Best Practices - Perplexity

Overview

This guide covers best practices for getting the most out of Perplexity’s Embeddings API, including dimension reduction, batch processing, RAG patterns, and error handling.

Matryoshka Dimension Reduction

Perplexity embeddings support Matryoshka representation learning, allowing you to reduce embedding dimensions while maintaining quality. This enables faster similarity search and reduced storage costs.

from perplexity import Perplexity

client = Perplexity()

# Full dimensions (2560 for 4b model)
full_response = client.embeddings.create(
    input=["Your text here"],
    model="pplx-embed-v1-4b"
)
print(f"Full: {full_response.data[0].embedding}")  # 2560-dim base64 string

# Reduced dimensions - faster search, smaller storage
reduced_response = client.embeddings.create(
    input=["Your text here"],
    model="pplx-embed-v1-4b",
    dimensions=512
)
print(f"Reduced: {reduced_response.data[0].embedding}")  # 512-dim base64 string

import Perplexity from '@perplexity-ai/perplexity_ai';

const client = new Perplexity();

// Full dimensions (2560 for 4b model)
const fullResponse = await client.embeddings.create({
    input: ["Your text here"],
    model: "pplx-embed-v1-4b"
});
console.log(`Full: ${fullResponse.data![0].embedding}`);

// Reduced dimensions - faster search, smaller storage
const reducedResponse = await client.embeddings.create({
    input: ["Your text here"],
    model: "pplx-embed-v1-4b",
    dimensions: 512
});
console.log(`Reduced: ${reducedResponse.data![0].embedding}`);

Trade-off: Lower dimensions = faster search + less storage, but slightly lower quality. Start with full dimensions and reduce if needed.

Encoding Formats

Control precision and size of embedding outputs:

Format	Description	Decoded Size	Similarity Metric	Use Case
`base64_int8`	Base64-encoded signed int8 (-128 to 127)	dimensions bytes	Cosine similarity	Default, good balance of quality and size
`base64_binary`	Base64-encoded packed bits (1 bit per dimension, LSB first)	dimensions / 8 bytes	Hamming distance	Maximum compression for large-scale retrieval

import base64
import numpy as np

# Decode base64_int8 (default)
response = client.embeddings.create(
    input=["Your text"],
    model="pplx-embed-v1-4b"
)
int8_embedding = np.frombuffer(
    base64.b64decode(response.data[0].embedding), dtype=np.int8
)

# Binary embeddings for large-scale retrieval systems
response = client.embeddings.create(
    input=["Your text"],
    model="pplx-embed-v1-4b",
    encoding_format="base64_binary"
)
binary_bytes = np.frombuffer(
    base64.b64decode(response.data[0].embedding), dtype=np.uint8
)
# Unpack bits: each byte contains 8 dimensions (LSB first)
binary_embedding = np.unpackbits(binary_bytes, bitorder="little")

// Decode base64_int8 (default)
const response = await client.embeddings.create({
    input: ["Your text"],
    model: "pplx-embed-v1-4b"
});
const buffer = Buffer.from(response.data![0].embedding, 'base64');
const int8Embedding = new Int8Array(buffer.buffer, buffer.byteOffset, buffer.byteLength);

// Binary embeddings for large-scale retrieval systems
const binaryResponse = await client.embeddings.create({
    input: ["Your text"],
    model: "pplx-embed-v1-4b",
    encoding_format: "base64_binary"
});
const binaryBuffer = Buffer.from(binaryResponse.data![0].embedding, 'base64');
// Each byte contains 8 dimensions as packed bits (LSB first)

base64_int8 produces the same quality as bfloat16 with significantly reduced storage. Use base64_binary for extreme compression in large-scale systems.

Similarity Metrics

Perplexity embedding models produce unnormalized embeddings. Choosing the correct similarity metric is critical for accurate retrieval.

pplx-embed-v1 and pplx-embed-context-v1 natively produce unnormalized int8-quantized embeddings. You must compare them via cosine similarity. Using inner product or L2 distance directly will produce incorrect results because most embedding models are pre-normalized, but Perplexity embeddings are not.

int8 Embeddings (`base64_int8`)

Compare using cosine similarity. If your vector database does not support cosine similarity natively, convert the embeddings to float32 and L2-normalize them before storing:

import base64
import numpy as np

def decode_and_normalize(b64_string):
    """Decode and L2-normalize for vector DBs that only support inner product."""
    embedding = np.frombuffer(base64.b64decode(b64_string), dtype=np.int8).astype(np.float32)
    norm = np.linalg.norm(embedding)
    if norm > 0:
        embedding = embedding / norm
    return embedding

