diff --git a/.vscode/settings.json b/.vscode/settings.json
index 5af6191..44f3e0e 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -4,5 +4,6 @@
     ],
     "python.testing.unittestEnabled": false,
     "python.testing.pytestEnabled": true,
-    "python.analysis.typeCheckingMode": "basic"
+    "python.analysis.typeCheckingMode": "basic",
+    "makefile.configureOnOpen": false
 }
\ No newline at end of file
diff --git a/examples/audio_to_paper.sh b/examples/audio_to_paper.sh
index f3d79ee..557b889 100755
--- a/examples/audio_to_paper.sh
+++ b/examples/audio_to_paper.sh
@@ -132,7 +132,10 @@ echo "Generating Documents..."
     sed -E 's/\[\[([0-9]+)\]\]\([^)]+\)//g' |
     sed -E 's/\[([0-9]+)\]//g' |
     tee \
-        >(pandoc -o "$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-no-refs.pdf" --from markdown --pdf-engine=xelatex) >/dev/null
+        >(pandoc -o "$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-no-refs.pdf" --from markdown --pdf-engine=xelatex) \
+        >(pandoc -o "$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-no-refs.docx" --from markdown) \
+        >(cat >"$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-no-refs.md") \
+        >/dev/null
 
 # With References
 (
@@ -149,33 +152,6 @@ echo "Generating Documents..."
     tee \
         >(pandoc -o "$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-refs.pdf" --from markdown+header_attributes --pdf-engine=xelatex) >/dev/null
 
-# Check if images are requested
-if [ "$IMAGES" = true ]; then
-    echo "Extracting images..."
-    IMAGE_OUTPUT=$(plato --images "$URL" --lang "$LANG" | sed '/^$/d' | sed -e :a -e '/^\n*$/{$d;N;ba' -e '}')
-    
-    if [ ! -z "$IMAGE_OUTPUT" ]; then
-        # Create a temporary directory for images
-        TMP_IMG_DIR=$(mktemp -d)
-        
-        # Save image paths to temporary files
-        echo "$IMAGE_OUTPUT" | while read -r img_path; do
-            cp ".platogram-cache/$img_path" "$TMP_IMG_DIR/"
-        done
-        
-        # Create zip file with images
-        ZIP_FILE="$(echo "$TITLE" | sed 's/[^a-zA-Z0-9]/_/g')-images.zip"
-        zip -j "$ZIP_FILE" "$TMP_IMG_DIR"/*
-        
-        # Clean up temporary directory
-        rm -rf "$TMP_IMG_DIR"
-        
-        echo "Images saved to $ZIP_FILE"
-    else
-        echo "No images found or extracted."
-    fi
-fi
-
 wait
 
 if [ "$VERBOSE" = true ]; then
diff --git a/platogram/cli.py b/platogram/cli.py
index 9eb7ee2..7f62e4f 100644
--- a/platogram/cli.py
+++ b/platogram/cli.py
@@ -77,17 +77,18 @@ def process_url(
     with tqdm(total=4, desc=f"Processing {url}", file=sys.stderr) as pbar:
         transcript = plato.extract_transcript(url, asr, lang=lang)
         pbar.update(1)
-        pbar.set_description("Indexing content")
-        content = plato.index(transcript, llm, lang=lang)
-        pbar.update(1)
+        images = []
         if extract_images:
             pbar.set_description("Extracting images")
             images_dir = library.home / id
             images_dir.mkdir(exist_ok=True)
-            timestamps_ms = [event.time_ms for event in content.transcript]
+            timestamps_ms = [event.time_ms for event in transcript]
             images = ingest.extract_images(url, images_dir, timestamps_ms)
-            content.images = [str(image.relative_to(library.home)) for image in images]
+            images = {i: image for i, image in enumerate(images)}
             pbar.update(1)
+        pbar.set_description("Indexing content")
+        content = plato.index(transcript, images, llm, lang=lang)
+        pbar.update(1)
         pbar.set_description("Saving content")
         library.put(id, content)
         pbar.update(1)
@@ -242,17 +243,20 @@ def get_chapter(passage_marker: int) -> int | None:
             passages = ""
             if args.chapters:
                 current_chapter = None
-                for passage in content.passages:
-                    passage_markers = [int(m) for m in re.findall(r"【(\d+)】", passage)]
-                    chapter_marker = get_chapter(passage_markers[0]) if passage_markers else None
-                    if chapter_marker is not None and chapter_marker != current_chapter:
-                        passages += f"### {content.chapters[chapter_marker]}\n\n"
-                        current_chapter = chapter_marker
-                    passages += f"{passage.strip()}\n\n"
+                for text in content.text:
+                    for passage in text.split("\n\n"):
+                        passage_markers = [int(m) for m in re.findall(r"【(\d+)】", passage)]
+                        chapter_marker = get_chapter(passage_markers[0]) if passage_markers else None
+                        if chapter_marker is not None and chapter_marker != current_chapter:
+                            passages += f"### {content.chapters[chapter_marker]}\n\n"
+                            current_chapter = chapter_marker
+                        passages += f"{passage.strip()}\n\n"
+                        if content.figures:
+                            for image_marker in set(passage_markers) & set(content.figures):
+                                figure_html = f"![{content.figures[image_marker][0]}]({content.figures[image_marker][1]}){{ width=80% }}\n\n"
+                                passages += figure_html
             else:
-                passages = "\n\n".join(
-                    passage.strip() for passage in content.passages
-                )
+                passages = content.text
 
             result += f"""{passages}\n\n\n\n"""
 
diff --git a/platogram/ingest.py b/platogram/ingest.py
index 58ef5e3..61f1a26 100644
--- a/platogram/ingest.py
+++ b/platogram/ingest.py
@@ -1,19 +1,18 @@
 import logging
 import mimetypes
+import subprocess
 from functools import lru_cache
 from pathlib import Path
 from tempfile import TemporaryDirectory
 
 import requests  # type: ignore
 from yt_dlp import YoutubeDL  # type: ignore
-import subprocess
 
