Skip to main content
Pinecone Local is an in-memory Pinecone Database emulator available as a Docker image. This page shows you how to build a CI/CD workflow with Pinecone Local and GitHub Actions to test your integration without connecting to your Pinecone account, affecting production data, or incurring any usage or storage fees.
Pinecone Local is not suitable for production. See Limitations for details.
This feature is in public preview.

​
Limitations

Pinecone Local has the following limitations:
  • Pinecone Local uses the 2025-01 API version, which is not the latest stable version.
  • Pinecone Local is available in Docker only.
  • Pinecone Local is an in-memory emulator and is not suitable for production. Records loaded into Pinecone Local do not persist after it is stopped.
  • Pinecone Local does not authenticate client requests. API keys are ignored.
  • Max number of records per index: 100,000.
Pinecone Local does not currently support the following features:

​
1. Write your tests

Running code against Pinecone Local is just like running code against your Pinecone account, with the following differences:
Be sure to review the limitations of Pinecone Local before using it for development or testing.
Example The following example assumes that you have started Pinecone Local without indexes. It initializes a client, creates a dense index and a sparse index, upserts records into the indexes, checks their record counts, and queries the indexes. 
from pinecone.grpc import PineconeGRPC, GRPCClientConfig from pinecone import ServerlessSpec  # Initialize a client. # API key is required, but the value does not matter. # Host and port of the Pinecone Local instance # is required when starting without indexes.  pc = PineconeGRPC(  api_key="pclocal",   host="http://localhost:5080"  )   # Create two indexes, one dense and one sparse dense_index_name = "dense-index" sparse_index_name = "sparse-index"  if not pc.has_index(dense_index_name):   dense_index_model = pc.create_index(  name=dense_index_name,  vector_type="dense",  dimension=2,  metric="cosine",  spec=ServerlessSpec(cloud="aws", region="us-east-1"),  deletion_protection="disabled",  tags={"environment": "development"}  )  print("Dense index model:\n", dense_index_model)  if not pc.has_index(sparse_index_name):   sparse_index_model = pc.create_index(  name=sparse_index_name,  vector_type="sparse",  metric="dotproduct",  spec=ServerlessSpec(cloud="aws", region="us-east-1"),  deletion_protection="disabled",  tags={"environment": "development"}  )  print("\nSparse index model:\n", sparse_index_model)  # Target each index, disabling tls dense_index_host = pc.describe_index(name=dense_index_name).host dense_index = pc.Index(host=dense_index_host, grpc_config=GRPCClientConfig(secure=False)) sparse_index_host = pc.describe_index(name=sparse_index_name).host sparse_index = pc.Index(host=sparse_index_host, grpc_config=GRPCClientConfig(secure=False))  # Upsert records into the dense index dense_index.upsert(  vectors=[  {  "id": "vec1",   "values": [1.0, -2.5],  "metadata": {"genre": "drama"}  },  {  "id": "vec2",   "values": [3.0, -2.0],  "metadata": {"genre": "documentary"}  },  {  "id": "vec3",   "values": [0.5, -1.5],  "metadata": {"genre": "documentary"}  }  ],  namespace="example-namespace" )  # Upsert records into the sparse index sparse_index.upsert(  namespace="example-namespace",  vectors=[  {  "id": "vec1",  "sparse_values": {  "values": [1.7958984, 0.41577148, 2.828125, 2.8027344, 2.8691406, 1.6533203, 5.3671875, 1.3046875, 0.49780273, 0.5722656, 2.71875, 3.0820312, 2.5019531, 4.4414062, 3.3554688],  "indices": [822745112, 1009084850, 1221765879, 1408993854, 1504846510, 1596856843, 1640781426, 1656251611, 1807131503, 2543655733, 2902766088, 2909307736, 3246437992, 3517203014, 3590924191]  },  "metadata": {  "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.",  "category": "technology",  "quarter": "Q3"  }  },  {  "id": "vec2",  "sparse_values": {  "values": [0.4362793, 3.3457031, 2.7714844, 3.0273438, 3.3164062, 5.6015625, 2.4863281, 0.38134766, 1.25, 2.9609375, 0.34179688, 1.4306641, 0.34375, 3.3613281, 1.4404297, 2.2558594, 2.2597656, 4.8710938, 0.5605469],  "indices": [131900689, 592326839, 710158994, 838729363, 1304885087, 1640781426, 1690623792, 1807131503, 2066971792, 2428553208, 2548600401, 2577534050, 3162218338, 3319279674, 3343062801, 3476647774, 3485013322, 3517203014, 4283091697]  },  "metadata": {  "chunk_text": "Analysts suggest that AAPL'\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market.",  "category": "technology",  "quarter": "Q4"  }  },  {  "id": "vec3",  "sparse_values": {  "values": [2.6875, 4.2929688, 3.609375, 3.0722656, 2.1152344, 5.78125, 3.7460938, 3.7363281, 1.2695312, 3.4824219, 0.7207031, 0.0826416, 4.671875, 3.7011719, 2.796875, 0.61621094],  "indices": [8661920, 350356213, 391213188, 554637446, 1024951234, 1640781426, 1780689102, 1799010313, 2194093370, 2632344667, 2641553256, 2779594451, 3517203014, 3543799498, 3837503950, 4283091697]  },  "metadata": {  "chunk_text": "AAPL'\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production",  "category": "technology",  "quarter": "Q3"  }  }  ] )  # Check the number of records in each index print("\nDense index stats:\n", dense_index.describe_index_stats()) print("\nSparse index stats:\n", sparse_index.describe_index_stats())  # Query the dense index with a metadata filter dense_response = dense_index.query(  namespace="example-namespace",  vector=[3.0, -2.0],  filter={"genre": {"$eq": "documentary"}},  top_k=1,  include_values=False,  include_metadata=True )  print("\nDense query response:\n", dense_response)  # Query the sparse index with a metadata filter sparse_response = sparse_index.query(  namespace="example-namespace",  sparse_vector={  "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],  "indices": [767227209, 1640781426, 1690623792, 2021799277, 2152645940, 2295025838, 2443437770, 2779594451, 2956155693, 3476647774, 3818127854, 4283091697]  },   filter={  "quarter": {"$eq": "Q4"}  },  top_k=1,  include_values=False,  include_metadata=True )  print("/nSparse query response:\n", sparse_response)  # Delete the indexes pc.delete_index(name=dense_index_name) pc.delete_index(name=sparse_index_name)

​
2. Set up GitHub Actions

Set up a GitHub Actions workflow to do the following:
  1. Pull the Pinecone Local Docker image.
  2. Start a Pinecone Local instance for each test run.
  3. Execute tests against the local instance.
  4. Tear down the instance after tests complete.
Here’s a sample GitHub Actions workflow that you can extend for your own needs: 
name: CI/CD with Pinecone Local on:  pull_request:  branches:  - main  push:  branches:  - main  jobs:  pc-local-tests:  name: Pinecone Local tests  runs-on: ubuntu-latest  services:  pc-local:  image: ghcr.io/pinecone-io/pinecone-local:latest  env:  PORT: 5080  ports:  - "5080-6000:5080-6000"  steps:  - name: Check out repository code  uses: actions/checkout@v4   - name: Install dependencies  run: |  python -m pip install --upgrade pip  pip install "pinecone[grpc]"   - name: Run tests  run: |  pytest test/

​
3. Run your tests

GitHub Actions will automaticaly run your tests against Pinecone Local when the events you specified in your workflow occur. For a list of the events that can trigger a workflow and more details about using GitHub Actions for CI/CD, see the GitHub Actions documentation.