add cache for embeddings

[azawalich]

Figured out that embeddings are often generated for the same question multiple times. Added caching it to reduce number of requests sent to embedding models.

[llamapreview]

Auto Pull Request Review from LlamaPReview

1. Change Overview

1.1 Core Changes

• Primary purpose and scope: The primary purpose of this PR is to add caching functionality for embeddings to improve performance by reducing redundant computations.
• Key components modified: The caching mechanism is introduced in multiple embedding generation classes across different modules such as ZhipuAI_embeddings, azuresearch_vector, chromadb_vector, faiss, bigquery_vector, milvus_vector, openai_embeddings, pinecone_vector, qdrant, Qianfan_embeddings, QianwenAI_embeddings, and weaviate_vector.
• Cross-component impacts: The change impacts how embeddings are generated and stored across various vector stores and embedding models.
• Business value alignment: This change aligns with the need to optimize performance and reduce computational overhead, which can lead to faster response times and lower costs.

1.2 Technical Architecture

• System design modifications: The introduction of an embedding cache modifies the system design by adding a layer of caching to the embedding generation process.
• Component interaction changes: The interaction between the embedding generation functions and the caching mechanism is modified to first check the cache before generating new embeddings.
• Integration points impact: The integration points with various embedding models and vector stores are impacted as the caching mechanism is introduced.
• Dependency changes and implications: There are no significant dependency changes, but the introduction of caching implies that the system now relies on the cache for performance optimization.

2. Deep Technical Analysis

2.1 Code Logic Analysis

src/vanna/ZhipuAI/ZhipuAI_embeddings.py - ZhipuAI_Embeddings.generate_embedding

• Submitted PR Code:

  def generate_embedding(self, data: str, **kwargs) -> List[float]:
      if self.embedding_cache['question'] == data:
          return self.embedding_cache['embedding'].data[0].embedding
      else:
          embedding = self.client.embeddings.create(
              model="embedding-2",
              input=data,
          )
          self.embedding_cache['question'] = data
          self.embedding_cache['embedding'] = embedding
          return embedding.data[0].embedding

  • Analysis:
    • The current logic introduces a caching mechanism that stores the last generated embedding.
    • If the same data is requested again, the cached embedding is returned, avoiding redundant API calls.
    • This improves performance by reducing the number of API calls to the embedding service.
  • LlamaPReview Suggested Improvements:

    def generate_embedding(self, data: str, **kwargs) -> List[float]:
        if self.embedding_cache['question'] == data:
            return self.embedding_cache['embedding'].data[0].embedding
        else:
            try:
                embedding = self.client.embeddings.create(
                    model="embedding-2",
                    input=data,
                )
                self.embedding_cache['question'] = data
                self.embedding_cache['embedding'] = embedding
                return embedding.data[0].embedding
            except Exception as e:
                # Log the exception and handle it appropriately
                print(f"Error generating embedding: {e}")
                raise

  • Improvement rationale:
    • Adding exception handling ensures that any errors during the embedding generation process are logged and handled appropriately, improving the robustness of the code.

src/vanna/azuresearch/azuresearch_vector.py - AzureSearchVectorStore.generate_embedding

• Submitted PR Code:

  def generate_embedding(self, data: str, **kwargs) -> List[float]:
      if self.embedding_cache['question'] == data:
          return self.embedding_cache['embedding'].tolist()
      else:
          embedding = next(embedding_model.embed(data))
          self.embedding_cache['question'] = data
          self.embedding_cache['embedding'] = embedding
          return embedding.tolist()

  • Analysis:
    • Similar to the previous example, this introduces a caching mechanism to avoid redundant embedding generation.
    • The cache is checked before generating a new embedding, which improves performance.
  • LlamaPReview Suggested Improvements:

    def generate_embedding(self, data: str, **kwargs) -> List[float]:
        if self.embedding_cache['question'] == data:
            return self.embedding_cache['embedding'].tolist()
        else:
            try:
                embedding = next(embedding_model.embed(data))
                self.embedding_cache['question'] = data
                self.embedding_cache['embedding'] = embedding
                return embedding.tolist()
            except Exception as e:
                # Log the exception and handle it appropriately
                print(f"Error generating embedding: {e}")
                raise

  • Improvement rationale:
    • Adding exception handling ensures that any errors during the embedding generation process are logged and handled appropriately, improving the robustness of the code.

