Claude Code transcript - page 5/9

Perfect! I've successfully read the ingest.py file and fetched the Firecrawl agent documentation. Here's a comprehensive breakdown of what I found:

## Current Implementation in ingest.py

**File location:** `/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/ingest.py`

### API Configuration
- **Base URL:** `https://api.firecrawl.dev/v1`
- **Endpoint:** `/scrape` (lines 144)
- **Authentication:** Bearer token via `FIRECRAWL_API_KEY` environment variable

### Request Format (lines 143-153)
```python
POST /scrape
Headers:
  Authorization: Bearer {FIRECRAWL_API_KEY}
  Content-Type: application/json

Payload:
{
  "url": url,
  "formats": ["markdown"]
}
```

### Response Parsing (lines 155-164)
- Expects JSON response with structure: `response.data.markdown`
- Extracts the markdown content from the response
- Falls back to empty list if no markdown is found

### Review Extraction Strategy (lines 167-212)
After getting markdown, the code uses `_extract_reviews_from_markdown()` to:
1. Split markdown on patterns: horizontal rules (`---`), asterisks (`***`), or headers (`#`)
2. Extract rating from star symbols (`★`) or patterns like "X/5" or "X out of 5"
3. Extract dates using multiple formats
4. Cap each review to 2000 characters
5. Guess platform from URL or use provided platform parameter

---

## Firecrawl Agent API

**Endpoint:** `https://api.firecrawl.dev/v2/agent`

### Key Differences from Current `/scrape` Endpoint

| Aspect | Current `/scrape` | Agent Endpoint |
|--------|-------------------|----------------|
| **API Version** | v1 | v2 |
| **Input** | URL + format selection | Natural language prompt (up to 10,000 chars) |
| **Autonomy** | Passive—scrapes single URL as-is | Active—searches, navigates, and gathers data autonomously |
| **Multi-page** | No built-in support | Built-in—processes multiple sources in parallel |
| **Output** | Raw markdown | Structured data matching optional JSON schema |
| **Models** | N/A | Two options: `spark-1-mini` (default, cheaper) or `spark-1-pro` |

### Request Format for Agent
```json
{
  "prompt": "natural language description",
  "urls": ["optional", "specific", "urls"],
  "schema": {JSON schema for structured output},
  "model": "spark-1-mini" or "spark-1-pro",
  "maxCredits": 2500
}
```

### Response Format for Agent
```json
{
  "success": boolean,
  "status": "processing|completed|failed|cancelled",
  "data": {extracted information matching schema},
  "creditsUsed": number,
  "expiresAt": "24-hour availability window"
}
```

### Important Notes
- **Pagination/Multi-step:** The agent documentation emphasizes it "autonomously searches and navigates deep into sites" and "processes multiple sources in parallel," but specific mechanics for handling pagination aren't detailed in the docs
- **Cost:** The agent has a credit-based model (tracked via `maxCredits` and `creditsUsed`)
- **Timeout:** Current implementation uses 60-second timeout; agent behavior under this constraint isn't specified
- **Schema Support:** Supports Pydantic or Zod schema definitions for structured extraction

---

## Summary of Key Insights

1. **Current approach** is simple and stateless—fetch URL, convert to markdown, regex-parse reviews
2. **Agent approach** is much more sophisticated—natural language driven, can navigate multi-page sites, returns structured data
3. **No pagination handling** in current code; agent claims to handle it autonomously
4. The agent would likely be better for complex review sites with JavaScript rendering, multi-page navigation, or varied HTML structures

agentId: adfd96a743cc82b66 (use SendMessage with to: 'adfd96a743cc82b66' to continue this agent)
<usage>total_tokens: 26474
tool_uses: 2
duration_ms: 16041</usage>

Excellent! I have comprehensive information. Let me compile a detailed report for you.

## Firecrawl Agent Endpoint - Comprehensive Documentation

Based on my research, here's detailed information about Firecrawl's agent endpoint:

### 1. **API Endpoint URL & HTTP Method**
- **Endpoint:** `https://api.firecrawl.dev/v2/agent`
- **HTTP Method:** POST
- **Authentication:** Bearer token in Authorization header

### 2. **Request Payload Schema (All Parameters)**

| Parameter | Type | Required | Description | Notes |
|-----------|------|----------|-------------|-------|
| `prompt` | string | **Yes** | Describes the data you want to extract | Max 10,000 characters |
| `model` | string | No | Which AI model to use | `spark-1-mini` (default, 60% cheaper) or `spark-1-pro` (higher accuracy) |
| `urls` | array | No | Optional list of target pages to focus on | If omitted, agent discovers URLs via web search |
| `schema` | object | No | JSON schema for structured output | Define using Pydantic (Python) or Zod (Node.js) |
| `maxCredits` | number | No | Spending limit for the job | Default 2,500 credits |
| `strictConstrainToURLs` | boolean | No | Force agent to only visit provided URLs | Restricts autonomous navigation |

### 3. **Response Format**

**Successful Response (HTTP 200):**
```json
{
  "success": boolean,
  "id": "uuid",
  "status": "processing|completed|failed|cancelled",
  "data": { /* extracted results matching your schema */ },
  "creditsUsed": number,
  "expiresAt": "ISO 8601 timestamp"
}
```

**Key response characteristics:**
- Jobs run **asynchronously** — returns immediately with a Job ID
- Results are available for **24 hours** after completion
- Can be retrieved via the Job ID using polling
- Status can be: `processing`, `completed`, `failed`, or `cancelled`

**Error Responses:**
- **402:** Payment required (insufficient credits)
- **429:** Rate limit exceeded

### 4. **Pagination Handling**

The agent endpoint handles pagination **automatically and autonomously**:
- The agent itself navigates through paginated content by clicking buttons, following "next" links, and exploring multiple pages
- No explicit pagination parameters needed in the request
- The agent "clicks through authentication flows, navigates nested menus, handles dropdowns, and explores multi-step processes"
- This differs from the earlier `/extract` endpoint which required manual pagination configuration
- All results from multiple pages are automatically aggregated and structured according to your schema