-from platogram.parsers import parse_subtitles, parse_waffly
 from platogram.asr import ASRModel
+from platogram.parsers import parse_subtitles, parse_waffly
 from platogram.types import SpeechEvent
 from platogram.utils import get_sha256_hash
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -164,33 +163,35 @@ def extract_images(
 
     image_paths = []
     try:
-        for timestamp_ms in timestamps_ms:
-            timestamp_s = timestamp_ms / 1000
-            image_path = Path(output_dir) / f"image_{timestamp_ms:09d}.png"
-
-            subprocess.run(
-                [
-                    "ffmpeg",
-                    "-ss",
-                    f"{timestamp_s:.3f}",
-                    "-i",
-                    str(video_path),
-                    "-frames:v",
-                    "1",
-                    "-q:v",
-                    "2",
-                    "-f",
-                    "image2",
-                    str(image_path),
-                ],
-                check=True,
-                stdout=subprocess.DEVNULL,
-                stderr=subprocess.DEVNULL,
-            )
-
-            image_paths.append(image_path)
+        # Convert timestamps to seconds and create the select filter
+        timestamps_s = sorted(list(set(ts // 1000 for ts in timestamps_ms)))
+        select_filter = '+'.join([f"eq(t,{t})" for t in timestamps_s])
+
+        # Construct the FFmpeg command
+        ffmpeg_command = [
+            "ffmpeg",
+            "-i", str(video_path),
+            "-vf", f"select='{select_filter}',setpts=N/FRAME_RATE/TB",
+            "-vsync", "0",
+            "-q:v", "2",
+            "-f", "image2",
+            str(output_dir / "image_%d.png")
+        ]
+
+        # Run FFmpeg command to extract all images at once
+        subprocess.run(
+            ffmpeg_command,
+            check=True,
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
+        )
+
+        # Get all image files in the output directory and sort them alphabetically
+        image_paths = [output_dir / f"image_{timestamps_s.index(ts // 1000) + 1}.png" for ts in timestamps_ms]
+
+        if not image_paths:
+            raise RuntimeError("No images were extracted from the video.")
     finally:
-        # Delete the downloaded video file
         if video_path:
             video_path.unlink()
 
diff --git a/platogram/llm/__init__.py b/platogram/llm/__init__.py
index 96da4de..fbc82a9 100644
--- a/platogram/llm/__init__.py
+++ b/platogram/llm/__init__.py
@@ -1,5 +1,6 @@
 from typing import Protocol, Literal, Generator, Sequence
 from platogram.types import Content, User, Assistant
+from pathlib import Path
 
 
 class LanguageModel(Protocol):
@@ -10,8 +11,12 @@ def get_meta(
     ) -> tuple[str, str]: ...
 
     def get_chapters(
-        self, passages: list[str], max_tokens: int = 4096, temperature: float = 0.5, lang: str | None = None
+        self, passages: list[str], context: dict[int, str], chapter_size_words: int, temperature: float = 0.5, lang: str | None = None
     ) -> dict[int, str]: ...
+    
+    def expand_chapter_text(self, chapter_text: str, images: dict[int, Path]) -> str: ...
+
+    def get_figures(self, chapter_text: str, images: list[Path]) -> dict[int, str]: ...
 
     def get_paragraphs(
         self,
diff --git a/platogram/llm/anthropic.py b/platogram/llm/anthropic.py
index 674a989..1b90fa9 100644
--- a/platogram/llm/anthropic.py
+++ b/platogram/llm/anthropic.py
@@ -1,5 +1,6 @@
 import os
 import re
+from pathlib import Path
 from typing import Any, Generator, Literal, Sequence
 
 import anthropic
@@ -40,6 +41,30 @@ def __init__(self, model: str, key: str | None = None) -> None:
 
     def count_tokens(self, text: str) -> int:
         return self.client.count_tokens(text)
+    
+    def load_images(self, images: dict[str, Path] | None = None) -> list[dict]:
+        if images is None:
+            return []
+
+        loaded_images = []
+        for image_text, image_path in images.items():
+            with open(image_path, "rb") as image_file:
+                import base64
+                image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
+                loaded_images.append({
+                    "type": "image",
+                    "source": {
+                        "type": "base64",
+                        "media_type": f"image/{image_path.suffix[1:]}",
+                        "data": image_base64
+                    }
+                })
+                loaded_images.append({
+                    "type": "text",
+                    "text": image_text
+                })
+        return loaded_images
+
 
     def prompt_model(
         self,
@@ -68,11 +93,14 @@ def get_response(messages):
                     temperature=temperature,
                     extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"},
                     messages=[
-                        {"role": m.role, "content": m.content}
+                        {
+                            "role": m.role,
+                            "content": self.load_images(m.images) + [{"type": "text", "text": m.content}]
+                        }
                         if not m.cache
                         else {
                             "role": m.role,
-                            "content": [{"type": "text", "text": m.content, "cache_control": {"type": "ephemeral"}}],
+                            "content": self.load_images(m.images) +[{"type": "text", "text": m.content, "cache_control": {"type": "ephemeral"}}],
                         }
                         for m in messages
                     ],
@@ -188,7 +216,8 @@ def get_meta(
     def get_chapters(
         self,
         passages: list[str],
-        max_tokens: int = 4096,
+        context: dict[int, str],
+        chapter_size_words: int,
         temperature: float = 0.5,
         lang: str | None = None,
     ) -> dict[int, str]:
@@ -196,16 +225,17 @@ def get_chapters(
             lang = "en"
 
         system_prompt = {
-            "en": """<role>
+            "en": f"""<role>
 You are a very capable editor, speaker, educator, and author who is really good at reading text that represents transcript of human speech and rewriting it into well-structured, information-dense written text.
 </role>
 <task>
 You will be given <passages> of text in a format "<p>text【0】text【1】... text 【2】</p>". Where each【number】is a <marker> and goes AFTER the text it references. Markers are zero-based and are in sequential order.
+You will also be given <context> chat contains prior <chapters>.
 
