diff --git a/docs/indexes/pinecone_async.ipynb b/docs/indexes/pinecone_async.ipynb new file mode 100644 index 00000000..9019db2f --- /dev/null +++ b/docs/indexes/pinecone_async.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -qU \"semantic-router[pinecone]==0.0.47\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router import Route\n", + "\n", + "# we could use this as a guide for our chatbot to avoid political conversations\n", + "politics = Route(\n", + " name=\"politics\",\n", + " utterances=[\n", + " \"isn't politics the best thing ever\",\n", + " \"why don't you tell me about your political opinions\",\n", + " \"don't you just love the president\" \"don't you just hate the president\",\n", + " \"they're going to destroy this country!\",\n", + " \"they will save the country!\",\n", + " ],\n", + ")\n", + "\n", + "# this could be used as an indicator to our chatbot to switch to a more\n", + "# conversational prompt\n", + "chitchat = Route(\n", + " name=\"chitchat\",\n", + " utterances=[\n", + " \"how's the weather today?\",\n", + " \"how are things going?\",\n", + " \"lovely weather today\",\n", + " \"the weather is horrendous\",\n", + " \"let's go to the chippy\",\n", + " ],\n", + ")\n", + "\n", + "# we place both of our decisions together into single list\n", + "routes = [politics, chitchat]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As of 13 June 2024, two encoders support async functionality:\n", + "\n", + "* `AzureOpenAIEncoder`\n", + "* `OpenAIEncoder`\n", + "\n", + "To use either of these encoders in async mode we simply initialize them as we usually would. When we then include them within a `RouteLayer` and run `acall` the route layer will automatically run the encoders in async mode.\n", + "\n", + "**Azure OpenAI:**\n", + "\n", + "```python\n", + "from semantic_router.encoders import AzureOpenAIEncoder\n", + "\n", + "encoder = AzureOpenAIEncoder(\n", + " api_key=\"YOUR_AZURE_OPENAI_API_KEY\",\n", + " deployment_name=\"YOUR_DEPLOYMENT_NAME\",\n", + " azure_endpoint=\"YOUR_ENDPOINT\",\n", + " api_version=\"2024-02-01\",\n", + " model=\"text-embedding-3-small\",\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**OpenAI:**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "from semantic_router.encoders import OpenAIEncoder\n", + "\n", + "# get at platform.openai.com\n", + "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass(\n", + " \"Enter OpenAI API key: \"\n", + ")\n", + "encoder = OpenAIEncoder(name=\"text-embedding-3-small\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see encoder details, including default `score_threshold` like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OpenAIEncoder(name='text-embedding-3-small', score_threshold=0.3, type='openai', client=, async_client=, dimensions=NOT_GIVEN, token_limit=8192)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can create embeddings asynchronously via our encoder using the `encoder.acall` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[-0.009877119213342667,\n", + " 0.0015331337926909328,\n", + " 0.015642808750271797,\n", + " -0.05476367473602295,\n", + " -0.006405937951058149,\n", + " ...],\n", + " [0.012598802335560322,\n", + " -0.0037690235767513514,\n", + " 0.025685198605060577,\n", + " -0.08883649855852127,\n", + " 0.0013644769787788391,\n", + " ...]]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await encoder.acall(docs=[\"test\", \"test 2\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For our `PineconeIndex` we do the exact same thing, ie we initialize as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from semantic_router.index.pinecone import PineconeIndex\n", + "\n", + "# get at app.pinecone.io\n", + "os.environ[\"PINECONE_API_KEY\"] = os.environ.get(\"PINECONE_API_KEY\") or getpass(\n", + " \"Enter Pinecone API key: \"\n", + ")\n", + "pc_index = PineconeIndex(dimensions=1536)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several async methods we can call directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'indexes': []}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "await pc_index._async_list_indexes()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But unless we're using the index directly, we don't need to use