### 5. **Structured Data Extraction**

The agent extracts structured data through:
- **Schema-based extraction:** You provide a JSON schema (Pydantic for Python, Zod for Node.js) defining the desired output structure
- **Natural language prompts:** Simply describe what data you need; the AI understands context and intent
- **Autonomous processing:** The agent "plans and takes actions to uncover data" by navigating websites intelligently
- **Format:** Results conform strictly to your specified schema, delivered as clean JSON ready for downstream processing
- **No pre-specified URLs required:** The agent can search the web to discover relevant websites before extracting data

### 6. **Python SDK Usage Examples**

**Basic Agent Usage:**
```python
from firecrawl import Firecrawl

firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")

# Simple autonomous data gathering
result = firecrawl.agent(
    prompt="Find the founders of Stripe"
)
print(result.data)

# With structured output schema
result = firecrawl.agent(
    prompt="Find all product names and prices from an e-commerce site",
    schema={
        "products": [
            {
                "name": "string",
                "price": "number"
            }
        ]
    }
)
print(result.data)

# With specific URLs to focus on
result = firecrawl.agent(
    prompt="Extract all job postings",
    urls=["https://example.com/careers"],
    model="spark-1-pro"  # Higher accuracy for complex extraction
)
```

**Installation:**
```bash
pip install firecrawl-py
```

### 7. **Pricing & Rate Limits**

**Pricing Model (Research Preview):**
- **Credit-based billing** with dynamic costs based on task complexity
- **Simple single-page tasks:** Fewer credits consumed
- **Complex multi-domain research:** Higher credit consumption
- **Typical runs:** Consume "a few hundred credits"
- **Free daily runs:** 5 free runs per day included
- **Parallel processing (Spark-1 Fast):** 10 credits per cell

**Rate Limits:**
- **Free tier:** 5 runs per day
- **Paid tier:** 10 requests per minute maximum (from `/extract` endpoint which uses similar infrastructure)
- **402 errors** returned when credit limit exceeded

**Note:** Firecrawl provides a credit calculator on their website to estimate costs for specific tasks.

### 8. **Differences from Regular Scrape/Crawl Endpoints**

| Feature | `/scrape` | `/crawl` | `/agent` |
|---------|-----------|----------|---------|
| **URLs Required** | Yes (single) | Yes (starting URL) | No — uses web search |
| **Navigation** | None | Follows links within domain | Autonomous multi-site navigation |
| **Pagination** | Manual | Manual config required | Automatic autonomous |
| **Form Interaction** | No | No | Yes — clicks, fills forms |
| **Authentication Flows** | No | No | Yes — handles login flows |
| **Web Search** | No | No | Yes — discovers relevant sites |
| **Output Format** | Markdown/HTML | Markdown/HTML | Structured JSON (schema-based) |
| **Schema Support** | No | No | Yes — required for structure |
| **Model Options** | N/A | N/A | spark-1-mini or spark-1-pro |
| **Job Processing** | Synchronous | Asynchronous | Asynchronous with 24h availability |
| **Primary Use Case** | Extract from known page | Crawl known site structure | Autonomous research & extraction |

### Key Advantages of `/agent`

- **Successor to `/extract`:** Faster, more reliable, no URLs required
- **Autonomous discovery:** Searches for and finds data automatically
- **Complex interaction support:** Navigates authentication, forms, dynamic content
- **Multi-page intelligence:** Aggregates data across paginated results
- **Natural language:** Just describe what you need in plain language
- **Type-safe output:** Strict schema compliance for downstream processing

---

## Sources:
- [Agent | Firecrawl Features](https://docs.firecrawl.dev/features/agent)
- [Agent API Reference](https://docs.firecrawl.dev/api-reference/endpoint/agent)
- [FIRE-1 AI Agent Documentation](https://docs.firecrawl.dev/agents/fire-1)
- [Python SDK Documentation](https://docs.firecrawl.dev/sdks/python)
- [Introducing /agent Blog Post](https://www.firecrawl.dev/blog/introducing-agent)
- [How to Build AI Agents with Claude and Firecrawl](https://www.firecrawl.dev/blog/claude-agent-sdk-firecrawl)
- [Firecrawl Python SDK Repository](https://github.com/firecrawl/firecrawl-py)
- [Firecrawl Main Repository](https://github.com/firecrawl/firecrawl)

agentId: a0f1ad3ea9deaf8af (use SendMessage with to: 'a0f1ad3ea9deaf8af' to continue this agent)
<usage>total_tokens: 30175
tool_uses: 10
duration_ms: 45122</usage>

Now I have a complete picture. Let me formulate the plan.

---

## Implementation Plan: Replace Firecrawl v1/scrape with v2/agent

### Answers to Design Questions

**1. Raw httpx vs firecrawl-py SDK**

Use raw httpx. Reasons:
- The project already depends on httpx and uses it for the current Firecrawl call.
- Adding `firecrawl-py` introduces a new dependency for exactly two HTTP calls (submit job + poll).
- The SDK's `firecrawl.agent()` method is synchronous (or has its own async story) which may not integrate cleanly with the existing `async with httpx.AsyncClient` pattern.
- The v2/agent API surface is simple: one POST to submit, one GET to poll. Two requests do not justify an SDK.

**2. Prompt Design**

The prompt should be specific about what constitutes a "review" and what fields to extract. Something like:

```
Extract all customer/user reviews from this page. For each review, extract:
- The full review text (the main body of what the reviewer wrote)
- The star rating as a number from 1 to 5 (if present)
- The date the review was posted (in YYYY-MM-DD format if possible)
- The author/reviewer name (if present)

Only extract actual user-submitted reviews. Do not extract editorial content, product descriptions, or page navigation text. If reviews span multiple pages, navigate through all available pages.
```

This is under 600 characters, well within the 10,000 char limit. The platform hint from `_guess_platform()` can be prepended (e.g., "This is an Amazon product page.") to help the agent understand context.

