mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 19:24:27 +00:00
implemented RestAPI and added support for cloud and local Qdrant clusters
This commit is contained in:
Haadi Rakhangi
parent
a047df3825
commit
9df92923d8
3 changed files with 238 additions and 129 deletions
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@ -146,11 +146,6 @@ assert response1.id == response2.id
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<TabItem value="qdrant-sem" label="qdrant-semantic cache">
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Install redis
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```shell
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pip install qdrant-client
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```
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You can set up your own cloud Qdrant cluster by following this: https://qdrant.tech/documentation/quickstart-cloud/
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To set up a Qdrant cluster locally follow: https://qdrant.tech/documentation/quickstart/
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@ -166,12 +161,12 @@ random_number = random.randint(
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print("testing semantic caching")
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litellm.cache = Cache(
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type="qdrant-semantic",
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qdrant_host_type="cloud", # can be either 'cloud' or 'local'
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qdrant_url=os.environ["QDRANT_URL"],
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qdrant_username=os.environ["QDRANT_USERNAME"]",
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qdrant_password=os.environ["QDRANT_PASSWORD"],
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qdrant_api_key=os.environ["QDRANT_API_KEY"],
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qdrant_collection_name="your_collection_name", # any name of your collection
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similarity_threshold=0.7, # similarity threshold for cache hits, 0 == no similarity, 1 = exact matches, 0.5 == 50% similarity
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qdrant_quantization_config = "binary", # can be one of 'binary', 'product' or 'scalar' quantizations that is supported by qdrant
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qdrant_quantization_config ="binary", # can be one of 'binary', 'product' or 'scalar' quantizations that is supported by qdrant
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qdrant_semantic_cache_embedding_model="text-embedding-ada-002", # this model is passed to litellm.embedding(), any litellm.embedding() model is supported here
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)
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@ -496,12 +491,12 @@ def __init__(
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disk_cache_dir=None,
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# qdrant cache params
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qdrant_username: Optional[str] = None,
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qdrant_password: Optional[str] = None,
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qdrant_url: Optional[str] = None,
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qdrant_api_key: Optional[str] = None,
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qdrant_collection_name: Optional[str] = None,
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qdrant_quantization_config: Optional[str] = None,
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qdrant_semantic_cache_embedding_model="text-embedding-ada-002",
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qdrant_host_type: Optional[Literal["local","cloud"]] = "local",
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**kwargs
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):
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@ -1220,16 +1220,18 @@ class RedisSemanticCache(BaseCache):
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class QdrantSemanticCache(BaseCache):
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def __init__(
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self,
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qdrant_username=None,
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qdrant_password=None,
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qdrant_url=None,
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qdrant_api_key = None,
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collection_name=None,
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similarity_threshold=None,
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quantization_config=None,
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embedding_model="text-embedding-ada-002"
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embedding_model="text-embedding-ada-002",
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host_type = None
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):
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from qdrant_client import models, AsyncQdrantClient, QdrantClient
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import base64
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from litellm.llms.custom_httpx.http_handler import (
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_get_httpx_client,
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_get_async_httpx_client
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)
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if collection_name is None:
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raise Exception("collection_name must be provided, passed None")
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@ -1244,73 +1246,109 @@ class QdrantSemanticCache(BaseCache):
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self.similarity_threshold = similarity_threshold
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self.embedding_model = embedding_model
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if qdrant_url is None or qdrant_username is None or qdrant_password is None:
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if host_type=="cloud":
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import os
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if qdrant_url is None:
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qdrant_url = os.getenv('QDRANT_URL')
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if qdrant_api_key is None:
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qdrant_api_key = os.getenv('QDRANT_API_KEY')
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if qdrant_url is not None and qdrant_api_key is not None:
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headers = {
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"api-key": qdrant_api_key,
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"Content-Type": "application/json"
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}
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else:
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raise Exception("Qdrant url and api_key must be provided for qdrant cloud hosting")
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elif host_type=="local":
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import os
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if qdrant_url is None:
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qdrant_url = os.getenv('QDRANT_URL')
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if qdrant_url is None:
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raise Exception("Qdrant url must be provided for qdrant local hosting")
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if qdrant_api_key is None:
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qdrant_api_key = os.getenv('QDRANT_API_KEY')
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if qdrant_api_key is None:
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print_verbose('Running locally without API Key.')
