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community[minor]: Unify Titan Takeoff Integrations and Adding Embedding Support (#18775)
**Community: Unify Titan Takeoff Integrations and Adding Embedding Support** **Description:** Titan Takeoff no longer reflects this either of the integrations in the community folder. The two integrations (TitanTakeoffPro and TitanTakeoff) where causing confusion with clients, so have moved code into one place and created an alias for backwards compatibility. Added Takeoff Client python package to do the bulk of the work with the requests, this is because this package is actively updated with new versions of Takeoff. So this integration will be far more robust and will not degrade as badly over time. **Issue:** Fixes bugs in the old Titan integrations and unified the code with added unit test converge to avoid future problems. **Dependencies:** Added optional dependency takeoff-client, all imports still work without dependency including the Titan Takeoff classes but just will fail on initialisation if not pip installed takeoff-client **Twitter** @MeryemArik9 Thanks all :) --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This commit is contained in:
pjb157
GitHub
@@ -357,6 +357,7 @@ _module_lookup = {
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"VolcanoEmbeddings": "langchain_community.embeddings.volcengine",
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"VoyageEmbeddings": "langchain_community.embeddings.voyageai",
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"XinferenceEmbeddings": "langchain_community.embeddings.xinference",
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"TitanTakeoffEmbed": "langchain_community.embeddings.titan_takeoff",
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"PremAIEmbeddings": "langchain_community.embeddings.premai",
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"YandexGPTEmbeddings": "langchain_community.embeddings.yandex",
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}
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207
libs/community/langchain_community/embeddings/titan_takeoff.py
Normal file
207
libs/community/langchain_community/embeddings/titan_takeoff.py
Normal file
@@ -0,0 +1,207 @@
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from enum import Enum
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from typing import Any, List, Optional, Set, Union
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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class TakeoffEmbeddingException(Exception):
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"""Exceptions experienced with interfacing with Takeoff Embedding Wrapper"""
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class MissingConsumerGroup(TakeoffEmbeddingException):
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"""Exception raised when no consumer group is provided on initialization of
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TitanTakeoffEmbed or in embed request"""
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class Device(str, Enum):
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"""The device to use for inference, cuda or cpu"""
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cuda = "cuda"
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cpu = "cpu"
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class ReaderConfig(BaseModel):
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class Config:
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protected_namespaces = ()
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model_name: str
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"""The name of the model to use"""
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device: Device = Device.cuda
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"""The device to use for inference, cuda or cpu"""
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consumer_group: str = "primary"
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"""The consumer group to place the reader into"""
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class TitanTakeoffEmbed(Embeddings):
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"""Titan Takeoff Embed is a wrapper to interface with Takeoff Inference API
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for embedding models
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You can use this wrapper to send embedding requests and to deploy embedding
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readers with Takeoff.
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Examples:
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This is an example how to deploy an embedding model and send requests.
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.. code-block:: python
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# Import the TitanTakeoffEmbed class from community package
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import time
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from langchain_community.embeddings import TitanTakeoffEmbed
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# Specify the embedding reader you'd like to deploy
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reader_1 = {
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"model_name": "avsolatorio/GIST-large-Embedding-v0",
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"device": "cpu",
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"consumer_group": "embed"
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}
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# For every reader you pass into models arg Takeoff will spin up a reader
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# according to the specs you provide. If you don't specify the arg no models
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# are spun up and it assumes you have already done this separately.
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embed = TitanTakeoffEmbed(models=[reader_1])
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# Wait for the reader to be deployed, time needed depends on the model size
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# and your internet speed
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time.sleep(60)
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# Returns the embedded query, ie a List[float], sent to `embed` consumer
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# group where we just spun up the embedding reader
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print(embed.embed_query(
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"Where can I see football?", consumer_group="embed"
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))
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# Returns a List of embeddings, ie a List[List[float]], sent to `embed`
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# consumer group where we just spun up the embedding reader
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print(embed.embed_document(
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["Document1", "Document2"],
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consumer_group="embed"
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))
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"""
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base_url: str = "http://localhost"
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"""The base URL of the Titan Takeoff (Pro) server. Default = "http://localhost"."""
