two version of R2R are here HEAD master

1 files changed, 430 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/caching/qdrant_semantic_cache.py b/.venv/lib/python3.12/site-packages/litellm/caching/qdrant_semantic_cache.py
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+"""
+Qdrant Semantic Cache implementation
+
+Has 4 methods:
+    - set_cache
+    - get_cache
+    - async_set_cache
+    - async_get_cache
+"""
+
+import ast
+import asyncio
+import json
+from typing import Any
+
+import litellm
+from litellm._logging import print_verbose
+
+from .base_cache import BaseCache
+
+
+class QdrantSemanticCache(BaseCache):
+    def __init__(  # noqa: PLR0915
+        self,
+        qdrant_api_base=None,
+        qdrant_api_key=None,
+        collection_name=None,
+        similarity_threshold=None,
+        quantization_config=None,
+        embedding_model="text-embedding-ada-002",
+        host_type=None,
+    ):
+        import os
+
+        from litellm.llms.custom_httpx.http_handler import (
+            _get_httpx_client,
+            get_async_httpx_client,
+            httpxSpecialProvider,
+        )
+        from litellm.secret_managers.main import get_secret_str
+
+        if collection_name is None:
+            raise Exception("collection_name must be provided, passed None")
+
+        self.collection_name = collection_name
+        print_verbose(
+            f"qdrant semantic-cache initializing COLLECTION - {self.collection_name}"
+        )
+
+        if similarity_threshold is None:
+            raise Exception("similarity_threshold must be provided, passed None")
+        self.similarity_threshold = similarity_threshold
+        self.embedding_model = embedding_model
+        headers = {}
+
+        # check if defined as os.environ/ variable
+        if qdrant_api_base:
+            if isinstance(qdrant_api_base, str) and qdrant_api_base.startswith(
+                "os.environ/"
+            ):
+                qdrant_api_base = get_secret_str(qdrant_api_base)
+        if qdrant_api_key:
+            if isinstance(qdrant_api_key, str) and qdrant_api_key.startswith(
+                "os.environ/"
+            ):
+                qdrant_api_key = get_secret_str(qdrant_api_key)
+
+        qdrant_api_base = (
+            qdrant_api_base or os.getenv("QDRANT_URL") or os.getenv("QDRANT_API_BASE")
+        )
+        qdrant_api_key = qdrant_api_key or os.getenv("QDRANT_API_KEY")
+        headers = {"Content-Type": "application/json"}
+        if qdrant_api_key:
+            headers["api-key"] = qdrant_api_key
+
+        if qdrant_api_base is None:
+            raise ValueError("Qdrant url must be provided")
+
+        self.qdrant_api_base = qdrant_api_base
+        self.qdrant_api_key = qdrant_api_key
+        print_verbose(f"qdrant semantic-cache qdrant_api_base: {self.qdrant_api_base}")
+
+        self.headers = headers
+
+        self.sync_client = _get_httpx_client()
+        self.async_client = get_async_httpx_client(
+            llm_provider=httpxSpecialProvider.Caching
+        )
+
+        if quantization_config is None:
+            print_verbose(
+                "Quantization config is not provided. Default binary quantization will be used."
