RAG Basics answer by Google Gemini AI
AI Knowledge Pipeline: Complete Archive
1. Why do we need chunking?
2. Is chunking mandatory for small files? Limits?
3. Embedding Model vs LLM?
4. Same company model? Single embed model for multiple LLMs? Rebuild needed?
5. Is embed model also an LLM? Can it answer, know English, code?
6. What does embed model know? Maths, programs, history, stocks?
7. Can it calculate 25*40? Will not vs cannot?
8. Does it use frozen weights? Need GPU like LLM?
9. Size? RAM vs Disk? Where is data retrieved from?
10. Deep Dive: Manual vector handling, VDB as MySQL, loop logic, soft-switch, internal embeddings, why called embedding?
11. Pulling internal representations, why LLM heavier, VDB useless without model? Where chunks stored? Token matching?
12. What to do with hidden states? Use VDB + embed without LLM?
13. Clarification: Why allow hidden states + pure semantic search use-case
14. Is VDB free locally? Embedding model free? Must they match?
15. Cloud cost vs open-source, data security risks
Turn 1: Why do we need to chunk?
Chunking means two different things in AI and Psychology, but both solve a memory limit problem.
A) In AI / RAG Systems
Breaking a huge document into smaller meaningful segments before embedding. Needed for:
- Bypassing Token Limits: LLMs have hard context windows (e.g. 8k, 128k tokens). A full book will error.
- Improving Retrieval Accuracy: Searching a 100-page report as one vector is noisy. Targeted 300-word chunks let vector search isolate the exact paragraph.
- Preventing Context Dilution / Lost-in-Middle: If you give LLM 50k tokens of irrelevant text, the true answer drowns and hallucinations rise.
- Reducing Cost & Latency: APIs bill per token. Unchunked files spike cost and response time.
B) In Cognitive Psychology
- Working Memory Limit: Humans hold ~3-4 items at once.
- Mental Folders: 123-456-7890 vs 1234567890 — grouping turns 10 digits into 3 chunks.
- Expert Chunking: Chess masters bundle openings into one mental model, freeing brain for strategy.
Turn 2: Is chunking mandatory if files are already small?
No, not mandatory if files are already paragraph-sized. Treat each file as its own chunk.
Lower Limit — When too small?
No hard error, but functional limit: A chunk must be a complete semantic thought.
If file = 1-5 words like “Apple” or “$42” or “2024-01-01”, embedding model has no context → accuracy collapses (Apple fruit vs company).
Upper Limit — When must chunk?
Check Embedding Model limit, not LLM limit. Popular embed models: 512 to 8192 tokens (400-6000 words).
If your “small file” is 10 pages (~4000 words) and model limit is 512 tokens, it silently truncates after ~400 words. Rest is invisible to search.
Rule of thumb
- Under 200-300 words Self-contained (review, FAQ, product desc) → Do NOT chunk.
- 300-1000 words Multiple topics → Split by paragraph / semantic boundary.
- 1000+ words Always chunk (recursive or semantic).
Turn 3: Embedding Model (not just the LLM)??
RAG uses TWO different models with separate limits.
| Feature | 🧠 Embedding Model | 💬 LLM |
|---|---|---|
| Role | Librarian: Reads text → list of numbers (vector) for search | Writer: Takes search results → writes answer |
| Examples | text-embedding-3-small, bge-large-en, all-MiniLM | GPT-4o, Claude 3.5, Llama 3 |
| Token Limits | Very Small: 512-8192 tokens | Very Large: 128k-1M+ tokens |
| If exceeded? | Silently truncates — data lost | Error or forgets early context |
Analogy: LLM = giant desk holding many books. Embedding model = tiny scanner that can only scan few pages at a time to index them.
A 5000-word file fits easily on LLM desk, but 512-token embed scanner throws away 4600 words.
Takeaway: Even if file fits LLM, it must fit embedding model’s smaller limit.
Turn 4: Same company? One embed model for many LLMs? Rebuild?
1. Must embedding and LLM be same company?
No. Mix and match freely. You can embed with OpenAI, retrieve, and answer with Claude or Llama. They never talk mathematically — only via plain English text chunks.
2. Can one embedding model serve multiple LLMs?
Yes, standard practice. Vectors live in your DB forever. Today route top chunks to GPT-4o, tomorrow to Claude — no DB change.