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port: int = 3000
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"""The port of the Titan Takeoff (Pro) server. Default = 3000."""
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mgmt_port: int = 3001
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"""The management port of the Titan Takeoff (Pro) server. Default = 3001."""
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client: Any = None
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"""Takeoff Client Python SDK used to interact with Takeoff API"""
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embed_consumer_groups: Set[str] = set()
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"""The consumer groups in Takeoff which contain embedding models"""
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def __init__(
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self,
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base_url: str = "http://localhost",
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port: int = 3000,
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mgmt_port: int = 3001,
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models: List[ReaderConfig] = [],
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):
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"""Initialize the Titan Takeoff embedding wrapper.
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Args:
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base_url (str, optional): The base url where Takeoff Inference Server is
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listening. Defaults to "http://localhost".
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port (int, optional): What port is Takeoff Inference API listening on.
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Defaults to 3000.
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mgmt_port (int, optional): What port is Takeoff Management API listening on.
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Defaults to 3001.
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models (List[ReaderConfig], optional): Any readers you'd like to spin up on.
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Defaults to [].
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Raises:
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ImportError: If you haven't installed takeoff-client, you will get an
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ImportError. To remedy run `pip install 'takeoff-client==0.4.0'`
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"""
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self.base_url = base_url
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self.port = port
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self.mgmt_port = mgmt_port
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try:
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from takeoff_client import TakeoffClient
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except ImportError:
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raise ImportError(
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"takeoff-client is required for TitanTakeoff. "
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"Please install it with `pip install 'takeoff-client==0.4.0'`."
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)
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self.client = TakeoffClient(
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self.base_url, port=self.port, mgmt_port=self.mgmt_port
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)
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for model in models:
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self.client.create_reader(model)
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if isinstance(model, dict):
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self.embed_consumer_groups.add(model.get("consumer_group"))
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else:
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self.embed_consumer_groups.add(model.consumer_group)
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super(TitanTakeoffEmbed, self).__init__()
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def _embed(
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self, input: Union[List[str], str], consumer_group: Optional[str]
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) -> dict:
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"""Embed text.
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Args:
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input (List[str]): prompt/document or list of prompts/documents to embed
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consumer_group (Optional[str]): what consumer group to send the embedding
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request to. If not specified and there is only one
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consumer group specified during initialization, it will be used. If there
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are multiple consumer groups specified during initialization, you must
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specify which one to use.
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Raises:
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MissingConsumerGroup: The consumer group can not be inferred from the
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initialization and must be specified with request.
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Returns:
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Dict[str, Any]: Result of query, {"result": List[List[float]]} or
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{"result": List[float]}
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"""
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if not consumer_group:
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if len(self.embed_consumer_groups) == 1:
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consumer_group = list(self.embed_consumer_groups)[0]
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elif len(self.embed_consumer_groups) > 1:
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raise MissingConsumerGroup(
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"TakeoffEmbedding was initialized with multiple embedding reader"
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"groups, you must specify which one to use."
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)
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else:
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raise MissingConsumerGroup(
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"You must specify what consumer group you want to send embedding"
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"response to as TitanTakeoffEmbed was not initialized with an "
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"embedding reader."
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)
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return self.client.embed(input, consumer_group)
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def embed_documents(
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self, texts: List[str], consumer_group: Optional[str] = None
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) -> List[List[float]]:
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"""Embed documents.
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Args:
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texts (List[str]): List of prompts/documents to embed
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consumer_group (Optional[str], optional): Consumer group to send request
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to containing embedding model. Defaults to None.
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Returns:
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List[List[float]]: List of embeddings
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"""
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return self._embed(texts, consumer_group)["result"]
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def embed_query(
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self, text: str, consumer_group: Optional[str] = None
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) -> List[float]:
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"""Embed query.
