   313→                    "content": [
   314→                        {
   315→                            "type": "text",
   316→                            "text": json.dumps({"error": "keyword parameter required"}),
   317→                        }
   318→                    ]
   319→                }
   320→            count = sum(1 for r in all_reviews if keyword in r["text"].lower())
   321→            result["keyword"] = keyword
   322→            result["count"] = count
   323→            result["percentage"] = round(count / len(all_reviews) * 100, 1)
   324→
   325→        elif operation == "summary_stats":
   326→            ratings = [r["metadata"].get("rating") for r in all_reviews if r["metadata"].get("rating") is not None]
   327→            result["total_reviews"] = len(all_reviews)
   328→            result["total_with_ratings"] = len(ratings)
   329→            if ratings:
   330→                result["average_rating"] = round(sum(ratings) / len(ratings), 2)
   331→                result["min_rating"] = min(ratings)
   332→                result["max_rating"] = max(ratings)
   333→            dates = [r["metadata"].get("date", "") for r in all_reviews if r["metadata"].get("date")]
   334→            if dates:
   335→                result["earliest_review"] = min(dates)
   336→                result["latest_review"] = max(dates)
   337→            avg_length = sum(len(r["text"]) for r in all_reviews) / len(all_reviews)
   338→            result["average_review_length"] = round(avg_length)
   339→
   340→        await emit_fn(
   341→            session_id,
   342→            f"Calculated stats: {operation}",
   343→            "tool",
   344→        )
   345→
   346→        return {
   347→            "content": [
   348→                {
   349→                    "type": "text",
   350→                    "text": json.dumps(result),
   351→                }
   352→            ]
   353→        }
   354→
   355→    # ── suggest_follow_ups ───────────────────────────────────────────
   356→
   357→    @tool(
   358→        name="suggest_follow_ups",
   359→        description="Generate contextual follow-up question buttons based on the current conversation. Call this at the END of every response. The questions should be specific to what was just discussed and lead the user deeper into the data.",
   360→        input_schema={
   361→            "type": "object",
   362→            "properties": {
   363→                "questions": {
   364→                    "type": "array",
   365→                    "items": {"type": "string"},
   366→                    "description": "3-4 contextual follow-up questions. Make them specific and actionable.",
   367→                    "minItems": 2,
   368→                    "maxItems": 4,
   369→                },
   370→            },
   371→            "required": ["questions"],
   372→        },
   373→    )
   374→    async def suggest_follow_ups_tool(args: dict[str, Any]) -> dict[str, Any]:
   375→        questions = args["questions"]
   376→
   377→        await emit_fn(
   378→            session_id,
   379→            f"Suggested {len(questions)} follow-up questions",
   380→            "tool",
   381→        )
   382→
   383→        return {
   384→            "content": [
   385→                {
   386→                    "type": "text",
   387→                    "text": json.dumps(
   388→                        {
   389→                            "follow_ups": questions,
   390→                            "instruction": "These will appear as clickable buttons below your message. Do not repeat them in your text response.",
   391→                        }
   392→                    ),
   393→                }
   394→            ]
   395→        }
   396→
   397→    # ── Build and return the MCP server ──────────────────────────────
   398→
   399→    return create_sdk_mcp_server(
   400→        name="reviewlens",
   401→        version="1.0.0",
   402→        tools=[
   403→            search_reviews_tool,
   404→            analyze_sentiment_tool,
   405→            generate_chart_tool,
   406→            calculate_stats_tool,
   407→            suggest_follow_ups_tool,
   408→        ],
   409→    )
   410→