3. If you change embedding model, rebuild?
Yes, must re-embed everything. No training needed (models pre-trained), but must re-run.
- Different Dimensions: OpenAI 1536 dims vs bge-small 384 dims vs bge-large 1024 dims — DB will crash on mismatch.
- Different Coordinate System: Even same dimension size, Model A’s “Apple” at [0.1,0.9] is Model B’s “Car” location. Like English library with Japanese dictionary.
Procedure: Load new model → loop over raw text chunks → generate new vectors → overwrite old collection.
Turn 5: Is embed model an LLM? Can it answer, know English, code?
No, not an LLM in generative sense. Same transformer family, opposite job.
- LLM = generative: Predicts next token → writes essay/code.
- Embedding = mathematical: Input “Write Python sort” → outputs
[0.0023, -0.0145, 0.4321, ...]— list of 384-1536 numbers. No text.
Does it know English?
Yes, silent conceptual knowledge. Knows “king’s daughter” ≈ “princess” → gives very similar vectors. Understands grammar, context, synonyms, but has no mouth to reply.
| Capability | Embedding | LLM |
|---|---|---|
| Primary Output | Numbers (Vectors) | Text / Code |
| Can chat? | ❌ No | ✅ Yes |
| Can code? | ❌ No | ✅ Yes |
| Understands English? | ✅ For matching | ✅ For generation |
| Analogy | Barcode scanner → SKU | Clerk who talks |
Turn 6: What all does embed model know? Maths, programs, history, stocks?
It knows everything an LLM knows, but as patterns & concepts, not speakable facts. Silent savant who read all books but lost voice.
📊 Maths & Probability
Knows “Bayes’ Theorem” ≈ “conditional probability” ≈ “statistical inference”. Searching “chance of rain given clouds” matches probability docs. Cannot solve equation.
💻 Programs & Code
Knows def bubble_sort(arr): means same as doc “inefficient adjacent swap sort”. Matches Python code to English description. Cannot write/fix/execute code.
📜 History
Knows 1789 + Bastille + Guillotine + Louis XVI → cluster around “French Revolution”. Search “overthrowing French monarchy” finds Louis XVI docs even without word “monarchy”. Cannot narrate story.
📈 Stock Market
Knows bull market ↔ quantitative easing ↔ aggressive growth; “revenue plummeted” ↔ bearish signal. Cannot give live prices or buy recommendation.
Core: It doesn’t know words, it knows what words mean in relation to each other.
Turn 7: Can it solve 25*40? Will not vs Cannot?
It literally cannot calculate — not “will not”, but architecturally cannot.
1. No Generative Head
To output 25*40=1000, model must generate new tokens “1” “0” “0” “0”. LLM has decoder head for that. Embedding model’s only output is fixed-size float array (vector). No token generator.
2. Understands Meaning, Not Logic
Given “25 * 40”, it thinks: “Ah, topic = multiplication, arithmetic”. Assigns vector near other multiplication terms. Doesn’t execute steps.
Proof
Feed “25 * 40” → [0.012, -0.043, 0.91, ...] (1536 floats)
Feed “1000” → [-0.21, 0.88, 0.03, ...] — completely different. No internal bridge linking equation to solution. It recognizes topic, cannot compute.
Turn 8: Frozen weights? Needs GPU like LLM?
Exactly! Uses pre-trained frozen weights as static knowledge. Doesn’t learn on the fly. Just translates using what it knows.
Needs GPU?
No, not strictly. Runs great on CPU.
- LLM: Loops: guess next word → read own output → guess next → repeat 100s times → heavy, needs GPU VRAM.
- Embedding: Single forward pass: reads text once → instantly outputs vector. No loop.
Size comparison
- LLMs: 8B, 70B+ parameters → needs 16-80 GB VRAM
- Embed models: 30M-300M params (all-MiniLM 22M, bge-small 33M, bge-base 110M, bge-large 335M) → fits in 200MB RAM
When GPU helps?
- Bulk initial upload: Millions of docs → GPU batching 10x faster
- Single query: “What is refund policy?” → CPU ~5-20ms, GPU unnecessary
- Hosted APIs (OpenAI) → runs on their GPUs, your machine needs none.
Turn 9: Size? RAM vs Disk? Where retrieval happens?