# After normalization, cosine similarity == inner product

function decodeAndNormalize(b64String: string): Float32Array {
    const buffer = Buffer.from(b64String, 'base64');
    const int8 = new Int8Array(buffer.buffer, buffer.byteOffset, buffer.byteLength);
    const float32 = new Float32Array(int8.length);

    // Convert to float32
    let norm = 0;
    for (let i = 0; i < int8.length; i++) {
        float32[i] = int8[i];
        norm += float32[i] * float32[i];
    }

    // L2-normalize so inner product == cosine similarity
    norm = Math.sqrt(norm);
    if (norm > 0) {
        for (let i = 0; i < float32.length; i++) {
            float32[i] /= norm;
        }
    }
    return float32;
}

Binary Embeddings (`base64_binary`)

Compare using Hamming distance. Binary embeddings encode each dimension as a single bit, so the natural distance metric is the number of differing bits between two vectors.

import numpy as np

def hamming_distance(a: np.ndarray, b: np.ndarray) -> int:
    """Hamming distance between two binary vectors (as uint8 packed bits)."""
    return np.unpackbits(np.bitwise_xor(a, b)).sum()

Most vector databases (Pinecone, Weaviate, Qdrant, Milvus) support cosine similarity as a distance metric. Verify your database’s configuration before indexing embeddings.

RAG Pattern

Combine embeddings with Perplexity’s Agentic Research API for retrieval-augmented generation:

import base64
import numpy as np
from perplexity import Perplexity

client = Perplexity()

def decode_embedding(b64_string):
    return np.frombuffer(base64.b64decode(b64_string), dtype=np.int8).astype(np.float32)

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# 1. Your knowledge base (embed once, store in vector DB)
knowledge_base = [
    "Perplexity API provides web-grounded AI responses",
    "The Embeddings API supports Matryoshka dimension reduction",
    "Contextualized embeddings share context across document chunks"
]

kb_response = client.embeddings.create(input=knowledge_base, model="pplx-embed-v1-4b")
kb_embeddings = [decode_embedding(emb.embedding) for emb in kb_response.data]

# 2. User query
user_query = "How do I reduce embedding dimensions?"

# 3. Find relevant context
query_response = client.embeddings.create(input=[user_query], model="pplx-embed-v1-4b")
query_embedding = decode_embedding(query_response.data[0].embedding)

scores = [(i, cosine_similarity(query_embedding, emb)) for i, emb in enumerate(kb_embeddings)]
top_docs = sorted(scores, key=lambda x: x[1], reverse=True)[:2]
context = "\n".join([knowledge_base[i] for i, _ in top_docs])

# 4. Generate answer with context
response = client.responses.create(
    model="openai/gpt-5.5",
    input=f"Answer using this context:\n\n{context}\n\nQuestion: {user_query}"
)

print(response.output[0].content[0].text)

import Perplexity from '@perplexity-ai/perplexity_ai';

const client = new Perplexity();

function decodeEmbedding(b64String: string): Int8Array {
    const buffer = Buffer.from(b64String, 'base64');
    return new Int8Array(buffer.buffer, buffer.byteOffset, buffer.byteLength);
}

function cosineSimilarity(a: Int8Array, b: Int8Array): number {
    let dotProduct = 0, normA = 0, normB = 0;
    for (let i = 0; i < a.length; i++) {
        dotProduct += a[i] * b[i];
        normA += a[i] * a[i];
        normB += b[i] * b[i];
    }
    return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}

// 1. Your knowledge base
const knowledgeBase = [
    "Perplexity API provides web-grounded AI responses",
    "The Embeddings API supports Matryoshka dimension reduction",
    "Contextualized embeddings share context across document chunks"
];

const kbResponse = await client.embeddings.create({
    input: knowledgeBase,
    model: "pplx-embed-v1-4b"
});
const kbEmbeddings = kbResponse.data!.map(emb => decodeEmbedding(emb.embedding!));

// 2. User query
const userQuery = "How do I reduce embedding dimensions?";

// 3. Find relevant context
const queryResponse = await client.embeddings.create({
    input: [userQuery],
    model: "pplx-embed-v1-4b"
});
const queryEmbedding = decodeEmbedding(queryResponse.data![0].embedding!);

const scores = kbEmbeddings.map((emb, i) => ({
    index: i,
    score: cosineSimilarity(queryEmbedding, emb)
}));
const topDocs = scores.sort((a, b) => b.score - a.score).slice(0, 2);
const context = topDocs.map(d => knowledgeBase[d.index]).join("\n");