 Follow these steps to transform the <passages> into a dictionary of chapters:
-1. Read the <passages> carefully and come up with a list of <chapters> that equally cover the <passages>.
-2. Review <chapters> and <passages> and for each chapter generate <title> and first <marker> from the relevant passage and create a <chapter> object and add it to the list.
-3. Call <chapter_tool> and pass the list of <chapter> objects.
+1. Read <passages> and <context> carefully and come up with a list of <chapters> that cover at least two sequential passages (two or more) and each chapter has around {chapter_size_words} words.
+2. For each chapter generate <title> and first <marker> from the first <passage> in the chapter.
+3. For each chapter call <chapter_tool> and pass <title> and <marker>.
 </task>
 """.strip(),
             "es": """<role>
@@ -262,12 +292,12 @@ def get_chapters(
         }
 
         text = "\n".join([f"<p>{passage}</p>" for passage in passages])
+        context_str = "\n".join([f"<chapter><marker>{int(marker)}</marker><title>{title}</title></chapter>" for marker, title in context.items()])
 
         chapters = self.prompt_model(
             system=system_prompt[lang],
-            messages=[User(content=f"<passages>{text}</passages>")],
+            messages=[User(content=f"<passages>{text}</passages><context>{context_str}</context>")],
             tools=[tool_definition],
-            max_tokens=max_tokens,
             temperature=temperature,
         )
 
@@ -462,3 +492,97 @@ def prompt(
         )
         assert isinstance(response, str), f"Expected LLM to return str, got {response}"
         return response
+
+
+    def expand_chapter_text(self, chapter_text: str, images: dict[int, Path], temperature: float = 0.5) -> str:
+        system_prompt = """<role>
+You are a very capable editor, speaker, educator, and author who is really good at reviewing text and images to create meaningful connections.
+</role>
+<task>
+You will be given images with labels. Each image label corresponds to a marker in the <chapter_text>.
+You will be given a <chapter_text> that contains multiple paragraphs separated by two new lines.
+Each paragraph contains special markers in the format of "text【number】text【number】... text 【number】". All numbers are unique.
+Study the images and <chapter_text>. Select the most relevant images.
+Stick to the style of <chapter_text> and paraphrase text around markers to include the information from the most relevant images.
+Keep all the original markers in the <chapter_text>.
+Avoid referring to the images directly in the <chapter_text>.
+</task>
+<output_format>
+Use markdown for code and lists you transferred from the images.
+Output in the following format:
+<improved_chapter_text>
+Improved <chapter_text>
+</improved_chapter_text>
+</output_format>
+"""
+        response = self.prompt_model(
+            system=system_prompt,
+            messages=[
+                User(content=f"<chapter_text>{chapter_text}</chapter_text>", images={f"【{i}】": p for i, p in images.items()}),
+                Assistant(content="<improved_chapter_text>"),],
+            temperature=temperature,
+        )
+        assert isinstance(response, str), f"Expected LLM to return str, got {response}"
+        response = response.replace("</improved_chapter_text>", "").strip()
+        return response
+    
+    
+    def get_figures(self, chapter_text: str, images: list[Path]) -> dict[int, str]:
+        system_prompt = """<role>
+You are a very capable editor, speaker, educator, and author with expertise in analyzing text and images to create meaningful connections.
+</role>
+<task>
+You will be given a <chapter_text> that contains multiple paragraphs separated by two new lines.
+Each paragraph contains special markers in the format of "text【number】text【number】... text 【number】". All numbers are unique.
+You will also be given a set of images with labels corresponding to markers in paragraphs.
+
+Your task is to:
+1. Analyze the <chapter_text> and the provided images.
+2. Identify zero or more unique images that are highly relevant to the content of the <chapter_text>.
+3. For each relevant image, create a brief but informative caption that relates it to the nearby text.
+4. Call the <add_figure> tool and pass all markers of relevant images and their captions.
+
+Only select images that are truly relevant and add value to the text. It's acceptable to use no images if none are sufficiently relevant.
+Ensure that the marker numbers are correctly extracted from the text and passed as integers.
+</task>
+"""
+        tool_definition = {
+            "name": "add_figure",
+            "description": "Adds a figure with its marker and caption",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "entities": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "marker": {"type": "integer", "description": "The marker number for the figure"},
+                                "caption": {"type": "string", "description": "A brief, informative caption for the figure"}
+                            },
+                            "required": ["marker", "caption"]
+                        }
+                    }
+                },
+                "required": ["entities"]
+            }
+        }
+
+        response = self.prompt_model(
+            system=system_prompt,
+            messages=[User(content=f"<chapter_text>{chapter_text}</chapter_text>", images={f"【{i}】": p for i, p in enumerate(images)})],
+            tools=[tool_definition],
+            temperature=0.5
+        )
+
+        assert isinstance(response, dict), f"Expected LLM to return dict, got {response}"
+        assert isinstance(response["entities"], list), f"Expected LLM to return list of entities, got {response['entities']}"
+
+        figures = {}
+        for figure in response["entities"]:
+            marker = figure["marker"]
+            caption = figure["caption"]
+            if isinstance(marker, int) and isinstance(caption, str):
+                figures[marker] = caption
+
+        return figures
diff --git a/platogram/ops.py b/platogram/ops.py
index 914d097..5db18dd 100644
--- a/platogram/ops.py
+++ b/platogram/ops.py
@@ -1,6 +1,12 @@
+import functools
+import random
 import re
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from tempfile import TemporaryDirectory
 from typing import Callable
 
+from PIL import Image
 from tqdm import tqdm  # type: ignore
 
 from platogram.llm import LanguageModel
@@ -149,7 +155,8 @@ def get_paragraphs(
         lang = "en"
 
     examples = {
-        str(example["input"]): list(example["output"]) for example in rewrite_examples[lang]
+        str(example["input"]): list(example["output"])
+        for example in rewrite_examples[lang]
     }
     tail = ""
     paragraphs = []
@@ -166,7 +173,11 @@ def get_paragraphs(
 
             # limit the number of output tokens to chunk size
             for paragraph in llm.get_paragraphs(
-                content, examples, max_tokens=max_tokens, temperature=temperature, lang=lang,
+                content,
+                examples,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                lang=lang,
             ):
                 # we are not parsing again, because sometimes model returns paragraphs without trailing marker
                 paragraphs.append(
@@ -206,35 +217,253 @@ def get_paragraphs(
     return paragraphs
 