     1→"""Conversational agent loop — invoked per-message, not per-session."""
     2→
     3→from __future__ import annotations
     4→
     5→import json
     6→import os
     7→from typing import Any
     8→
     9→from claude_agent_sdk import (
    10→    AssistantMessage,
    11→    ClaudeAgentOptions,
    12→    ClaudeSDKClient,
    13→    HookMatcher,
    14→    ResultMessage,
    15→    TextBlock,
    16→)
    17→
    18→from .models import ChatMessage, IngestionSummary
    19→from .prompts import build_system_prompt
    20→from .tools import create_review_tools_server
    21→
    22→DEFAULT_MODEL = "claude-sonnet-4-6"
    23→
    24→
    25→async def handle_message(
    26→    session_id: str,
    27→    user_message: str,
    28→    conversation_history: list[ChatMessage],
    29→    summary: IngestionSummary,
    30→    emit_fn,
    31→) -> ChatMessage:
    32→    """Run the agent for a single user message. Returns the assistant's response."""
    33→
    34→    model = os.getenv("CLAUDE_MODEL", DEFAULT_MODEL)
    35→    system_prompt = build_system_prompt(summary)
    36→
    37→    # Build conversation context for the agent
    38→    messages_for_context = []
    39→    for msg in conversation_history[-20:]:  # Last 20 messages for context window
    40→        messages_for_context.append(f"{'User' if msg.role == 'user' else 'Assistant'}: {msg.content}")
    41→
    42→    prompt_parts = []
    43→    if messages_for_context:
    44→        prompt_parts.append("Previous conversation:\n" + "\n".join(messages_for_context))
    45→    prompt_parts.append(f"User: {user_message}")
    46→    prompt = "\n\n".join(prompt_parts)
    47→
    48→    # Create per-request MCP server (closure over session_id)
    49→    server = create_review_tools_server(
    50→        session_id=session_id,
    51→        emit_fn=emit_fn,
    52→    )
    53→
    54→    # Track tool outputs for charts and follow-ups
    55→    charts: list[dict[str, Any]] = []
    56→    follow_ups: list[str] = []
    57→
    58→    async def post_tool_hook(input_data, tool_use_id, context):
    59→        """Intercept tool results to extract charts and follow-ups."""
    60→        # The tool result is in context
    61→        try:
    62→            result = context.get("result", {})
    63→            content = result.get("content", [])
    64→            for block in content:
    65→                if block.get("type") == "text":
    66→                    data = json.loads(block["text"])
    67→                    if "chart" in data:
    68→                        charts.append(data["chart"])
    69→                    if "follow_ups" in data:
    70→                        follow_ups.extend(data["follow_ups"])
    71→        except (json.JSONDecodeError, KeyError, TypeError):
    72→            pass
    73→        return {}
    74→
    75→    options = ClaudeAgentOptions(
    76→        system_prompt=system_prompt,
    77→        allowed_tools=["mcp__reviewlens__*"],
    78→        permission_mode="bypassPermissions",
    79→        max_turns=15,
    80→        model=model,
    81→        mcp_servers={"reviewlens": server},
    82→        hooks={
    83→            "PostToolUse": [HookMatcher(matcher=".*", hooks=[post_tool_hook])],
    84→        },
    85→    )
    86→
    87→    response_text = ""
    88→
    89→    try:
    90→        async with ClaudeSDKClient(options=options) as client:
    91→            await client.query(prompt)
    92→            async for message in client.receive_response():
    93→                if isinstance(message, AssistantMessage):
    94→                    for block in message.content:
    95→                        if isinstance(block, TextBlock) and block.text.strip():
    96→                            response_text += block.text.strip() + "\n"
    97→                elif isinstance(message, ResultMessage):
    98→                    if message.result and not response_text.strip():
    99→                        response_text = message.result
   100→
   101→    except Exception as e:
   102→        response_text = f"I encountered an error processing your question. Please try again.\n\nError: {str(e)}"
   103→        await emit_fn(session_id, f"Agent error: {e}", "error")
   104→
   105→    return ChatMessage(
   106→        role="assistant",
   107→        content=response_text.strip(),
   108→        charts=charts,
   109→        follow_ups=follow_ups,
   110→    )
   111→

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/prompts.py has been updated successfully.

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/agent.py has been updated successfully.

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py has been updated successfully.

Updated task #5 status

Updated task #6 status

Updated task #7 status

Updated task #8 status

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.

Updated task #8 status

  app/knowledge.py: OK
  app/models.py: OK
  app/store.py: OK
  app/prompts.py: OK
  app/tools.py: OK
  app/agent.py: OK
  app/main.py: OK
All files parse successfully.

File created successfully at: /Users/shaynelarocque/.claude/projects/-Users-shaynelarocque-Documents-GitHub-futuresight/memory/MEMORY.md

File created successfully at: /Users/shaynelarocque/.claude/projects/-Users-shaynelarocque-Documents-GitHub-futuresight/memory/user_profile.md

File created successfully at: /Users/shaynelarocque/.claude/projects/-Users-shaynelarocque-Documents-GitHub-futuresight/memory/project_reviewlens.md

37051
54347

Command running in background with ID: bqav6nagk. Output is being written to: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/bqav6nagk.output

INFO:     Will watch for changes in these directories: ['/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens']
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [54481] using WatchFiles
Process SpawnProcess-1:
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/multiprocessing/process.py", line 313, in _bootstrap
    self.run()
    ~~~~~~~~^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/multiprocessing/process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
    ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/_subprocess.py", line 80, in subprocess_started
    target(sockets=sockets)
    ~~~~~~^^^^^^^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/server.py", line 75, in run
    return asyncio_run(self.serve(sockets=sockets), loop_factory=self.config.get_loop_factory())
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/runners.py", line 194, in run
    return runner.run(main)
           ~~~~~~~~~~^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/runners.py", line 118, in run
    return self._loop.run_until_complete(task)
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
  File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/server.py", line 79, in serve
    await self._serve(sockets)
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/server.py", line 86, in _serve
    config.load()
    ~~~~~~~~~~~^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/config.py", line 441, in load
    self.loaded_app = import_from_string(self.app)
                      ~~~~~~~~~~~~~~~~~~^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/importer.py", line 22, in import_from_string
    raise exc from None
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/site-packages/uvicorn/importer.py", line 19, in import_from_string
    module = importlib.import_module(module_str)
  File "/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/importlib/__init__.py", line 88, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
           ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 1022, in exec_module
  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
  File "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py", line 23, in <module>
    from . import knowledge, store, vectordb
  File "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/vectordb.py", line 9, in <module>
    import chromadb
ModuleNotFoundError: No module named 'chromadb'

/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/.venv/bin/uvicorn

Command running in background with ID: bj4ycd3eg. Output is being written to: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/bj4ycd3eg.output

{"status":"ok"}