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Args:
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text (str): Prompt/document to embed
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consumer_group (Optional[str], optional): Consumer group to send request
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to containing embedding model. Defaults to None.
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Returns:
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List[float]: Embedding
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"""
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return self._embed(text, consumer_group)["result"]
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@@ -549,9 +549,9 @@ def _import_titan_takeoff() -> Type[BaseLLM]:
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def _import_titan_takeoff_pro() -> Type[BaseLLM]:
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from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro
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from langchain_community.llms.titan_takeoff import TitanTakeoff
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return TitanTakeoffPro
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return TitanTakeoff
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def _import_together() -> Type[BaseLLM]:
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@@ -1,61 +1,155 @@
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from typing import Any, Iterator, List, Mapping, Optional
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from enum import Enum
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from typing import Any, Iterator, List, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import GenerationChunk
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from requests.exceptions import ConnectionError
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_community.llms.utils import enforce_stop_tokens
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class Device(str, Enum):
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"""The device to use for inference, cuda or cpu"""
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cuda = "cuda"
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cpu = "cpu"
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class ReaderConfig(BaseModel):
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class Config:
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protected_namespaces = ()
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model_name: str
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"""The name of the model to use"""
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device: Device = Device.cuda
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"""The device to use for inference, cuda or cpu"""
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consumer_group: str = "primary"
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"""The consumer group to place the reader into"""
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tensor_parallel: Optional[int] = None
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"""The number of gpus you would like your model to be split across"""
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max_seq_length: int = 512
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"""The maximum sequence length to use for inference, defaults to 512"""
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max_batch_size: int = 4
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"""The max batch size for continuous batching of requests"""
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class TitanTakeoff(LLM):
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"""Titan Takeoff API LLMs."""
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"""Titan Takeoff API LLMs.
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base_url: str = "http://localhost:8000"
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"""Specifies the baseURL to use for the Titan Takeoff API.
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Default = http://localhost:8000.
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Titan Takeoff is a wrapper to interface with Takeoff Inference API for
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generative text to text language models.
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You can use this wrapper to send requests to a generative language model
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and to deploy readers with Takeoff.
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Examples:
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This is an example how to deploy a generative language model and send
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requests.
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.. code-block:: python
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# Import the TitanTakeoff class from community package
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import time
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from langchain_community.llms import TitanTakeoff
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# Specify the embedding reader you'd like to deploy
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reader_1 = {
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"model_name": "TheBloke/Llama-2-7b-Chat-AWQ",
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"device": "cuda",
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"tensor_parallel": 1,
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"consumer_group": "llama"
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}
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# For every reader you pass into models arg Takeoff will spin
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# up a reader according to the specs you provide. If you don't
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# specify the arg no models are spun up and it assumes you have
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# already done this separately.
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llm = TitanTakeoff(models=[reader_1])
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# Wait for the reader to be deployed, time needed depends on the
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# model size and your internet speed
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time.sleep(60)
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# Returns the query, ie a List[float], sent to `llama` consumer group
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# where we just spun up the Llama 7B model
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print(embed.invoke(
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"Where can I see football?", consumer_group="llama"
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))
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# You can also send generation parameters to the model, any of the
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# following can be passed in as kwargs:
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# https://docs.titanml.co/docs/next/apis/Takeoff%20inference_REST_API/generate#request
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# for instance:
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print(embed.invoke(
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"Where can I see football?", consumer_group="llama", max_new_tokens=100
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))
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"""
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generate_max_length: int = 128
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"""Maximum generation length. Default = 128."""
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base_url: str = "http://localhost"
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"""The base URL of the Titan Takeoff (Pro) server. Default = "http://localhost"."""
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sampling_topk: int = 1
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"""Sample predictions from the top K most probable candidates. Default = 1."""
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port: int = 3000
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"""The port of the Titan Takeoff (Pro) server. Default = 3000."""
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sampling_topp: float = 1.0
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"""Sample from predictions whose cumulative probability exceeds this value.
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Default = 1.0.
