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docs: CrateDB: Educate readers about full and semantic cache components (#29000)

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Dear @ccurme and @efriis,

following up on our initial patch adding documentation about CrateDB
[^1], with version 0.1.0, just released, the [CrateDB
provider](https://python.langchain.com/docs/integrations/providers/cratedb/)
starts providing `CrateDBCache` and `CrateDBSemanticCache` classes. This
little patch updates the documentation accordingly.

Happy New Year!

With kind regards,
Andreas.

[^1]: Thanks for merging
https://github.com/langchain-ai/langchain/pull/28877 so quickly.

/cc @kneth, @simonprickett


#### Preview
- [Full
Cache](https://langchain-git-fork-crate-workbench-docs-cratedb-cache-langchain.vercel.app/docs/integrations/providers/cratedb/#full-cache)
- [Semantic
Cache](https://langchain-git-fork-crate-workbench-docs-cratedb-cache-langchain.vercel.app/docs/integrations/providers/cratedb/#semantic-cache)
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docs/docs/integrations/providers/cratedb.mdx
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@ -27,7 +27,7 @@ docker run --name=cratedb --rm \
[free trial][CrateDB Cloud Console].
### Install Client
Install the most recent version of the `langchain-cratedb` package
Install the most recent version of the [langchain-cratedb] package
and a few others that are needed for this tutorial.
```bash
pip install --upgrade langchain-cratedb langchain-openai unstructured
@ -120,13 +120,78 @@ message_history = CrateDBChatMessageHistory(
message_history.add_user_message("hi!")
```
### Full Cache
The standard / full cache avoids invoking the LLM when the supplied
prompt is exactly the same as one encountered already.
See also [CrateDBCache Example].
To use the full cache in your applications:
```python
import sqlalchemy as sa
from langchain.globals import set_llm_cache
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_cratedb import CrateDBCache
# Configure cache.
engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
set_llm_cache(CrateDBCache(engine))
# Invoke LLM conversation.
llm = ChatOpenAI(
model_name="chatgpt-4o-latest",
temperature=0.7,
)
print()
print("Asking with full cache:")
answer = llm.invoke("What is the answer to everything?")
print(answer.content)
```
### Semantic Cache
The semantic cache allows users to retrieve cached prompts based on semantic
similarity between the user input and previously cached inputs. It also avoids
invoking the LLM when not needed.
See also [CrateDBSemanticCache Example].
To use the semantic cache in your applications:
```python
import sqlalchemy as sa
from langchain.globals import set_llm_cache
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_cratedb import CrateDBSemanticCache
# Configure embeddings.
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
# Configure cache.
engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
set_llm_cache(
CrateDBSemanticCache(
embedding=embeddings,
connection=engine,
search_threshold=1.0,
)
)
# Invoke LLM conversation.
llm = ChatOpenAI(model_name="chatgpt-4o-latest")
print()
print("Asking with semantic cache:")
answer = llm.invoke("What is the answer to everything?")
print(answer.content)
```
[all features of CrateDB]: https://cratedb.com/docs/guide/feature/
[CrateDB]: https://cratedb.com/database
[CrateDB Cloud]: https://cratedb.com/database/cloud
[CrateDB Cloud Console]: https://console.cratedb.cloud/?utm_source=langchain&utm_content=documentation
[CrateDB installation options]: https://cratedb.com/docs/guide/install/
[CrateDBCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
[CrateDBSemanticCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
[CrateDBChatMessageHistory Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb
[CrateDBLoader Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb
[CrateDBVectorStore Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
[langchain-cratedb]: https://pypi.org/project/langchain-cratedb/
[using LangChain with CrateDB]: https://cratedb.com/docs/guide/integrate/langchain/