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community[patch]: Add OpenSearch as semantic cache (#20254)

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### Description
Use OpenSearch vector store as Semantic Cache.

### Twitter Handle
**@OpenSearchProj**

---------

Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Co-authored-by: Harish Tatikonda <harishtatikonda@Harishs-MacBook-Air.local>
Co-authored-by: EC2 Default User <ec2-user@ip-172-31-31-155.ec2.internal>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Naveen Tatikonda 2024-04-26 19:20:24 -05:00 committed by GitHub
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4 changed files with 431 additions and 21 deletions
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128
docs/docs/integrations/llms/llm_caching.ipynb
View File

@ -12,12 +12,12 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 9,
"id": "10ad9224",
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-18T01:01:08.425930Z",
"start_time": "2024-03-18T01:01:08.327196Z"
"end_time": "2024-04-12T02:05:57.319706Z",
"start_time": "2024-04-12T02:05:57.303868Z"
}
},
"outputs": [],
@ -1358,7 +1358,10 @@
"cell_type": "markdown",
"id": "40624c26e86b57a4",
"metadata": {
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Azure Cosmos DB Semantic Cache\n",
@ -1435,7 +1438,10 @@
"end_time": "2024-03-12T00:12:57.462226Z",
"start_time": "2024-03-12T00:12:55.166201Z"
},
"collapsed": false
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
@ -1865,6 +1871,116 @@
"source": [
"!rm .langchain.db sqlite.db"
]
},
{
"cell_type": "markdown",
"id": "544a90cbdd9894ba",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "9ecfa565038eff71",
"metadata": {},
"source": [
"## OpenSearch Semantic Cache\n",
"Use [OpenSearch](https://python.langchain.com/docs/integrations/vectorstores/opensearch/) as a semantic cache to cache prompts and responses and evaluate hits based on semantic similarity."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7379fd5aa83ee500",
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-12T02:06:03.766873Z",
"start_time": "2024-04-12T02:06:03.754481Z"
}
},
"outputs": [],
"source": [
"from langchain_community.cache import OpenSearchSemanticCache\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"set_llm_cache(\n",
" OpenSearchSemanticCache(\n",
" opensearch_url=\"http://localhost:9200\", embedding=OpenAIEmbeddings()\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fecb26634bf27e93",
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-12T02:06:08.734403Z",
"start_time": "2024-04-12T02:06:07.178381Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 39.4 ms, sys: 11.8 ms, total: 51.2 ms\n",
"Wall time: 1.55 s\n"
]
},
{
"data": {
"text/plain": [
"\"\\n\\nWhy don't scientists trust atoms?\\n\\nBecause they make up everything.\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# The first time, it is not yet in cache, so it should take longer\n",
"llm(\"Tell me a joke\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "43b24b725ea4ba98",
"metadata": {
"ExecuteTime": {
"end_time": "2024-04-12T02:06:12.073448Z",
"start_time": "2024-04-12T02:06:11.957571Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4.66 ms, sys: 1.1 ms, total: 5.76 ms\n",
"Wall time: 113 ms\n"
]
},
{
"data": {
"text/plain": [
"\"\\n\\nWhy don't scientists trust atoms?\\n\\nBecause they make up everything.\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# The second time, while not a direct hit, the question is semantically similar to the original question,\n",
"# so it uses the cached result!\n",
"llm(\"Tell me one joke\")"
]
}
],
"metadata": {
@ -1883,7 +1999,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.9.1"
}
},
"nbformat": 4,

