RAG-Anything Tutorial: Build a Multimodal Retrieval Pipeline for Text, Tables, Equations, and Images in Colab

import os
import re
import sys
import json
import time
import shutil
import hashlib
import asyncio
import inspect
import getpass
import subprocess
import importlib
import importlib.metadata
from pathlib import Path
from typing import List, Dict, Any
def run_shell(cmd, check=True):
   print(f"n$ {cmd}")
   result = subprocess.run(cmd, shell=True, text=True)
   if check and result.returncode != 0:
       raise RuntimeError(f"Command failed: {cmd}")
   return result.returncode
print("=" * 80)
print("RAG-Anything Advanced Colab Tutorial")
print("=" * 80)
print("n[1/10] Installing dependencies...")
for module_name in list(sys.modules):
   if module_name == "PIL" or module_name.startswith("PIL."):
       del sys.modules[module_name]
run_shell(
   'pip -q install -U '
   '"raganything[image,text]" '
   '"openai>=1.0.0" '
   '"python-dotenv" '
   '"reportlab" '
   '"pandas" '
   '"matplotlib" '
   '"tabulate"'
)
run_shell('pip -q install --no-cache-dir --force-reinstall "pillow==11.3.0"')
for module_name in list(sys.modules):
   if module_name == "PIL" or module_name.startswith("PIL."):
       del sys.modules[module_name]
importlib.invalidate_caches()
try:
   print("Pillow version:", importlib.metadata.version("Pillow"))
except Exception as e:
   print("Could not read Pillow version:", repr(e))
print("n[2/10] Importing libraries...")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.units import inch
from openai import AsyncOpenAI
from raganything import RAGAnything, RAGAnythingConfig
from lightrag.utils import EmbeddingFunc
print("Imports successful.")

print("n[3/10] Preparing directories and runtime settings...")
BASE_DIR = Path("/content/raganything_advanced_tutorial") if Path("/content").exists() else Path.cwd() / "raganything_advanced_tutorial"
ASSET_DIR = BASE_DIR / "assets"
OUTPUT_DIR = BASE_DIR / "output"
WORKING_DIR = BASE_DIR / "rag_storage"
LOG_DIR = BASE_DIR / "logs"
RESET_STORAGE = True
RUN_FULL_DOCUMENT_PARSE = False
PARSER_FOR_FULL_PARSE = "mineru"
PARSE_METHOD = "auto"
for d in [BASE_DIR, ASSET_DIR, OUTPUT_DIR, WORKING_DIR, LOG_DIR]:
   d.mkdir(parents=True, exist_ok=True)
if RESET_STORAGE and WORKING_DIR.exists():
   shutil.rmtree(WORKING_DIR)
   WORKING_DIR.mkdir(parents=True, exist_ok=True)
os.environ["LOG_DIR"] = str(LOG_DIR)
os.environ["SUMMARY_LANGUAGE"] = "English"
os.environ["ENABLE_LLM_CACHE"] = "false"
os.environ["ENABLE_LLM_CACHE_FOR_EXTRACT"] = "false"
os.environ["MAX_ASYNC"] = "2"
os.environ["CHUNK_SIZE"] = "900"
os.environ["CHUNK_OVERLAP_SIZE"] = "120"
os.environ["TIMEOUT"] = "240"
for var in [
   "OPENAI_API_KEY",
   "OPENAI_ORG_ID",
   "OPENAI_ORGANIZATION",
   "OPENAI_PROJECT",
   "OPENAI_DEFAULT_HEADERS",
   "LLM_BINDING_API_KEY",
   "LLM_BINDING_HOST",
]:
   os.environ.pop(var, None)
print(f"Base directory: {BASE_DIR}")
print(f"Assets directory: {ASSET_DIR}")
print(f"Storage directory: {WORKING_DIR}")
print("n[4/10] Entering OpenAI API key securely...")
def clean_api_key(raw_value: str) -> str:
   raw_value = str(raw_value or "").strip()
   raw_value = raw_value.replace("Bearer ", "").replace("bearer ", "").strip()
   raw_value = raw_value.strip("'").strip('"').strip("`").strip()
   if "=" in raw_value:
       raw_value = raw_value.split("=", 1)[1].strip().strip("'").strip('"').strip("`")
   raw_value = re.sub(r"s+", "", raw_value)
   raw_value = raw_value.encode("ascii", errors="ignore").decode("ascii").strip()
   return raw_value
OPENAI_API_KEY_RAW = getpass.getpass("Paste your OpenAI API key here. Input is hidden: ")
OPENAI_API_KEY = clean_api_key(OPENAI_API_KEY_RAW)
if not OPENAI_API_KEY:
   raise ValueError(
       "No API key was captured. Paste the key into the hidden input box and press Enter."
