intermediate⏱45 minLesson 2 of 5

Vector Stores, Embeddings and RAG Architecture

Learn how embeddings turn text into vectors, how vector databases store them, and how RAG pipelines retrieve relevant context for LLMs.

Vector Stores, Embeddings and RAG Architecture

Retrieval-Augmented Generation (RAG) is the dominant pattern for grounding LLM responses in external knowledge. At its core are embeddings — numerical representations of meaning — and vector databases that index and search them.

What Are Embeddings?

An embedding is a dense vector (list of floats) that captures the semantic meaning of a piece of text. Texts with similar meaning cluster together in vector space.

"The cat sat on the mat"
        |
        v
[0.023, -0.145, 0.312, ..., 0.078]   ← 384-dimensional vector
        |
        v
"A dog slept on the rug"
        |
        v
[0.019, -0.138, 0.305, ..., 0.081]   ← close in vector space

Embedding models (e.g., text-embedding-3-small, all-MiniLM-L6-v2) map text to a fixed-size vector regardless of input length.

ℹ️Note

Embedding models have a maximum input token limit (typically 512 tokens for open-source models, 8192 for OpenAI's text-embedding-3-large). Documents longer than this limit must be split into chunks before embedding — this is why chunking is a critical part of any RAG pipeline.

Common Embedding Models

Model	Dimensions	Max Tokens	Cost	Best For
`text-embedding-3-small`	512-1536	8192	$0.02/1K tokens	General purpose, cost-sensitive
`text-embedding-3-large`	256-3072	8192	$0.13/1K tokens	High-accuracy needs
`all-MiniLM-L6-v2`	384	512	Free (local)	Local/offline, prototyping
`intfloat/e5-large-v2`	1024	512	Free (local)	High-quality open-source
`BAAI/bge-large-en-v1.5`	1024	512	Free (local)	English retrieval tasks

Cosine Similarity

The most common way to compare two embeddings is cosine similarity:

cosine_similarity(A, B) = (A · B) / (||A|| * ||B||)

Ranges from -1 (opposite meaning) to 1 (identical meaning)
Values above 0.8 typically indicate strong semantic similarity
Used by vector databases to rank search results

⚠️Warning

Cosine similarity assumes all dimensions are equally weighted. If your embedding model is biased or poorly trained, similarity scores may not reflect true semantic relevance. Always evaluate retrieval quality on your specific domain.

Distance Metrics Comparison

Metric	Range	Use Case	Speed
Cosine similarity	[-1, 1]	Semantic text search	Fast
Euclidean (L2)	[0, ∞)	Image embeddings, clustering	Fast
Dot product	(-∞, ∞)	Normalized vectors, efficiency	Very fast
Manhattan (L1)	[0, ∞)	Sparse vectors	Moderate
Hamming	[0, dim]	Binary embeddings	Very fast

Embedding Dimensionality and Performance

The dimensionality of your embeddings has a direct impact on both search accuracy and storage cost. Higher dimensions capture more nuanced meaning but require more storage and slower search.

python

# OpenAI lets you reduce embedding dimensions via `dimensions` parameter
import openai

# text-embedding-3-large supports dimensions from 256 to 3072
# Smaller dimensions = faster search, less storage, potentially less accuracy
embeddings_256 = openai.embeddings.create(
    input="Your text here",
    model="text-embedding-3-large",
    dimensions=256,  # reduce from default 3072 to 256
)
embeddings_3072 = openai.embeddings.create(
    input="Your text here",
    model="text-embedding-3-large",  # default: 3072 dimensions
)

print(f"Reduced: {len(embeddings_256.data[0].embedding)} dims")
print(f"Full: {len(embeddings_3072.data[0].embedding)} dims")
# Output: Reduced: 256 dims
#         Full: 3072 dims

Dimensions	Storage (1M vectors)	Search Latency	Accuracy Retention	Best For
256	~1 GB	Very fast	~95%	Cost-sensitive, high-throughput
512	~2 GB	Fast	~98%	General purpose, balanced
1024	~4 GB	Moderate	~99%	High accuracy requirements
1536	~6 GB	Slow	~100%	Maximum accuracy (default for 3-small)
3072	~12 GB	Slowest	~100%	Maximum accuracy (default for 3-large)

💡Tip

Always test with reduced dimensions before committing to the full vector size. Many applications retain >95% accuracy at 256-512 dimensions, dramatically reducing storage and speeding up search. OpenAI's text-embedding-3 models support native dimensionality reduction via the dimensions parameter — no post-processing needed.