 
+def chunk_paragraphs(
+    paragraphs: list[str],
+    chunk_size: int,
+    token_count_fn: Callable[[str], int],
+) -> list[list[str]]:
+    chunks = [[]]
+    current_chunk_size = 0
+    for paragraph in paragraphs:
+        if current_chunk_size > chunk_size:
+            chunks.append([])
+            current_chunk_size = 0
+        current_chunk_size += token_count_fn(paragraph)
+        chunks[-1].append(paragraph)
+
+    if not chunks[-1]:
+        chunks.pop()
+
+    return chunks
+
+
+def get_chapters(
+    paragraphs: list[str],
+    llm: LanguageModel,
+    temperature: float,
+    chapter_size_words: int,
+    chunk_size_tokens: int,
+    lang: str | None = None,
+) -> dict[int, str]:
+    if not lang:
+        lang = "en"
+
+    tail: list[str] = []
+    chunks = chunk_paragraphs(paragraphs, chunk_size_tokens, llm.count_tokens)
+    chapters: dict[int, str] = {}
+    with tqdm(total=len(chunks), initial=0) as pbar:
+        pbar.update(0)
+
+        while chunks:
+            chunk = chunks.pop(0)
+            chunk = tail + chunk
+
+            base_marker = sorted(
+                [int(marker) for marker in re.findall(r"【(\d+)】", chunk[0])]
+            )[0]
+
+            content = [
+                re.sub(
+                    r"【(\d+)】",
+                    lambda match: f"【{int(match.group(1)) - base_marker}】",
+                    paragraph,
+                )
+                for paragraph in chunk
+            ]
+
+            last_marker = 0
+            _chapters = llm.get_chapters(
+                content,
+                context=chapters,
+                chapter_size_words=chapter_size_words,
+                temperature=temperature,
+                lang=lang,
+            ).items()
+            for marker, title in _chapters:
+                chapters[marker + base_marker] = title
+                last_marker = marker + base_marker
+
+            if chunks:
+                chapters.pop(last_marker)
+                tail = [
+                    paragraph
+                    for paragraph in chunk
+                    if sorted(
+                        [int(marker) for marker in re.findall(r"【(\d+)】", paragraph)]
+                    )[0]
+                    >= last_marker
+                ]
+
+            pbar.update(1)
+
+    return chapters
+
+
+def get_chapter(chapters: dict[int, str], passage_marker: int) -> int | None:
+    chapter_markers = list(chapters.keys())
+    for start, end in zip(chapter_markers[:-1], chapter_markers[1:]):
+        if start <= passage_marker < end:
+            return start
+    if passage_marker >= chapter_markers[-1]:
+        return chapter_markers[-1]
+    return None
+
+
+def assemble_chapters(
+    chapters: dict[int, str], passages: list[str]
+) -> dict[int, tuple[str, list[str]]]:
+    def get_passage_marker(passage: str) -> int | None:
+        markers = [int(m) for m in re.findall(r"【(\d+)】", passage)]
+        return markers[0] if markers else None
+
+    def update_chapter(
+        acc: dict[int, tuple[str, list[str]]], passage: str
+    ) -> dict[int, tuple[str, list[str]]]:
+        marker = get_passage_marker(passage)
+        chapter_marker = get_chapter(chapters, marker) if marker is not None else None
+
+        if chapter_marker is not None and chapter_marker not in acc:
+            acc[chapter_marker] = (chapters[chapter_marker], [])
+
+        if chapter_marker is not None:
+            acc[chapter_marker][1].append(passage.strip())
+
+        return acc
+
+    return functools.reduce(update_chapter, passages, {})
+
+
+def expand_and_add_figures(
+    passages: list[str],
+    images: dict[int, Path],
+    llm: LanguageModel,
+    max_image_edge_length_px: int = 1024,
+    num_image_samples: int = 16,
+) -> tuple[str, dict[int, str]]:
+    chapter_text = "\n\n".join(passages)
+
+    if not images:
+        return chapter_text, {}
+
+    with TemporaryDirectory() as tmpdir:
+        def resize_image(image_path: Path, output_dir: Path, max_edge_length: int) -> Path:
+            with Image.open(image_path) as img:
+                img.thumbnail((max_edge_length, max_edge_length), Image.LANCZOS)
+                output_path = output_dir / image_path.name
+                img.save(output_path, optimize=True)
+            return output_path
+
+        with ThreadPoolExecutor() as executor:
+            futures = [
+                executor.submit(resize_image, img_path, Path(tmpdir), max_image_edge_length_px)
+                for img_path in images.values()
+            ]
+            resized_images = {
+                marker: future.result()
+                for marker, future in zip(images.keys(), futures)
+            }
+
+        images = resized_images
+
+        all_markers = [
+            int(match) for match in re.findall(r"【(\d+)】", chapter_text)
+        ]
+        image_markers = sorted(
+            list(random.sample(all_markers, min(num_image_samples, len(all_markers))))
+        )
+        expanded_chapter_text = llm.expand_chapter_text(
+            re.sub(
+                r"【(\d+)】",
+                lambda m: f"【{all_markers.index(int(m.group(1)))}】",
+                chapter_text,
+            ),
+            {all_markers.index(marker): images[marker] for marker in image_markers},
+        )
+        expanded_chapter_text = re.sub(
+            r"【(\d+)】",
+            lambda m: f"【{all_markers[int(m.group(1))]}】",
+            expanded_chapter_text,
+        )
+
+        figures = llm.get_figures(
+            re.sub(
+                r"【(\d+)】",
+                lambda m: f"【{image_markers.index(marker)}】"
+                if (marker := int(m.group(1))) in image_markers
+                else "",
+                expanded_chapter_text,
+            ),
+            [images[marker] for marker in image_markers],
+        )
+
+        return expanded_chapter_text, {
+            image_markers[marker]: title for marker, title in figures.items()
+        }
+
+
 def index(
     transcript: list[SpeechEvent],
+    images: dict[int, Path],
     llm: LanguageModel,
     max_tokens: int = 4096,
     temperature: float = 0.5,
-    chunk_size: int = 2048,
+    chunk_size_tokens: int = 2048,
+    chapter_size_words: int = 1024,
+    num_image_samples_per_chapter: int = 16,
+    num_workers: int = 8,
     lang: str | None = None,
 ) -> Content:
     text = render({i: event.text for i, event in enumerate(transcript)})
-    paragraphs = get_paragraphs(text, llm, max_tokens, temperature, chunk_size, lang=lang)
-
-    try:
-        title, summary = llm.get_meta(paragraphs, lang=lang)
-    except Exception:
-        title, summary = "Missing Title", "Missing Summary"
-    
-    try:
-        chapters = llm.get_chapters(paragraphs, lang=lang)
-    except Exception:
-        chapters = {0: "All Content"}
-
-    return Content(
-        title=title,
-        summary=summary,
-        passages=paragraphs,  # NOTE: we should experiment and settle on passage vs. paragraph
-        transcript=transcript,
-        chapters=chapters,
+    paragraphs = get_paragraphs(
+        text, llm, max_tokens, temperature, chunk_size_tokens, lang=lang
+    )
+
+    title, summary = llm.get_meta(
+        paragraphs, max_tokens=max_tokens, temperature=temperature, lang=lang
+    )
+    chapters = get_chapters(
+        paragraphs,
+        llm,
+        chapter_size_words=chapter_size_words,
+        chunk_size_tokens=chunk_size_tokens,
+        temperature=temperature,
+        lang=lang,
     )
 
