AgentSkillsCN

RAG Pipeline

检索增强生成流水线、摄取和向量搜索的细节。

SKILL.md
--- frontmatter
name: RAG Pipeline
description: Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.

RAG Pipeline Logic

Ingestion

  • •Script: backend/ingest.py
  • •Process:
    1. •Scans docs/.
    2. •Cleans MDX (removes frontmatter/imports).
    3. •Chunks text (1000 chars, 100 overlap).
    4. •Embeds using models/text-embedding-004.
    5. •Upserts to Qdrant collection physical_ai_book.
  • •Run: python backend/ingest.py

Vector Search (Qdrant)

  • •Client: qdrant-client
  • •Collection: physical_ai_book
  • •Vector Size: 768 (Gecko-004)
  • •Similarity: Cosine

Prompt Engineering

  • •File: backend/utils/helpers.py.
  • •RAG Prompt: Constructs a prompt containing retrieved context chunks.
  • •Personalization: backend/personalization.py creates system instructions based on software_background and hardware_background of the user.

Agentic Flow

We use a custom Agent class (backend/agents.py) that wraps the LLM calls, allowing for future expansion into multi-agent workflows.