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headers= {
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"Content-Type": "application/json"
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}
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else:
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print_verbose("Running locally with API Key")
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headers = {
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"api-key": qdrant_api_key,
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"Content-Type": "application/json"
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}
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else:
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raise Exception("Host type can be either 'local' or 'cloud'")
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qdrant_url = os.getenv('QDRANT_URL')
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qdrant_username = os.getenv('QDRANT_USERNAME')
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qdrant_password = os.getenv('QDRANT PASSWORD')
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if qdrant_url is None or qdrant_username is None or qdrant_password is None:
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raise Exception("Qdrant url, username and password must be provided")
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self.qdrant_url = qdrant_url
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self.qdrant_api_key = qdrant_api_key
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print_verbose(f"qdrant semantic-cache qdrant_url: {self.qdrant_url}")
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print_verbose(f"qdrant semantic-cache qdrant_url: {qdrant_url}")
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self.credentials = f"{qdrant_username}:{qdrant_password}"
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self.encoded_credentials = base64.b64encode(self.credentials.encode()).decode()
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self.headers = {
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"Authorization": f"Basic {self.encoded_credentials}"
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}
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self.headers = headers
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self.qdrant_client = QdrantClient(
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url= qdrant_url,
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timeout=1200,
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headers=self.headers
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)
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self.sync_client = _get_httpx_client()
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self.async_client = _get_async_httpx_client()
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self.qdrant_client_async = AsyncQdrantClient(
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url= qdrant_url,
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timeout=1200,
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headers=self.headers
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)
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if quantization_config is None:
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print('Quantization config is not provided. Default binary quantization will be used.')
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if self.qdrant_client.collection_exists(collection_name=f"{self.collection_name}"):
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self.collection_info = self.qdrant_client.get_collection(f"{self.collection_name}")
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collection_exists = self.sync_client.get(
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url= f"{self.qdrant_url}/collections/{self.collection_name}/exists",
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headers=self.headers
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)
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if collection_exists.json()['result']['exists']:
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collection_details = self.sync_client.get(
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url=f"{self.qdrant_url}/collections/{self.collection_name}",
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headers=self.headers
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)
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self.collection_info = collection_details.json()
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print_verbose(f'Collection already exists.\nCollection details:{self.collection_info}')
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else:
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if quantization_config is None or quantization_config == 'binary':
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quantization_params = models.BinaryQuantization(
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binary= models.BinaryQuantizationConfig(always_ram=False),
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)
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quantization_params = {
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"binary": {
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"always_ram": False,
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}
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}
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elif quantization_config == 'scalar':
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quantization_params = models.ScalarQuantization(
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scalar=models.ScalarQuantizationConfig(
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type=models.ScalarType.INT8,
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quantile=0.99,
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always_ram=False,
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),
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)
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quantization_params = {
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"scalar": {
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"type": "int8",
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"quantile": 0.99,
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"always_ram": False
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}
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}
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elif quantization_config == 'product':
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quantization_params = models.ProductQuantization(
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product=models.ProductQuantizationConfig(
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compression=models.CompressionRatio.X16,
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always_ram=False,
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),
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)
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quantization_params = {
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"product": {
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"compression": "x16",
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"always_ram": False
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}
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}
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else:
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raise Exception("Quantization config must be one of 'scalar', 'binary' or 'product'")
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self.qdrant_client.create_collection(
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collection_name=f"{self.collection_name}",
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vectors_config= models.VectorParams(
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size=1536,
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distance= models.Distance.COSINE
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),
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quantization_config= quantization_params
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new_collection_status = self.sync_client.put(
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url=f"{self.qdrant_url}/collections/{self.collection_name}",