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"""
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sampling_temperature: float = 1.0
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"""Sample with randomness. Bigger temperatures are associated with
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more randomness and 'creativity'. Default = 1.0.
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"""
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repetition_penalty: float = 1.0
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"""Penalise the generation of tokens that have been generated before.
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Set to > 1 to penalize. Default = 1 (no penalty).
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"""
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no_repeat_ngram_size: int = 0
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"""Prevent repetitions of ngrams of this size. Default = 0 (turned off)."""
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mgmt_port: int = 3001
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"""The management port of the Titan Takeoff (Pro) server. Default = 3001."""
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streaming: bool = False
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"""Whether to stream the output. Default = False."""
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@property
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def _default_params(self) -> Mapping[str, Any]:
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"""Get the default parameters for calling Titan Takeoff Server."""
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params = {
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"generate_max_length": self.generate_max_length,
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"sampling_topk": self.sampling_topk,
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"sampling_topp": self.sampling_topp,
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"sampling_temperature": self.sampling_temperature,
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"repetition_penalty": self.repetition_penalty,
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"no_repeat_ngram_size": self.no_repeat_ngram_size,
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}
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return params
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client: Any = None
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"""Takeoff Client Python SDK used to interact with Takeoff API"""
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def __init__(
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self,
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base_url: str = "http://localhost",
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port: int = 3000,
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mgmt_port: int = 3001,
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streaming: bool = False,
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models: List[ReaderConfig] = [],
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):
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"""Initialize the Titan Takeoff language wrapper.
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Args:
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base_url (str, optional): The base URL where the Takeoff
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Inference Server is listening. Defaults to `http://localhost`.
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port (int, optional): What port is Takeoff Inference API
|
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listening on. Defaults to 3000.
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mgmt_port (int, optional): What port is Takeoff Management API
|
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listening on. Defaults to 3001.
|
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streaming (bool, optional): Whether you want to by default use the
|
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generate_stream endpoint over generate to stream responses.
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Defaults to False. In reality, this is not significantly different
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as the streamed response is buffered and returned similar to the
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non-streamed response, but the run manager is applied per token
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generated.
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models (List[ReaderConfig], optional): Any readers you'd like to
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spin up on. Defaults to [].
|
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|
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Raises:
|
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ImportError: If you haven't installed takeoff-client, you will
|
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get an ImportError. To remedy run `pip install 'takeoff-client==0.4.0'`
|
||||
"""
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super().__init__(
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base_url=base_url, port=port, mgmt_port=mgmt_port, streaming=streaming
|
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)
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try:
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from takeoff_client import TakeoffClient
|
||||
except ImportError:
|
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raise ImportError(
|
||||
"takeoff-client is required for TitanTakeoff. "
|
||||
"Please install it with `pip install 'takeoff-client>=0.4.0'`."
|
||||
)
|
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self.client = TakeoffClient(
|
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self.base_url, port=self.port, mgmt_port=self.mgmt_port
|
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)
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for model in models:
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self.client.create_reader(model)
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|
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@property
|
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def _llm_type(self) -> str:
|
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@@ -69,11 +163,12 @@ class TitanTakeoff(LLM):
|
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run_manager: Optional[CallbackManagerForLLMRun] = None,
|
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**kwargs: Any,
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) -> str:
|
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"""Call out to Titan Takeoff generate endpoint.
|
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"""Call out to Titan Takeoff (Pro) generate endpoint.
|
||||
|
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
|
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run_manager: Optional callback manager to use when streaming.
|
||||
|
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Returns:
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The string generated by the model.
|
||||
@@ -81,41 +176,31 @@ class TitanTakeoff(LLM):
|
||||
Example:
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||||
.. code-block:: python
|
||||
|
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model = TitanTakeoff()
|
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|
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prompt = "What is the capital of the United Kingdom?"