Embedding Generation Pipeline

The embedding process transforms raw text into searchable vectors:

💡 Tip: Use Ctrl + Mouse Wheel to zoom, or click the fullscreen icon

Document Chunking

Raw documents must be split into chunks before embedding. Chunking strategy directly impacts retrieval quality.

python

from langchain.text_splitter import RecursiveCharacterTextSplitter

# Load a document
with open("report.md", "r") as f:
    text = f.read()

# Create a recursive text splitter
splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,        # target characters per chunk
    chunk_overlap=50,      # overlap between chunks
    separators=["\n\n", "\n", ".", " "],  # priority order
    length_function=len,
)

chunks = splitter.split_text(text)
print(f"Split into {len(chunks)} chunks")
# Output: Split into 23 chunks

Overlap ensures that sentences or ideas split across chunk boundaries are not lost.

Chunking Strategies Comparison

Strategy	Split Unit	Preserves Structure	Overlap Support	Best For
RecursiveCharacter	Characters (by separator)	Moderate	Yes	General text, most use cases
Token	Tokens (model-aware)	Low	Yes	LLM-context-aligned chunks
MarkdownHeader	Markdown headers	High	No	Documentation, wikis
RecursiveJson	JSON keys	High	No	Structured JSON data
HTMLHeader	HTML tags (h1, h2, etc.)	High	No	Web pages, HTML docs
Semantic	Sentence embeddings	High	No	Coherent passage preservation

python

from langchain.text_splitter import (
    TokenTextSplitter,
    MarkdownHeaderTextSplitter,
)

# Token-aware splitting (matches LLM tokenizers)
token_splitter = TokenTextSplitter(
    chunk_size=256,      # tokens, not characters
    chunk_overlap=50,
)

# Markdown-aware splitting (preserves header structure)
headers_to_split_on = [
    ("#", "Header 1"),
    ("##", "Header 2"),
]
markdown_splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=headers_to_split_on,
)

# Semantic chunking using sentence boundaries
from langchain.text_splitter import SentenceTransformersTokenTextSplitter

semantic_splitter = SentenceTransformersTokenTextSplitter(
    chunk_size=256,
    chunk_overlap=0,
    model_name="sentence-transformers/all-mpnet-base-v2",
)

Approximate Nearest Neighbor (ANN) Algorithms

Vector databases do not perform exact nearest-neighbor search at scale — that would be too slow for millions of vectors. Instead, they use Approximate Nearest Neighbor algorithms that trade a small amount of accuracy for massive speed gains.

Algorithm	Speed	Accuracy	Memory Use	Supported By
HNSW (Hierarchical Navigable Small World)	Very fast	High	High	Chroma, Qdrant, Weaviate
IVF (Inverted File Index)	Fast	Moderate	Moderate	Pinecone, Faiss
IVFPQ (IVF + Product Quantization)	Fast	Moderate	Low	Faiss, Qdrant
LSH (Locality-Sensitive Hashing)	Fast	Low	Low	Annoy, Spark
Flat (Brute force)	Slow	Exact	High	Small datasets (<10K)

python

# Most vector DBs let you configure the ANN algorithm
import chromadb
from chromadb.config import Settings

# Chroma uses HNSW by default but allows configuration
client = chromadb.Client(Settings(
    chroma_db_impl="duckdb+parquet",
    persist_directory="./chroma_data",
))