106
libs/community/langchain_community/cache.py
View File

@ -19,6 +19,7 @@ Cache directly competes with Memory. See documentation for Pros and Cons.
BaseCache --> <name>Cache # Examples: InMemoryCache, RedisCache, GPTCache
"""
from __future__ import annotations
import hashlib
@ -76,6 +77,9 @@ from langchain_community.utilities.astradb import (
_AstraDBCollectionEnvironment,
)
from langchain_community.vectorstores import AzureCosmosDBVectorSearch
from langchain_community.vectorstores import (
OpenSearchVectorSearch as OpenSearchVectorStore,
)
from langchain_community.vectorstores.redis import Redis as RedisVectorstore
logger = logging.getLogger(__file__)
@ -2049,3 +2053,105 @@ class AzureCosmosDBSemanticCache(BaseCache):
def _validate_enum_value(value: Any, enum_type: Type[Enum]) -> None:
if not isinstance(value, enum_type):
raise ValueError(f"Invalid enum value: {value}. Expected {enum_type}.")
class OpenSearchSemanticCache(BaseCache):
"""Cache that uses OpenSearch vector store backend"""
def __init__(
self, opensearch_url: str, embedding: Embeddings, score_threshold: float = 0.2
):
"""
Args:
opensearch_url (str): URL to connect to OpenSearch.
embedding (Embedding): Embedding provider for semantic encoding and search.
score_threshold (float, 0.2):
Example:
.. code-block:: python
import langchain
from langchain.cache import OpenSearchSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = OpenSearchSemanticCache(
opensearch_url="http//localhost:9200",
embedding=OpenAIEmbeddings()
)
"""
self._cache_dict: Dict[str, OpenSearchVectorStore] = {}
self.opensearch_url = opensearch_url
self.embedding = embedding
self.score_threshold = score_threshold
def _index_name(self, llm_string: str) -> str:
hashed_index = _hash(llm_string)
return f"cache_{hashed_index}"
def _get_llm_cache(self, llm_string: str) -> OpenSearchVectorStore:
index_name = self._index_name(llm_string)
# return vectorstore client for the specific llm string
if index_name in self._cache_dict:
return self._cache_dict[index_name]
# create new vectorstore client for the specific llm string
self._cache_dict[index_name] = OpenSearchVectorStore(
opensearch_url=self.opensearch_url,
index_name=index_name,
embedding_function=self.embedding,
)
# create index for the vectorstore
vectorstore = self._cache_dict[index_name]
if not vectorstore.index_exists():
_embedding = self.embedding.embed_query(text="test")
vectorstore.create_index(len(_embedding), index_name)
return vectorstore
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
llm_cache = self._get_llm_cache(llm_string)
generations: List = []
# Read from a Hash
results = llm_cache.similarity_search(
query=prompt,
k=1,
score_threshold=self.score_threshold,
)
if results:
for document in results:
try:
generations.extend(loads(document.metadata["return_val"]))
except Exception:
logger.warning(
"Retrieving a cache value that could not be deserialized "
"properly. This is likely due to the cache being in an "
"older format. Please recreate your cache to avoid this "
"error."
)
generations.extend(
_load_generations_from_json(document.metadata["return_val"])
)
return generations if generations else None
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
for gen in return_val:
if not isinstance(gen, Generation):
raise ValueError(
"OpenSearchSemanticCache only supports caching of "
f"normal LLM generations, got {type(gen)}"
)
llm_cache = self._get_llm_cache(llm_string)
metadata = {
"llm_string": llm_string,
"prompt": prompt,
"return_val": dumps([g for g in return_val]),
}
llm_cache.add_texts(texts=[prompt], metadatas=[metadata])
def clear(self, **kwargs: Any) -> None:
"""Clear semantic cache for a given llm_string."""
index_name = self._index_name(kwargs["llm_string"])
if index_name in self._cache_dict:
self._cache_dict[index_name].delete_index(index_name=index_name)
del self._cache_dict[index_name]

159
libs/community/langchain_community/vectorstores/opensearch_vector_search.py
View File