**3. JSON Schema**

The schema sent to the agent endpoint should match this structure:

```json
{
  "type": "object",
  "properties": {
    "reviews": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "text": {"type": "string", "description": "Full review text"},
          "rating": {"type": ["number", "null"], "description": "Star rating 1-5"},
          "date": {"type": ["string", "null"], "description": "Date in YYYY-MM-DD format"},
          "author": {"type": ["string", "null"], "description": "Reviewer name"}
        },
        "required": ["text"]
      }
    }
  },
  "required": ["reviews"]
}
```

This keeps it simple: an object with a `reviews` array. Each item has `text` (required) plus optional `rating`, `date`, `author`.

**4. Polling Strategy**

- **Interval:** 2 seconds between polls. The agent needs time to navigate pages, and more frequent polling wastes API calls.
- **Timeout:** 120 seconds total (matching the current `httpx.AsyncClient(timeout=60.0)` but doubled since the agent does more work). Use `asyncio.sleep(2)` between polls.
- **Max polls:** 60 (120s / 2s). After that, raise or return empty.
- Use the same job URL: `GET https://api.firecrawl.dev/v2/agent/{job_id}` with the auth header.

**5. Keep the old fallback or remove it?**

Remove it entirely. The old `_extract_reviews_from_markdown()` function and the regex-based parsing are the whole reason for this change -- they are fragile. The agent endpoint replaces both the scraping and the parsing. Delete `_extract_reviews_from_markdown()`. Keep `_guess_platform()`, `_parse_date()`, and `_parse_rating()` as they are useful for normalizing agent output.

### Step-by-Step Implementation

**Step 1: Update constants**

In `app/ingest.py`, change:
- `FIRECRAWL_API_URL` from `"https://api.firecrawl.dev/v1"` to `"https://api.firecrawl.dev/v2"`

Add new constants:
- `_AGENT_POLL_INTERVAL = 2` (seconds)
- `_AGENT_TIMEOUT = 120` (seconds)
- `_AGENT_MAX_CREDITS = 500` (conservative cap; `spark-1-mini` default is 2500 but we want to stay cheap)

Add import: `import asyncio` (for `asyncio.sleep`).

**Step 2: Define the extraction schema as a module-level dict**

Create `_REVIEW_SCHEMA` as the JSON schema dict described above.

**Step 3: Define the prompt builder**

A small helper `_build_agent_prompt(url: str, platform: str) -> str` that constructs the extraction prompt, optionally including the platform name for context.

**Step 4: Rewrite `scrape_url()`**

The new flow:
1. Check `FIRECRAWL_API_KEY` (same as now).
2. POST to `{FIRECRAWL_API_URL}/agent` with body:
   ```json
   {
     "urls": [url],
     "prompt": _build_agent_prompt(url, platform),
     "schema": _REVIEW_SCHEMA,
     "model": "spark-1-mini",
     "maxCredits": _AGENT_MAX_CREDITS
   }
   ```
3. Extract `job_id` from the response.
4. Poll `GET {FIRECRAWL_API_URL}/agent/{job_id}` every `_AGENT_POLL_INTERVAL` seconds.
5. On `status == "completed"`, extract `data["reviews"]` from the response.
6. On `status == "failed"` or timeout, return `[]`.
7. Convert each raw review dict into a `Review` object, using `_parse_date()` and `_parse_rating()` to normalize values. Use `_guess_platform(url)` if platform not provided.
8. Return the list.

The httpx client timeout should be set to 30s per individual request (not the total polling duration). Use a single `httpx.AsyncClient` context for both the submit and all poll requests.

**Step 5: Remove dead code**

Delete `_extract_reviews_from_markdown()` entirely. It is only called from the old `scrape_url()`.

**Step 6: No changes needed to `app/main.py`**

The call site at line 156 (`reviews = await scrape_url(url, platform=platform)`) remains identical. The function signature is preserved.

**Step 7: No changes to `requirements.txt`**

No new dependencies. `httpx` is already present. We are not using `firecrawl-py`.

### Potential Challenges

- **Agent job duration:** The agent may take longer than 120 seconds for sites with many review pages. The 120-second timeout is a reasonable starting point; if it proves insufficient, increase it. The FastAPI endpoint at `/scrape` will be holding the connection open during polling. Consider whether the frontend uses HTMX with a loading state (it does, based on the HTMX patterns visible in main.py). The user will see the loading spinner during the wait.

- **Schema compliance:** The agent may not perfectly follow the schema. Defensive code is needed when iterating `data["reviews"]` -- skip entries without `text`, handle missing fields gracefully.

- **Credit exhaustion:** With `maxCredits: 500` and `spark-1-mini`, a single extraction should cost well under that. But if the free tier has a low monthly cap, multiple scrapes could exhaust it. This is acceptable since URL scraping is the secondary path.

- **Rate limits:** The polling loop makes ~60 GET requests over 120 seconds. This should be fine for Firecrawl's API.

### Critical Files for Implementation
- `/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/ingest.py` - Primary file to modify: rewrite `scrape_url()`, add schema/prompt constants, remove `_extract_reviews_from_markdown()`
- `/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/models.py` - Review model definition that the JSON schema must align with
- `/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py` - Call site for `scrape_url()` at line 156; verify no changes needed
- `/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/requirements.txt` - Confirm no new dependencies are needed

agentId: ab24596fb7dcc1eda (use SendMessage with to: 'ab24596fb7dcc1eda' to continue this agent)
<usage>total_tokens: 21159
tool_uses: 7
duration_ms: 60654</usage>