   )
print("Captured key length:", len(OPENAI_API_KEY))
print("Captured key prefix:", OPENAI_API_KEY[:12] + "...")
print("Captured key suffix:", "..." + OPENAI_API_KEY[-6:])
LLM_MODEL = "gpt-4o-mini"
VISION_MODEL = "gpt-4o-mini"
EMBEDDING_MODEL = "text-embedding-3-small"
EMBEDDING_DIM = 1536
openai_client = AsyncOpenAI(api_key=OPENAI_API_KEY)
os.environ["LLM_MODEL"] = LLM_MODEL
os.environ["VISION_MODEL"] = VISION_MODEL
os.environ["EMBEDDING_MODEL"] = EMBEDDING_MODEL
os.environ["EMBEDDING_DIM"] = str(EMBEDDING_DIM)
print("Testing OpenAI chat API with the captured key...")
try:
   test_response = await openai_client.chat.completions.create(
       model=LLM_MODEL,
       messages=[{"role": "user", "content": "Reply with exactly: ok"}],
       temperature=0,
   )
   print("Chat API test response:", test_response.choices[0].message.content)
except Exception as e:
   raise RuntimeError(
       "The key was captured, but OpenAI rejected the request or the account/model access failed. "
       "Check billing, project permissions, and make sure this is an OpenAI Platform API key."
   ) from e
print("nTesting OpenAI embedding API...")
try:
   test_embedding = await openai_client.embeddings.create(
       model=EMBEDDING_MODEL,
       input=["RAG-Anything embedding test"],
   )
   print("Embedding vector length:", len(test_embedding.data[0].embedding))
except Exception as e:
   raise RuntimeError(
       "Chat worked, but embeddings failed. Make sure your API key has permission for embeddings."
   ) from e
print("OpenAI API key is working.")
print(f"Chat model: {LLM_MODEL}")
print(f"Vision model: {VISION_MODEL}")
print(f"Embedding model: {EMBEDDING_MODEL}")
print(f"Embedding dimension: {EMBEDDING_DIM}")

print("n[5/10] Creating a synthetic multimodal report...")
monthly_data = pd.DataFrame(
   {
       "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
       "Query Volume": [1200, 1700, 2100, 2600, 3300, 4100],
       "Hybrid Accuracy": [0.71, 0.74, 0.79, 0.83, 0.87, 0.91],
       "Average Latency ms": [980, 920, 850, 790, 760, 730],
   }
)
table_md = monthly_data.to_markdown(index=False)
plt.figure(figsize=(8, 4.8))
plt.plot(monthly_data["Month"], monthly_data["Query Volume"], marker="o", label="Query Volume")
plt.plot(monthly_data["Month"], monthly_data["Hybrid Accuracy"] * 4000, marker="s", label="Hybrid Accuracy scaled")
plt.title("Multimodal RAG Usage and Quality Trend")
plt.xlabel("Month")
plt.ylabel("Volume / Scaled Accuracy")
plt.legend()
plt.grid(True, alpha=0.3)
plt.text(
   0.02,
   0.95,
   "Synthetic figure: usage rises while latency falls",
   transform=plt.gca().transAxes,
   fontsize=9,
   verticalalignment="top",
   bbox=dict(boxstyle="round", alpha=0.15),
)
chart_path = ASSET_DIR / "raganything_quality_trend.png"
plt.tight_layout()
plt.savefig(chart_path, dpi=180)
plt.close()
report_pdf_path = ASSET_DIR / "synthetic_multimodal_rag_report.pdf"
c = canvas.Canvas(str(report_pdf_path), pagesize=letter)
width, height = letter
c.setFont("Helvetica-Bold", 18)
c.drawString(0.8 * inch, height - 0.8 * inch, "Synthetic Multimodal RAG Evaluation Report")
c.setFont("Helvetica", 10)
intro_lines = [
   "This report evaluates a synthetic multimodal RAG pipeline for enterprise documents.",
   "The knowledge base includes text, tables, equations, and visual evidence.",
   "The central hypothesis is that hybrid retrieval improves answer quality when evidence spans modalities.",
]
y = height - 1.25 * inch
for line in intro_lines:
   c.drawString(0.8 * inch, y, line)
   y -= 0.22 * inch