these. As with the encoder, once we pass the `PineconeIndex` to our route layer, the route layer will call all async methods automatically when we hit the `acall` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Async RouteLayer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `RouteLayer` class supports both sync and async operations by default, so we initialize as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.layer import RouteLayer\n", + "\n", + "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check our route layer and index information as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['politics', 'chitchat']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl.list_route_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(rl.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also view all of the records for a given route:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['politics#64069085d9d6e98e5a80915f69fabe82bac6c742f801bc305c5001dce88f0d19',\n", + " 'politics#af8b76111f260cf44fb34f04fcf82927dcbe08e8f47c30f4d571379c1512fac8',\n", + " 'politics#d1bb40236c3d95b9c695bfa86b314b6da4eb87e136699563fccae47fccea23e2',\n", + " 'politics#ed0f3dd7bd5dea12e55b1953bcd2c562a5ab19f501f6d5ff8c8927652c3904b8',\n", + " 'politics#fc6d15f9e6075e6de82b3fbef6722b64353e4eadc8d663b7312a4ed60c43e6f6']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl.index._get_route_ids(route_name=\"politics\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now for async vs. sync usage! To call in synchronous mode we simply hit `rl(...)`, to switch to async mode we hit `rl.acall(...)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'politics'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl(\"don't you love politics\").name # SYNC mode" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'politics'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = await rl.acall(\"don't you love politics?\") # ASYNC mode\n", + "out.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try a few more sync and async requests:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'chitchat'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl(\"how's the weather today?\").name" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'chitchat'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = await rl.acall(\"how's the weather today?\")\n", + "out.name" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "rl(\"I'm interested in learning about llama 2\").name" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "out = await rl.acall(\"I'm interested in learning about llama 2\")\n", + "out.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can delete or update routes using the usual synchronous methods:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(rl.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import time\n", + "\n", + "rl.delete(route_name=\"chitchat\")\n", + "time.sleep(3)\n", + "len(rl.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "out = await rl.acall(\"how's the weather today?\")\n", + "out.name" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('politics', \"why don't you tell me about your political opinions\"),\n", + " ('politics',\n", + " \"don't you just love the presidentdon't you just hate the president\"),\n", + " ('politics', \"isn't politics the best thing ever\"),\n", + " ('politics', \"they're going to destroy this country!\"),\n", + " ('politics', 'they will save the country!')]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl.index.get_routes()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'pinecone', 'dimensions': 1536, 'vectors': 5}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rl.index.