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**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> |
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package_name
This package contains the LangChain integration with ModuleName
Installation
pip install -U __package_name__
And you should configure credentials by setting the following environment variables:
- TODO: fill this out
Chat Models
Chat__ModuleName__ class exposes chat models from ModuleName.
from __module_name__ import Chat__ModuleName__
llm = Chat__ModuleName__()
llm.invoke("Sing a ballad of LangChain.")
Embeddings
__ModuleName__Embeddings class exposes embeddings from ModuleName.
from __module_name__ import __ModuleName__Embeddings
embeddings = __ModuleName__Embeddings()
embeddings.embed_query("What is the meaning of life?")
LLMs
__ModuleName__LLM class exposes LLMs from ModuleName.
from __module_name__ import __ModuleName__LLM
llm = __ModuleName__LLM()
llm.invoke("The meaning of life is")