// 4. Generate answer with context
const response = await client.responses.create({
    model: "openai/gpt-5.5",
    input: `Answer using this context:\n\n${context}\n\nQuestion: ${userQuery}`
});

console.log((response.output[0] as any).content[0].text);

Batch Processing

Process large datasets efficiently with async batching:

import asyncio
from perplexity import AsyncPerplexity

async def batch_embed(texts: list[str], batch_size: int = 100):
    async with AsyncPerplexity() as client:
        results = []
        for i in range(0, len(texts), batch_size):
            batch = texts[i:i + batch_size]
            response = await client.embeddings.create(
                input=batch,
                model="pplx-embed-v1-4b"
            )
            results.extend(response.data)
            print(f"Processed {min(i + batch_size, len(texts))}/{len(texts)}")
        return results

# Usage
texts = ["Document " + str(i) for i in range(1000)]
embeddings = asyncio.run(batch_embed(texts))
print(f"Generated {len(embeddings)} embeddings")

import Perplexity from '@perplexity-ai/perplexity_ai';

async function batchEmbed(texts: string[], batchSize: number = 100) {
    const client = new Perplexity();
    const results: any[] = [];

    for (let i = 0; i < texts.length; i += batchSize) {
        const batch = texts.slice(i, i + batchSize);
        const response = await client.embeddings.create({
            input: batch,
            model: "pplx-embed-v1-4b"
        });
        results.push(...(response.data ?? []));
        console.log(`Processed ${Math.min(i + batchSize, texts.length)}/${texts.length}`);
    }

    return results;
}

// Usage
const texts = Array.from({ length: 1000 }, (_, i) => `Document ${i}`);
const embeddings = await batchEmbed(texts);
console.log(`Generated ${embeddings.length} embeddings`);

Error Handling

import perplexity
from perplexity import Perplexity

client = Perplexity()

try:
    response = client.embeddings.create(
        input=["Your text"],
        model="pplx-embed-v1-4b"
    )
except perplexity.BadRequestError as e:
    print(f"Invalid request: {e}")
except perplexity.RateLimitError:
    print("Rate limited, please retry later")
except perplexity.APIStatusError as e:
    print(f"API error: {e.status_code}")

import Perplexity from '@perplexity-ai/perplexity_ai';

const client = new Perplexity();

try {
    const response = await client.embeddings.create({
        input: ["Your text"],
        model: "pplx-embed-v1-4b"
    });
} catch (error) {
    if (error instanceof Perplexity.BadRequestError) {
        console.error("Invalid request:", error.message);
    } else if (error instanceof Perplexity.RateLimitError) {
        console.error("Rate limited, please retry later");
    } else if (error instanceof Perplexity.APIError) {
        console.error(`API error: ${error.status}`);
    }
}

Tips

Batch requests

Send up to 512 texts per request to maximize throughput and reduce API calls.

Match models

Always use the same embedding model for both queries and documents to ensure consistent similarity scores.

Use cosine similarity

Perplexity embeddings are unnormalized. Always use cosine similarity for base64_int8 and Hamming distance for base64_binary. If your vector DB only supports inner product, L2-normalize the embeddings before storing.

Cache embeddings

Store computed embeddings in a vector database. Never recompute embeddings for the same text.

Use Matryoshka wisely

Start with full dimensions for best quality. Reduce dimensions only if you need faster search or smaller storage.

Binary for scale

Use base64_binary encoding format for large-scale retrieval systems where storage and speed are critical.

Quickstart

Get started with basic embeddings functionality.

Contextualized Embeddings

Document-aware embeddings for chunks with shared context.

API Reference

Complete Embeddings API documentation.

SDK Guide

Perplexity SDK features and best practices.

​Overview

​Matryoshka Dimension Reduction

​Encoding Formats

​Similarity Metrics

​int8 Embeddings (base64_int8)

​Binary Embeddings (base64_binary)

​RAG Pattern

​Batch Processing

​Error Handling

​Tips

​Related Resources

Quickstart

Contextualized Embeddings

API Reference

SDK Guide

Overview

Matryoshka Dimension Reduction

Encoding Formats

Similarity Metrics

int8 Embeddings (`base64_int8`)

Binary Embeddings (`base64_binary`)

RAG Pattern

Batch Processing

Error Handling

Tips

Related Resources