Add a conversation memory that combines a (optionally persistent) vectorstore history with a token buffer (#22155)

**langchain: ConversationVectorStoreTokenBufferMemory**

-**Description:** This PR adds ConversationVectorStoreTokenBufferMemory.
It is similar in concept to ConversationSummaryBufferMemory. It
maintains an in-memory buffer of messages up to a preset token limit.
After the limit is hit timestamped messages are written into a
vectorstore retriever rather than into a summary. The user's prompt is
then used to retrieve relevant fragments of the previous conversation.
By persisting the vectorstore, one can maintain memory from session to
session.
-**Issue:** n/a
-**Dependencies:** none
-**Twitter handle:** Please no!!!
- [X] **Add tests and docs**: I looked to see how the unit tests were
written for the other ConversationMemory modules, but couldn't find
anything other than a test for successful import. I need to know whether
you are using pytest.mock or another fixture to simulate the LLM and
vectorstore. In addition, I would like guidance on where to place the
documentation. Should it be a notebook file in docs/docs?

- [X] **Lint and test**: I am seeing some linting errors from a couple
of modules unrelated to this PR.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>

libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py

@@ -1,6 +1,7 @@
 """__ModuleName__ document loader."""
 
 from typing import Iterator
+
 from langchain_core.document_loaders.base import BaseLoader
 from langchain_core.documents import Document