• Removed the unused Pinecone REST‐client import; we now only import ServerlessSpec and the gRPC client.
• Enhanced close()
• Call self.client.close() to explicitly shut down the underlying gRPC channel.
• Log success or a warning on failure.
• Still tear down the thread‐pool executor afterward.
• Context‐manager support
• Added __enter__()/__exit__() so you can do:
with PineconeClient() as client:
client.insert(...)
# automatically calls client.close()
Now supports batched insert, upsert, and delete operations using a default batch size of 100, reducing API strain and improving throughput. All blocking calls to the Pinecone API are wrapped in asyncio.to_thread(...), ensuring async safety and preventing event loop blocking. The implementation includes zero-vector handling for efficient metadata-only queries, normalized cosine distance scores for accurate ranking, and protections against empty input operations. Logs for batch durations have been streamlined to minimize noise, while preserving key info-level success logs.
- Created `VectorDBBase` as an abstract base class to standardize vector database operations.
- Added required methods for common vector database operations: `has_collection`, `delete_collection`, `insert`, `upsert`, `search`, `query`, `get`, `delete`, `reset`.
- The base class can now be extended by any vector database implementation (e.g., Qdrant, Pinecone) to ensure a consistent API across different database systems.
Adds Pinecone as a supported vector database option.
- Implements `PineconeClient` with support for common operations: `add`, `query`, `delete`, `reset`.
- Emulates namespace support using metadata filtering (`collection_name` prefix).
- Dynamically configures Pinecone via the following env vars:
- `PINECONE_API_KEY`
- `PINECONE_ENVIRONMENT`
- `PINECONE_INDEX_NAME`
- `PINECONE_DIMENSION`
- `PINECONE_METRIC`
- `PINECONE_CLOUD`
- Integrates cleanly with the vector DB abstraction layer.
- Includes markdown documentation under `docs/getting-started/env-configuration.md`.
BREAKING CHANGE: None
- This PR aims to address the error 400: "**'OpenSearchClient' object has no attribute 'query'**".
- With the implemented query() method, this issue should be resolved and allow uploaded documents to be vectorized and retrieved based on the given query.