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "semantic_router_1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": 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["pillow", "torch", "torchvision", "transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "cb723f43017c81956776fc89703cf4c165c923e46bd0b541c109ccdfe561f2ab" +content-hash = "3b69fa6afcbfe049c4d56d0216bbd5cd326dc33b647c1b5906bd490d5d102728" diff --git a/pyproject.toml b/pyproject.toml index bb832b60..1b02184e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "semantic-router" -version = "0.0.46" +version = "0.0.47" description = "Super fast semantic router for AI decision making" authors = [ "James Briggs ", @@ -41,6 +41,7 @@ google-cloud-aiplatform = {version = "^1.45.0", optional = true} requests-mock = "^1.12.1" boto3 = { version = "^1.34.98", optional = true } botocore = {version = "^1.34.110", optional = true} +aiohttp = "^3.9.5" [tool.poetry.extras] hybrid = ["pinecone-text"] diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index b1e1311e..fcc5734d 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -1,15 +1,22 @@ -from typing import Any, List +from typing import Any, Coroutine, List, Optional -from pydantic.v1 import BaseModel, Field +from pydantic.v1 import BaseModel, Field, validator class BaseEncoder(BaseModel): name: str - score_threshold: float + score_threshold: Optional[float] = None type: str = Field(default="base") class Config: arbitrary_types_allowed = True + @validator("score_threshold", pre=True, always=True) + def set_score_threshold(cls, v): + return float(v) if v is not None else None + def __call__(self, docs: List[Any]) -> List[List[float]]: raise NotImplementedError("Subclasses must implement this method") + + def acall(self, docs: List[Any]) -> Coroutine[Any, Any, List[List[float]]]: + raise NotImplementedError("Subclasses must implement this method") diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py index bd68727f..e4acc5a4 100644 --- a/semantic_router/encoders/openai.py +++ b/semantic_router/encoders/openai.py @@ -1,3 +1,4 @@ +from asyncio import sleep as asleep import os from time import sleep from typing import Any, List, Optional, Union @@ -17,19 +18,26 @@ model_configs = { "text-embedding-ada-002": EncoderInfo( - name="text-embedding-ada-002", token_limit=8192 + name="text-embedding-ada-002", + token_limit=8192, + threshold=0.82, ), "text-embedding-3-small": EncoderInfo( - name="text-embedding-3-small", token_limit=8192 + name="text-embedding-3-small", + token_limit=8192, + threshold=0.3, ), "text-embedding-3-large": EncoderInfo( - name="text-embedding-3-large", token_limit=8192 + name="text-embedding-3-large", + token_limit=8192, + threshold=0.3, ), } class OpenAIEncoder(BaseEncoder): client: Optional[openai.Client] + async_client: Optional[openai.AsyncClient] dimensions: Union[int, NotGiven] = NotGiven() token_limit: int = 8192 # default value, should be replaced by config _token_encoder: Any = PrivateAttr() @@ -41,12 +49,24 @@ def __init__( openai_base_url: Optional[str] = None, openai_api_key: Optional[str] = None, openai_org_id: Optional[str] = None, - score_threshold: float = 0.82, + score_threshold: Optional[float] = None, dimensions: Union[int, NotGiven] = NotGiven(), ): if name is None: name = EncoderDefault.OPENAI.value["embedding_model"] - super().__init__(name=name, score_threshold=score_threshold) + if score_threshold is None and name in model_configs: + set_score_threshold = model_configs[name].threshold + elif score_threshold is None: + logger.warning( + f"Score threshold not set for model: {name}. Using default value." + ) + set_score_threshold = 0.82 + else: + set_score_threshold = score_threshold + super().__init__( + name=name, + score_threshold=set_score_threshold, + ) api_key = openai_api_key or os.getenv("OPENAI_API_KEY") base_url = openai_base_url or os.getenv("OPENAI_BASE_URL") openai_org_id = openai_org_id or os.getenv("OPENAI_ORG_ID") @@ -56,6 +76,9 @@ def __init__( self.client = openai.Client( base_url=base_url, api_key=api_key, organization=openai_org_id ) + self.async_client = openai.AsyncClient( + base_url=base_url, api_key=api_key, organization=openai_org_id + ) except Exception as e: raise ValueError( f"OpenAI API client failed to initialize. Error: {e}" @@ -126,3 +149,42 @@ def _truncate(self, text: str) -> str: logger.info(f"Trunc length: {len(self._token_encoder.encode(text))}") return text return text + + async def acall(self, docs: List[str], truncate: bool = True) -> List[List[float]]: + if self.async_client is None: + raise ValueError("OpenAI async client is not initialized.") + embeds = None + error_message = "" + + if truncate: + # check if any document exceeds token limit and truncate if so + docs = [self._truncate(doc) for doc in