2.2 Implementation Quality

• Code Structure:
  • The caching mechanism is consistently implemented across different modules, which ensures maintainability and reusability.
  • The code is modular, with each class responsible for its own caching logic.
• Error Handling:
  • The current implementation lacks robust error handling, which could lead to unhandled exceptions during embedding generation.
  • Suggested improvements include adding try-except blocks to handle potential errors gracefully.
• Performance Considerations:
  • The introduction of caching improves performance by reducing redundant API calls and computations.
  • However, the cache size and eviction policy are not defined, which could lead to memory issues if not managed properly.

3. Risk Assessment

3.1 Critical Issues

🔴 P0 (Must Fix):

• Issue: Lack of cache size management and eviction policy.
  • Impact:
    • Technical implications: Potential memory issues and degraded performance over time.
    • Business consequences: Increased operational costs and reduced system reliability.
    • User experience effects: Slower response times and potential system crashes.
  • Resolution:
    • Implement a cache size limit and eviction policy (e.g., LRU cache).
    • Monitor cache usage and performance metrics.

3.2 Important Improvements

🟡 P1 (Should Fix):

• Issue: Lack of robust error handling in embedding generation functions.
  • Current Impact:
    • Performance implications: Unhandled exceptions can lead to system crashes and degraded performance.
    • Maintenance overhead: Debugging and maintaining the code becomes more challenging without proper error handling.
    • Future scalability: As the system scales, the lack of error handling can lead to increased downtime and reduced reliability.
  • Suggested Solution:
    • Implementation approach: Add try-except blocks to handle potential errors gracefully.
    • Migration strategy: Gradually introduce error handling in all embedding generation functions.
    • Testing considerations: Ensure that all edge cases and potential errors are covered in unit tests.

3.3 Minor Suggestions

🟢 P2 (Consider):

• Area: Documentation and comments.
  • Improvement Opportunity:
    • Code quality enhancement: Add comments and docstrings to explain the caching mechanism and its benefits.
    • Best practice alignment: Ensure that the code adheres to best practices for documentation and maintainability.
    • Documentation updates: Update the project documentation to reflect the changes and the introduction of the caching mechanism.

4. Requirements Analysis

4.1 Functional Coverage

• Requirements mapping:
  • Implemented features: Caching mechanism for embedding generation.
  • Missing elements: Cache size management and eviction policy.
  • Edge cases handling: The current implementation does not handle edge cases related to cache size and eviction.
• Business Logic:
  • Use case coverage: The caching mechanism covers the use case of reducing redundant embedding generation.
  • Business rule implementation: The business rule of optimizing performance by reducing redundant computations is implemented.
  • Data flow correctness: The data flow is correct, with the cache being checked before generating new embeddings.

4.2 Non-functional Aspects

• Performance metrics: The introduction of caching improves performance by reducing redundant API calls and computations.
• Security considerations: The caching mechanism does not introduce any significant security risks.
• Scalability factors: The system's scalability is improved by reducing the computational overhead of embedding generation.
• Maintainability aspects: The code is maintainable due to its modular structure and consistent implementation of the caching mechanism.

5. Testing Strategy

• Test Coverage:
  • Unit test requirements: Ensure that all embedding generation functions are tested with and without the cache.
  • Integration test scenarios: Test the integration of the caching mechanism with various vector stores and embedding models.
  • Edge case validation: Validate edge cases related to cache size and eviction policy.
• Quality Metrics:
  • Current coverage: Ensure that all new and modified functions are covered by unit tests.
  • Critical paths: Test the critical paths related to embedding generation and caching.
  • Performance benchmarks: Benchmark the performance improvement due to the introduction of caching.

6. Final Assessment

6.1 Key Action Items

1. Critical Changes (P0):

   • Implement a cache size limit and eviction policy to prevent memory issues.

2. Important Improvements (P1):

   • Add robust error handling in embedding generation functions to improve reliability.

3. Suggested Enhancements (P2):

   • Update documentation and comments to explain the caching mechanism and its benefits.

6.2 Overall Evaluation

• Technical assessment: The introduction of caching improves performance but lacks cache size management and error handling.
• Business impact: The change aligns with the business need to optimize performance and reduce costs.
• Risk evaluation: The lack of cache size management and error handling poses risks to system reliability and performance.
• Implementation quality: The code is modular and maintainable, but it requires improvements in error handling and cache management.

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