c.setFont("Helvetica-Bold", 12)
c.drawString(0.8 * inch, y - 0.1 * inch, "Table 1. Monthly system measurements")
y -= 0.4 * inch
c.setFont("Courier", 7.5)
for row in table_md.splitlines():
   c.drawString(0.8 * inch, y, row[:120])
   y -= 0.17 * inch
c.setFont("Helvetica-Bold", 12)
c.drawString(0.8 * inch, y - 0.15 * inch, "Equation 1. Weighted multimodal score")
y -= 0.45 * inch
c.setFont("Helvetica", 9)
c.drawString(
   0.8 * inch,
   y,
   "Score(q, d) = alpha * Sim_text(q, d) + beta * Sim_graph(q, d) + gamma * Sim_visual(q, d)",
)
y -= 0.5 * inch
c.drawImage(str(chart_path), 0.8 * inch, y - 2.8 * inch, width=6.5 * inch, height=2.6 * inch)
c.showPage()
c.setFont("Helvetica-Bold", 16)
c.drawString(0.8 * inch, height - 0.8 * inch, "Interpretation and Findings")
c.setFont("Helvetica", 10)
findings = [
   "Hybrid retrieval combines semantic similarity with graph-based relationship navigation.",
   "The synthetic table shows accuracy improving from 0.71 to 0.91 over six months.",
   "The generated figure shows query volume increasing while latency gradually decreases.",
   "Equation-level retrieval is useful when the question depends on scoring logic rather than plain prose.",
   "A multimodal system should preserve page index, captions, footnotes, and local image paths for traceability.",
]
y = height - 1.25 * inch
for finding in findings:
   c.drawString(0.8 * inch, y, "- " + finding)
   y -= 0.28 * inch
c.save()
print(f"Created chart: {chart_path}")
print(f"Created PDF: {report_pdf_path}")
print("nSynthetic table:")
display(monthly_data)

print("n[6/10] Building direct multimodal content_list...")
content_list: List[Dict[str, Any]] = [
   {
       "type": "text",
       "text": (
           "This synthetic report evaluates a multimodal retrieval augmented generation system. "
           "The system indexes textual explanations, a structured performance table, a scoring equation, "
           "and a trend figure. The main goal is to answer questions whose evidence is distributed across "
           "several document modalities rather than one plain text passage."
       ),
       "page_idx": 0,
   },
   {
       "type": "table",
       "table_body": table_md,
       "table_caption": ["Table 1: Monthly query volume, hybrid accuracy, and average latency."],
       "table_footnote": ["Synthetic measurements created for a Colab tutorial."],
       "page_idx": 0,
   },
   {
       "type": "equation",
       "latex": r"Score(q,d)=alpha cdot Sim_{text}(q,d)+beta cdot Sim_{graph}(q,d)+gamma cdot Sim_{visual}(q,d)",
       "text": (
           "Weighted multimodal retrieval score. Alpha controls text similarity, beta controls graph relationship "
           "similarity, and gamma controls visual similarity."
       ),
       "page_idx": 0,
   },
   {
       "type": "image",
       "img_path": str(chart_path.resolve()),
       "image_caption": ["Figure 1: Multimodal RAG usage and quality trend."],
       "image_footnote": ["The line chart is synthetic and generated inside this tutorial."],
       "page_idx": 0,
   },
   {
       "type": "text",
       "text": (
           "The key finding is that hybrid retrieval is preferred for cross-modal questions. "
           "Local retrieval is useful for entity-specific lookup, global retrieval is useful for broader themes, "
           "and naive retrieval is a simpler baseline. In this report, hybrid accuracy rises from 0.71 in January "
           "to 0.91 in June, while average latency drops from 980 milliseconds to 730 milliseconds."
