040597e832
This PR improves on the `CassandraCache` and `CassandraSemanticCache` classes, mainly in the constructor signature, and also introduces several minor improvements around these classes. ### Init signature A (sigh) breaking change is tentatively introduced to the constructor. To me, the advantages outweigh the possible discomfort: the new syntax places the DB-connection objects `session` and `keyspace` later in the param list, so that they can be given a default value. This is what enables the pattern of _not_ specifying them, provided one has previously initialized the Cassandra connection through the versatile utility method `cassio.init(...)`. In this way, a much less unwieldy instantiation can be done, such as `CassandraCache()` and `CassandraSemanticCache(embedding=xyz)`, everything else falling back to defaults. A downside is that, compared to the earlier signature, this might turn out to be breaking for those doing positional instantiation. As a way to mitigate this problem, this PR typechecks its first argument trying to detect the legacy usage. (And to make this point less tricky in the future, most arguments are left to be keyword-only). If this is considered too harsh, I'd like guidance on how to further smoothen this transition. **Our plan is to make the pattern of optional session/keyspace a standard across all Cassandra classes**, so that a repeatable strategy would be ideal. A possibility would be to keep positional arguments for legacy reasons but issue a deprecation warning if any of them is actually used, to later remove them with 0.2 - please advise on this point. ### Other changes - class docstrings: enriched, completely moved to class level, added note on `cassio.init(...)` pattern, added tiny sample usage code. - semantic cache: revised terminology to never mention "distance" (it is in fact a similarity!). Kept the legacy constructor param with a deprecation warning if used. - `llm_caching` notebook: uniform flow with the Cassandra and Astra DB separate cases; better and Cassandra-first description; all imports made explicit and from community where appropriate. - cache integration tests moved to community (incl. the imported tools), env var bugfix for `CASSANDRA_CONTACT_POINTS`. --------- Co-authored-by: Erick Friis <erick@langchain.dev> |
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| .env.template | ||
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| README.md | ||
cassandra-synonym-caching
This template provides a simple chain template showcasing the usage of LLM Caching backed by Apache Cassandra® or Astra DB through CQL.
Environment Setup
To set up your environment, you will need the following:
- an Astra Vector Database (free tier is fine!). You need a Database Administrator token, in particular the string starting with
AstraCS:...; - likewise, get your Database ID ready, you will have to enter it below;
- an OpenAI API Key. (More info here, note that out-of-the-box this demo supports OpenAI unless you tinker with the code.)
Note: you can alternatively use a regular Cassandra cluster: to do so, make sure you provide the USE_CASSANDRA_CLUSTER entry as shown in .env.template and the subsequent environment variables to specify how to connect to it.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package cassandra-synonym-caching
If you want to add this to an existing project, you can just run:
langchain app add cassandra-synonym-caching
And add the following code to your server.py file:
from cassandra_synonym_caching import chain as cassandra_synonym_caching_chain
add_routes(app, cassandra_synonym_caching_chain, path="/cassandra-synonym-caching")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/cassandra-synonym-caching/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/cassandra-synonym-caching")
Reference
Stand-alone LangServe template repo: here.