RAG-Something-Tutorial: Erstellen Sie eine multimodale Retrieval-Pipeline für Textual content, Tabellen, Gleichungen und Bilder in Colab

print("n(5/10) Creating an artificial multimodal report...")
monthly_data = pd.DataFrame(
   {
       "Month": ("Jan", "Feb", "Mar", "Apr", "Could", "Jun"),
       "Question Quantity": (1200, 1700, 2100, 2600, 3300, 4100),
       "Hybrid Accuracy": (0.71, 0.74, 0.79, 0.83, 0.87, 0.91),
       "Common Latency ms": (980, 920, 850, 790, 760, 730),
   }
)
table_md = monthly_data.to_markdown(index=False)
plt.determine(figsize=(8, 4.8))
plt.plot(monthly_data("Month"), monthly_data("Question Quantity"), marker="o", label="Question Quantity")
plt.plot(monthly_data("Month"), monthly_data("Hybrid Accuracy") * 4000, marker="s", label="Hybrid Accuracy scaled")
plt.title("Multimodal RAG Utilization and High quality Pattern")
plt.xlabel("Month")
plt.ylabel("Quantity / Scaled Accuracy")
plt.legend()
plt.grid(True, alpha=0.3)
plt.textual content(
   0.02,
   0.95,
   "Artificial determine: utilization rises whereas latency falls",
   rework=plt.gca().transAxes,
   fontsize=9,
   verticalalignment="prime",
   bbox=dict(boxstyle="spherical", alpha=0.15),
)
chart_path = ASSET_DIR / "raganything_quality_trend.png"
plt.tight_layout()
plt.savefig(chart_path, dpi=180)
plt.shut()
report_pdf_path = ASSET_DIR / "synthetic_multimodal_rag_report.pdf"
c = canvas.Canvas(str(report_pdf_path), pagesize=letter)
width, peak = letter
c.setFont("Helvetica-Daring", 18)
c.drawString(0.8 * inch, peak - 0.8 * inch, "Artificial Multimodal RAG Analysis Report")
c.setFont("Helvetica", 10)
intro_lines = (
   "This report evaluates an artificial multimodal RAG pipeline for enterprise paperwork.",
   "The data base contains textual content, tables, equations, and visible proof.",
   "The central speculation is that hybrid retrieval improves reply high quality when proof spans modalities.",
)
y = peak - 1.25 * inch
for line in intro_lines:
   c.drawString(0.8 * inch, y, line)
   y -= 0.22 * inch
c.setFont("Helvetica-Daring", 12)
c.drawString(0.8 * inch, y - 0.1 * inch, "Desk 1. Month-to-month 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-Daring", 12)
c.drawString(0.8 * inch, y - 0.15 * inch, "Equation 1. Weighted multimodal rating")
y -= 0.45 * inch
c.setFont("Helvetica", 9)
c.drawString(
   0.8 * inch,
   y,
   "Rating(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, peak=2.6 * inch)
c.showPage()
c.setFont("Helvetica-Daring", 16)
c.drawString(0.8 * inch, peak - 0.8 * inch, "Interpretation and Findings")
c.setFont("Helvetica", 10)
findings = (
   "Hybrid retrieval combines semantic similarity with graph-based relationship navigation.",
   "The artificial desk exhibits accuracy enhancing from 0.71 to 0.91 over six months.",
   "The generated determine exhibits question quantity growing whereas latency step by step decreases.",
   "Equation-level retrieval is beneficial when the query depends upon scoring logic moderately than plain prose.",
   "A multimodal system ought to protect web page index, captions, footnotes, and native picture paths for traceability.",
)
y = peak - 1.25 * inch
for locating in findings:
   c.drawString(0.8 * inch, y, "- " + discovering)
   y -= 0.28 * inch
c.save()
print(f"Created chart: {chart_path}")
print(f"Created PDF: {report_pdf_path}")
print("nSynthetic desk:")
show(monthly_data)