+    if images:
+        text_and_figures = []
+        for chapter, content in assemble_chapters(chapters, paragraphs).items():
+            expanded_text, figures = expand_and_add_figures(
+                content[1],
+                images,
+                llm,
+                num_image_samples=num_image_samples_per_chapter,
+            )
+            text_and_figures.append((expanded_text, figures))
+
+        return Content(
+            title=title,
+            summary=summary,
+            passages=paragraphs,  # NOTE: we should experiment and settle on passage vs. paragraph
+            transcript=transcript,
+            chapters=chapters,
+            text=[text for text, _ in text_and_figures],
+            figures={
+                marker: (caption, str(images[marker]))
+                for _, figures in text_and_figures
+                for marker, caption in figures.items()
+            },
+        )
+    else:
+        return Content(
+            title=title,
+            summary=summary,
+            passages=paragraphs,
+            transcript=transcript,
+            chapters=chapters,
+            text=["\n\n".join(paragraphs) for _, paragraphs in assemble_chapters(chapters, paragraphs).values()],
+        )
+
 
 rewrite_examples = {
     "en": [
diff --git a/platogram/types.py b/platogram/types.py
index acb230c..4b16539 100644
--- a/platogram/types.py
+++ b/platogram/types.py
@@ -1,3 +1,4 @@
+from pathlib import Path
 from typing import Literal
 
 from pydantic import BaseModel
@@ -6,11 +7,13 @@
 class User(BaseModel):
     role: Literal["user"] = "user"
     content: str
+    images: dict[str, Path] | None = None
     cache: bool = False
 
 class Assistant(BaseModel):
     role: Literal["assistant"] = "assistant"
     content: str
+    images: dict[str, Path] | None = None
     cache: bool = False
 
 class SpeechEvent(BaseModel):
@@ -25,5 +28,7 @@ class Content(BaseModel):
     chapters: dict[int, str]
     passages: list[str]
     transcript: list[SpeechEvent]
+    text: list[str]
+    figures: dict[int, tuple[str, str]] | None = None
     images: list[str] | None = None
     origin: str | None = None
diff --git a/poetry.lock b/poetry.lock
index 26be3ab..a486ce8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -69,6 +69,23 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "asgiref"
+version = "3.8.1"
+description = "ASGI specs, helper code, and adapters"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
+
+[package.extras]
+tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
+
 [[package]]
 name = "assemblyai"
 version = "0.26.0"
@@ -121,6 +138,57 @@ files = [
 [package.dependencies]
 cryptography = "*"
 
+[[package]]
+name = "backoff"
+version = "1.11.1"
+description = "Function decoration for backoff and retry"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
+    {file = "backoff-1.11.1-py2.py3-none-any.whl", hash = "sha256:61928f8fa48d52e4faa81875eecf308eccfb1016b018bb6bd21e05b5d90a96c5"},
+    {file = "backoff-1.11.1.tar.gz", hash = "sha256:ccb962a2378418c667b3c979b504fdeb7d9e0d29c0579e3b13b86467177728cb"},
+]
+
+[[package]]
+name = "bcrypt"
+version = "4.2.0"
+description = "Modern password hashing for your software and your servers"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c02d944ca89d9b1922ceb8a46460dd17df1ba37ab66feac4870f6862a1533c00"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d84cf6d877918620b687b8fd1bf7781d11e8a0998f576c7aa939776b512b98d"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1bb429fedbe0249465cdd85a58e8376f31bb315e484f16e68ca4c786dcc04291"},
+    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:655ea221910bcac76ea08aaa76df427ef8625f92e55a8ee44fbf7753dbabb328"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:1ee38e858bf5d0287c39b7a1fc59eec64bbf880c7d504d3a06a96c16e14058e7"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:0da52759f7f30e83f1e30a888d9163a81353ef224d82dc58eb5bb52efcabc399"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3698393a1b1f1fd5714524193849d0c6d524d33523acca37cd28f02899285060"},
+    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:762a2c5fb35f89606a9fde5e51392dad0cd1ab7ae64149a8b935fe8d79dd5ed7"},
+    {file = "bcrypt-4.2.0-cp37-abi3-win32.whl", hash = "sha256:5a1e8aa9b28ae28020a3ac4b053117fb51c57a010b9f969603ed885f23841458"},
+    {file = "bcrypt-4.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:8f6ede91359e5df88d1f5c1ef47428a4420136f3ce97763e31b86dd8280fbdf5"},
+    {file = "bcrypt-4.2.0-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:c52aac18ea1f4a4f65963ea4f9530c306b56ccd0c6f8c8da0c06976e34a6e841"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3bbbfb2734f0e4f37c5136130405332640a1e46e6b23e000eeff2ba8d005da68"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3413bd60460f76097ee2e0a493ccebe4a7601918219c02f503984f0a7ee0aebe"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:8d7bb9c42801035e61c109c345a28ed7e84426ae4865511eb82e913df18f58c2"},
+    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d3a6d28cb2305b43feac298774b997e372e56c7c7afd90a12b3dc49b189151c"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:9c1c4ad86351339c5f320ca372dfba6cb6beb25e8efc659bedd918d921956bae"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:27fe0f57bb5573104b5a6de5e4153c60814c711b29364c10a75a54bb6d7ff48d"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:8ac68872c82f1add6a20bd489870c71b00ebacd2e9134a8aa3f98a0052ab4b0e"},
+    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:cb2a8ec2bc07d3553ccebf0746bbf3d19426d1c6d1adbd4fa48925f66af7b9e8"},
+    {file = "bcrypt-4.2.0-cp39-abi3-win32.whl", hash = "sha256:77800b7147c9dc905db1cba26abe31e504d8247ac73580b4aa179f98e6608f34"},
+    {file = "bcrypt-4.2.0-cp39-abi3-win_amd64.whl", hash = "sha256:61ed14326ee023917ecd093ee6ef422a72f3aec6f07e21ea5f10622b735538a9"},
+    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:39e1d30c7233cfc54f5c3f2c825156fe044efdd3e0b9d309512cc514a263ec2a"},
+    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f4f4acf526fcd1c34e7ce851147deedd4e26e6402369304220250598b26448db"},
+    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:1ff39b78a52cf03fdf902635e4c81e544714861ba3f0efc56558979dd4f09170"},
+    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:373db9abe198e8e2c70d12b479464e0d5092cc122b20ec504097b5f2297ed184"},
+    {file = "bcrypt-4.2.0.tar.gz", hash = "sha256:cf69eaf5185fd58f268f805b505ce31f9b9fc2d64b376642164e9244540c1221"},
+]
+
+[package.extras]
+tests = ["pytest (>=3.2.1,!=3.3.0)"]
+typecheck = ["mypy"]
+
 [[package]]
 name = "brotli"
 version = "1.1.0"
@@ -252,6 +320,31 @@ files = [
 [package.dependencies]
 cffi = ">=1.0.0"
 
