* fix(testing+linting): add nox lint+format directives

This change introduces new nox directives:
* blacken: `nox -s blacken`
* format: `nox -s format` to apply formatting to files
* lint: `nox -s lint` to flag linting issues
* integration: to run integration tests
* unit: to run unit tests locally

which are the basis to enable scalable development
and continuous testing as I prepare to bring in
Approximate Nearest Neighors (ANN) functionality into
this package.

* Rebased from main and re-ran nox -s format/lint

…TABLE, CREATE VECTOR INDEX) (#124)

* fix(testing+linting): add nox lint+format directives

This change introduces new nox directives:
* blacken: `nox -s blacken`
* format: `nox -s format` to apply formatting to files
* lint: `nox -s lint` to flag linting issues
* unit: to run unit tests locally

which are the basis to enable scalable development
and continuous testing as I prepare to bring in
Approximate Nearest Neighors (ANN) functionality into
this package.

Also while here, fixed a typo in the README.rst file
that didn't have the correct import path.

* feat: add Approximate Nearest Neighbor support to distance strategies

This change adds ANN distance strategies for GoogleSQL semantics.
While here started unit tests to effectively test out components
without having to have a running Cloud Spanner instance.

Updates #94

* Ensure vector fits within limits in sample

* Update ANN query names + test expectations

* Pass in strategy inferred from initialization

* Hook up get_documents_from_query_results

* Link up __search_by_ANN to similarity_search_by_vector

* Incorporate pre_filter and post_filter plus update tests

* Review addressing

* Simplified checking if using ANN

* Reduce the amount of changes

* More reductions

* More reductions to ease code review

* Fit with get_rows_by_similarity_search_ann

* Updates from nox

* Fix PostGreSQL

This change is carved out of PR #138, due to the fact that
at present, Google Cloud Spanner vector index creation and
deletion takes a very long time and is non-deterministic
hence unreliable for integration tests so these updates
which have unit tests are exclusive of the integration tests
and allow for usage directly as Google engineering figures
out the backend issues.

Updates #94.

… Jupyter Notebook (#139)

This change provides a copy+pastable guide for using the ANN algorithms
inside the Jupyter Notebook.

Updates #94

* Extension to SpannerGraphStore

- Allow a dynamic schema for nodes and edges:
  - nodes are stored in one table with dynamic label and properties (JSON)
- Allow more than one (src.node.id, target.node.id, edge.type) by
  introducing a uuid as part of the key;
- Prefix the table names with graph names to avoid conflicting.

* Fix linters

* Update notebook for illustrate flexible schema

* Remove the uuid key

* Resolve comments

* Avoid unnecessary overwrites based on the specified properties

Previously, we override all columns by schema;
with this change, we selectively write specified columns.

Note:
- this won't change the behavior if you always do a full blind write;

* Address comments

* Minor updates to the interface

* Add agent info for usage tracking

* Update src/langchain_google_spanner/graph_store.py

---------

Co-authored-by: Averi Kitsch <akitsch@google.com>

Co-authored-by: release-please[bot] <55107282+release-please[bot]@users.noreply.github.com>