   120→                rating=_parse_rating(rating_raw),
   121→                date=_parse_date(date_raw),
   122→                author=author,
   123→                platform=plat,
   124→                metadata=metadata,
   125→            )
   126→        )
   127→
   128→    return reviews
   129→
   130→
   131→# ── Firecrawl URL Scraping (secondary, best-effort) ─────────────────
   132→
   133→FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", "")
   134→FIRECRAWL_API_URL = "https://api.firecrawl.dev/v1"
   135→
   136→
   137→async def scrape_url(url: str, platform: str = "") -> list[Review]:
   138→    """Best-effort URL scraping via Firecrawl. Returns whatever we can get."""
   139→    if not FIRECRAWL_API_KEY:
   140→        raise ValueError("FIRECRAWL_API_KEY not set. Use CSV upload instead.")
   141→
   142→    async with httpx.AsyncClient(timeout=60.0) as client:
   143→        resp = await client.post(
   144→            f"{FIRECRAWL_API_URL}/scrape",
   145→            headers={
   146→                "Authorization": f"Bearer {FIRECRAWL_API_KEY}",
   147→                "Content-Type": "application/json",
   148→            },
   149→            json={
   150→                "url": url,
   151→                "formats": ["markdown"],
   152→            },
   153→        )
   154→        resp.raise_for_status()
   155→        data = resp.json()
   156→
   157→    markdown = data.get("data", {}).get("markdown", "")
   158→    if not markdown:
   159→        return []
   160→
   161→    # Try to extract individual reviews from the markdown.
   162→    # This is best-effort — review platforms have varied structures.
   163→    reviews = _extract_reviews_from_markdown(markdown, platform, url)
   164→    return reviews
   165→
   166→
   167→def _extract_reviews_from_markdown(
   168→    markdown: str, platform: str, url: str
   169→) -> list[Review]:
   170→    """Attempt to parse review blocks from scraped markdown."""
   171→    reviews: list[Review] = []
   172→
   173→    # Strategy: split on patterns that look like review boundaries.
   174→    # Common patterns: "★★★★☆", "Rating: X/5", numbered reviews, horizontal rules
   175→    blocks = re.split(r"\n(?:---+|\*\*\*+|#{1,3}\s)", markdown)
   176→
   177→    for i, block in enumerate(blocks):
   178→        block = block.strip()
   179→        if len(block) < 20:
   180→            continue
   181→
   182→        # Try to extract rating
   183→        rating = None
   184→        star_match = re.search(r"([★]{1,5})", block)
   185→        if star_match:
   186→            rating = float(len(star_match.group(1)))
   187→        else:
   188→            rating_match = re.search(r"(\d(?:\.\d)?)\s*(?:/\s*5|out of 5|stars?)", block, re.I)
   189→            if rating_match:
   190→                rating = float(rating_match.group(1))
   191→
   192→        # Try to extract date
   193→        date = None
   194→        date_match = re.search(
   195→            r"(\w+ \d{1,2},? \d{4}|\d{4}-\d{2}-\d{2}|\d{1,2}/\d{1,2}/\d{4})",
   196→            block,
   197→        )
   198→        if date_match:
   199→            date = _parse_date(date_match.group(1))
   200→
   201→        reviews.append(
   202→            Review(
   203→                id=f"scraped_{i}",
   204→                text=block[:2000],  # Cap individual review length
   205→                rating=rating,
   206→                date=date,
   207→                platform=platform or _guess_platform(url),
   208→                metadata={"source_url": url},
   209→            )
   210→        )
   211→
   212→    return reviews
   213→
   214→

File created successfully at: /Users/shaynelarocque/.claude/plans/splendid-discovering-bentley.md

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

User has approved your plan. You can now start coding. Start with updating your todo list if applicable

Your plan has been saved to: /Users/shaynelarocque/.claude/plans/splendid-discovering-bentley.md
You can refer back to it if needed during implementation.

## Approved Plan:
# Replace Firecrawl /scrape with /agent endpoint

## Context

The current URL scraping in `app/ingest.py` uses Firecrawl's `/v1/scrape` endpoint which returns raw markdown, then tries to regex-parse review blocks from it. This is fragile — review platforms have varied HTML structures and the regex splitting misses most of them. Firecrawl's newer `/v2/agent` endpoint is purpose-built for this: it accepts a natural language prompt + JSON schema, autonomously navigates paginated content, and returns structured data directly. This eliminates the brittle markdown-to-reviews parsing entirely.

## Changes

**Single file modified:** `app/ingest.py`

### 1. Update constants

- `FIRECRAWL_API_URL` → `https://api.firecrawl.dev/v2`
- Add `_AGENT_POLL_INTERVAL = 2` (seconds)
- Add `_AGENT_TIMEOUT = 120` (seconds)
- Add `_AGENT_MAX_CREDITS = 500` (conservative cap for zero-cost constraint)
- Add `import asyncio`

### 2. Add review extraction schema

Module-level `_REVIEW_SCHEMA` dict — an object with a `reviews` array, each item having `text` (required), `rating`, `date`, `author` (optional).

### 3. Add prompt builder

`_build_agent_prompt(url, platform)` — constructs a focused extraction prompt telling the agent to find all user reviews with text/rating/date/author fields and navigate pagination. Prepends platform context if known.

### 4. Rewrite `scrape_url()`

New flow (same signature: `async def scrape_url(url, platform) -> list[Review]`):

1. POST to `/agent` with `urls=[url]`, prompt, schema, `model="spark-1-mini"`, `maxCredits=500`
2. Extract job ID from response
3. Poll `GET /agent/{job_id}` every 2s, timeout at 120s
4. On `completed` → parse `data["reviews"]` into `Review` objects using existing `_parse_date()` / `_parse_rating()` for normalization
5. On `failed` / timeout → return `[]`

Uses raw `httpx` (already a dependency) — no need for `firecrawl-py` SDK for two HTTP calls.

### 5. Delete `_extract_reviews_from_markdown()`

No longer needed. The agent returns structured data directly.

### 6. Keep unchanged

- `_guess_platform()`, `_parse_date()`, `_parse_rating()` — reused for normalizing agent output
- `parse_csv()`, `build_summary()` — untouched
- `app/main.py` call site — `scrape_url()` signature unchanged
- `requirements.txt` — no new deps

## Verification

1. Set `FIRECRAWL_API_KEY` in `.env`
2. Run the app: `uvicorn app.main:app --reload --reload-exclude '.venv'`
3. Open modal → URL Scrape tab → paste a review page URL → submit
4. Confirm it either returns reviews (redirects to chat with data) or shows a graceful error
5. Verify CSV upload still works unchanged