+[[package]]
+name = "build"
+version = "1.2.1"
+description = "A simple, correct Python build frontend"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"},
+    {file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "os_name == \"nt\""}
+importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
+packaging = ">=19.1"
+pyproject_hooks = "*"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"]
+test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"]
+typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
+uv = ["uv (>=0.1.18)"]
+virtualenv = ["virtualenv (>=20.0.35)"]
+
 [[package]]
 name = "cachetools"
 version = "5.5.0"
@@ -452,6 +545,101 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
+[[package]]
+name = "chroma-hnswlib"
+version = "0.7.6"
+description = "Chromas fork of hnswlib"
+optional = false
+python-versions = "*"
+files = [
+    {file = "chroma_hnswlib-0.7.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f35192fbbeadc8c0633f0a69c3d3e9f1a4eab3a46b65458bbcbcabdd9e895c36"},
+    {file = "chroma_hnswlib-0.7.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f007b608c96362b8f0c8b6b2ac94f67f83fcbabd857c378ae82007ec92f4d82"},
+    {file = "chroma_hnswlib-0.7.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:456fd88fa0d14e6b385358515aef69fc89b3c2191706fd9aee62087b62aad09c"},
+    {file = "chroma_hnswlib-0.7.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5dfaae825499c2beaa3b75a12d7ec713b64226df72a5c4097203e3ed532680da"},
+    {file = "chroma_hnswlib-0.7.6-cp310-cp310-win_amd64.whl", hash = "sha256:2487201982241fb1581be26524145092c95902cb09fc2646ccfbc407de3328ec"},
+    {file = "chroma_hnswlib-0.7.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:81181d54a2b1e4727369486a631f977ffc53c5533d26e3d366dda243fb0998ca"},
+    {file = "chroma_hnswlib-0.7.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4b4ab4e11f1083dd0a11ee4f0e0b183ca9f0f2ed63ededba1935b13ce2b3606f"},
+    {file = "chroma_hnswlib-0.7.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:53db45cd9173d95b4b0bdccb4dbff4c54a42b51420599c32267f3abbeb795170"},
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+
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+
+[[package]]
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+description = "Chroma."
+optional = false
+python-versions = ">=3.8"
+files = [
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+httpx = ">=0.27.0"
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+kubernetes = ">=28.1.0"
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+numpy = ">=1.22.5,<2.0.0"
+onnxruntime = ">=1.14.1"
+opentelemetry-api = ">=1.2.0"
+opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
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+opentelemetry-sdk = ">=1.2.0"
+orjson = ">=3.9.12"
+overrides = ">=7.3.1"
+posthog = ">=2.4.0"
+pydantic = ">=1.9"
+pypika = ">=0.48.9"
+PyYAML = ">=6.0.0"
+tenacity = ">=8.2.3"
+tokenizers = ">=0.13.2"
+tqdm = ">=4.65.0"
+typer = ">=0.9.0"
+typing-extensions = ">=4.5.0"
+uvicorn = {version = ">=0.18.3", extras = ["standard"]}
+
+[[package]]
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+version = "8.1.7"
+description = "Composable command line interface toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -463,6 +651,23 @@ files = [
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+[[package]]
+name = "coloredlogs"
+version = "15.0.1"
+description = "Colored terminal output for Python's logging module"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+
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+
+[package.extras]
+cron = ["capturer (>=2.4)"]
+
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 version = "0.2.2"
@@ -815,6 +1020,17 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
+[[package]]
+name = "flatbuffers"
+version = "24.3.25"
+description = "The FlatBuffers serialization format for Python"
+optional = false
+python-versions = "*"
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+]
+
 [[package]]
 name = "fsspec"
 version = "2024.6.1"
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 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
+[[package]]
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+version = "1.65.5"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.8"
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+[package.extras]
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+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -1014,6 +1288,54 @@ files = [
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 pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0.2,<3.0.3 || >3.0.3,<4", markers = "python_version > \"3.0\""}
 