# HNSW parameters that affect search performance:
#   - M: number of connections per node (higher = more accurate, more memory)
#   - ef_construction: build time/quality trade-off
#   - ef_search: search time/quality trade-off
collection = client.create_collection(
    name="tuned_collection",
    metadata={
        "hnsw:space": "cosine",
        "hnsw:M": 16,           # default: 16
        "hnsw:ef_construction": 200,  # default: 200
        "hnsw:ef_search": 100,        # default: 100
    },
)

ℹ️Note

For datasets under 10,000 vectors, brute-force exact search is often fast enough and eliminates the accuracy trade-off. For datasets in the millions, HNSW is the current gold standard — it offers the best speed/accuracy balance. IVF with product quantization (IVFPQ) is preferred when memory is constrained.

Vector Databases

Vector databases specialize in storing embedding vectors and performing fast nearest-neighbor search.

Feature	Chroma	Pinecone	Qdrant	Weaviate
Deployment	Local / embedded	Cloud / serverless	Self-hosted / cloud	Self-hosted / cloud
Open Source	Yes	No	Yes	Yes (since 2024)
Filtering	Metadata filters	Metadata + namespace	Payload filters	GraphQL + metadata
Distance metrics	Cosine, L2, IP	Cosine, L2, IP	Cosine, L2, IP, Dot	Cosine, L2, IP, Dot
Free tier	Unlimited local	1 index, limited	1GB free cluster	1GB free (sandbox)
LangChain support	Native	Native	Native	Native
Scalability	Single-node	Auto-scaling	Horizontal sharding	Horizontal sharding

💡Tip

For prototyping and local development, Chroma is the best choice — it runs embedded with zero infrastructure. For production at scale, Pinecone or Qdrant offer managed auto-scaling. Qdrant is preferred if you need self-hosting.

Indexing Pipeline

A production indexing pipeline transforms raw documents into a searchable vector index:

Raw Documents (PDF, HTML, MD)
        |
        v
[ Text Extraction ]
        |
        v
[ Chunking (splitter) ]
        |
        v
[ Embedding (model) ]
        |
        v
[ Vector DB Insert ]
        |
        v
[ Metadata Index ]

python

import chromadb
from sentence_transformers import SentenceTransformer

# Initialize embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")

# Initialize Chroma client
client = chromadb.Client()
collection = client.create_collection("my_docs")

# Generate embeddings for chunks
embeddings = model.encode(chunks).tolist()

# Add to vector store with metadata
collection.add(
    documents=chunks,
    embeddings=embeddings,
    metadatas=[{"source": "report.md", "chunk_id": i}
               for i in range(len(chunks))],
    ids=[f"chunk_{i}" for i in range(len(chunks))],
)

📌Important

Always store metadata alongside embeddings. Metadata enables filtered search (e.g., "only return results from 2025"), document-level tracing, and incremental updates. Without metadata, you cannot selectively delete or update documents.

Retrieval-Augmented Generation Flow

RAG connects retrieval to generation in a single pipeline.

💡 Tip: Use Ctrl + Mouse Wheel to zoom, or click the fullscreen icon

python

from openai import OpenAI
import chromadb

client = OpenAI()
chroma_client = chromadb.Client()
collection = chroma_client.get_collection("my_docs")

def rag_answer(query: str, k: int = 3) -> str:
    # 1. Embed the query (using OpenAI embeddings)
    query_emb = client.embeddings.create(
        input=query,
        model="text-embedding-3-small"
    ).data[0].embedding

    # 2. Retrieve top-k chunks
    results = collection.query(
        query_embeddings=[query_emb],
        n_results=k,
    )
    context = "\n\n".join(results["documents"][0])

    # 3. Generate with context
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system",
             "content": "Answer using only the provided context."},
            {"role": "user",
             "content": f"Context:\n{context}\n\nQuestion: {query}"}
        ],
    )
    return response.choices[0].message.content

print(rag_answer("What is the return policy?"))

⚠️Warning

A common RAG failure mode is context stuffing — putting too many retrieved chunks into the prompt, which dilutes the signal-to-noise ratio. Retrieve only the top-k most relevant chunks (k=3 to 5 is typical), and consider adding a relevance score threshold to filter out low-quality matches.

