Pinecone Native Integration

[monami44]

Pinecone Integration with Comprehensive Testing Suite

Overview

This PR introduces a comprehensive Pinecone integration along with extensive testing coverage across vector operations, RAG implementations, inference capabilities, and assistant functionalities.

Key Features

PineconeProvider Class

• Implements a robust provider class that wraps Pinecone's functionality with instrumentation.
• Handles both data plane and control plane operations.
• Includes comprehensive error handling and event tracking.
• Supports both traditional Pinecone operations and newer features like the Assistant API.
• Note: Currently, there is no support for evaluating answers using the Assistant API.

Testing Suites

RAG Pipeline Test (pinecone_rag_test.py)

• Tests the complete RAG (Retrieval-Augmented Generation) workflow.
• Covers index creation, document embedding, and semantic search.
• Implements vector operations (upsert, update, delete).
• Tests collection management.
• Includes real-world query testing with context retrieval.

Inference API Test (pinecone_inference_test.py)

• Tests Pinecone's embedding generation capabilities.
• Implements document reranking functionality.
• Validates usage tracking and response formatting.
• Tests various embedding models and configurations.

Assistant API Test (pinecone_assistant_test.py)

• Tests Pinecone's Assistant API functionality.
• Covers assistant creation, updating, and deletion.
• Implements file upload and management.
• Tests chat completions using an OpenAI-compatible interface.
• Note: There is currently no support for evaluating answers within the Assistant API (premium tier feature)

Implementation Details

• Comprehensive error handling and logging are included.
• Event tracking for all operations is implemented.
• Support for both synchronous and streaming responses is provided.
• Proper cleanup of resources after each test is ensured.
• Integrated with AgentOps for monitoring and tracking.

Testing Instructions

1. Ensure that the required environment variables are set:

   • PINECONE_API_KEY
   • OPENAI_API_KEY (for RAG testing)

2. Run individual test suites with the following commands:

   python tests/core_manual_tests/providers/pinecone_rag_test.py
   python tests/core_manual_tests/providers/pinecone_inference_test.py
   python tests/core_manual_tests/providers/pinecone_assistant_test.py

Notes

• All tests are designed to be idempotent, ensuring no repeated impact on test results.
• Comprehensive resource cleanup is included in all test flows.
• Tests include rate limiting and appropriate delays to ensure API stability.
• Extensive error handling and detailed reporting are part of the suite.

Future Improvements

• Add support for batch operations to enhance efficiency.
• Implement retry logic for handling transient failures.
• Integrate custom embedding model support.
• Enhance streaming response handling for smoother real-time interactions.

For more information, refer to the Pinecone Documentation.

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[areibman]

Amazing-- this is actually the first vectorDB integration PR we've seen so far. Will take a look soon @monami44.

In the meantime-- can you resolve the merge conflict?

[monami44]

Hey @areibman , I just resolved the merge conflict. Also here is a video-walkthrough I sent to Adam as I submitted the PR. Hit me up if you want anything else to be changed

[areibman]

Super cool! @the-praxs and I can take a look