docs] + + # Exponential backoff + for j in range(1, 7): + try: + embeds = await self.async_client.embeddings.create( + input=docs, + model=self.name, + dimensions=self.dimensions, + ) + if embeds.data: + break + except OpenAIError as e: + await asleep(2**j) + error_message = str(e) + logger.warning(f"Retrying in {2**j} seconds...") + except Exception as e: + logger.error(f"OpenAI API call failed. Error: {error_message}") + raise ValueError(f"OpenAI API call failed. Error: {e}") from e + + if ( + not embeds + or not isinstance(embeds, CreateEmbeddingResponse) + or not embeds.data + ): + logger.info(f"Returned embeddings: {embeds}") + raise ValueError(f"No embeddings returned. Error: {error_message}") + + embeddings = [embeds_obj.embedding for embeds_obj in embeds.data] + return embeddings diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py index 55b2a40b..dba93600 100644 --- a/semantic_router/encoders/zure.py +++ b/semantic_router/encoders/zure.py @@ -1,3 +1,4 @@ +from asyncio import sleep as asleep import os from time import sleep from typing import List, Optional, Union @@ -14,6 +15,7 @@ class AzureOpenAIEncoder(BaseEncoder): client: Optional[openai.AzureOpenAI] = None + async_client: Optional[openai.AsyncAzureOpenAI] = None dimensions: Union[int, NotGiven] = NotGiven() type: str = "azure" api_key: Optional[str] = None @@ -77,7 +79,14 @@ def __init__( api_key=str(self.api_key), azure_endpoint=str(self.azure_endpoint), api_version=str(self.api_version), - # _strict_response_validation=True, + ) + self.async_client = openai.AsyncAzureOpenAI( + azure_deployment=( + str(self.deployment_name) if self.deployment_name else None + ), + api_key=str(self.api_key), + azure_endpoint=str(self.azure_endpoint), + api_version=str(self.api_version), ) except Exception as e: raise ValueError( @@ -86,7 +95,7 @@ def __init__( def __call__(self, docs: List[str]) -> List[List[float]]: if self.client is None: - raise ValueError("OpenAI client is not initialized.") + raise ValueError("Azure OpenAI client is not initialized.") embeds = None error_message = "" @@ -121,3 +130,41 @@ def __call__(self, docs: List[str]) -> List[List[float]]: embeddings = [embeds_obj.embedding for embeds_obj in embeds.data] return embeddings + + async def acall(self, docs: List[str]) -> List[List[float]]: + if self.async_client is None: + raise ValueError("Azure OpenAI async client is not initialized.") + embeds = None + error_message = "" + + # Exponential backoff + for j in range(3): + try: + embeds = await self.async_client.embeddings.create( + input=docs, + model=str(self.model), + dimensions=self.dimensions, + ) + if embeds.data: + break + except OpenAIError as e: + # print full traceback + import traceback + + traceback.print_exc() + await asleep(2**j) + error_message = str(e) + logger.warning(f"Retrying in {2**j} seconds...") + except Exception as e: + logger.error(f"Azure OpenAI API call failed. Error: {error_message}") + raise ValueError(f"Azure OpenAI API call failed. Error: {e}") from e + + if ( + not embeds + or not isinstance(embeds, CreateEmbeddingResponse) + or not embeds.data + ): + raise ValueError(f"No embeddings returned. Error: {error_message}") + + embeddings = [embeds_obj.embedding for embeds_obj in embeds.data] + return embeddings diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py index 5f223384..2aaf6f17 100644 --- a/semantic_router/hybrid_layer.py +++ b/semantic_router/hybrid_layer.py @@ -28,6 +28,11 @@ def __init__( aggregation: str = "sum", ): self.encoder = encoder + if self.encoder.score_threshold is None: + raise ValueError( + "No score threshold provided for encoder. Please set the score threshold " + "in the encoder config." + ) self.score_threshold = self.encoder.score_threshold if sparse_encoder is None: diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py index bc5adcca..e6638fb1 100644 --- a/semantic_router/index/base.py +++ b/semantic_router/index/base.py @@ -55,6 +55,18 @@ def query( """ raise NotImplementedError("This method should be implemented by subclasses.") + async def aquery( + self, + vector: np.ndarray, + top_k: int = 5, + route_filter: Optional[List[str]] = None, + ) -> Tuple[np.ndarray, List[str]]: + """ + Search the index for the query_vector and return top_k results. + This method should be implemented by subclasses. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + def delete_index(self): """ Deletes or resets the index. diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index de288524..7d3828f4 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -1,3 +1,5 @@ +import aiohttp +import asyncio import hashlib import os import time @@ -46,9 +48,11 @@ class PineconeIndex(BaseIndex): region: str = "us-west-2" host: str = "" client: Any = Field(default=None, exclude=True) + async_client: Any = Field(default=None, exclude=True) index: Optional[Any] = Field(default=None, exclude=True) ServerlessSpec: Any = Field(default=None, exclude=True) namespace: Optional[str] = "" + base_url: Optional[str] = "https://api.pinecone.io" def __init__( self, @@ -60,6 +64,7 @@ def __init__( region: str = "us-west-2", host: str = "", namespace: Optional[str] = "", + base_url: Optional[str] = "https://api.pinecone.io", ): super().__init__() self.index_name = index_name @@ -71,11 +76,13 @@ def __init__( self.namespace = namespace self.type = "pinecone" self.api_key = api_key or os.getenv("PINECONE_API_KEY") + self.base_url = base_url if self.api_key is None: raise ValueError("Pinecone API key is required.") self.client = self._initialize_client(api_key=self.api_key) + self.async_client = self._initialize_async_client(api_key=self.api_key) def _initialize_client(self, api_key: Optional[str] = None): try: @@ -88,12 +95,26 @@ def _initialize_client(self, api_key: Optional[str] = None): "You can install it with: " "`pip install 'semantic-router[pinecone]'`" ) - pinecone_args = {"api_key": api_key, "source_tag": "semantic-router"} + pinecone_args = {"api_key": api_key, "source_tag": "semanticrouter"} if self.namespace: pinecone_args["namespace"] = self.namespace return Pinecone(**pinecone_args) + def _initialize_async_client(self, api_key: Optional[str] = None): + api_key = api_key or self.api_key + if api_key is None: + raise ValueError("Pinecone API key is required.") + async_client = aiohttp.ClientSession( + headers={ + "Api-Key": api_key, + "Content-Type": "application/json", + "X-Pinecone-API-Version": "2024-07", + "User-Agent": "source_tag=semanticrouter", + } + ) + return async_client + def _init_index(self, force_create: bool = False) -> Union[Any, None]: """Initializing the index can be done after the object has been created to allow for the user to set the dimensions and other parameters. @@ -141,6 +162,39 @@ def _init_index(self, force_create: bool = False) -> Union[Any, None]: self.host = self.client.describe_index(self.index_name)["host"] return index + async def _init_async_index(self, force_create: bool = False): + index_stats = None + indexes = await self._async_list_indexes() + index_names = [i["name"] for i in indexes["indexes"]] + index_exists = self.index_name in index_names + if self.dimensions is not None and not index_exists: + await self._async_create_index( + name=self.index_name, + dimension=self.dimensions, + metric=self.metric, + cloud=self.cloud, + region=self.region, + ) + # TODO describe index and async sleep + index_ready = "false" + while index_ready != "true": + index_stats = await self._async_describe_index(self.index_name) + index_ready = index_stats["status"]["ready"] + await asyncio.sleep(1) + elif index_exists: + index_stats = await self._async_describe_index(self.index_name) + # grab dimensions for the index + self.dimensions = index_stats["dimension"] + elif force_create and self.dimensions is None: + raise ValueError( + "Cannot create an index without specifying the dimensions." + ) + else: + # if the index doesn't exist and we don't have the dimensions + # we raise warning + logger.warning("Index could not be initialized.") + self.host = index_stats["host"] if index_stats else None + def _batch_upsert(self, batch: List[Dict]): """Helper method for upserting a single batch of records.""" if self.index is not None: @@ -281,8 +335,83 @@ def query( route_names = [result["metadata"]["sr_route"] for result in results["matches"]] return np.array(scores), route_names + async def aquery( + self, + vector: np.ndarray, + top_k: int = 5, + route_filter: Optional[List[str]] = None, + ) -> Tuple[np.ndarray, List[str]]: + if self.async_client is None or self.host is None: + raise ValueError("Async client or host are not initialized.") + query_vector_list = vector.tolist() + if route_filter is not None: + filter_query = {"sr_route": {"$in": route_filter}} + else: + filter_query = None + results = await self._async_query( + vector=query_vector_list, + namespace=self.namespace or "", + filter=filter_query, + top_k=top_k, + include_metadata=True, + ) + scores = [result["score"] for result in results["matches"]] + route_names = [result["metadata"]["sr_route"] for result in results["matches"]] + return np.array(scores), route_names + def delete_index(self): self.client.delete_index(self.index_name) + # __ASYNC CLIENT METHODS__ + async def _async_query( + self, + vector: list[float], + namespace: str = "", + filter: Optional[dict] = None, + top_k: int = 5, + include_metadata: bool = False, + ): + params = { + "vector": vector, + "namespace": namespace, + "filter": filter, + "top_k": top_k, + "include_metadata": include_metadata, + } + async with self.async_client.post( + f"https://{self.host}/query", + json=params, + ) as response: + return await response.json(content_type=None) + + async def _async_list_indexes(self): + async with self.async_client.get(f"{self.base_url}/indexes") as response: + return await response.json(content_type=None) + + async def _async_create_index( + self, + name: str, + dimension: int, + cloud: str, + region: str, + metric: str = "cosine", + ): + params = { + "name": name, + "dimension": dimension, + "metric": metric, + "spec": {"serverless": {"cloud": cloud, "region": region}}, + } + async with self.async_client.post( + f"{self.base_url}/indexes", + headers={"Api-Key": self.api_key}, + json=params, + ) as response: + return await response.json(content_type=None) + + async def _async_describe_index(self, name: str): + async with self.async_client.get(f"{self.base_url}/indexes/{name}") as response: + return await response.json(content_type=None) + def __len__(self): return self.index.describe_index_stats()["total_vector_count"] diff --git a/semantic_router/layer.py b/semantic_router/layer.py index 0b882ff6..6b9ee1d2 100644 --- a/semantic_router/layer.py +++ b/semantic_router/layer.py @@ -185,7 +185,6 @@ def __init__( top_k: int = 5, aggregation: str = "sum", ): - logger.info("local") self.index: BaseIndex = index if index is not None else LocalIndex() if encoder is None: logger.warning( @@ -197,6 +196,11 @@ def __init__( self.encoder = encoder self.llm = llm self.routes: List[Route] = routes if routes is not None else [] + if self.encoder.score_threshold is None: + raise ValueError( + "No score threshold provided for encoder. Please set the score threshold " + "in the encoder config." + ) self.score_threshold = self.encoder.score_threshold self.top_k = top_k if self.top_k < 1: @@ -270,6 +274,43 @@ def __call__( # if no route passes threshold, return empty route choice return RouteChoice() + async def acall( + self, + text: Optional[str] = None, + vector: Optional[List[float]] = None, + simulate_static: bool = False, + route_filter: Optional[List[str]] = None, + ) -> RouteChoice: + # if no vector provided, encode text to get vector + if vector is None: + if text is None: + raise ValueError("Either text or vector must be provided") + vector = await self._async_encode(text=text) + + route, top_class_scores = await self._async_retrieve_top_route( + vector, route_filter + ) + passed = self._check_threshold(top_class_scores, route) + if passed and route is not None and not simulate_static: + if route.function_schemas and text is None: + raise ValueError( + "Route has a function schema, but no text was provided." + ) + if route.function_schemas and not isinstance(route.llm, BaseLLM): + raise NotImplementedError( + "Dynamic routes not yet supported for async calls." + ) + return route(text) + elif passed and route is not None and simulate_static: + return RouteChoice( + name=route.name, + function_call=None, + similarity_score=None, + ) + else: + # if no route passes threshold, return empty route choice + return RouteChoice() + def retrieve_multiple_routes( self, text: Optional[str] = None, @@ -313,6 +354,19 @@ def _retrieve_top_route( route = self.check_for_matching_routes(top_class) return