1. Size
- Small: all-MiniLM-L6-v2 ~80MB, bge-small-en-v1.5 ~133MB
- Medium: bge-base ~420MB
- Large: bge-large ~1.3GB, NV-Embed-v1 ~7GB (rare)
- Most common: 100MB-3GB
2. RAM or Disk?
File lives on SSD when app off. When app starts, must load entirely into RAM. Running directly from disk → massive slowdown (disk IO 1000x slower). Since <3GB, fits easily in 8-16GB laptop RAM.
3. Retrieval from RAM?
Yes. Ideal flow:
If millions of vectors too big for RAM → hybrid: keep index in RAM, keep raw text metadata on SSD, pull only winners into RAM.
Turn 10: Deep Dive — Manual vectors, VDB = MySQL, loop, soft-switch, internal embeddings
--- so should we manually take these weights..?? can we see the outputting weights or should we programatically take those weights then send to vector database..??? what is vectordatabase systems -- are they already built systems like mysql or mogodb etc --- we can just install them and push the data we got from embeding model like how we send the data to mysql tables using sql code... ??? so the embed model does the vector convertion as we give a chunck -- takes it as a query and converts to vector like any standard LLM... but there we cut the backpropagation and get those vectors to store then feed it with next chunk like giving it next question in a while loop repeat it until last chunk is over repeat getting vectors and store in VDB correct ..??? but if we give same to LLM the moment we give first chunck it starts generating answers and cant we stop that with a soft swicth like a mode -- answer mode and vectors only mode -- so we can use same LLM -- then when we use EMbed model to ask a question -- this new quation related to new data converting again to vectors is the part of embed model -- but who does match the vectors of questions with the data inside the vector database and extract the needed chunks or data ..??? is that implemented inside vector database itself like mysql takes complete responsibility to give us the matching data..??? then why is it so hard to implement a soft switch in Main LLM itself..??? and we can even give the control to it when to use vector mode to automate itself -- Technically, you could extract vectors from an LLM because LLMs use embeddings internally to read text. --- what do you mean by using embeddings internally ..?? do you mean they have another embedding model inside it ..??? so I should understand why an embedding model is calle dby its name -- is it doing any embedding in addition to just understanding the data and converting them to vectors as weights ..???
Your understanding is 100% correct on storage! Embedding model is a camera: looks at text, snaps picture as vector, hands to you, forgets file instantly. Size never grows. Data lives in external VDB.
1. Do we manually handle vectors? Can we see them?
Programmatically via Python. Yes you can print:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
vec = model.encode("Our refund policy is 30 days")
print(vec[:5]) # [0.0123, -0.0432, 0.0981, 0.334, -0.12]
print(len(vec)) # 384You capture list in variable and push to DB. No manual typing of numbers.
2. What is Vector Database? Like MySQL?
Yes, ready-built installable software like MySQL/MongoDB. You install, connect via driver, push data.
- Examples: ChromaDB (simplest, runs inside Python), FAISS (Meta, ultra-fast local lib), Qdrant / Milvus / Weaviate (production via Docker), pgvector (Postgres extension)
- Insert looks like SQL INSERT but with vector:
collection.add(
ids=["chunk_001"],
embeddings=[[0.14, -0.82, 0.05, ...]],
documents=["Our corporate refund policy allows 30 days..."],
metadatas=[{"source": "policy.pdf"}]
)3. Your Loop Logic is 100% Spot On
No backpropagation during inference. Model frozen. Exactly like giving next question in while loop.
4. Why not soft-switch LLM to vector-only mode?
Technically you CAN pull internal vectors from LLM via output_hidden_states=True. But 3 massive blockers:
- Pooling Problem — Math Wrong: LLM outputs 1 vector per token. 1000-token chunk → 1000 vectors. No compression. Embedding model has special pooling layer (mean pooling / CLS) trained to compress 1000 → 1 meaningful vector.
- Massive Resource Waste: 8B LLM for vector job = semi-truck to buy apple. 100M embed model = bicycle — 50x faster, 100x cheaper, fits in RAM.
- Vector Shifting: Fine-tune LLM → internal math shifts → entire DB breaks. Standalone embed model gives permanent mathematical standard.
Could you build soft switch? Some research models (e.g., LLM2Vec, NV-Embed) do exactly that — convert LLM into embedder by adding pooling. But production still uses tiny dedicated embedder for efficiency.