@ -274,10 +274,12 @@ def _default_approximate_search_query(
query_vector: List[float],
k: int = 4,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, this is the default query."""
return {
"size": k,
"min_score": score_threshold,
"query": {"knn": {vector_field: {"vector": query_vector, "k": k}}},
}
@ -288,10 +290,12 @@ def _approximate_search_query_with_boolean_filter(
k: int = 4,
vector_field: str = "vector_field",
subquery_clause: str = "must",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, with Boolean Filter."""
return {
"size": k,
"min_score": score_threshold,
"query": {
"bool": {
"filter": boolean_filter,
@ -308,11 +312,12 @@ def _approximate_search_query_with_efficient_filter(
efficient_filter: Dict,
k: int = 4,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Approximate k-NN Search, with Efficient Filter for Lucene and
Faiss Engines."""
search_query = _default_approximate_search_query(
query_vector, k=k, vector_field=vector_field
query_vector, k=k, vector_field=vector_field, score_threshold=score_threshold
)
search_query["query"]["knn"][vector_field]["filter"] = efficient_filter
return search_query
@ -324,6 +329,7 @@ def _default_script_query(
space_type: str = "l2",
pre_filter: Optional[Dict] = None,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Script Scoring Search, this is the default query."""
@ -332,6 +338,7 @@ def _default_script_query(
return {
"size": k,
"min_score": score_threshold,
"query": {
"script_score": {
"query": pre_filter,
@ -368,6 +375,7 @@ def _default_painless_scripting_query(
space_type: str = "l2Squared",
pre_filter: Optional[Dict] = None,
vector_field: str = "vector_field",
score_threshold: Optional[float] = 0.0,
) -> Dict:
"""For Painless Scripting Search, this is the default query."""
@ -377,6 +385,7 @@ def _default_painless_scripting_query(
source = __get_painless_scripting_source(space_type, vector_field=vector_field)
return {
"size": k,
"min_score": score_threshold,
"query": {
"script_score": {
"query": pre_filter,
@ -509,6 +518,72 @@ class OpenSearchVectorSearch(VectorStore):
is_aoss=self.is_aoss,
)
def delete_index(self, index_name: Optional[str] = None) -> Optional[bool]:
"""Deletes a given index from vectorstore."""
if index_name is None:
if self.index_name is None:
raise ValueError("index_name must be provided.")
index_name = self.index_name
try:
self.client.indices.delete(index=index_name)
return True
except Exception as e:
raise e
def index_exists(self, index_name: Optional[str] = None) -> Optional[bool]:
"""If given index present in vectorstore, returns True else False."""
if index_name is None:
if self.index_name is None:
raise ValueError("index_name must be provided.")
index_name = self.index_name
return self.client.indices.exists(index=index_name)
def create_index(
self,
dimension: int,
index_name: Optional[str] = uuid.uuid4().hex,
**kwargs: Any,
) -> Optional[str]:
"""Create a new Index with given arguments"""
is_appx_search = kwargs.get("is_appx_search", True)
vector_field = kwargs.get("vector_field", "vector_field")
kwargs.get("text_field", "text")
http_auth = kwargs.get("http_auth")
is_aoss = _is_aoss_enabled(http_auth=http_auth)
if is_aoss and not is_appx_search:
raise ValueError(
"Amazon OpenSearch Service Serverless only "
"supports `approximate_search`"
)
if is_appx_search:
engine = kwargs.get("engine", "nmslib")
space_type = kwargs.get("space_type", "l2")
ef_search = kwargs.get("ef_search", 512)
ef_construction = kwargs.get("ef_construction", 512)
m = kwargs.get("m", 16)
_validate_aoss_with_engines(is_aoss, engine)
mapping = _default_text_mapping(
dimension,
engine,
space_type,
ef_search,
ef_construction,
m,
vector_field,
)
else:
mapping = _default_scripting_text_mapping(dimension)
if self.index_exists(index_name):
raise RuntimeError(f"The index, {index_name} already exists.")
self.client.indices.create(index=index_name, body=mapping)
return index_name
def add_texts(
self,
texts: Iterable[str],
@ -659,7 +734,11 @@ class OpenSearchVectorSearch(VectorStore):
)
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
self,
query: str,
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
@ -669,6 +748,8 @@ class OpenSearchVectorSearch(VectorStore):
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents most similar to the query.
@ -717,20 +798,30 @@ class OpenSearchVectorSearch(VectorStore):
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {"match_all": {}}
"""
docs_with_scores = self.similarity_search_with_score(query, k, **kwargs)
docs_with_scores = self.similarity_search_with_score(
query, k, score_threshold, **kwargs
)
return [doc[0] for doc in docs_with_scores]
def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to the embedding vector."""
docs_with_scores = self.similarity_search_with_score_by_vector(
embedding, k, **kwargs
embedding, k, score_threshold, **kwargs
)
return [doc[0] for doc in docs_with_scores]
def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
self,
query: str,
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and it's scores most similar to query.
@ -740,6 +831,8 @@ class OpenSearchVectorSearch(VectorStore):
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents along with its scores most similar to the query.
@ -748,10 +841,16 @@ class OpenSearchVectorSearch(VectorStore):
same as `similarity_search`
"""
embedding = self.embedding_function.embed_query(query)
return self.similarity_search_with_score_by_vector(embedding, k, **kwargs)
return self.similarity_search_with_score_by_vector(
embedding, k, score_threshold, **kwargs
)
def similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and it's scores most similar to the embedding vector.
@ -761,6 +860,8 @@ class OpenSearchVectorSearch(VectorStore):
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of Documents along with its scores most similar to the query.
@ -772,7 +873,7 @@ class OpenSearchVectorSearch(VectorStore):
metadata_field = kwargs.get("metadata_field", "metadata")
hits = self._raw_similarity_search_with_score_by_vector(
embedding=embedding, k=k, **kwargs
embedding=embedding, k=k, score_threshold=score_threshold, **kwargs
)
documents_with_scores = [
@ -792,7 +893,11 @@ class OpenSearchVectorSearch(VectorStore):
return documents_with_scores
def _raw_similarity_search_with_score_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
self,
embedding: List[float],
k: int = 4,
score_threshold: Optional[float] = 0.0,
**kwargs: Any,
) -> List[dict]:
"""Return raw opensearch documents (dict) including vectors,
scores most similar to the embedding vector.
@ -803,6 +908,8 @@ class OpenSearchVectorSearch(VectorStore):
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
score_threshold: Specify a score threshold to return only documents
above the threshold. Defaults to 0.0.
Returns:
List of dict with its scores most similar to the embedding.
@ -868,10 +975,15 @@ class OpenSearchVectorSearch(VectorStore):
k=k,
vector_field=vector_field,
subquery_clause=subquery_clause,
score_threshold=score_threshold,
)
elif efficient_filter != {}:
search_query = _approximate_search_query_with_efficient_filter(
embedding, efficient_filter, k=k, vector_field=vector_field
embedding,
efficient_filter,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
elif lucene_filter != {}:
warnings.warn(
@ -879,23 +991,40 @@ class OpenSearchVectorSearch(VectorStore):
" `efficient_filter`"
)
search_query = _approximate_search_query_with_efficient_filter(
embedding, lucene_filter, k=k, vector_field=vector_field
embedding,
lucene_filter,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
else:
search_query = _default_approximate_search_query(
embedding, k=k, vector_field=vector_field
embedding,
k=k,
vector_field=vector_field,
score_threshold=score_threshold,
)
elif search_type == SCRIPT_SCORING_SEARCH:
space_type = kwargs.get("space_type", "l2")
pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY)
search_query = _default_script_query(
embedding, k, space_type, pre_filter, vector_field
embedding,
k,
space_type,
pre_filter,
vector_field,
score_threshold=score_threshold,
)
elif search_type == PAINLESS_SCRIPTING_SEARCH:
space_type = kwargs.get("space_type", "l2Squared")
pre_filter = kwargs.get("pre_filter", MATCH_ALL_QUERY)
search_query = _default_painless_scripting_query(
embedding, k, space_type, pre_filter, vector_field
embedding,
k,
space_type,
pre_filter,
vector_field,
score_threshold=score_threshold,
)
else:
raise ValueError("Invalid `search_type` provided as an argument")