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json={
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"vectors": {
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"size": 1536,
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"distance": "Cosine"
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},
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"quantization_config": quantization_params
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},
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headers=self.headers
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)
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self.collection_info = self.qdrant_client.get_collection(f"{self.collection_name}")
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print_verbose(f'New collection created.\nCollection details:{self.collection_info}')
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if new_collection_status.json()["result"]:
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collection_details = self.sync_client.get(
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url=f"{self.qdrant_url}/collections/{self.collection_name}",
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headers=self.headers
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)
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self.collection_info = collection_details.json()
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print_verbose(f'New collection created.\nCollection details:{self.collection_info}')
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else:
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raise Exception("Error while creating new collection")
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def _get_cache_logic(self, cached_response: Any):
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if cached_response is None:
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@ -1325,7 +1363,6 @@ class QdrantSemanticCache(BaseCache):
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def set_cache(self, key, value, **kwargs):
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print_verbose(f"qdrant semantic-cache set_cache, kwargs: {kwargs}")
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from qdrant_client import models
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import uuid
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# get the prompt
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@ -1347,24 +1384,27 @@ class QdrantSemanticCache(BaseCache):
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value = str(value)
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assert isinstance(value, str)
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keys = self.qdrant_client.upsert(
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collection_name=f"{self.collection_name}",
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points=[
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models.PointStruct(
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id=str(uuid.uuid4()),
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payload={
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data = {
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"points": [
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{
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"id": str(uuid.uuid4()),
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"vector": embedding,
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"payload": {
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"text": prompt,
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"response": value,
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},
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vector= embedding,
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),
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}
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},
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]
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}
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keys = self.sync_client.put(
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url=f"{self.qdrant_url}/collections/{self.collection_name}/points",
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headers=self.headers,
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json=data
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)
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return
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def get_cache(self, key, **kwargs):
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print_verbose(f"sync qdrant semantic-cache get_cache, kwargs: {kwargs}")
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from qdrant_client import models
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# get the messages
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messages = kwargs["messages"]
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@ -1382,19 +1422,25 @@ class QdrantSemanticCache(BaseCache):
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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results = self.qdrant_client.search(
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collection_name=self.collection_name,
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query_vector= embedding,
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search_params= models.SearchParams(
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quantization= models.QuantizationSearchParams(
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ignore=False,
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rescore=True,
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oversampling=3.0,
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),
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exact=False,
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),
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limit=1
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data = {
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"vector": embedding,
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"params": {
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"quantization": {
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"ignore": False,
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"rescore": True,
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"oversampling": 3.0,
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}
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},
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"limit":1,
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"with_payload": True
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}
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search_response = self.sync_client.post(
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url=f"{self.qdrant_url}/collections/{self.collection_name}/points/search",
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headers=self.headers,
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json=data
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)
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results = search_response.json()["result"]
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if results == None:
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return None
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@ -1402,8 +1448,8 @@ class QdrantSemanticCache(BaseCache):
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if len(results) == 0:
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return None
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similarity = results[0].score
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cached_prompt = results[0].payload['text']
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similarity = results[0]["score"]
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cached_prompt = results[0]["payload"]["text"]
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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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@ -1411,7 +1457,7 @@ class QdrantSemanticCache(BaseCache):
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)
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if similarity >= self.similarity_threshold:
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# cache hit !