+            )
+        collection_exists = self.sync_client.get(
+            url=f"{self.qdrant_api_base}/collections/{self.collection_name}/exists",
+            headers=self.headers,
+        )
+        if collection_exists.status_code != 200:
+            raise ValueError(
+                f"Error from qdrant checking if /collections exist {collection_exists.text}"
+            )
+
+        if collection_exists.json()["result"]["exists"]:
+            collection_details = self.sync_client.get(
+                url=f"{self.qdrant_api_base}/collections/{self.collection_name}",
+                headers=self.headers,
+            )
+            self.collection_info = collection_details.json()
+            print_verbose(
+                f"Collection already exists.\nCollection details:{self.collection_info}"
+            )
+        else:
+            if quantization_config is None or quantization_config == "binary":
+                quantization_params = {
+                    "binary": {
+                        "always_ram": False,
+                    }
+                }
+            elif quantization_config == "scalar":
+                quantization_params = {
+                    "scalar": {"type": "int8", "quantile": 0.99, "always_ram": False}
+                }
+            elif quantization_config == "product":
+                quantization_params = {
+                    "product": {"compression": "x16", "always_ram": False}
+                }
+            else:
+                raise Exception(
+                    "Quantization config must be one of 'scalar', 'binary' or 'product'"
+                )
+
+            new_collection_status = self.sync_client.put(
+                url=f"{self.qdrant_api_base}/collections/{self.collection_name}",
+                json={
+                    "vectors": {"size": 1536, "distance": "Cosine"},
+                    "quantization_config": quantization_params,
+                },
+                headers=self.headers,
+            )
+            if new_collection_status.json()["result"]:
+                collection_details = self.sync_client.get(
+                    url=f"{self.qdrant_api_base}/collections/{self.collection_name}",
+                    headers=self.headers,
+                )
+                self.collection_info = collection_details.json()
+                print_verbose(
+                    f"New collection created.\nCollection details:{self.collection_info}"
+                )
+            else:
+                raise Exception("Error while creating new collection")
+
+    def _get_cache_logic(self, cached_response: Any):
+        if cached_response is None:
+            return cached_response
+        try:
+            cached_response = json.loads(
+                cached_response
+            )  # Convert string to dictionary
+        except Exception:
+            cached_response = ast.literal_eval(cached_response)
+        return cached_response
+
+    def set_cache(self, key, value, **kwargs):
+        print_verbose(f"qdrant semantic-cache set_cache, kwargs: {kwargs}")
+        import uuid
+
+        # get the prompt
+        messages = kwargs["messages"]
+        prompt = ""
+        for message in messages:
+            prompt += message["content"]
+
+        # create an embedding for prompt
+        embedding_response = litellm.embedding(
+            model=self.embedding_model,
+            input=prompt,
+            cache={"no-store": True, "no-cache": True},
+        )
+
+        # get the embedding
+        embedding = embedding_response["data"][0]["embedding"]
+
+        value = str(value)
+        assert isinstance(value, str)
+
+        data = {
+            "points": [
+                {
+                    "id": str(uuid.uuid4()),
+                    "vector": embedding,
+                    "payload": {
+                        "text": prompt,
+                        "response": value,
+                    },
+                },
+            ]
+        }
+        self.sync_client.put(
+            url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points",
+            headers=self.headers,
+            json=data,
+        )
+        return
+
+    def get_cache(self, key, **kwargs):
+        print_verbose(f"sync qdrant semantic-cache get_cache, kwargs: {kwargs}")
+
+        # get the messages
+        messages = kwargs["messages"]
+        prompt = ""
+        for message in messages:
+            prompt += message["content"]
+
+        # convert to embedding
+        embedding_response = litellm.embedding(
+            model=self.embedding_model,
+            input=prompt,
+            cache={"no-store": True, "no-cache": True},
+        )
+
+        # get the embedding
+        embedding = embedding_response["data"][0]["embedding"]
+
+        data = {
+            "vector": embedding,
+            "params": {
+                "quantization": {
+                    "ignore": False,
+                    "rescore": True,
+                    "oversampling": 3.0,
+                }
+            },
+            "limit": 1,
+            "with_payload": True,
+        }
+
+        search_response = self.sync_client.post(
+            url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points/search",
+            headers=self.headers,
+            json=data,
+        )
+        results = search_response.json()["result"]
+
+        if results is None:
+            return None
+        if isinstance(results, list):
+            if len(results) == 0:
+                return None
+
+        similarity = results[0]["score"]
+        cached_prompt = results[0]["payload"]["text"]
+
+        # check similarity, if more than self.similarity_threshold, return results
+        print_verbose(
+            f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
+        )
+        if similarity >= self.similarity_threshold:
+            # cache hit !
+            cached_value = results[0]["payload"]["response"]
+            print_verbose(
+                f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
+            )
+            return self._get_cache_logic(cached_response=cached_value)
+        else:
+            # cache miss !
