Erste Schritte mit Pinecone
Sie erstellen einen Pinecone-Index, führen ein Upsert von Vektoren mit Metadaten durch, stellen Ähnlichkeitsabfragen mit Filtern und verwalten Namespaces, um verschiedene Datensammlungen zu trennen.
Erste Schritte mit Pinecone ist eine kostenlose AI Engineering Academy-Lektion auf CoddyKit. Dies ist Lektion 2 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des AI Engineering Academy-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der AI Engineering Academy-Kurs umfasst insgesamt 4 Lektionen.
Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.
What Is Pinecone?
Pinecone is a fully managed vector database that handles infrastructure, scaling, and backups automatically. You interact with it through a Python SDK, sending vectors and metadata via API calls. Pinecone stores them on its servers and answers similarity queries at low latency, even at billions of vectors.
It offers a free tier that is sufficient for learning and small production workloads.
Installing the Pinecone SDK
Install the Pinecone client and initialize it with your API key. Store the API key in an environment variable — never hardcode secrets in source files. The Pinecone class is the main entry point for all operations.
# pip install pinecone-client
import os
from pinecone import Pinecone
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
# List existing indexes to verify connection
existing = pc.list_indexes().names()
print('Existing indexes:', existing)Creating a Pinecone Index
A Pinecone index is the collection that stores your vectors. When creating an index you must specify the dimension (must match your embedding model) and the metric for similarity calculation. For OpenAI embeddings, use dimension=1536 and metric='cosine'.
Index creation is idempotent: the serverless spec creates the index on-demand without pre-allocating capacity.
from pinecone import Pinecone, ServerlessSpec
import os
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
index_name = 'my-rag-index'
if index_name not in pc.list_indexes().names():
pc.create_index(
name=index_name,
dimension=1536, # text-embedding-3-small dimension
metric='cosine',
spec=ServerlessSpec(
cloud='aws',
region='us-east-1'
)
)
print(f'Created index: {index_name}')
else:
print(f'Index already exists: {index_name}')
index = pc.Index(index_name)Upserting Vectors with Metadata
Pinecone uses upsert (insert or update) operations. Each vector requires a unique id string, the values (the embedding as a list of floats), and optional metadata (a dict of filterable fields). If you upsert with an existing id, the old vector is replaced.
from openai import OpenAI
oai = OpenAI()
documents = [
{'id': 'doc-001', 'text': 'What is RAG and how does it work?', 'category': 'faq'},
{'id': 'doc-002', 'text': 'Pinecone is a managed vector database.', 'category': 'docs'},
{'id': 'doc-003', 'text': 'OpenAI embeddings have 1536 dimensions.', 'category': 'docs'}
]
texts = [d['text'] for d in documents]
resp = oai.embeddings.create(model='text-embedding-3-small', input=texts)
vectors = [
{
'id': doc['id'],
'values': resp.data[i].embedding,
'metadata': {'text': doc['text'], 'category': doc['category']}
}
for i, doc in enumerate(documents)
]
index.upsert(vectors=vectors)
print(f'Upserted {len(vectors)} vectors')Querying the Pinecone Index
To search, embed your query and call index.query() with the query vector and top_k for how many results to return. Set include_metadata=True to get the stored metadata back alongside the match ids and scores.
from openai import OpenAI
oai = OpenAI()
query = 'How many dimensions do OpenAI embeddings have?'
q_resp = oai.embeddings.create(model='text-embedding-3-small', input=query)
q_vec = q_resp.data[0].embedding
results = index.query(
vector=q_vec,
top_k=3,
include_metadata=True
)
for match in results['matches']:
score = match['score']
text = match['metadata']['text']
print(f'{score:.4f}: {text}')Filtering by Metadata
Pinecone supports pre-filtering — narrowing the search to vectors that match a metadata condition before running ANN. Use the filter parameter with MongoDB-style operators: $eq, $in, $gt, $lt, $and, $or.
Always index the metadata fields you plan to filter on when creating the index to ensure filter performance.
# Filter to only 'docs' category results
results = index.query(
vector=q_vec,
top_k=5,
filter={
'category': {'$eq': 'docs'}
},
include_metadata=True
)
print(f'Filtered results: {len(results["matches"])}')
for m in results['matches']:
print(f' [{m["metadata"]["category"]}] {m["metadata"]["text"][:60]}')Namespaces for Data Isolation
Pinecone namespaces partition a single index into isolated segments. Queries in one namespace never return results from another, making namespaces ideal for multi-tenant RAG systems where each customer should only search their own documents.