Retrieval with Metadata Filters

Real-world RAG systems need to combine semantic search with structured filtering:

python

def filtered_rag_answer(
    query: str,
    department: str | None = None,
    max_year: int | None = None,
    k: int = 3,
) -> str:
    # Build metadata filter
    where_filter = {}
    if department:
        where_filter["department"] = department
    if max_year:
        where_filter["year"] = {"$lte": max_year}

    # Embed query
    query_emb = client.embeddings.create(
        input=query,
        model="text-embedding-3-small"
    ).data[0].embedding

    # Retrieve with filter
    results = collection.query(
        query_embeddings=[query_emb],
        n_results=k,
        where=where_filter,
    )

    context = "\n\n".join(results["documents"][0])

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "system",
             "content": "Answer using only the provided context."},
            {"role": "user",
             "content": f"Context:\n{context}\n\nQuestion: {query}"}
        ],
    )
    return response.choices[0].message.content

# Example: query only engineering documents from 2025
# answer = filtered_rag_answer("API rate limits",
#                              department="engineering",
#                              max_year=2025)

💡Tip

Metadata filters dramatically improve retrieval precision by narrowing the search space. Common filter fields include: source document, date range, document type, department, author, and language. Design your metadata schema before building the ingestion pipeline.

Hybrid Search: Vector + Keyword

Pure vector search can miss exact matches. Hybrid search combines vector similarity with keyword (BM25) ranking:

python

def hybrid_search(query: str, k: int = 5, alpha: float = 0.5) -> list[str]:
    """
    Hybrid search combining vector and keyword scores.
    alpha=1.0: pure vector search
    alpha=0.0: pure keyword search
    """
    # Vector search scores
    query_emb = client.embeddings.create(
        input=query, model="text-embedding-3-small"
    ).data[0].embedding
    vector_results = collection.query(
        query_embeddings=[query_emb], n_results=k * 2
    )

    # Keyword search (simplified BM25-like scoring)
    query_terms = set(query.lower().split())
    keyword_scores = {}
    for i, doc in enumerate(chunks):
        doc_terms = set(doc.lower().split())
        overlap = query_terms & doc_terms
        if overlap:
            keyword_scores[i] = len(overlap) / (len(doc_terms) ** 0.5)

    # Normalize and combine scores
    # (In production, use a proper hybrid retriever like
    #  LangChain's EnsembleRetriever)
    combined_scores = {}
    # ... normalization and alpha-weighted combination ...

    return sorted(combined_scores, key=combined_scores.get, reverse=True)[:k]

5 Practice Questions

Practice Question

What is an embedding?

Practice Question

Which similarity metric is most common in vector search?

Practice Question

Why is document chunking necessary in RAG?

Practice Question

Which vector database is open-source and runs locally?

Practice Question

In the RAG flow, what happens before the LLM generates a response?

Practice Question

A support agent keeps retrieving irrelevant HR documents when users ask technical questions. Which fix would most likely help?

✅Success

Key Takeaways

Embeddings map text to dense vectors that encode semantic meaning.
Cosine similarity measures the angle between vectors; values near 1 mean high similarity.
Vector databases (Chroma, Pinecone, Qdrant) specialize in fast nearest-neighbor search over embeddings.
Document chunking with overlap is essential for high-quality retrieval.
A RAG pipeline embeds a query, searches a vector store, retrieves top-k chunks, and feeds them as context to an LLM.
Metadata filtering and chunk overlap improve retrieval precision.
Hybrid search combines vector and keyword methods for better recall.
The RAG architecture is model-agnostic and works with any embedding + LLM combination.

Progress40%

Foundations of Agent Memory

Memory Stores, Summaries and Entity Extraction

Course Content

1. Foundations of Agent Memory 2. Vector Stores, Embeddings and RAG Architecture 3. Memory Stores, Summaries and Entity Extraction 4. Knowledge Base Integration and Document Processing 5. Advanced Retrieval Strategies