+[[package]]
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+version = "0.6.1"
+description = "A collection of framework independent HTTP protocol utils."
+optional = false
+python-versions = ">=3.8.0"
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+]
+
+[package.extras]
+test = ["Cython (>=0.29.24,<0.30.0)"]
+
 [[package]]
 name = "httpx"
 version = "0.27.0"
@@ -1072,6 +1394,20 @@ testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gr
 torch = ["safetensors[torch]", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
+[[package]]
+name = "humanfriendly"
+version = "10.0"
+description = "Human friendly output for text interfaces using Python"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+]
+
+[package.dependencies]
+pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
+
 [[package]]
 name = "idna"
 version = "3.7"
@@ -1102,6 +1438,25 @@ doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linke
 perf = ["ipython"]
 test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
+[[package]]
+name = "importlib-resources"
+version = "6.4.4"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -1329,6 +1684,32 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "kubernetes"
+version = "30.1.0"
+description = "Kubernetes python client"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+certifi = ">=14.05.14"
+google-auth = ">=1.0.1"
+oauthlib = ">=3.2.2"
+python-dateutil = ">=2.5.3"
+pyyaml = ">=5.4.1"
+requests = "*"
+requests-oauthlib = "*"
+six = ">=1.9.0"
+urllib3 = ">=1.24.2"
+websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
+
+[package.extras]
+adal = ["adal (>=1.0.2)"]
+
 [[package]]
 name = "logfire"
 version = "0.50.1"
@@ -1417,6 +1798,126 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
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+python-versions = "*"
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 version = "1.26.0"
@@ -1542,6 +2154,26 @@ files = [
 [package.dependencies]
 opentelemetry-proto = "1.26.0"
 
+[[package]]
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+version = "1.26.0"
+description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
+optional = false
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+
 [[package]]
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 version = "1.26.0"
@@ -1578,6 +2210,48 @@ opentelemetry-api = ">=1.4,<2.0"
 setuptools = ">=16.0"
 wrapt = ">=1.0.0,<2.0.0"
 
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+
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+
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 [[package]]
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 version = "1.26.0"
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 opentelemetry-api = "1.26.0"
 
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 name = "packaging"
 version = "24.1"
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 [package.dependencies]
 ptyprocess = ">=0.5"
 
+[[package]]
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+
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+fpx = ["olefile"]
+mic = ["olefile"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
+
 [[package]]
 name = "platformdirs"
 version = "4.2.2"
@@ -1694,6 +2553,29 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
+name = "posthog"
+version = "3.5.2"
+description = "Integrate PostHog into any python application."
+optional = false
+python-versions = "*"
+files = [
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+
+[package.dependencies]
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+monotonic = ">=1.5"
+python-dateutil = ">2.1"
+requests = ">=2.7,<3.0"
+six = ">=1.5"
+
+[package.extras]
+dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
+sentry = ["django", "sentry-sdk"]
+test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-timeout"]
+
 [[package]]
 name = "prompt-toolkit"
 version = "3.0.47"
@@ -2044,6 +2926,38 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
+[[package]]
+name = "pypika"
+version = "0.48.9"
+description = "A SQL query builder API for Python"
+optional = false
+python-versions = "*"
+files = [
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+
+[[package]]
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+description = "Wrappers to call pyproject.toml-based build backend hooks."
+optional = false
+python-versions = ">=3.7"
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+]
+
+[[package]]
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+version = "3.4.1"
+description = "A python implementation of GNU readline."
+optional = false
+python-versions = "*"
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+
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 version = "7.4.4"
@@ -2136,6 +3050,20 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -2455,6 +3383,17 @@ core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.te
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+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
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@@ -2528,6 +3467,23 @@ files = [
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 typing-extensions = {version = ">=4.5.0", markers = "python_version >= \"3.7\""}
 
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+description = "Computer algebra system (CAS) in Python"
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+
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 version = "8.5.0"
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 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
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+optional = false
+python-versions = ">=3.7"
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+
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 version = "4.66.0.20240417"
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 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
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+    {file = "watchfiles-0.23.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:3af1b05361e1cc497bf1be654a664750ae61f5739e4bb094a2be86ec8c6db9b6"},
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+    {file = "watchfiles-0.23.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efadd40fca3a04063d40c4448c9303ce24dd6151dc162cfae4a2a060232ebdcb"},
+    {file = "watchfiles-0.23.0-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:556347b0abb4224c5ec688fc58214162e92a500323f50182f994f3ad33385dcb"},
+    {file = "watchfiles-0.23.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:1cf7f486169986c4b9d34087f08ce56a35126600b6fef3028f19ca16d5889071"},
+    {file = "watchfiles-0.23.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f18de0f82c62c4197bea5ecf4389288ac755896aac734bd2cc44004c56e4ac47"},
+    {file = "watchfiles-0.23.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:532e1f2c491274d1333a814e4c5c2e8b92345d41b12dc806cf07aaff786beb66"},
+    {file = "watchfiles-0.23.0.tar.gz", hash = "sha256:9338ade39ff24f8086bb005d16c29f8e9f19e55b18dcb04dfa26fcbc09da497b"},
+]
+
+[package.dependencies]
+anyio = ">=3.0.0"
+
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -2787,6 +3929,22 @@ files = [
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
 ]
 
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
+    {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
 [[package]]
 name = "websockets"
 version = "13.0"
@@ -3014,4 +4172,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10"
-content-hash = "d4610a1216f4c51e8965d6bfaf7c26aebc7d172ddad821846f4736e4e53989b6"
+content-hash = "c6301de7448abe4dff875bb375df1b01571204cf06d2d812c2e4657977b7e1a7"
diff --git a/pyproject.toml b/pyproject.toml
index 7a788d0..7ba83f3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,9 @@ google-auth = "^2.33.0"
 google-auth-oauthlib = "^1.2.1"
 google-api-python-client = "^2.141.0"
 stripe = "^10.8.0"
+pillow = "^10.4.0"
+chromadb = "^0.5.5"
+openai = "^1.42.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.3"
diff --git a/samples/images/0.png b/samples/images/0.png
new file mode 100644
index 0000000..bab61ce
Binary files /dev/null and b/samples/images/0.png differ
diff --git a/samples/images/1.png b/samples/images/1.png
new file mode 100644
index 0000000..2696380
Binary files /dev/null and b/samples/images/1.png differ
diff --git a/samples/images/2.png b/samples/images/2.png
new file mode 100644
index 0000000..22200e3
Binary files /dev/null and b/samples/images/2.png differ
diff --git a/samples/images/3.png b/samples/images/3.png
new file mode 100644
index 0000000..22200e3
Binary files /dev/null and b/samples/images/3.png differ
diff --git a/samples/images/4.png b/samples/images/4.png
new file mode 100644
index 0000000..22200e3
Binary files /dev/null and b/samples/images/4.png differ
diff --git a/samples/images/5.png b/samples/images/5.png
new file mode 100644
index 0000000..22200e3
Binary files /dev/null and b/samples/images/5.png differ
diff --git a/tests/test_cli.py b/tests/test_cli.py
index 0d21199..74a3e70 100644
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -1,16 +1,17 @@
 import os
-from pathlib import Path
 import tempfile
+from pathlib import Path
+
+import platogram as plato
 import platogram.cli as cli
 from platogram.types import Content
-import platogram as plato
 
