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couchbase: Add standard and semantic caches (#23607)

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Thank you for contributing to LangChain!

**Description:** Add support for caching (standard + semantic) LLM
responses using Couchbase


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

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

---------

Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Nithish Raghunandanan
2024-07-12 22:30:03 +02:00
committed by GitHub
parent 8d82a0d483
commit f1618ec540
9 changed files with 1182 additions and 226 deletions
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350
libs/partners/couchbase/langchain_couchbase/cache.py Normal file
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"""
LangChain Couchbase Caches
Functions "_hash", "_loads_generations" and "_dumps_generations"
are duplicated in this utility from modules:
- "libs/community/langchain_community/cache.py"
"""
import hashlib
import json
import logging
from typing import Any, Dict, Optional, Union
from couchbase.cluster import Cluster
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.embeddings import Embeddings
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.outputs import Generation
from langchain_couchbase.vectorstores import CouchbaseVectorStore
logger = logging.getLogger(__file__)
def _hash(_input: str) -> str:
"""Use a deterministic hashing approach."""
return hashlib.md5(_input.encode()).hexdigest()
def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
"""
Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
Args:
generations (RETURN_VAL_TYPE): A list of language model generations.
Returns:
str: a single string representing a list of generations.
This function (+ its counterpart `_loads_generations`) rely on
the dumps/loads pair with Reviver, so are able to deal
with all subclasses of Generation.
Each item in the list can be `dumps`ed to a string,
then we make the whole list of strings into a json-dumped.
"""
return json.dumps([dumps(_item) for _item in generations])
def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
"""
Deserialization of a string into a generic RETURN_VAL_TYPE
(i.e. a sequence of `Generation`).
See `_dumps_generations`, the inverse of this function.
Args:
generations_str (str): A string representing a list of generations.
Compatible with the legacy cache-blob format
Does not raise exceptions for malformed entries, just logs a warning
and returns none: the caller should be prepared for such a cache miss.
Returns:
RETURN_VAL_TYPE: A list of generations.
"""
try:
generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
return generations
except (json.JSONDecodeError, TypeError):
# deferring the (soft) handling to after the legacy-format attempt
pass
try:
gen_dicts = json.loads(generations_str)
# not relying on `_load_generations_from_json` (which could disappear):
generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
logger.warning(
f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
)
return generations
except (json.JSONDecodeError, TypeError):
logger.warning(
f"Malformed/unparsable cached blob encountered: '{generations_str}'"
)
return None
class CouchbaseCache(BaseCache):
"""Couchbase LLM Cache
LLM Cache that uses Couchbase as the backend
"""
PROMPT = "prompt"
LLM = "llm"
RETURN_VAL = "return_val"
def _check_bucket_exists(self) -> bool:
"""Check if the bucket exists in the linked Couchbase cluster"""
bucket_manager = self._cluster.buckets()
try:
bucket_manager.get_bucket(self._bucket_name)
return True
except Exception:
return False
def _check_scope_and_collection_exists(self) -> bool:
"""Check if the scope and collection exists in the linked Couchbase bucket
Raises a ValueError if either is not found"""
scope_collection_map: Dict[str, Any] = {}
# Get a list of all scopes in the bucket
for scope in self._bucket.collections().get_all_scopes():
scope_collection_map[scope.name] = []
# Get a list of all the collections in the scope
for collection in scope.collections:
scope_collection_map[scope.name].append(collection.name)
# Check if the scope exists
if self._scope_name not in scope_collection_map.keys():
raise ValueError(
f"Scope {self._scope_name} not found in Couchbase "
f"bucket {self._bucket_name}"
)
# Check if the collection exists in the scope
if self._collection_name not in scope_collection_map[self._scope_name]:
raise ValueError(
f"Collection {self._collection_name} not found in scope "
f"{self._scope_name} in Couchbase bucket {self._bucket_name}"
)
return True
def __init__(
self,
cluster: Cluster,
bucket_name: str,
scope_name: str,
collection_name: str,
**kwargs: Dict[str, Any],
) -> None:
"""Initialize the Couchbase LLM Cache
Args:
cluster (Cluster): couchbase cluster object with active connection.
bucket_name (str): name of the bucket to store documents in.
scope_name (str): name of the scope in bucket to store documents in.
collection_name (str): name of the collection in the scope to store
documents in.
"""
if not isinstance(cluster, Cluster):
raise ValueError(
