mirror of
https://github.com/BerriAI/litellm.git
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d9a71650e3
* fix remove qdrant semantic caching to it's own folder * refactor use 1 file for s3 caching * fix use sep files for in mem and redis caching * fix refactor caching * add readme.md for caching folder
424 lines
14 KiB
Python
424 lines
14 KiB
Python
"""
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Qdrant Semantic Cache implementation
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Has 4 methods:
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- set_cache
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- get_cache
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- async_set_cache
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- async_get_cache
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"""
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import ast
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import json
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from typing import Any
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import litellm
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from litellm._logging import print_verbose
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from litellm.types.caching import LiteLLMCacheType
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from .base_cache import BaseCache
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class QdrantSemanticCache(BaseCache):
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def __init__(
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self,
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qdrant_api_base=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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host_type=None,
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):
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import os
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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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httpxSpecialProvider,
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)
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from litellm.secret_managers.main import get_secret_str
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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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self.collection_name = collection_name
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print_verbose(
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f"qdrant semantic-cache initializing COLLECTION - {self.collection_name}"
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)
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if similarity_threshold is None:
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raise Exception("similarity_threshold must be provided, passed None")
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self.similarity_threshold = similarity_threshold
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self.embedding_model = embedding_model
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headers = {}
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# check if defined as os.environ/ variable
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if qdrant_api_base:
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if isinstance(qdrant_api_base, str) and qdrant_api_base.startswith(
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"os.environ/"
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):
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qdrant_api_base = get_secret_str(qdrant_api_base)
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if qdrant_api_key:
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if isinstance(qdrant_api_key, str) and qdrant_api_key.startswith(
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"os.environ/"
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):
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qdrant_api_key = get_secret_str(qdrant_api_key)
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qdrant_api_base = (
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qdrant_api_base or os.getenv("QDRANT_URL") or os.getenv("QDRANT_API_BASE")
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)
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qdrant_api_key = qdrant_api_key or os.getenv("QDRANT_API_KEY")
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headers = {"Content-Type": "application/json"}
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if qdrant_api_key:
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headers["api-key"] = qdrant_api_key
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if qdrant_api_base is None:
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raise ValueError("Qdrant url must be provided")
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self.qdrant_api_base = qdrant_api_base
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self.qdrant_api_key = qdrant_api_key
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print_verbose(f"qdrant semantic-cache qdrant_api_base: {self.qdrant_api_base}")
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self.headers = headers
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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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llm_provider=httpxSpecialProvider.Caching
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)
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if quantization_config is None:
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print_verbose(
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"Quantization config is not provided. Default binary quantization will be used."
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)
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collection_exists = self.sync_client.get(
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url=f"{self.qdrant_api_base}/collections/{self.collection_name}/exists",
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headers=self.headers,
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)
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if collection_exists.status_code != 200:
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raise ValueError(
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f"Error from qdrant checking if /collections exist {collection_exists.text}"
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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_api_base}/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(
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f"Collection already exists.\nCollection details:{self.collection_info}"
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)
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else:
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if quantization_config is None or quantization_config == "binary":
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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 = {
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"scalar": {"type": "int8", "quantile": 0.99, "always_ram": False}
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}
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elif quantization_config == "product":
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quantization_params = {
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"product": {"compression": "x16", "always_ram": False}
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}
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else:
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raise Exception(
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"Quantization config must be one of 'scalar', 'binary' or 'product'"
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)
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new_collection_status = self.sync_client.put(
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url=f"{self.qdrant_api_base}/collections/{self.collection_name}",
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json={
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"vectors": {"size": 1536, "distance": "Cosine"},
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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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if new_collection_status.json()["result"]:
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collection_details = self.sync_client.get(
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url=f"{self.qdrant_api_base}/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(
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f"New collection created.\nCollection details:{self.collection_info}"
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)
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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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return cached_response
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try:
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cached_response = json.loads(
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cached_response
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) # Convert string to dictionary
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except Exception:
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cached_response = ast.literal_eval(cached_response)
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return cached_response
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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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import uuid
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# get the prompt
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messages = kwargs["messages"]
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prompt = ""
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for message in messages:
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prompt += message["content"]
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# create an embedding for prompt
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embedding_response = litellm.embedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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value = str(value)
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assert isinstance(value, str)
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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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},
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]
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}
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self.sync_client.put(
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url=f"{self.qdrant_api_base}/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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# get the messages
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messages = kwargs["messages"]
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prompt = ""
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for message in messages:
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prompt += message["content"]
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# convert to embedding
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embedding_response = litellm.embedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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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_api_base}/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 is None:
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return None
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if isinstance(results, list):
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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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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
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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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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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return self._get_cache_logic(cached_response=cached_value)
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else:
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# cache miss !
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return None
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pass
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async def async_set_cache(self, key, value, **kwargs):
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import uuid
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from litellm.proxy.proxy_server import llm_model_list, llm_router
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print_verbose(f"async qdrant semantic-cache set_cache, kwargs: {kwargs}")
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# get the prompt
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messages = kwargs["messages"]
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prompt = ""
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for message in messages:
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prompt += message["content"]
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# create an embedding for prompt
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router_model_names = (
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[m["model_name"] for m in llm_model_list]
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if llm_model_list is not None
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else []
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)
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if llm_router is not None and self.embedding_model in router_model_names:
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user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
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embedding_response = await llm_router.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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metadata={
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"user_api_key": user_api_key,
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"semantic-cache-embedding": True,
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"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
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},
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)
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else:
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# convert to embedding
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embedding_response = await litellm.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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value = str(value)
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assert isinstance(value, str)
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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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},
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]
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}
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await self.async_client.put(
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url=f"{self.qdrant_api_base}/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 litellm.proxy.proxy_server import llm_model_list, llm_router
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# get the messages
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messages = kwargs["messages"]
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prompt = ""
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for message in messages:
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prompt += message["content"]
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router_model_names = (
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[m["model_name"] for m in llm_model_list]
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if llm_model_list is not None
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else []
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)
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if llm_router is not None and self.embedding_model in router_model_names:
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user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
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embedding_response = await llm_router.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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metadata={
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"user_api_key": user_api_key,
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"semantic-cache-embedding": True,
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"trace_id": kwargs.get("metadata", {}).get("trace_id", None),
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},
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)
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else:
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# convert to embedding
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embedding_response = await litellm.aembedding(
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model=self.embedding_model,
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input=prompt,
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cache={"no-store": True, "no-cache": True},
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)
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# get the embedding
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embedding = embedding_response["data"][0]["embedding"]
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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_api_base}/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 is None:
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kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
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return None
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if isinstance(results, list):
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if len(results) == 0:
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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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# check similarity, if more than self.similarity_threshold, return results
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print_verbose(
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f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
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)
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# update kwargs["metadata"] with similarity, don't rewrite the original metadata
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kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
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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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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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return self._get_cache_logic(cached_response=cached_value)
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else:
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# cache miss !
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return None
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pass
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async def _collection_info(self):
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return self.collection_info
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