 
 def test_process_url():
     anthropic_api_key = os.environ["ANTHROPIC_API_KEY"]
     assemblyai_api_key = os.environ.get("ASSEMBLYAI_API_KEY")
 
-    url = "https://www.youtube.com/shorts/XsLK3tPy9SI"
+    url = "file:///home/astaff/Downloads/visualjj-demo.mov"
 
     with tempfile.TemporaryDirectory() as temp_dir:
         library = plato.library.get_semantic_local_chroma(Path(temp_dir))
diff --git a/tests/test_ingest.py b/tests/test_ingest.py
index d4b451d..026c8a5 100644
--- a/tests/test_ingest.py
+++ b/tests/test_ingest.py
@@ -1,15 +1,16 @@
-import platogram
-
 from pathlib import Path
+
+import platogram
 from platogram import ingest
 
 
 def test_extract_images(tmp_path):
     images = ingest.extract_images(
         "https://www.youtube.com/shorts/XsLK3tPy9SI",
-        timestamps_ms=[0],
+        timestamps_ms=[0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 9009],
         output_dir=Path(tmp_path),
     )
+    assert len(images) == 11
     assert images and all(image.exists() for image in images)
 
 
diff --git a/tests/test_llm.py b/tests/test_llm.py
index 9621b9c..244b7f3 100644
--- a/tests/test_llm.py
+++ b/tests/test_llm.py
@@ -10,13 +10,6 @@ def test_get_meta() -> None:
     assert meta
 
 
-def test_get_chapters() -> None:
-    llm = platogram.llm.get_model("anthropic/claude-3-5-sonnet")
-    chapters = llm.get_chapters(
-        ["First Asset Sentence one and two【0】", "First Asset Sentence three【2】"]
-    )
-    assert chapters
-
 
 def test_prompt() -> None:
     llm = platogram.llm.get_model("anthropic/claude-3-5-sonnet")
diff --git a/tests/test_ops.py b/tests/test_ops.py
index 17fbdb4..b02685a 100644
--- a/tests/test_ops.py
+++ b/tests/test_ops.py
@@ -1,6 +1,15 @@
-import platogram
+from pathlib import Path
+
 import pytest
-from platogram.ops import chunk_text, get_paragraphs, parse
+
+import platogram
+from platogram.ops import (
+    chunk_text,
+    expand_and_add_figures,
+    get_chapters,
+    get_paragraphs,
+    parse,
+)
 
 
 def test_get_paragraphs() -> None:
@@ -19,6 +28,58 @@ def test_get_paragraphs_es() -> None:
         text, llm, max_tokens=2048, temperature=0.5, chunk_size=1024, lang="es"
     )
     assert len(paragraphs) > 2
+    
+
+def test_get_chapters() -> None:
+    llm = platogram.llm.get_model("anthropic/claude-3-5-sonnet")
+    chapters = get_chapters(
+        [
+            "This is the first paragraph of the first chapter.【0】This is the second paragraph of the first chapter.【1】",
+            "This is the third paragraph of the first chapter.【2】",
+            "This is the first paragraph of the second chapter.【3】This is the second paragraph of the second chapter.【4】",
+            "This is the third paragraph of the second chapter.【5】",
+            "This is the first paragraph of the third chapter.【6】This is the second paragraph of the third chapter.【7】",
+            "This is the third paragraph of the third chapter.【8】",
+            "This is the first paragraph of the fourth chapter.【9】This is the second paragraph of the fourth chapter.【10】",
+            "This is the third paragraph of the fourth chapter.【11】",
+            "This is the first paragraph of the fifth chapter.【12】This is the second paragraph of the fifth chapter.【13】",
+            "This is the third paragraph of the fifth chapter.【14】",
+            "This is the first paragraph of the sixth chapter.【15】This is the second paragraph of the sixth chapter.【16】",
+            "This is the third paragraph of the sixth chapter.【17】",
+            "This is the first paragraph of the seventh chapter.【18】This is the second paragraph of the seventh chapter.【19】",
+            "This is the third paragraph of the seventh chapter.【20】",
+            "This is the first paragraph of the eighth chapter.【21】This is the second paragraph of the eighth chapter.【22】",
+            "This is the third paragraph of the eighth chapter.【23】",
+        ],
+        llm,
+        temperature=0.5,
+        chapter_size_words=48,
+        chunk_size_tokens=64,
+    )
+
+    assert list(chapters.keys()) == [0, 3, 6, 9, 12, 15, 18, 21], f"chapters: {chapters}"
+
+
+def test_expand_and_add_figures() -> None:
+    llm = platogram.llm.get_model("anthropic/claude-3-5-sonnet")
+    passages = [
+        "This is the first paragraph of the first chapter.【0】This is the second paragraph of the first chapter.【1】",
+        "This is the third paragraph of the first chapter.【2】This is the fourth paragraph of the first chapter.【3】",
+        "This is the fifth paragraph of the first chapter. This is the sixth paragraph of the first chapter.【5】",
+    ]
+
+    images = {
+        0: Path("samples/images/0.png"),
+        1: Path("samples/images/1.png"),
+        2: Path("samples/images/2.png"),
+        3: Path("samples/images/3.png"),
+        4: Path("samples/images/4.png"),
+        5: Path("samples/images/5.png"),
+    }
+    text, figures = expand_and_add_figures(passages, images, llm, num_image_samples=4)
+
+    assert text
+    assert figures
 
 
 def test_chunk_empty_input_string():