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response = model(prompt)
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# Use of model(prompt), ie `__call__` was deprecated in LangChain 0.1.7,
|
||||
# use model.invoke(prompt) instead.
|
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response = model.invoke(prompt)
|
||||
|
||||
"""
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||||
try:
|
||||
if self.streaming:
|
||||
text_output = ""
|
||||
for chunk in self._stream(
|
||||
prompt=prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
):
|
||||
text_output += chunk.text
|
||||
return text_output
|
||||
if self.streaming:
|
||||
text_output = ""
|
||||
for chunk in self._stream(
|
||||
prompt=prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
):
|
||||
text_output += chunk.text
|
||||
return text_output
|
||||
|
||||
url = f"{self.base_url}/generate"
|
||||
params = {"text": prompt, **self._default_params}
|
||||
response = self.client.generate(prompt, **kwargs)
|
||||
text = response["text"]
|
||||
|
||||
response = requests.post(url, json=params)
|
||||
response.raise_for_status()
|
||||
response.encoding = "utf-8"
|
||||
text = ""
|
||||
|
||||
if "message" in response.json():
|
||||
text = response.json()["message"]
|
||||
else:
|
||||
raise ValueError("Something went wrong.")
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
except ConnectionError:
|
||||
raise ConnectionError(
|
||||
"Could not connect to Titan Takeoff server. \
|
||||
Please make sure that the server is running."
|
||||
)
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
@@ -124,14 +209,12 @@ class TitanTakeoff(LLM):
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
"""Call out to Titan Takeoff stream endpoint.
|
||||
"""Call out to Titan Takeoff (Pro) stream endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
run_manager: Optional callback manager to use when streaming.
|
||||
|
||||
Yields:
|
||||
A dictionary like object containing a string token.
|
||||
@@ -139,23 +222,40 @@ class TitanTakeoff(LLM):
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
model = TitanTakeoff()
|
||||
|
||||
prompt = "What is the capital of the United Kingdom?"
|
||||
response = model(prompt)
|
||||
response = model.stream(prompt)
|
||||
|
||||
# OR
|
||||
|
||||
model = TitanTakeoff(streaming=True)
|
||||
|
||||
response = model.invoke(prompt)
|
||||
|
||||
"""
|
||||
url = f"{self.base_url}/generate_stream"
|
||||
params = {"text": prompt, **self._default_params}
|
||||
response = self.client.generate_stream(prompt, **kwargs)
|
||||
buffer = ""
|
||||
for text in response:
|
||||
buffer += text.data
|
||||
if "data:" in buffer:
|
||||
# Remove the first instance of "data:" from the buffer.
|
||||
if buffer.startswith("data:"):
|
||||
buffer = ""
|
||||
if len(buffer.split("data:", 1)) == 2:
|
||||
content, _ = buffer.split("data:", 1)
|
||||
buffer = content.rstrip("\n")
|
||||
# Trim the buffer to only have content after the "data:" part.
|
||||
if buffer: # Ensure that there's content to process.
|
||||
chunk = GenerationChunk(text=buffer)
|
||||
buffer = "" # Reset buffer for the next set of data.
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token=chunk.text)
|
||||
|
||||
response = requests.post(url, json=params, stream=True)
|
||||
response.encoding = "utf-8"
|
||||
for text in response.iter_content(chunk_size=1, decode_unicode=True):
|
||||
if text:
|
||||
chunk = GenerationChunk(text=text)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token=chunk.text)
|
||||
yield chunk
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {"base_url": self.base_url, **{}, **self._default_params}
|
||||
# Yield any remaining content in the buffer.
|
||||
if buffer:
|
||||
chunk = GenerationChunk(text=buffer.replace("</s>", ""))
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token=chunk.text)
|
||||
|
||||
@@ -1,217 +0,0 @@
|
||||
from typing import Any, Iterator, List, Mapping, Optional
|
||||
|
||||
import requests
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.outputs import GenerationChunk
|
||||
from requests.exceptions import ConnectionError
|
||||
|
||||
from langchain_community.llms.utils import enforce_stop_tokens
|
||||
|
||||
|
||||
class TitanTakeoffPro(LLM):
|
||||
"""Titan Takeoff Pro is a language model that can be used to generate text."""
