Update pinecone_vector.py

[nb0309]

In newer versions of the Pinecone client, the fetch() method returns a FetchResponse object, not a simple dictionary. And this object is not subscriptable.

[nb0309]

After overriding the change, it works

[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Overview

1.1 Core Changes

• Addresses incompatibility with newer Pinecone client library versions.
• Modifies _check_if_embedding_exists method in pinecone_vector.py to use attribute access for fetch_response.vectors.
• No significant cross-component impacts.
• Ensures continued functionality for users using newer Pinecone client libraries, preventing potential application errors.

1.2 Technical Architecture

2. Critical Findings

2.1 Must Fix (P0🔴)

[No critical issues identified]

2.2 Should Fix (P1🟡)

[No issues identified]

2.3 Consider (P2🟢)

Area: Code Readability and Maintainability in _check_if_embedding_exists

• Analysis Confidence: High
• Improvement Opportunity: Adding a comment to explain the change from dictionary access to attribute access for fetch_response.vectors will improve code understanding and future maintainability.

2.4 Summary of Action Items

• P2🟢 Consider: Add a comment to _check_if_embedding_exists explaining the change for Pinecone client compatibility. Timeline: Before merging.

3. Technical Analysis

3.1 Code Logic Analysis

📁 src/vanna/pinecone/pinecone_vector.py - PineconeVectorDB._check_if_embedding_exists

• Submitted PR Code:

    def _check_if_embedding_exists(self, id: str, namespace: str) -> bool:
        fetch_response = self.Index.fetch(ids=[id], namespace=namespace)
        if fetch_response.vectors == {}:
            return False
        return True

• Analysis:
  • The code checks if an embedding exists in Pinecone by fetching vectors and verifying if the vectors attribute of the FetchResponse object is empty.
  • This change adapts to newer Pinecone client libraries where fetch() returns a FetchResponse object with vectors accessible as an attribute (fetch_response.vectors) instead of a dictionary key (fetch_response["vectors"]).
  • It correctly addresses the incompatibility issue caused by the Pinecone client library update.
• LlamaPReview Suggested Improvements:

    def _check_if_embedding_exists(self, id: str, namespace: str) -> bool:
        fetch_response = self.Index.fetch(ids=[id], namespace=namespace)
        # In newer Pinecone client versions, fetch() returns a FetchResponse object,
        # and vectors are accessed as an attribute, not a dictionary key.
        if fetch_response.vectors == {}:
            return False
        return True

• Improvement rationale:
  • Technical benefits: Improves code readability and maintainability by providing context for the change, making it easier for developers to understand the code's purpose and the reason for attribute access.
  • Business value: Reduces potential debugging time and maintenance costs in the future by making the code more self-explanatory.

3.2 Key Quality Aspects

4. Overall Evaluation

• Technical assessment: The PR provides a necessary bug fix to maintain compatibility with newer Pinecone client libraries. The code change is minimal and correct.
• Business impact: High. Resolves a compatibility issue that could break functionality for users on newer Pinecone client versions, ensuring continued service and preventing potential errors.
• Risk evaluation: Low. The change is localized and directly addresses the identified issue.
• Notable positive aspects and good practices: Quick response to dependency update, targeted fix addressing the root cause.
• Implementation quality: Good. The code change is simple and effective. Adding the suggested comment will further improve quality.
• Final recommendation: Approve with the suggestion to add the comment for improved code clarity.

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