route, top_class_scores + async def _async_retrieve_top_route( + self, vector: List[float], route_filter: Optional[List[str]] = None + ) -> Tuple[Optional[Route], List[float]]: + # get relevant results (scores and routes) + results = await self._async_retrieve( + xq=np.array(vector), top_k=self.top_k, route_filter=route_filter + ) + # decide most relevant routes + top_class, top_class_scores = await self._async_semantic_classify(results) + # TODO do we need this check? + route = self.check_for_matching_routes(top_class) + return route, top_class_scores + def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: """ Check if the route's score passes the specified threshold. @@ -425,6 +479,13 @@ def _encode(self, text: str) -> Any: xq = np.squeeze(xq) # Reduce to 1d array. return xq + async def _async_encode(self, text: str) -> Any: + """Given some text, encode it.""" + # create query vector + xq = np.array(await self.encoder.acall(docs=[text])) + xq = np.squeeze(xq) # Reduce to 1d array. + return xq + def _retrieve( self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None ) -> List[Dict]: @@ -435,6 +496,16 @@ def _retrieve( ) return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] + async def _async_retrieve( + self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None + ) -> List[Dict]: + """Given a query vector, retrieve the top_k most similar records.""" + # get scores and routes + scores, routes = await self.index.aquery( + vector=xq, top_k=top_k, route_filter=route_filter + ) + return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] + def _set_aggregation_method(self, aggregation: str = "sum"): if aggregation == "sum": return lambda x: sum(x) @@ -464,6 +535,25 @@ def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float logger.warning("No classification found for semantic classifier.") return "", [] + async def _async_semantic_classify( + self, query_results: List[Dict] + ) -> Tuple[str, List[float]]: + scores_by_class = await self.async_group_scores_by_class(query_results) + + # Calculate total score for each class + total_scores = { + route: self.aggregation_method(scores) + for route, scores in scores_by_class.items() + } + top_class = max(total_scores, key=lambda x: total_scores[x], default=None) + + # Return the top class and its associated scores + if top_class is not None: + return str(top_class), scores_by_class.get(top_class, []) + else: + logger.warning("No classification found for semantic classifier.") + return "", [] + def get(self, name: str) -> Optional[Route]: for route in self.routes: if route.name == name: @@ -507,6 +597,19 @@ def group_scores_by_class( scores_by_class[route] = [score] return scores_by_class + async def async_group_scores_by_class( + self, query_results: List[Dict] + ) -> Dict[str, List[float]]: + scores_by_class: Dict[str, List[float]] = {} + for result in query_results: + score = result["score"] + route = result["route"] + if route in scores_by_class: + scores_by_class[route].append(score) + else: + scores_by_class[route] = [score] + return scores_by_class + def _pass_threshold(self, scores: List[float], threshold: float) -> bool: if scores: return max(scores) > threshold diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 86ab1233..b444c988 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -21,6 +21,7 @@ class EncoderType(Enum): class EncoderInfo(BaseModel): name: str token_limit: int + threshold: Optional[float] = None class RouteChoice(BaseModel): diff --git a/semantic_router/splitters/rolling_window.py b/semantic_router/splitters/rolling_window.py index dc393b55..7fdcc63e 100644 --- a/semantic_router/splitters/rolling_window.py +++ b/semantic_router/splitters/rolling_window.py @@ -95,6 +95,11 @@ def __call__(self, docs: List[str]) -> List[DocumentSplit]: if self.dynamic_threshold: self._find_optimal_threshold(docs, similarities) else: + if self.encoder.score_threshold is None: + raise ValueError( + "No score threshold provided for encoder. Please set the score threshold " + "in the encoder config." + ) self.calculated_threshold = self.encoder.score_threshold split_indices = self._find_split_indices(similarities=similarities) splits = self._split_documents(docs, split_indices, similarities)