     1→"""Ingestion module: CSV parsing and Firecrawl URL scraping."""
     2→
     3→from __future__ import annotations
     4→
     5→import csv
     6→import io
     7→import os
     8→import re
     9→import uuid
    10→from collections import Counter
    11→from datetime import datetime
    12→from typing import Any
    13→
    14→import httpx
    15→
    16→from .models import IngestionSummary, Review
    17→
    18→
    19→# ── CSV Ingestion (primary path) ────────────────────────────────────
    20→
    21→# Common column name variants we normalise to our schema.
    22→_COL_MAP: dict[str, list[str]] = {
    23→    "text": ["text", "review", "review_text", "content", "body", "comment", "review_body", "reviews", "feedback"],
    24→    "rating": ["rating", "score", "stars", "star_rating", "review_rating", "overall_rating"],
    25→    "date": ["date", "review_date", "created_at", "timestamp", "time", "posted_date", "review_time"],
    26→    "author": ["author", "reviewer", "user", "username", "reviewer_name", "name", "user_name"],
    27→    "platform": ["platform", "source", "site", "channel"],
    28→}
    29→
    30→
    31→def _normalise_col(name: str) -> str:
    32→    return re.sub(r"[^a-z0-9]", "", name.lower().strip())
    33→
    34→
    35→def _map_columns(headers: list[str]) -> dict[str, str]:
    36→    """Map CSV column names → our field names. Returns {our_field: csv_col}."""
    37→    mapping: dict[str, str] = {}
    38→    normalised = {_normalise_col(h): h for h in headers}
    39→
    40→    for field, aliases in _COL_MAP.items():
    41→        for alias in aliases:
    42→            norm = _normalise_col(alias)
    43→            if norm in normalised:
    44→                mapping[field] = normalised[norm]
    45→                break
    46→    return mapping
    47→
    48→
    49→def _parse_date(val: str) -> datetime | None:
    50→    for fmt in (
    51→        "%Y-%m-%d",
    52→        "%Y-%m-%dT%H:%M:%S",
    53→        "%Y-%m-%dT%H:%M:%SZ",
    54→        "%Y-%m-%d %H:%M:%S",
    55→        "%m/%d/%Y",
    56→        "%d/%m/%Y",
    57→        "%B %d, %Y",
    58→        "%b %d, %Y",
    59→        "%Y/%m/%d",
    60→    ):
    61→        try:
    62→            return datetime.strptime(val.strip(), fmt)
    63→        except (ValueError, AttributeError):
    64→            continue
    65→    return None
    66→
    67→
    68→def _parse_rating(val: str) -> float | None:
    69→    try:
    70→        # Handle "4/5", "4 out of 5", or plain "4.5"
    71→        val = val.strip()
    72→        match = re.match(r"([\d.]+)\s*(?:/|out of)\s*\d+", val)
    73→        if match:
    74→            return float(match.group(1))
    75→        return float(val)
    76→    except (ValueError, TypeError):
    77→        return None
    78→
    79→
    80→def parse_csv(content: str | bytes, platform: str = "", product_name: str = "") -> list[Review]:
    81→    """Parse CSV content into Review objects. Handles flexible column names."""
    82→    if isinstance(content, bytes):
    83→        content = content.decode("utf-8-sig")  # Handle BOM
    84→
    85→    reader = csv.DictReader(io.StringIO(content))
    86→    if not reader.fieldnames:
    87→        return []
    88→
    89→    col_map = _map_columns(list(reader.fieldnames))
    90→
    91→    if "text" not in col_map:
    92→        # Try: if there's only one text-like long column, use it
    93→        for h in reader.fieldnames:
    94→            if h not in col_map.values():
    95→                col_map["text"] = h
    96→                break
    97→
    98→    if "text" not in col_map:
    99→        return []
   100→
   101→    reviews: list[Review] = []
   102→    for i, row in enumerate(reader):
   103→        text = row.get(col_map.get("text", ""), "").strip()
   104→        if not text:
   105→            continue
   106→
   107→        rating_raw = row.get(col_map.get("rating", ""), "")
   108→        date_raw = row.get(col_map.get("date", ""), "")
   109→        author = row.get(col_map.get("author", ""), "").strip()
   110→        plat = row.get(col_map.get("platform", ""), "").strip() or platform
   111→
   112→        # Collect unmapped columns as metadata
   113→        mapped_cols = set(col_map.values())
   114→        metadata = {k: v for k, v in row.items() if k not in mapped_cols and v}
   115→
   116→        reviews.append(
   117→            Review(
   118→                id=f"review_{i}",
   119→                text=text,
   120→                rating=_parse_rating(rating_raw),
   121→                date=_parse_date(date_raw),
   122→                author=author,
   123→                platform=plat,
   124→                metadata=metadata,
   125→            )
   126→        )
   127→
   128→    return reviews
   129→
   130→
   131→# ── Firecrawl URL Scraping (secondary, best-effort) ─────────────────
   132→
   133→FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", "")
   134→FIRECRAWL_API_URL = "https://api.firecrawl.dev/v1"
   135→
   136→
   137→async def scrape_url(url: str, platform: str = "") -> list[Review]:
   138→    """Best-effort URL scraping via Firecrawl. Returns whatever we can get."""
   139→    if not FIRECRAWL_API_KEY:
   140→        raise ValueError("FIRECRAWL_API_KEY not set. Use CSV upload instead.")
   141→
   142→    async with httpx.AsyncClient(timeout=60.0) as client:
   143→        resp = await client.post(
   144→            f"{FIRECRAWL_API_URL}/scrape",
   145→            headers={
   146→                "Authorization": f"Bearer {FIRECRAWL_API_KEY}",
   147→                "Content-Type": "application/json",
   148→            },
   149→            json={
   150→                "url": url,
   151→                "formats": ["markdown"],
   152→            },
   153→        )
   154→        resp.raise_for_status()
   155→        data = resp.json()
   156→
   157→    markdown = data.get("data", {}).get("markdown", "")
   158→    if not markdown:
   159→        return []
   160→
   161→    # Try to extract individual reviews from the markdown.
   162→    # This is best-effort — review platforms have varied structures.
   163→    reviews = _extract_reviews_from_markdown(markdown, platform, url)
   164→    return reviews
   165→
   166→
   167→def _extract_reviews_from_markdown(
   168→    markdown: str, platform: str, url: str
   169→) -> list[Review]:
   170→    """Attempt to parse review blocks from scraped markdown."""
   171→    reviews: list[Review] = []
   172→
   173→    # Strategy: split on patterns that look like review boundaries.
   174→    # Common patterns: "★★★★☆", "Rating: X/5", numbered reviews, horizontal rules
   175→    blocks = re.split(r"\n(?:---+|\*\*\*+|#{1,3}\s)", markdown)
   176→
   177→    for i, block in enumerate(blocks):
   178→        block = block.strip()
   179→        if len(block) < 20:
   180→            continue
   181→
   182→        # Try to extract rating
   183→        rating = None
   184→        star_match = re.search(r"([★]{1,5})", block)
   185→        if star_match:
   186→            rating = float(len(star_match.group(1)))
   187→        else:
   188→            rating_match = re.search(r"(\d(?:\.\d)?)\s*(?:/\s*5|out of 5|stars?)", block, re.I)
   189→            if rating_match:
   190→                rating = float(rating_match.group(1))
   191→
   192→        # Try to extract date
   193→        date = None
   194→        date_match = re.search(
   195→            r"(\w+ \d{1,2},? \d{4}|\d{4}-\d{2}-\d{2}|\d{1,2}/\d{1,2}/\d{4})",
   196→            block,
   197→        )
   198→        if date_match:
   199→            date = _parse_date(date_match.group(1))
   200→
   201→        reviews.append(
   202→            Review(
   203→                id=f"scraped_{i}",
   204→                text=block[:2000],  # Cap individual review length
   205→                rating=rating,
   206→                date=date,
   207→                platform=platform or _guess_platform(url),
   208→                metadata={"source_url": url},
   209→            )
   210→        )
   211→
   212→    return reviews
   213→
   214→
   215→def _guess_platform(url: str) -> str:
   216→    url_lower = url.lower()
   217→    if "amazon" in url_lower:
   218→        return "Amazon"
   219→    if "google" in url_lower and "maps" in url_lower:
   220→        return "Google Maps"
   221→    if "g2" in url_lower:
   222→        return "G2"
   223→    if "capterra" in url_lower:
   224→        return "Capterra"
   225→    if "yelp" in url_lower:
   226→        return "Yelp"
   227→    if "trustpilot" in url_lower:
   228→        return "Trustpilot"
   229→    return "Web"
   230→
   231→
   232→# ── Summary builder ──────────────────────────────────────────────────
   233→
   234→def build_summary(
   235→    reviews: list[Review],
   236→    source_type: str,
   237→    product_name: str = "",
   238→    platform: str = "",
   239→) -> IngestionSummary:
   240→    """Build an ingestion summary from parsed reviews."""
   241→    if not reviews:
   242→        return IngestionSummary(source_type=source_type)
   243→
   244→    ratings = [r.rating for r in reviews if r.rating is not None]
   245→    dates = [r.date for r in reviews if r.date is not None]
   246→
   247→    # Rating distribution (bucket by integer star)
   248→    dist: dict[str, int] = {}
   249→    for r in ratings:
   250→        bucket = str(int(round(r)))
   251→        dist[bucket] = dist.get(bucket, 0) + 1
   252→
   253→    # Date range
   254→    date_range = ""
   255→    if dates:
   256→        earliest = min(dates).strftime("%Y-%m-%d")
   257→        latest = max(dates).strftime("%Y-%m-%d")
   258→        date_range = f"{earliest} to {latest}" if earliest != latest else earliest
   259→
   260→    # Platform detection
   261→    platforms = [r.platform for r in reviews if r.platform]
   262→    detected_platform = platform or (Counter(platforms).most_common(1)[0][0] if platforms else "Unknown")
   263→
   264→    return IngestionSummary(
   265→        total_reviews=len(reviews),
   266→        date_range=date_range,
   267→        rating_distribution=dist,
   268→        average_rating=round(sum(ratings) / len(ratings), 2) if ratings else None,
   269→        platform=detected_platform,
   270→        product_name=product_name or "Unknown Product",
   271→        source_type=source_type,
   272→    )
   273→