5. Who matches question vector to stored vectors?
Vector Database takes 100% responsibility — just like MySQL does WHERE/JOIN internally.
You: embedding_model.encode("What is refund?") → query_vector → pass to VDB
VDB: runs cosine similarity / Euclidean / dot-product across millions, returns top 3 IDs + raw text.
6. What does “LLM uses embeddings internally” mean?
Every LLM has embedding layer as its first layer:
Standalone embedding model = essentially front half of LLM (tokenizer + embedding + attention) but ripped off generation mouth and tuned to output single pooled vector.
7. Why called “Embedding”?
Math term: to embed = take object from one space and place into another space preserving structure.
It takes human language (discrete words) and embeds into continuous multi-dimensional geometric space where similar meanings are physically close. Hence embedding model = projects thoughts into coordinates.
8. What gets sent to LLM during retrieval?
NOT vectors/weights. Entire raw text chunk (English words).
- Google Search: query → index → links + snippets → you read
- RAG: query → VDB → exact paragraphs → system secretly bundles: “Background: [raw chunk] + Question: ... Answer based strictly on background” → LLM writes conversational answer
Human text is universal bridge, so embed math and LLM math can be totally different languages (1536 vs 4096 dims) — never need to match.
Turn 11: Pulling internal representations, LLM heavy vs embed, VDB manual use, where chunks stored, token matching
1. Pulling internal representations — How?
# Hugging Face — steal mid-thought math
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("meta-llama/Llama-3-8B")
outputs = model(input_ids, output_hidden_states=True)
# What you get: tuple of layers, each shape [batch, tokens, hidden_dim]
# e.g., layer 32: [1, 500, 4096] = 500 token vectors of 4096 floats each
internal_vectors = outputs.hidden_states[-1]What you get is sprawling 3D tensor — vector for every token at that layer. Highly complex, needs manual pooling to become single search vector.
Why allow it? Advanced uses: custom classifier heads, AI explainability (track at which layer model connects symptom → disease), feature extraction for recommendation engines.
2. Why LLM much heavier if same knowledge?
They don’t have same knowledge amount.
- Dictionary vs Professor: Embedding = multilingual dictionary (needs enough to know meanings). LLM = living professor who also needs billions extra connections to reason, calculate, argue, write essays, debug code, remember timelines sequentially.
- Layer depth & width: Embed: 12-24 layers, hidden 384-1024 wide. LLM: 32-100+ layers, hidden 4096-8192 wide + extra feed-forward 3x bigger. Each layer massively wider.
3. Is VDB useless for manual human querying?
Yes, fundamentally useless without embedding model.
MySQL: SELECT * WHERE name="John" direct string match.
VDB: Cannot WHERE text="sad feelings" — DB only knows numbers. Manual query would need SEARCH VECTOR [0.012, -0.943, 0.231, ... 1536 numbers]. Humans can’t type that. So VDB absolutely requires embed model as interpreter.
4. Where does VDB store full chunks?
On Hard Disk (SSD) for persistence + RAM cache for speed. Stores actual raw text alongside vector as metadata, like MySQL TEXT/BLOB column. Layout conceptually:
| ID | Vector (RAM index) | Metadata / Raw Chunk (Disk + RAM) |
|---|---|---|
| chunk_001 | [0.14, -0.82, 0.05...] | “Our corporate refund policy allows 30 days...” |
| chunk_002 | [0.91, 0.02, -0.41...] | “To write a loop in Python, use while...” |
Not just links — holds actual string. When winning vector found, grabs attached raw text and returns.
5. Must embedding and generation head match vocab numbers?
No, completely independent.
Embed model may map “Apple” → token ID 432 → 384 dims.
LLM may map “Apple” → token ID 12054 → 4096 dims.
They never talk mathematically. Only handoff is human English text chunk. So internal vocabularies can be different languages.
Turn 12: What to do with hidden states? Use VDB without LLM?
Why allow pulling hidden states?
- Custom classifier: Cut off LLM mouth, attach tiny head to internal matrix → use Llama brain to classify risk high/medium/low for finance docs.
- Explainability: See at which layer model connects “fever + rash” → “measles”.
- Deep feature extraction: Use raw token matrices as features for other ML systems (recommendations).
Do people use only embedding + VDB without LLM?