59
libs/langchain/tests/integration_tests/cache/test_opensearch_cache.py vendored Normal file
View File

@ -0,0 +1,59 @@
from langchain_community.cache import OpenSearchSemanticCache
from langchain_core.outputs import Generation
from langchain.globals import get_llm_cache, set_llm_cache
from tests.integration_tests.cache.fake_embeddings import (
FakeEmbeddings,
)
from tests.unit_tests.llms.fake_llm import FakeLLM
DEFAULT_OPENSEARCH_URL = "http://localhost:9200"
def test_opensearch_semantic_cache() -> None:
"""Test opensearch semantic cache functionality."""
set_llm_cache(
OpenSearchSemanticCache(
embedding=FakeEmbeddings(),
opensearch_url=DEFAULT_OPENSEARCH_URL,
score_threshold=0.0,
)
)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
cache_output = get_llm_cache().lookup("bar", llm_string)
assert cache_output == [Generation(text="fizz")]
get_llm_cache().clear(llm_string=llm_string)
output = get_llm_cache().lookup("bar", llm_string)
assert output != [Generation(text="fizz")]
def test_opensearch_semantic_cache_multi() -> None:
set_llm_cache(
OpenSearchSemanticCache(
embedding=FakeEmbeddings(),
opensearch_url=DEFAULT_OPENSEARCH_URL,
score_threshold=0.0,
)
)
llm = FakeLLM()
params = llm.dict()
params["stop"] = None
llm_string = str(sorted([(k, v) for k, v in params.items()]))
get_llm_cache().update(
"foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
)
# foo and bar will have the same embedding produced by FakeEmbeddings
cache_output = get_llm_cache().lookup("bar", llm_string)
assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]
# clear the cache
get_llm_cache().clear(llm_string=llm_string)
output = get_llm_cache().lookup("bar", llm_string)
assert output != [Generation(text="fizz"), Generation(text="Buzz")]