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

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

Syntax OK

All imports OK
Prompt: This is a Amazon review page. Extract all customer/user reviews from https://ama ...
Schema keys: ['text', 'rating', 'date', 'author']

HTTP/1.1 200 OK
date: Sat, 21 Mar 2026 22:23:30 GMT
server: uvicorn
hx-redirect: /chat/1e3dcd60-5164-419c-8150-7cf854180802
content-length: 83

# FIRECRAWL_API_KEY=fc-c1a794a831494cbdab6308fe1aebd585

     1→# Required
     2→ANTHROPIC_API_KEY=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
     3→
     4→# Optional: model override (default: claude-sonnet-4-6-20250514)
     5→# CLAUDE_MODEL=claude-opus-4-6
     6→
     7→# Optional: Firecrawl for URL scraping (secondary path)
     8→# FIRECRAWL_API_KEY=fc-c1a794a831494cbdab6308fe1aebd585
     9→
    10→# Optional: data directory (default: data)
    11→# DATA_DIR=data
    12→
    13→# Optional: ChromaDB directory (default: data/chroma)
    14→# CHROMA_DIR=data/chroma
    15→

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/.env has been updated successfully.

   131→
   132→# ── Firecrawl URL Scraping (secondary, best-effort) ─────────────────
   133→# Uses the /v2/agent endpoint which autonomously navigates pagination
   134→# and returns structured data — no brittle markdown regex parsing.
   135→
   136→FIRECRAWL_API_KEY = os.getenv("FIRECRAWL_API_KEY", "")
   137→FIRECRAWL_API_URL = "https://api.firecrawl.dev/v2"
   138→
   139→_AGENT_POLL_INTERVAL = 2   # seconds between status checks
   140→_AGENT_TIMEOUT = 120       # total seconds before giving up
   141→_AGENT_MAX_CREDITS = 500   # conservative cap for zero-cost constraint
   142→
   143→_REVIEW_SCHEMA = {
   144→    "type": "object",
   145→    "properties": {

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

   173→        "navigate through all available pages to collect them."
   174→    )
   175→
   176→
   177→async def scrape_url(url: str, platform: str = "") -> list[Review]:
   178→    """Best-effort URL scraping via Firecrawl agent. Returns structured reviews."""
   179→    if not FIRECRAWL_API_KEY:
   180→        raise ValueError("FIRECRAWL_API_KEY not set. Use CSV upload instead.")
   181→
   182→    detected_platform = platform or _guess_platform(url)

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

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/ingest.py has been updated. All occurrences were successfully replaced.