+            return None
+        pass
+
+    async def async_set_cache(self, key, value, **kwargs):
+        import uuid
+
+        from litellm.proxy.proxy_server import llm_model_list, llm_router
+
+        print_verbose(f"async qdrant semantic-cache set_cache, kwargs: {kwargs}")
+
+        # get the prompt
+        messages = kwargs["messages"]
+        prompt = ""
+        for message in messages:
+            prompt += message["content"]
+        # create an embedding for prompt
+        router_model_names = (
+            [m["model_name"] for m in llm_model_list]
+            if llm_model_list is not None
+            else []
+        )
+        if llm_router is not None and self.embedding_model in router_model_names:
+            user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
+            embedding_response = await llm_router.aembedding(
+                model=self.embedding_model,
+                input=prompt,
+                cache={"no-store": True, "no-cache": True},
+                metadata={
+                    "user_api_key": user_api_key,
+                    "semantic-cache-embedding": True,
+                    "trace_id": kwargs.get("metadata", {}).get("trace_id", None),
+                },
+            )
+        else:
+            # convert to embedding
+            embedding_response = await litellm.aembedding(
+                model=self.embedding_model,
+                input=prompt,
+                cache={"no-store": True, "no-cache": True},
+            )
+
+        # get the embedding
+        embedding = embedding_response["data"][0]["embedding"]
+
+        value = str(value)
+        assert isinstance(value, str)
+
+        data = {
+            "points": [
+                {
+                    "id": str(uuid.uuid4()),
+                    "vector": embedding,
+                    "payload": {
+                        "text": prompt,
+                        "response": value,
+                    },
+                },
+            ]
+        }
+
+        await self.async_client.put(
+            url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points",
+            headers=self.headers,
+            json=data,
+        )
+        return
+
+    async def async_get_cache(self, key, **kwargs):
+        print_verbose(f"async qdrant semantic-cache get_cache, kwargs: {kwargs}")
+        from litellm.proxy.proxy_server import llm_model_list, llm_router
+
+        # get the messages
+        messages = kwargs["messages"]
+        prompt = ""
+        for message in messages:
+            prompt += message["content"]
+
+        router_model_names = (
+            [m["model_name"] for m in llm_model_list]
+            if llm_model_list is not None
+            else []
+        )
+        if llm_router is not None and self.embedding_model in router_model_names:
+            user_api_key = kwargs.get("metadata", {}).get("user_api_key", "")
+            embedding_response = await llm_router.aembedding(
+                model=self.embedding_model,
+                input=prompt,
+                cache={"no-store": True, "no-cache": True},
+                metadata={
+                    "user_api_key": user_api_key,
+                    "semantic-cache-embedding": True,
+                    "trace_id": kwargs.get("metadata", {}).get("trace_id", None),
+                },
+            )
+        else:
+            # convert to embedding
+            embedding_response = await litellm.aembedding(
+                model=self.embedding_model,
+                input=prompt,
+                cache={"no-store": True, "no-cache": True},
+            )
+
+        # get the embedding
+        embedding = embedding_response["data"][0]["embedding"]
+
+        data = {
+            "vector": embedding,
+            "params": {
+                "quantization": {
+                    "ignore": False,
+                    "rescore": True,
+                    "oversampling": 3.0,
+                }
+            },
+            "limit": 1,
+            "with_payload": True,
+        }
+
+        search_response = await self.async_client.post(
+            url=f"{self.qdrant_api_base}/collections/{self.collection_name}/points/search",
+            headers=self.headers,
+            json=data,
+        )
+
+        results = search_response.json()["result"]
+
+        if results is None:
+            kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
+            return None
+        if isinstance(results, list):
+            if len(results) == 0:
+                kwargs.setdefault("metadata", {})["semantic-similarity"] = 0.0
+                return None
+
+        similarity = results[0]["score"]
+        cached_prompt = results[0]["payload"]["text"]
+
+        # check similarity, if more than self.similarity_threshold, return results
+        print_verbose(
+            f"semantic cache: similarity threshold: {self.similarity_threshold}, similarity: {similarity}, prompt: {prompt}, closest_cached_prompt: {cached_prompt}"
+        )
+
+        # update kwargs["metadata"] with similarity, don't rewrite the original metadata
+        kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
+
+        if similarity >= self.similarity_threshold:
+            # cache hit !
+            cached_value = results[0]["payload"]["response"]
+            print_verbose(
+                f"got a cache hit, similarity: {similarity}, Current prompt: {prompt}, cached_prompt: {cached_prompt}"
+            )
+            return self._get_cache_logic(cached_response=cached_value)
+        else:
+            # cache miss !
+            return None
+        pass
+
+    async def _collection_info(self):
+        return self.collection_info
+
+    async def async_set_cache_pipeline(self, cache_list, **kwargs):
+        tasks = []
+        for val in cache_list:
+            tasks.append(self.async_set_cache(val[0], val[1], **kwargs))
+        await asyncio.gather(*tasks)