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cached_value = results[0].payload['response']
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cached_value = results[0]["payload"]["response"]
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print_verbose(
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f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
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)
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@ -1423,7 +1469,6 @@ class QdrantSemanticCache(BaseCache):
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async def async_set_cache(self, key, value, **kwargs):
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from litellm.proxy.proxy_server import llm_router, llm_model_list
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from qdrant_client import models
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import uuid
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print_verbose(f"async qdrant semantic-cache set_cache, kwargs: {kwargs}")
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@ -1464,24 +1509,28 @@ class QdrantSemanticCache(BaseCache):
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value = str(value)
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assert isinstance(value, str)
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keys = await self.qdrant_client_async.upsert(
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collection_name=f"{self.collection_name}",
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points=[
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models.PointStruct(
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id=str(uuid.uuid4()),
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payload={
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data = {
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"points": [
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{
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"id": str(uuid.uuid4()),
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"vector": embedding,
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"payload": {
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"text": prompt,
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"response": value,
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},
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vector= embedding,
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),
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}
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},
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]
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}
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keys = await self.async_client.put(
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url=f"{self.qdrant_url}/collections/{self.collection_name}/points",
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headers=self.headers,
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json=data
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)
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return
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async def async_get_cache(self, key, **kwargs):
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print_verbose(f"async qdrant semantic-cache get_cache, kwargs: {kwargs}")
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from qdrant_client import models
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from litellm.proxy.proxy_server import llm_router, llm_model_list
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# get the messages
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@ -1518,20 +1567,27 @@ class QdrantSemanticCache(BaseCache):
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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results = await self.qdrant_client_async.search(
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collection_name=self.collection_name,
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query_vector= embedding,
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search_params= models.SearchParams(
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quantization= models.QuantizationSearchParams(
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ignore=False,
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rescore=True,
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oversampling=3.0,
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),
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exact=False,
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),
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limit=1
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data = {
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"vector": embedding,
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"params": {
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"quantization": {
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"ignore": False,
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"rescore": True,
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"oversampling": 3.0,
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}
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},
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"limit":1,
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"with_payload": True
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}
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search_response = await self.async_client.post(
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url=f"{self.qdrant_url}/collections/{self.collection_name}/points/search",
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headers=self.headers,
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json=data
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)
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results = search_response.json()["result"]
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if results == None:
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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@ -1540,8 +1596,8 @@ class QdrantSemanticCache(BaseCache):
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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similarity = results[0].score
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cached_prompt = results[0].payload['text']
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similarity = results[0]["score"]
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cached_prompt = results[0]["payload"]["text"]
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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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@ -1553,7 +1609,7 @@ class QdrantSemanticCache(BaseCache):
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if similarity >= self.similarity_threshold:
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# cache hit !
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cached_value = results[0].payload['response']
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cached_value = results[0]["payload"]["response"]
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print_verbose(
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f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
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)
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@ -2070,12 +2126,12 @@ class Cache:
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redis_semantic_cache_embedding_model="text-embedding-ada-002",
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redis_flush_size=None,
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disk_cache_dir=None,
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qdrant_username: Optional[str] = None,
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qdrant_password: Optional[str] = None,
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qdrant_url: Optional[str] = None,
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qdrant_api_key: Optional[str] = None,
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qdrant_collection_name: Optional[str] = None,
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qdrant_quantization_config: Optional[str] = None,
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qdrant_semantic_cache_embedding_model="text-embedding-ada-002",
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qdrant_host_type: Optional[Literal["local","cloud"]] = "local",
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**kwargs,
|
||||
):
|
||||
"""
|
||||
|
|
@ -2086,11 +2142,11 @@ class Cache:
|
|||
host (str, optional): The host address for the Redis cache. Required if type is "redis".
|
||||
port (int, optional): The port number for the Redis cache. Required if type is "redis".
|
||||
password (str, optional): The password for the Redis cache. Required if type is "redis".
|
||||
qdrant_url (str, optional): The url for your qdrant cluster. Required if type is "qdrant-semantic"
|
||||
qdrant_username (str, optional): The username for the qdrant cluster. Required if type is "qdrant-semantic"
|
||||
qdrant_password (str, optional): The password for the qdrant cluster. Required if type is "qdrant-semantic"
|
||||
qdrant_collection_name (str, optional): The name for your qdrant collection. Required if type is "qdrant-semantic"
|
||||
similarity_threshold (float, optional): The similarity threshold for semantic-caching, Required if type is "redis-semantic" or "qdrant-semantic"
|
||||
qdrant_url (str, optional): The url for your qdrant cluster. Required if type is "qdrant-semantic".
|
||||
qdrant_api_key (str, optional): The api_key for the local or cloud qdrant cluster. Required if qdrant_host_type is "cloud" and optional if qdrant_host_type is "local".