       ),
       "page_idx": 1,
   },
]
content_list_path = ASSET_DIR / "content_list.json"
with open(content_list_path, "w", encoding="utf-8") as f:
   json.dump(content_list, f, indent=2, ensure_ascii=False)
print(f"Saved content list: {content_list_path}")

print("n[7/10] Defining clean OpenAI model and embedding functions...")
async def llm_model_func(prompt, system_prompt=None, history_messages=None, **kwargs):
   messages = []
   if system_prompt:
       messages.append({"role": "system", "content": str(system_prompt)})
   for msg in history_messages or []:
       if isinstance(msg, dict) and "role" in msg and "content" in msg:
           messages.append(msg)
   messages.append({"role": "user", "content": str(prompt)})
   allowed_kwargs = {}
   for key in ["temperature", "top_p", "max_tokens", "response_format"]:
       if key in kwargs and kwargs[key] is not None:
           allowed_kwargs[key] = kwargs[key]
   response = await openai_client.chat.completions.create(
       model=LLM_MODEL,
       messages=messages,
       **allowed_kwargs,
   )
   return response.choices[0].message.content or ""
async def vision_model_func(
   prompt,
   system_prompt=None,
   history_messages=None,
   image_data=None,
   messages=None,
   **kwargs,
):
   allowed_kwargs = {}
   for key in ["temperature", "top_p", "max_tokens", "response_format"]:
       if key in kwargs and kwargs[key] is not None:
           allowed_kwargs[key] = kwargs[key]
   if messages:
       clean_messages = [m for m in messages if m is not None]
       response = await openai_client.chat.completions.create(
           model=VISION_MODEL,
           messages=clean_messages,
           **allowed_kwargs,
       )
       return response.choices[0].message.content or ""
   built_messages = []
   if system_prompt:
       built_messages.append({"role": "system", "content": str(system_prompt)})
   for msg in history_messages or []:
       if isinstance(msg, dict) and "role" in msg and "content" in msg:
           built_messages.append(msg)
   if image_data:
       built_messages.append(
           {
               "role": "user",
               "content": [
                   {"type": "text", "text": str(prompt)},
                   {
                       "type": "image_url",
                       "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
                   },
               ],
           }
       )
   else:
       built_messages.append({"role": "user", "content": str(prompt)})
   response = await openai_client.chat.completions.create(
       model=VISION_MODEL,
       messages=built_messages,
       **allowed_kwargs,
   )
   return response.choices[0].message.content or ""
async def openai_embedding_func(texts, **kwargs):
   if isinstance(texts, str):
       texts = [texts]
   texts = [str(t) for t in texts]
   response = await openai_client.embeddings.create(
       model=EMBEDDING_MODEL,
       input=texts,
   )
   vectors = [item.embedding for item in response.data]
   return np.array(vectors, dtype=np.float32)
embedding_func = EmbeddingFunc(
   embedding_dim=EMBEDDING_DIM,
   max_token_size=8192,
   func=openai_embedding_func,
)
print("Model and embedding functions ready.")

print("n[8/10] Initializing RAG-Anything...")
config = RAGAnythingConfig(
   working_dir=str(WORKING_DIR),
   parser=PARSER_FOR_FULL_PARSE,
   parse_method=PARSE_METHOD,
   enable_image_processing=True,
   enable_table_processing=True,
   enable_equation_processing=True,
)
rag = RAGAnything(
   config=config,
   llm_model_func=llm_model_func,
   vision_model_func=vision_model_func,
   embedding_func=embedding_func,
)
async def maybe_await(value):
   if inspect.isawaitable(value):
       return await value
   return value
if hasattr(rag, "initialize_storages"):
   try:
       await maybe_await(rag.initialize_storages())
       print("RAG-Anything storages initialized.")
   except Exception as e:
       print("Storage initialization skipped or already handled:", repr(e))
print(f"Working directory: {WORKING_DIR}")
print("n[9/10] Inserting multimodal content and running retrieval queries...")
async def insert_demo_content():
   await rag.insert_content_list(
       content_list=content_list,
       file_path=str(report_pdf_path.name),
       split_by_character=None,
       split_by_character_only=False,
       doc_id="synthetic-multimodal-rag-report",
       display_stats=True,
   )
await insert_demo_content()
print("Insertion complete.")
