Pinecone Adapter for rag_control

A production-ready Pinecone vector database adapter that integrates with rag_control for secure, governed RAG (Retrieval-Augmented Generation) applications.

Overview

The Pinecone adapter implements the rag_control.adapters.VectorStore interface, enabling seamless integration of Pinecone's vector similarity search with rag_control's governance and access control framework. This enables:

• Semantic Search: Vector similarity-based retrieval from Pinecone
• Metadata Filtering: Rich filtering support with AND/OR logic
• Access Control: Per-user namespace isolation via user context
• Governance: Full compliance with rag_control's security and audit framework
• Error Handling: Graceful error management with detailed diagnostics

Installation

Install the package via pip:

pip install pinecone-adapter

Or using uv:

uv pip install pinecone-adapter

Requirements

• Python >= 3.10
• pinecone >= 3.0.0
• rag-control == 0.2.0

Quick Start

1. Initialize the Adapter

from pinecone_adapter import PineconeVectorStoreAdapter

adapter = PineconeVectorStoreAdapter(
    api_key="your-pinecone-api-key",
    index_name="your-index-name",
    embedding_model="text-embedding-3-small"
)

Integration with rag_control

Complete RAG Pipeline Example

from pinecone_adapter import PineconeVectorStoreAdapter
from rag_control import RAGControl
from rag_control.models import UserContext

# 1. Initialize adapters
vector_store = PineconeVectorStoreAdapter(
    api_key="your-pinecone-api-key",
    index_name="documents",
    embedding_model="text-embedding-3-small"
)

# Import or implement your embedding and LLM adapters
from your_embedding_provider import OpenAIEmbeddingAdapter
from your_llm_provider import OpenAIAdapter

query_embedding = OpenAIEmbeddingAdapter(
    api_key="your-openai-api-key",
    model="text-embedding-3-small"
)

llm = OpenAIAdapter(
    api_key="your-openai-api-key",
    model="gpt-4"
)

# 2. Configure rag_control - : Load from YAML
rag_control = RAGControl(
    llm=llm,
    query_embedding=query_embedding,
    vector_store=vector_store,
    config_path="rag_control_config.yaml"
)


# 3. Run a query
user_context = UserContext(
    user_id="user123",
    org_id="org456",
    attributes={"namespace": "department-finance"}
)

response = rag_control.run(
    query="What are our Q1 financial results?",
    user_context=user_context
)

print(response.response.text)
print(f"Retrieved {response.retrieved_count} documents")
print(f"Policy applied: {response.policy_name}")

Streaming Responses

# Stream responses for real-time output
stream_response = rag_control.stream(
    query="What are our Q1 financial results?",
    user_context=user_context
)

# Response includes enforcement metadata
print(f"Enforcement attached: {stream_response.enforcement_attached}")

for chunk in stream_response.response:
    print(chunk.text, end="", flush=True)

Metadata Filtering

The adapter supports all rag_control filter operators:

Operator	Description
equals	Exact value match
in	Value in list
lt	Less than
lte	Less than or equal
gt	Greater than
gte	Greater than or equal
exists	Field exists
intersects	Array intersection (approximated as in)

The selected filter is applied based on the org's document_policy.filter_name.

YAML Configuration File

For production deployments, use YAML config files:

# rag_control_config.yaml
orgs:
  - org_id: org456
    document_policy:
      filter_name: published
      top_k: 10
    default_policy: default
    policy_rules: []

filters:
  - name: published
    condition:
      field: status
      operator: equals
      value: published

  - name: finance_2024
    and:
      - condition:
          field: department
          operator: equals
          value: finance
      - condition:
          field: year
          operator: gte
          value: 2024

policies:
  - name: default
    generation:
      temperature: 0.7
      filter_name: finance_2024
    enforcement:
      max_output_tokens: 2048

Namespace Isolation

Use user_context.attributes["namespace"] to isolate results per user or tenant:

# Different users see different results
user_context_1 = UserContext(
    user_id="user1",
    org_id="org456",
    attributes={"namespace": "user1"}
)

user_context_2 = UserContext(
    user_id="user2",
    org_id="org456",
    attributes={"namespace": "user2"}
)

response_1 = adapter.search(embedding=query_embedding, user_context=user_context_1)
response_2 = adapter.search(embedding=query_embedding, user_context=user_context_2)

If no namespace is specified, the search defaults to an empty namespace (all documents).

API Reference

PineconeVectorStoreAdapter

Constructor

PineconeVectorStoreAdapter(
    api_key: str,
    index_name: str,
    embedding_model: str
)

Parameters:

• api_key: Pinecone API key
• index_name: Name of Pinecone index to query
• embedding_model: Embedding model identifier (e.g., "text-embedding-3-small")

Raises:

• VectorStoreAdapterError: If index doesn't exist or API key is invalid

embedding_model Property

model: str = adapter.embedding_model

Returns the embedding model identifier. Must match the embedding model in your query embedding adapter.

search() Method

response = adapter.search(
    embedding: list[float],
    top_k: int = 5,
    user_context: UserContext | None = None,
    filter: Filter | None = None
) -> VectorStoreSearchResponse

Parameters:

• embedding: Query embedding vector
• top_k: Number of results (default: 5). In rag_control, controlled by org's document_policy.top_k
• user_context: User context with org_id, user_id, and optional attributes
• filter: Metadata filter. In rag_control, provided by filter registry

Returns:

• VectorStoreSearchResponse with:
  • records: List of VectorStoreRecord objects
  • metadata: VectorStoreSearchMetadata with latency, provider, etc.

Raises:

• VectorStoreAdapterError: If search fails

Error Handling

from rag_control.exceptions import VectorStoreAdapterError

try:
    response = adapter.search(embedding=query_embedding)
except VectorStoreAdapterError as e:
    print(f"Search failed: {e}")

Development

See DEVELOPMENT.md for setup, testing, and contribution guidelines.

Quick start:

make venv
source .venv/bin/activate
make install-dev
make test

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Support

For issues, questions, or feature requests:

• Open a GitHub issue
• Contact: support@retrievallabs.com

Related Resources

• rag_control - RAG governance framework
• Pinecone Documentation
• Pinecone Python Client