|
||||
qdrant_host_type (str, optional): Can be either "local" or "cloud". Should be "local" when you are running a local qdrant cluster or "cloud" when you are using a qdrant cloud cluster.
|
||||
qdrant_collection_name (str, optional): The name for your qdrant collection. Required if type is "qdrant-semantic".
|
||||
similarity_threshold (float, optional): The similarity threshold for semantic-caching, Required if type is "redis-semantic" or "qdrant-semantic".
|
||||
|
||||
supported_call_types (list, optional): List of call types to cache for. Defaults to cache == on for all call types.
|
||||
**kwargs: Additional keyword arguments for redis.Redis() cache
|
||||
|
|
@ -2117,13 +2173,13 @@ class Cache:
|
|||
)
|
||||
elif type == "qdrant-semantic":
|
||||
self.cache = QdrantSemanticCache(
|
||||
qdrant_username= qdrant_username,
|
||||
qdrant_password= qdrant_password,
|
||||
qdrant_url= qdrant_url,
|
||||
qdrant_api_key= qdrant_api_key,
|
||||
collection_name= qdrant_collection_name,
|
||||
similarity_threshold= similarity_threshold,
|
||||
quantization_config= qdrant_quantization_config,
|
||||
embedding_model= qdrant_semantic_cache_embedding_model,
|
||||
host_type=qdrant_host_type
|
||||
)
|
||||
elif type == "local":
|
||||
self.cache = InMemoryCache()
|
||||
|
|
|
|||
|
|
@ -114,6 +114,48 @@ class AsyncHTTPHandler:
|
|||
except Exception as e:
|
||||
raise e
|
||||
|
||||
async def put(
|
||||
self,
|
||||
url: str,
|
||||
data: Optional[Union[dict, str]] = None, # type: ignore
|
||||
json: Optional[dict] = None,
|
||||
params: Optional[dict] = None,
|
||||
headers: Optional[dict] = None,
|
||||
stream: bool = False,
|
||||
):
|
||||
try:
|
||||
req = self.client.build_request(
|
||||
"PUT", url, data=data, json=json, params=params, headers=headers # type: ignore
|
||||
)
|
||||
response = await self.client.send(req, stream=stream)
|
||||
response.raise_for_status()
|
||||
return response
|
||||
except (httpx.RemoteProtocolError, httpx.ConnectError):
|
||||
# Retry the request with a new session if there is a connection error
|
||||
new_client = self.create_client(timeout=self.timeout, concurrent_limit=1)
|
||||
try:
|
||||
return await self.single_connection_post_request(
|
||||
url=url,
|
||||
client=new_client,
|
||||
data=data,
|
||||
json=json,
|
||||
params=params,
|
||||
headers=headers,
|
||||
stream=stream,
|
||||
)
|
||||
finally:
|
||||
await new_client.aclose()
|
||||
except httpx.HTTPStatusError as e:
|
||||
setattr(e, "status_code", e.response.status_code)
|
||||
if stream is True:
|
||||
setattr(e, "message", await e.response.aread())
|
||||
else:
|
||||
setattr(e, "message", e.response.text)
|
||||
raise e
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
async def single_connection_post_request(
|
||||
self,
|
||||
url: str,
|
||||
|
|
@ -200,6 +242,22 @@ class HTTPHandler:
|
|||
response = self.client.send(req, stream=stream)
|
||||
return response
|
||||
|
||||
def put(
|
||||
self,
|
||||
url: str,
|
||||
data: Optional[Union[dict, str]] = None,
|
||||
json: Optional[dict] = None,
|
||||
params: Optional[dict] = None,
|
||||
headers: Optional[dict] = None,
|
||||
stream: bool = False,
|
||||
):
|
||||
req = self.client.build_request(
|
||||
"PUT", url, data=data, json=json, params=params, headers=headers # type: ignore
|
||||
)
|
||||
response = self.client.send(req, stream=stream)
|
||||
return response
|
||||
|
||||
|
||||
def __del__(self) -> None:
|
||||
try:
|
||||
self.close()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue