Pinecone 入门
创建 Pinecone 索引，写入带元数据的向量，使用筛选条件执行相似度查询，并通过管理命名空间来分隔不同的数据集合。
Pinecone 入门 是 CoddyKit 上的免费 AI Engineering Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AI Engineering Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Engineering Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
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.
常见问题解答
「Pinecone 入门」课时是免费的吗？
是的 — 「Pinecone 入门」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Engineering Academy 课程的其余内容，请升级到 CoddyKit PRO。 AI Engineering Academy 课程共包含 4 节课。
「Pinecone 入门」这节课中我会学到什么？
创建 Pinecone 索引，写入带元数据的向量，使用筛选条件执行相似度查询，并通过管理命名空间来分隔不同的数据集合。 你通过在浏览器中直接运行的动手代码来练习 AI Engineering Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 AI Engineering Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 AI Engineering Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「Pinecone 入门」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 AI Engineering Academy 课中编写并运行代码吗？
能。每节 AI Engineering Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。