Both upsert and query accept an optional namespace string. The default namespace is an empty string.
# Upsert into a specific namespace
index.upsert(
vectors=[{'id': 'doc-001', 'values': q_vec, 'metadata': {'text': 'Tenant A doc'}}],
namespace='tenant-a'
)
# Query only tenant-a's documents
results = index.query(
vector=q_vec,
top_k=5,
namespace='tenant-a',
include_metadata=True
)
print(f'Tenant-A results: {len(results["matches"])}')Batch Upserting for Large Corpora
When indexing thousands of documents, split your vectors into batches of up to 100 vectors per upsert call (Pinecone's recommended batch size). Larger batches may hit size limits due to the payload size constraint of ~4MB per request.
def batch_upsert(index, vectors, batch_size=100):
total = len(vectors)
for start in range(0, total, batch_size):
batch = vectors[start:start + batch_size]
index.upsert(vectors=batch)
print(f'Upserted {min(start + batch_size, total)}/{total}')
# Usage:
# batch_upsert(index, all_vectors, batch_size=100)Checking Index Statistics
Use index.describe_index_stats() to check how many vectors are stored in each namespace and the total vector count. This is useful for verifying that an upsert completed successfully and for monitoring index growth over time.
stats = index.describe_index_stats()
print(f'Total vectors: {stats["total_vector_count"]}')
print(f'Namespaces:')
for ns, info in stats.get('namespaces', {}).items():
print(f' {ns!r}: {info["vector_count"]} vectors')Deleting and Updating Vectors
Use index.delete(ids=[...]) to remove specific vectors by id. To update a vector (for example when a document is revised), simply upsert with the same id — Pinecone will overwrite the existing entry.
To delete all vectors in a namespace (for example to re-index after a full refresh), use index.delete(delete_all=True, namespace='...').
# Delete specific vectors by id
index.delete(ids=['doc-001', 'doc-002'])
# Update a vector (upsert overwrites by id)
index.upsert(vectors=[{
'id': 'doc-003',
'values': q_vec, # new embedding
'metadata': {'text': 'Updated content.'}
}])
# Delete all vectors in a namespace
# index.delete(delete_all=True, namespace='tenant-a')Pinecone Pricing and Free Tier
Pinecone offers a free serverless tier with 2GB of storage, enough for roughly 300,000 vectors at 1536 dimensions. Beyond that, serverless pricing charges per read unit and write unit consumed.
For production workloads, estimate your monthly cost by calculating: (queries per day × 30 × cost per query) + (total vectors × storage cost). Serverless is often more cost-effective than pod-based plans for variable-traffic workloads.
Quick Check
Test your understanding of AI Engineering concepts from this lesson.
Lesson Recap
In this lesson you learned: Pinecone indexes require matching dimension and metric to your embedding model, upsert operations use a unique id so updating a document simply re-upserts with the same id, and namespaces isolate vector collections within a single index for multi-tenant use cases. Next up we explore pgvector, which brings vector search directly into PostgreSQL.
Häufig gestellte Fragen
Ist die Lektion „Erste Schritte mit Pinecone“ kostenlos?
Ja — der vollständige Text von „Erste Schritte mit Pinecone“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des AI Engineering Academy-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der AI Engineering Academy-Kurs umfasst insgesamt 4 Lektionen.
Was lerne ich in „Erste Schritte mit Pinecone“?
Sie erstellen einen Pinecone-Index, führen ein Upsert von Vektoren mit Metadaten durch, stellen Ähnlichkeitsabfragen mit Filtern und verwalten Namespaces, um verschiedene Datensammlungen zu trennen. Du übst AI Engineering Academy mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.
Brauche ich Erfahrung, um AI Engineering Academy zu starten?
Keine Vorkenntnisse erforderlich. AI Engineering Academy auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 2 von 4.
Wie lange dauert die Lektion „Erste Schritte mit Pinecone“?
Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.
Kann ich in dieser AI Engineering Academy-Lektion Code schreiben und ausführen?
Ja. Jede AI Engineering Academy-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.
Alle Lektionen in diesem Kurs
- Warum Sie eine Vektordatenbank benötigen
- Erste Schritte mit Pinecone
- pgvector: Embeddings in PostgreSQL
- Vektorspeicher auswählen und benchmarken