queries = [
   "What is the main purpose of the multimodal RAG report?",
   "How did hybrid accuracy and latency change from January to June?",
   "Why is hybrid retrieval better than naive retrieval for this report?",
   "What does the weighted multimodal score equation mean?",
]
async def safe_aquery(question, mode="hybrid", vlm_enhanced=False):
   try:
       return await rag.aquery(question, mode=mode, vlm_enhanced=vlm_enhanced)
   except TypeError:
       return await rag.aquery(question, mode=mode)
async def run_query_suite():
   results = []
   for mode in ["naive", "local", "global", "hybrid"]:
       print("n" + "=" * 80)
       print(f"QUERY MODE: {mode.upper()}")
       print("=" * 80)
       for q in queries:
           print(f"nQuestion: {q}")
           try:
               answer = await safe_aquery(q, mode=mode, vlm_enhanced=False)
           except Exception as e:
               answer = f"Query failed in mode={mode}: {repr(e)}"
           print("nAnswer:")
           print(answer)
           print("-" * 80)
           results.append(
               {
                   "mode": mode,
                   "question": q,
                   "answer_preview": str(answer)[:700],
               }
           )
   return pd.DataFrame(results)
query_results_df = await run_query_suite()
print("nQuery result preview:")
display(query_results_df)

print("n[10/10] Running explicit multimodal queries...")
async def run_multimodal_queries():
   multimodal_cases = [
       {
           "name": "Table-aware query",
           "question": (
               "Using the supplied table, identify the month with the highest hybrid accuracy, "
               "the month with the lowest latency, and explain whether the trend supports the report conclusion."
           ),
           "multimodal_content": [
               {
                   "type": "table",
                   "table_data": table_md,
                   "table_caption": "Monthly performance table",
               }
           ],
       },
       {
           "name": "Equation-aware query",
           "question": (
               "Explain how this scoring equation should affect retrieval when the user's question needs "
               "textual, graph, and visual evidence at the same time."
           ),
           "multimodal_content": [
               {
                   "type": "equation",
                   "latex": r"Score(q,d)=alpha Sim_{text}(q,d)+beta Sim_{graph}(q,d)+gamma Sim_{visual}(q,d)",
                   "equation_caption": "Weighted multimodal retrieval score",
               }
           ],
       },
       {
           "name": "Combined multimodal query",
           "question": (
               "Connect the table, equation, and document conclusion into one explanation of why a multimodal "
               "hybrid retriever is useful."
           ),
           "multimodal_content": [
               {
                   "type": "table",
                   "table_data": table_md,
                   "table_caption": "Monthly performance table",
               },
               {
                   "type": "equation",
                   "latex": r"Score(q,d)=alpha Sim_{text}(q,d)+beta Sim_{graph}(q,d)+gamma Sim_{visual}(q,d)",
                   "equation_caption": "Weighted multimodal retrieval score",
               },
           ],
       },
   ]
   outputs = []
   for case in multimodal_cases:
       print("n" + "=" * 80)
       print(case["name"])
       print("=" * 80)
       print("Question:", case["question"])
       try:
           answer = await rag.aquery_with_multimodal(
               case["question"],
               multimodal_content=case["multimodal_content"],
               mode="hybrid",
           )
       except Exception as e:
           answer = f"Multimodal query failed: {repr(e)}"
       print("nAnswer:")
       print(answer)
       outputs.append(
           {
               "case": case["name"],
               "question": case["question"],
               "answer_preview": str(answer)[:900],
           }
       )
   return pd.DataFrame(outputs)
multimodal_results_df = await run_multimodal_queries()
print("nMultimodal result preview:")
display(multimodal_results_df)
print("nOptional full-parser path:")
print("RUN_FULL_DOCUMENT_PARSE is currently:", RUN_FULL_DOCUMENT_PARSE)
async def optional_full_document_parse():
   if not RUN_FULL_DOCUMENT_PARSE:
       print(
           "Skipping parser-based PDF ingestion. "
           "Set RUN_FULL_DOCUMENT_PARSE=True near the top to test MinerU/Docling/PaddleOCR parsing."
       )
       return
   print("Starting full document parsing.")
   await rag.process_document_complete(
       file_path=str(report_pdf_path),
       output_dir=str(OUTPUT_DIR),
       parse_method=PARSE_METHOD,
       parser=PARSER_FOR_FULL_PARSE,
       display_stats=True,
       doc_id="parser-processed-synthetic-report",
   )
   answer = await safe_aquery(
       "After full parsing, what figures, tables, and equations are present in the report?",
       mode="hybrid",
       vlm_enhanced=False,
   )
   print(answer)
await optional_full_document_parse()
print("n" + "=" * 80)
print("Tutorial complete.")
print("=" * 80)
print(f"Assets directory: {ASSET_DIR}")
print(f"RAG storage directory: {WORKING_DIR}")
print(f"Output directory: {OUTPUT_DIR}")
print("nGenerated files:")
for path in sorted(ASSET_DIR.glob("*")):
   print(" -", path)