SpannerGraphVectorContextRetriever Doesn't Support Approximate Nearest Neighbor Search

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Introduction

The SpannerGraphVectorContextRetriever is a powerful tool for vector search in Google Cloud Spanner. However, it has a limitation when it comes to Approximate Nearest Neighbor (ANN) search. In this article, we will explore the issue and provide a solution to create a Spanner Graph query that uses ANN vector index.

Problem Statement

The SpannerGraphVectorContextRetriever does not support the APPROX_DOT_PRODUCT metric for vector search. Upon checking the implementation, we found that the __get_distance_function method only supports COSINE and EUCLIDEAN distance.

Environment Details

To reproduce the issue, we need to provide the following environment details:

  • OS type and version: MacOS Darwin Kernel Version 23.6.0 arm64
  • Python version: 3.12.9
  • pip version: pip 25.0.1
  • langchain-google-spanner version: 0.8.1

Steps to Reproduce

To reproduce the issue, we can try the following steps:

  1. Create a Spanner Graph query that uses the APPROX_DOT_PRODUCT metric for vector search.
  2. Check the implementation of the SpannerGraphVectorContextRetriever to see if it supports the APPROX_DOT_PRODUCT metric.

Example Spanner Graph Query

Here is an example Spanner Graph query that we are trying to create:

GRAPH TestGraph
MATCH (node:Node1)
WHERE node.embedding is not NULL
ORDER BY APPROX_DOT_PRODUCT(node.embedding, ARRAY<FLOAT32>[
      -0.036149498,
      0.07207133,
      -0.020961108,
      -0.005176733,
      0.047590617,
      0.006568967,
      -0.047031555,
      0.04741984
    ],
    OPTIONS => JSON '{"num_leaves_to_search": 5}') DESC
  LIMIT 5
  RETURN node.col1, node.col2;

Solution

Unfortunately, the SpannerGraphVectorContextRetriever does not support the APPROX_DOT_PRODUCT metric for vector search. However, we can use a workaround to achieve similar results.

One possible solution is to use the COSINE distance metric instead of APPROX_DOT_PRODUCT. We can modify the Spanner Graph query to use the COSINE distance metric:

GRAPH TestGraph
MATCH (node:Node1)
WHERE node.embedding is not NULL
ORDER BY COSINE_DISTANCE(node.embedding, ARRAY<FLOAT32>[
      -0.036149498,
      0.07207133,
      -0.020961108,
      -0.005176733,
      0.047590617,
      0.006568967,
      -0.047031555,
      0.04741984
    ],
    OPTIONS => JSON '{"num_leaves_to_search": 5}') DESC
  LIMIT 5
  RETURN node.col1, node.col2;

Conclusion

In conclusion, the SpannerGraphVectorContextRetriever does not support the APPROX_DOT_PRODUCT metric for vector search. However, we can use a workaround to achieve similar results by using the COSINE distance metric instead. We hope that this solution will be helpful to you.

Future Work

We hope that the Google Cloud Spanner team will consider adding support for the APPROX_DOT_PRODUCT metric in the future. This will make it easier for users to perform Approximate Nearest Neighbor search in Spanner Graph.

References

Acknowledgments

Q: What is the issue with the SpannerGraphVectorContextRetriever?

A: The SpannerGraphVectorContextRetriever does not support the APPROX_DOT_PRODUCT metric for vector search. This means that you cannot use the APPROX_DOT_PRODUCT metric in your Spanner Graph queries.

Q: Why is this a problem?

A: The APPROX_DOT_PRODUCT metric is a powerful tool for Approximate Nearest Neighbor (ANN) search. It allows you to search for similar vectors in a large dataset. Without support for this metric, you may need to use alternative methods that are less efficient or less accurate.

Q: What are the alternatives to the APPROX_DOT_PRODUCT metric?

A: There are several alternatives to the APPROX_DOT_PRODUCT metric that you can use in your Spanner Graph queries. These include:

  • COSINE distance: This metric measures the cosine of the angle between two vectors. It is a good choice when you want to search for similar vectors in a large dataset.
  • EUCLIDEAN distance: This metric measures the straight-line distance between two vectors. It is a good choice when you want to search for vectors that are close to each other in a high-dimensional space.

Q: How can I modify my Spanner Graph query to use the COSINE distance metric?

A: To modify your Spanner Graph query to use the COSINE distance metric, you can replace the APPROX_DOT_PRODUCT metric with the COSINE_DISTANCE metric. Here is an example:

GRAPH TestGraph
MATCH (node:Node1)
WHERE node.embedding is not NULL
ORDER BY COSINE_DISTANCE(node.embedding, ARRAY<FLOAT32>[
      -0.036149498,
      0.07207133,
      -0.020961108,
      -0.005176733,
      0.047590617,
      0.006568967,
      -0.047031555,
      0.04741984
    ],
    OPTIONS => JSON '{"num_leaves_to_search": 5}') DESC
  LIMIT 5
  RETURN node.col1, node.col2;

Q: What are the limitations of the COSINE distance metric?

A: The COSINE distance metric has several limitations. These include:

  • It is sensitive to the scale of the vectors: If the vectors in your dataset have different scales, the COSINE distance metric may not work well.
  • It is sensitive to the orientation of the vectors: If the vectors in your dataset have different orientations, the COSINE distance metric may not work well.

Q: What are the limitations of the EUCLIDEAN distance metric?

A: The EUCLIDEAN distance metric has several limitations. These include:

  • It is sensitive to the dimensionality of the vectors: If the vectors in your dataset have a high dimensionality, the EUCLIDEAN distance metric may not work well.
  • It is sensitive to the presence of outliers: If there are outliers in your dataset, the EUCLIDEAN distance metric may not work well.

Q: What are the future plans for the SpannerGraphVectorContextRetriever?

A: We are working on adding support for the APPROX_DOT_PRODUCT metric to the SpannerGraphVectorContextRetriever. This will make it easier for users to perform Approximate Nearest Neighbor search in Spanner Graph.

Q: How can I get involved in the development of the SpannerGraphVectorContextRetriever?

A: We welcome contributions from the community. If you are interested in contributing to the development of the SpannerGraphVectorContextRetriever, please contact us through the GitHub issue tracker.