Key loaded: True (fc-c1a79...)

HTTP/1.1 400 Bad Request
date: Sat, 21 Mar 2026 22:27:18 GMT
server: uvicorn
content-length: 118
content-type: text/html; charset=utf-8

<div class="error-banner">
    <p>No reviews could be extracted from that URL. Try uploading a CSV instead.</p>
</div>

Submit status: 200
Job response: {
  "success": true,
  "id": "019d1284-c3d1-7641-9dbb-7b0050037eee"
}
Poll 1: status=processing
Poll 2: status=processing
Poll 3: status=processing
Poll 4: status=processing
Poll 5: status=processing
Poll 6: status=processing
Poll 7: status=processing
Poll 8: status=processing
Poll 9: status=processing
Poll 10: status=processing
Poll 11: status=processing
Poll 12: status=processing
Poll 13: status=processing
Poll 14: status=processing
Poll 15: status=processing
Poll 16: status=processing
Poll 17: status=processing
Poll 18: status=processing
Poll 19: status=processing
Poll 20: status=processing
Poll 21: status=processing
Poll 22: status=processing
Poll 23: status=processing
Poll 24: status=processing
Poll 25: status=processing
Poll 26: status=processing
Poll 27: status=processing
Poll 28: status=processing
Poll 29: status=processing
Poll 30: status=processing

   120→    summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
   121→
   122→    session = Session(
   123→        session_id=session_id,
   124→        product_name=summary.product_name,
   125→        platform=summary.platform,
   126→        summary=summary,
   127→        status="indexing",
   128→    )
   129→    store.save_session(session)
   130→    store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
   131→
   132→    indexed = vectordb.index_reviews(session_id, reviews)
   133→    summary.total_reviews = indexed
   134→    store.update_summary(session_id, summary)
   135→    store.set_status(session_id, "ready")
   136→
   137→    return HTMLResponse(
   138→        status_code=200,
   139→        content=f'<script>window.location.href="/chat/{session_id}";</script>',
   140→        headers={"HX-Redirect": f"/chat/{session_id}"},
   141→    )
   142→
   143→
   144→# ── URL Scrape ───────────────────────────────────────────────────────
   145→
   146→@app.post("/scrape")
   147→async def scrape(
   148→    request: Request,
   149→    url: str = Form(...),
   150→    product_name: str = Form(""),
   151→    platform: str = Form(""),
   152→):
   153→    session_id = str(uuid.uuid4())
   154→
   155→    try:
   156→        reviews = await scrape_url(url, platform=platform)
   157→    except Exception as e:
   158→        return templates.TemplateResponse(
   159→            "partials/error.html",
   160→            {"request": request, "error": f"Scraping failed: {e}. Try uploading a CSV instead."},
   161→            status_code=400,
   162→        )
   163→
   164→    if not reviews:
   165→        return templates.TemplateResponse(
   166→            "partials/error.html",
   167→            {"request": request, "error": "No reviews could be extracted from that URL. Try uploading a CSV instead."},
   168→            status_code=400,
   169→        )
   170→
   171→    summary = build_summary(reviews, source_type="url", product_name=product_name, platform=platform)
   172→
   173→    session = Session(
   174→        session_id=session_id,
   175→        product_name=summary.product_name,
   176→        platform=summary.platform,
   177→        summary=summary,
   178→        status="indexing",
   179→    )
   180→    store.save_session(session)
   181→    store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
   182→
   183→    indexed = vectordb.index_reviews(session_id, reviews)
   184→    summary.total_reviews = indexed

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

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

    86→                        <span class="session-detail">{{ s.summary.total_reviews }} reviews &middot; {{ s.platform }}</span>
    87→                    </a>
    88→                    {% else %}
    89→                    <p class="empty-history">No analyses yet</p>
    90→                    {% endfor %}
    91→                </nav>
    92→            </div>
    93→        </aside>
    94→
    95→        <!-- ── Main Area ────────────────────────────────────────── -->
    96→        <main class="main-area">
    97→            {% if session %}
    98→            <!-- Chat view -->
    99→            <div class="chat-messages" id="chat-messages">
   100→                {% for msg in messages %}
   101→                    {% if msg.role == 'assistant' %}
   102→                    <div class="message assistant-message">
   103→                        <div class="message-content">{{ msg.content }}</div>
   104→                    </div>
   105→                    {% else %}
   106→                    <div class="message user-message">
   107→                        <div class="message-content"><p>{{ msg.content }}</p></div>
   108→                    </div>
   109→                    {% endif %}
   110→                {% endfor %}
   111→
   112→                {% if not messages %}
   113→                <div class="message assistant-message welcome">
   114→                    <div class="message-content">
   115→                        <p>I have <strong>{{ summary.total_reviews }} reviews</strong> loaded for <strong>{{ summary.product_name }}</strong> from <strong>{{ summary.platform }}</strong>{% if summary.date_range %}, spanning <strong>{{ summary.date_range }}</strong>{% endif %}.</p>
   116→                        <p>I can only answer questions grounded in this dataset. What would you like to explore?</p>
   117→                    </div>
   118→                    <div class="follow-ups">
   119→                        <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What's the overall sentiment breakdown?">Sentiment breakdown</button>
   120→                        <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What are the most common complaints?">Top complaints</button>
   121→                        <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="Show me the rating distribution over time">Ratings over time</button>
   122→                        <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What do people praise the most?">Most praised aspects</button>
   123→                    </div>
   124→                </div>
   125→                {% endif %}
   126→            </div>
   127→
   128→            <div class="chat-input-area">
   129→                <form id="chat-form" onsubmit="sendMessage(event)">
   130→                    <div class="input-row">
   131→                        <textarea id="chat-input" name="message" placeholder="Ask about the reviews..." rows="1" autofocus></textarea>
   132→                        <button type="submit" class="btn-send" id="send-btn">
   133→                            <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
   134→                                <line x1="5" y1="12" x2="19" y2="12"/><polyline points="12 5 19 12 12 19"/>
   135→                            </svg>
   136→                        </button>
   137→                    </div>
   138→                </form>
   139→                <p class="input-hint">Analyses only the uploaded dataset. No external knowledge.</p>
   140→            </div>

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/templates/app.html has been updated successfully.