f"cluster should be an instance of couchbase.Cluster, "
f"got {type(cluster)}"
)
self._cluster = cluster
self._bucket_name = bucket_name
self._scope_name = scope_name
self._collection_name = collection_name
# Check if the bucket exists
if not self._check_bucket_exists():
raise ValueError(
f"Bucket {self._bucket_name} does not exist. "
" Please create the bucket before searching."
)
try:
self._bucket = self._cluster.bucket(self._bucket_name)
self._scope = self._bucket.scope(self._scope_name)
self._collection = self._scope.collection(self._collection_name)
except Exception as e:
raise ValueError(
"Error connecting to couchbase. "
"Please check the connection and credentials."
) from e
# Check if the scope and collection exists. Throws ValueError if they don't
try:
self._check_scope_and_collection_exists()
except Exception as e:
raise e
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up from cache based on prompt and llm_string."""
try:
doc = self._collection.get(
self._generate_key(prompt, llm_string)
).content_as[dict]
return _loads_generations(doc[self.RETURN_VAL])
except Exception:
return None
def _generate_key(self, prompt: str, llm_string: str) -> str:
"""Generate the key based on prompt and llm_string."""
return _hash(prompt + llm_string)
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
doc = {
self.PROMPT: prompt,
self.LLM: llm_string,
self.RETURN_VAL: _dumps_generations(return_val),
}
try:
self._collection.upsert(
key=self._generate_key(prompt, llm_string), value=doc
)
except Exception:
logger.error("Error updating cache")
def clear(self, **kwargs: Any) -> None:
"""Clear the cache.
This will delete all documents in the collection. This requires an index on the
collection.
"""
try:
query = f"DELETE FROM `{self._collection_name}`"
self._scope.query(query).execute()
except Exception:
logger.error("Error clearing cache. Please check if you have an index.")
class CouchbaseSemanticCache(BaseCache, CouchbaseVectorStore):
"""Couchbase Semantic Cache
Cache backed by a Couchbase Server with Vector Store support
"""
LLM = "llm_string"
RETURN_VAL = "return_val"
def __init__(
self,
cluster: Cluster,
embedding: Embeddings,
bucket_name: str,
scope_name: str,
collection_name: str,
index_name: str,
score_threshold: Optional[float] = None,
) -> None:
"""Initialize the Couchbase LLM Cache
Args:
cluster (Cluster): couchbase cluster object with active connection.
embedding (Embeddings): embedding model to use.
bucket_name (str): name of the bucket to store documents in.
scope_name (str): name of the scope in bucket to store documents in.
collection_name (str): name of the collection in the scope to store
documents in.
index_name (str): name of the Search index to use.
score_threshold (float): score threshold to use for filtering results.
"""
if not isinstance(cluster, Cluster):
raise ValueError(
f"cluster should be an instance of couchbase.Cluster, "
f"got {type(cluster)}"
)
self._cluster = cluster
self._bucket_name = bucket_name
self._scope_name = scope_name
self._collection_name = collection_name
# Check if the bucket exists
if not self._check_bucket_exists():
raise ValueError(
f"Bucket {self._bucket_name} does not exist. "
" Please create the bucket before searching."
)
try:
self._bucket = self._cluster.bucket(self._bucket_name)
self._scope = self._bucket.scope(self._scope_name)
self._collection = self._scope.collection(self._collection_name)
except Exception as e:
raise ValueError(
"Error connecting to couchbase. "
"Please check the connection and credentials."
) from e
# Check if the scope and collection exists. Throws ValueError if they don't
try:
self._check_scope_and_collection_exists()
except Exception as e:
raise e
self.score_threshold = score_threshold
# Initialize the vector store
super().__init__(
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up from cache based on the semantic similarity of the prompt"""
search_results = self.similarity_search_with_score(
prompt, k=1, search_options={f"metadata.{self.LLM}": llm_string}
)
if search_results:
selected_doc, score = search_results[0]
else:
return None
# Check if the score is above the threshold if a threshold is provided
if self.score_threshold:
if score < self.score_threshold:
return None
# Note that the llm_string might not match the vector search result.
# So if the llm_string does not match, do not return the result.
if selected_doc.metadata["llm_string"] != llm_string:
return None
return _loads_generations(selected_doc.metadata[self.RETURN_VAL])
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on the prompt and llm_string"""
try:
self.add_texts(
texts=[prompt],
metadatas=[
{
self.LLM: llm_string,
self.RETURN_VAL: _dumps_generations(return_val),
}
],
)
except Exception:
logger.error("Error updating cache")
def clear(self, **kwargs: Any) -> None:
"""Clear the cache.
This will delete all documents in the collection.
This requires an index on the collection.
"""
try:
query = f"DELETE FROM `{self._collection_name}`"
self._scope.query(query).execute()
except Exception:
logger.error("Error clearing cache. Please check if you have an index.")