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

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)

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