   370→    color: var(--text-3);
   371→    padding: 0.5rem 0;
   372→}
   373→
   374→/* ═══════════════════════════════════════════════════════════════════
   375→   MAIN AREA
   376→   ═══════════════════════════════════════════════════════════════════ */
   377→
   378→.main-area {
   379→    display: flex;

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/style.css has been updated successfully.

   250→
   251→function removeThinking() {
   252→  var el = document.getElementById("thinking-indicator");
   253→  if (el) el.remove();
   254→}
   255→
   256→function appendError(msg) {
   257→  var div = document.createElement("div");
   258→  div.className = "message assistant-message error";
   259→  div.innerHTML = '<div class="message-content"><p>' + escapeHtml(msg) + "</p></div>";
   260→  chatMessages.appendChild(div);
   261→  scrollToBottom();
   262→}
   263→
   264→function sendFollowUp(btn) {

   338→          color: textColor,
   339→          padding: { bottom: 12 },
   340→        },
   341→        legend: {
   342→          labels: { color: textColor, font: { family: "'DM Sans', sans-serif", size: 11 } },
   343→        },
   344→      },
   345→      scales:
   346→        config.type !== "pie" && config.type !== "doughnut"
   347→          ? {
   348→              x: {
   349→                ticks: { color: textColor, font: { family: "'IBM Plex Mono', monospace", size: 10 } },
   350→                grid: { color: gridColor },
   351→              },
   352→              y: {
   353→                ticks: { color: textColor, font: { family: "'IBM Plex Mono', monospace", size: 10 } },
   354→                grid: { color: gridColor },
   355→                beginAtZero: true,
   356→              },
   357→            }
   358→          : undefined,
   359→    },
   360→  });
   361→}
   362→
   363→function runChartScripts() {
   364→  if (!chatMessages) return;
   365→  var scripts = chatMessages.querySelectorAll("script");
   366→  scripts.forEach(function (script) {
   367→    if (script.textContent.indexOf("renderChart") !== -1 && !script.dataset.executed) {
   368→      script.dataset.executed = "true";
   369→      eval(script.textContent);
   370→    }
   371→  });
   372→}
   373→
   374→// ── Utilities ────────────────────────────────────────────────────────
   375→
   376→function escapeHtml(str) {
   377→  var div = document.createElement("div");
   378→  div.appendChild(document.createTextNode(str));
   379→  return div.innerHTML;
   380→}
   381→
   382→if (chatMessages) {
   383→  var observer = new MutationObserver(scrollToBottom);
   384→  observer.observe(chatMessages, { childList: true, subtree: true });
   385→}
   386→
   387→scrollToBottom();
   388→

The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/app.js has been updated successfully.

All OK

HTTP/1.1 200 OK
date: Sat, 21 Mar 2026 22:33:09 GMT
server: uvicorn
hx-redirect: /chat/0c1d8495-38eb-4cdb-bd9a-bb4d87bf4966
content-length: 83
content-type: text/html; charset=utf-8

<script>window.location.href="/chat/0c1d8495-38eb-4cdb-bd9a-bb4d87bf4966";</script>

scraping-view
scraping-view
Scraping reviews

{"status":"scraping"}

--- Server restarted ---

(Bash completed with no output)

ERROR: HTTPStatusError: Server error '502 Bad Gateway' for url 'https://api.firecrawl.dev/v2/agent/019d128d-5f0a-708d-9f67-73e2568e6b38'
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/502

   177→async def scrape_url(url: str, platform: str = "") -> list[Review]:
   178→    """Best-effort URL scraping via Firecrawl agent. Returns structured reviews."""
   179→    api_key = os.getenv("FIRECRAWL_API_KEY", "")
   180→    if not api_key:
   181→        raise ValueError("FIRECRAWL_API_KEY not set. Use CSV upload instead.")
   182→
   183→    detected_platform = platform or _guess_platform(url)
   184→    headers = {
   185→        "Authorization": f"Bearer {api_key}",
   186→        "Content-Type": "application/json",
   187→    }
   188→
   189→    async with httpx.AsyncClient(timeout=30.0) as client:
   190→        # Submit agent job
   191→        resp = await client.post(
   192→            f"{FIRECRAWL_API_URL}/agent",
   193→            headers=headers,
   194→            json={
   195→                "urls": [url],
   196→                "prompt": _build_agent_prompt(url, detected_platform),
   197→                "schema": _REVIEW_SCHEMA,
   198→                "model": "spark-1-mini",
   199→                "maxCredits": _AGENT_MAX_CREDITS,
   200→            },
   201→        )
   202→        resp.raise_for_status()
   203→        job = resp.json()
   204→
   205→        job_id = job.get("id")
   206→        if not job_id:

   206→        if not job_id:
   207→            return []
   208→
   209→        # Poll for completion
   210→        elapsed = 0.0
   211→        while elapsed < _AGENT_TIMEOUT:
   212→            await asyncio.sleep(_AGENT_POLL_INTERVAL)
   213→            elapsed += _AGENT_POLL_INTERVAL
   214→
   215→            poll = await client.get(
   216→                f"{FIRECRAWL_API_URL}/agent/{job_id}",
   217→                headers=headers,
   218→            )
   219→            poll.raise_for_status()
   220→            result = poll.json()
   221→
   222→            status = result.get("status", "")
   223→            if status == "completed":
   224→                return _parse_agent_reviews(result.get("data", {}), detected_platform, url)
   225→            if status in ("failed", "cancelled"):
   226→                return []
   227→
   228→    # Timed out
   229→    return []
   230→

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