Use Case Guide

Build Production RAG Applications

Implement retrieval-augmented generation with Vector Panda's distributed architecture. Scale from prototype to billions of documents with zero configuration changes.

RAG Architecture with Vector Panda

Simple flow, powerful results

📄

Document Processing

Chunk and embed your documents

→
🐼

Vector Storage

Store in Vector Panda

→
🔍

Semantic Search

Query relevant context

→
🤖

LLM Generation

Generate accurate responses

Step-by-Step Implementation

From zero to production RAG in minutes

1

Initialize Vector Panda

Connect to Vector Panda with your API key. No configuration needed - our PCA indexing handles everything automatically.

Python
from veep import Client
from openai import OpenAI
# Initialize clients
panda = Client("your-vector-panda-key")
openai = OpenAI()
# Create or connect to collection
collection = panda.collection("knowledge-base")
2

Process and Store Documents

Chunk your documents, generate embeddings, and store them with metadata. Vector Panda handles billions of vectors without breaking a sweat.

Python
def process_document(text, doc_id, metadata):
# Chunk document (simple example)
chunks = [text[i:i+1000] for i in range(0, len(text), 800)]
# Generate embeddings and store
for idx, chunk in enumerate(chunks):
embedding = openai.embeddings.create(
input=chunk,
model="text-embedding-3-small"
).data[0].embedding
collection.upsert(
id=f"{doc_id}_chunk_{idx}",
vector=embedding,
metadata={
"text": chunk,
"doc_id": doc_id,
"chunk_index": idx,
**metadata
}
)
3

Implement RAG Query Pipeline

Query relevant context from Vector Panda and use it to generate accurate, grounded responses. Our 100% recall ensures you never miss important information.

Python
def rag_query(question, k=5):
# Generate query embedding
query_embedding = openai.embeddings.create(
input=question,
model="text-embedding-3-small"
).data[0].embedding
# Search Vector Panda (100% recall guaranteed)
results = collection.search(
vector=query_embedding,
k=k,
include_metadata=True
)
# Build context from results
context = "\n\n".join([
f"[{r.metadata['doc_id']}]: {r.metadata['text']}"
for r in results
])
# Generate response with context
response = openai.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "Answer based on the context."},
{"role": "user", "content": f"""
Context:
{context}
Question: {question}
"""}
]
)
return response.choices[0].message.content
4

Scale with Confidence

As your knowledge base grows, Vector Panda scales automatically. Switch between hot, warm, and cold storage tiers based on access patterns.

Python
# Monitor collection stats
stats = collection.stats()
print(f"Vectors: {stats.vector_count:,}")
print(f"Storage tier: {stats.tier}")
print(f"Query latency: {stats.avg_latency_ms}ms")
# Optimize storage tier for your needs
if stats.vector_count > 10_000_000:
# Move older documents to warm tier
collection.optimize_storage(
strategy="access_pattern",
hot_retention_days=7
)

RAG Performance Metrics

Real results from production deployments

100%
Recall Rate
Never miss relevant context with our PCA indexing
12ms
Avg Query Time
Fast retrieval even with billions of vectors
45%
Cost Reduction
vs traditional vector databases with usage pricing
10B+
Vectors Handled
Scale without limits or configuration changes

Best Practices

Optimize your RAG implementation

📊

Chunk Strategically

Use overlapping chunks of 500-1000 tokens for optimal context retrieval. Include document structure in metadata for better ranking.

🏷️

Rich Metadata

Store source, timestamp, section headers, and document type. Use metadata filters to improve relevance and reduce noise.

🔄

Hybrid Search

Combine semantic search with keyword filters for precision. Use Vector Panda's metadata queries for exact matches.

📈

Dynamic k Selection

Adjust the number of retrieved chunks based on query complexity. Start with k=5 and increase for open-ended questions.

🚀

Async Processing

Use batch operations for document ingestion. Vector Panda handles 10k+ vectors per second in production.

💾

Smart Tiering

Keep recent docs in hot storage, move historical data to warm. Save 80%+ on storage costs without sacrificing performance.

Ready to Build Your RAG System?

Start with our Python SDK and scale to billions of documents. No configuration, no complexity, just results.

Get Started Free →