Initial add IBM DB2 vector store .

[yiweiHeOSS]

This PR adds a new folder under libs for the DB2 vector store langchain connector code, i.e., libs/langchain-db2.
Other companies are doing the same things for their vector store (e.g. Google, AWS and Azure). Thanks for @ccurme to give me some guidance about where to put the code and @MateuszOssGit gave me the access to this repo .

Later, I will create a package on Pypi of the vector store connector, i.e. pip install langchain-db2. Note that it should have no impact on the current package pip install langchain-ibm. I just need a separate directory libs/langchain-db2 to put open-source code.

The major changes in this PR are in the files below:

• libs/langchain-db2/langchain_db2/db2vs.py - this is the source file of the db2 vector store
• libs/langchain-db2/docs/db2.ipynb - this is the Jupyter notebook for the demo and steps on how to use the db2 vector store
• libs/langchain-db2/tests/integration_tests/test_db2vs.py - This is the integration test

[shaikhq]

can we add support for all 6 distance strategies that we support at Db2?

[yiweiHeOSS]

I saw the db2 support:

metric

Identifies the metric used to compute the distance between vector1 and vector2. The supported metrics are:

COSINE
DOT
EUCLIDEAN
EUCLIDEAN_SQUARED
HAMMING
MANHATTAN

However, the langchain I only found:

class DistanceStrategy(str, Enum):
    """Enumerator of the Distance strategies for calculating distances
    between vectors."""

    EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE"
    MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT"
    DOT_PRODUCT = "DOT_PRODUCT"
    JACCARD = "JACCARD"
    COSINE = "COSINE"

So I guess I can only add the 3 strategies as I did now. Please tell me if I am wrong.