Yes, very common — called Semantic Search / Vector Search. Companies skip LLM because they want hyper-smart Google for private data, not AI writer.
Example — Legal Firm:
- Lawyer types: “cases where contract broken due to unexpected bad weather”
- Embed model → vector
- VDB finds top 5 paragraphs even if they said “Force Majeure” or “blizzard”, not “bad weather”
- System shows exact raw paragraphs + links — no LLM summary
Why skip LLM?
- Zero hallucinations — exact source text
- Cost 100x lower — vector search ms, pennies
- Speed — no generation loop
- Uses: product recommendations, image retrieval, support ticket deduplication, legal discovery
Turn 13: Clarification — what..?
This turn was clarification after previous deep dive. Recap:
Pulling hidden states purpose: Researchers need to see inside model mid-thought for diagnostics, building classifiers, or using massive brain as feature extractor.
Pure vector search without LLM is production standard: Many apps never use LLM — they just want semantic matching. Example flow without LLM:
This is how Spotify finds similar songs, e-commerce finds similar products by description, and internal enterprise search works without any chat component.
Turn 14: Is VDB free locally? Embed model free? Must they match?
1. Is Vector Database free locally?
Yes, completely free, open-source, offline forever.
- ChromaDB: Simplest — pip install, runs inside Python, stores in local folder
- FAISS (Meta): Hyper-fast lib for local vector math
- Qdrant / Milvus / Weaviate / LanceDB: Production-grade, run via Docker locally
- pgvector: Free Postgres extension turns Postgres into vector DB
2. Is embedding model free locally?
Yes, if you choose open-source. Via Hugging Face / Ollama / Sentence-Transformers:
- bge-small-en-v1.5 (384 dims, 133MB), all-MiniLM-L6-v2 (384 dims, 80MB), bge-base (768 dims), e5-large — all free, no API key, runs offline CPU ~5-20ms per chunk
- Cloud models (OpenAI text-embedding-3-small) charge ~$0.00002 per 1k tokens, but require internet
3. Must VDB and embedding model match?
No. VDB is blind vault. Can store any vectors from any model.
Golden Rule: Must use exact same embedding model for storing AND retrieving within one project.
- Store with Model A (384 nums) + Search with Model B (1536 nums) → crash dimension mismatch
- Even same dimension but different model → coordinates mean different things → search returns garbage (English map with Japanese coordinates)
As long as you stick to one model for project, you can plug into any VDB.
# 100% free local example — 10 lines
from sentence_transformers import SentenceTransformer
import chromadb
model = SentenceTransformer('all-MiniLM-L6-v2') # free, local
client = chromadb.PersistentClient(path="./my_vdb")
col = client.get_or_create_collection("docs")
chunks = ["refund is 30 days", "Python loop uses while"]
vectors = model.encode(chunks)
col.add(ids=["1","2"], embeddings=vectors.tolist(), documents=chunks)
# query
q_vec = model.encode("how long to return?").tolist()
results = col.query(query_embeddings=[q_vec], n_results=1)
print(results['documents']) # -> refund is 30 daysTurn 15: Cloud models data security issue?
Yes, exactly — biggest enterprise blocker. When you send raw chunks to cloud API for embedding, data leaves your machine → internet → third-party server.
1. Data Breaches & Exposure
PII (names, credit cards, medical records), proprietary code, contracts — if provider hacked or misconfigured, your data exposed.
2. Model Training Leakage
Some providers used API data to train next models (unless explicitly opted-out). Secret blueprint could be memorized and leaked to another user later. OpenAI says paid API data not used for training, but many legal teams still consider risk too high.
3. Compliance Violations
GDPR (EU), HIPAA (healthcare), SOC2, banking regs often ban sending sensitive customer data to external servers without complex DPA agreements. Local solves instantly.
100% Secure Solution: Local Offline
- Data never leaves machine — no internet touch
- Pull internet plug → search still works
- Complete ownership — your logs, your permissions
- Zero network dependency, zero vendor lock-in
- Costs fixed — no per-token billing surprise
Recommendation for sensitive files: Use Hugging Face model + ChromaDB/FAISS local, both in Python, 100% offline. If you must use cloud, use VPC/private endpoint + zero-retention agreement + encrypt chunks before sending + PII scrubbing.
Comments
Post a Comment