|
||||
|
||||
base_url: Optional[str] = "http://localhost:3000"
|
||||
"""Specifies the baseURL to use for the Titan Takeoff Pro API.
|
||||
Default = http://localhost:3000.
|
||||
"""
|
||||
|
||||
max_new_tokens: Optional[int] = None
|
||||
"""Maximum tokens generated."""
|
||||
|
||||
min_new_tokens: Optional[int] = None
|
||||
"""Minimum tokens generated."""
|
||||
|
||||
sampling_topk: Optional[int] = None
|
||||
"""Sample predictions from the top K most probable candidates."""
|
||||
|
||||
sampling_topp: Optional[float] = None
|
||||
"""Sample from predictions whose cumulative probability exceeds this value.
|
||||
"""
|
||||
|
||||
sampling_temperature: Optional[float] = None
|
||||
"""Sample with randomness. Bigger temperatures are associated with
|
||||
more randomness and 'creativity'.
|
||||
"""
|
||||
|
||||
repetition_penalty: Optional[float] = None
|
||||
"""Penalise the generation of tokens that have been generated before.
|
||||
Set to > 1 to penalize.
|
||||
"""
|
||||
|
||||
regex_string: Optional[str] = None
|
||||
"""A regex string for constrained generation."""
|
||||
|
||||
no_repeat_ngram_size: Optional[int] = None
|
||||
"""Prevent repetitions of ngrams of this size. Default = 0 (turned off)."""
|
||||
|
||||
streaming: bool = False
|
||||
"""Whether to stream the output. Default = False."""
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Mapping[str, Any]:
|
||||
"""Get the default parameters for calling Titan Takeoff Server (Pro)."""
|
||||
return {
|
||||
**(
|
||||
{"regex_string": self.regex_string}
|
||||
if self.regex_string is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"sampling_temperature": self.sampling_temperature}
|
||||
if self.sampling_temperature is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"sampling_topp": self.sampling_topp}
|
||||
if self.sampling_topp is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"repetition_penalty": self.repetition_penalty}
|
||||
if self.repetition_penalty is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"max_new_tokens": self.max_new_tokens}
|
||||
if self.max_new_tokens is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"min_new_tokens": self.min_new_tokens}
|
||||
if self.min_new_tokens is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"sampling_topk": self.sampling_topk}
|
||||
if self.sampling_topk is not None
|
||||
else {}
|
||||
),
|
||||
**(
|
||||
{"no_repeat_ngram_size": self.no_repeat_ngram_size}
|
||||
if self.no_repeat_ngram_size is not None
|
||||
else {}
|
||||
),
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "titan_takeoff_pro"
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to Titan Takeoff (Pro) generate endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
prompt = "What is the capital of the United Kingdom?"
|
||||
response = model(prompt)
|
||||
|
||||
"""
|
||||
try:
|
||||
if self.streaming:
|
||||
text_output = ""
|
||||
for chunk in self._stream(
|
||||
prompt=prompt,
|
||||
stop=stop,
|
||||
run_manager=run_manager,
|
||||
):
|
||||
text_output += chunk.text
|
||||
return text_output
|
||||
url = f"{self.base_url}/generate"
|
||||
params = {"text": prompt, **self._default_params}
|
||||
|
||||
response = requests.post(url, json=params)
|
||||
response.raise_for_status()
|
||||
response.encoding = "utf-8"
|
||||
|
||||
text = ""
|
||||
if "text" in response.json():
|
||||
text = response.json()["text"]
|
||||
text = text.replace("</s>", "")
|
||||
else:
|
||||
raise ValueError("Something went wrong.")
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
except ConnectionError:
|
||||
raise ConnectionError(
|
||||
"Could not connect to Titan Takeoff (Pro) server. \
|
||||
Please make sure that the server is running."
|
||||
)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[GenerationChunk]:
|
||||
"""Call out to Titan Takeoff (Pro) stream endpoint.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Yields:
|
||||
A dictionary like object containing a string token.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
prompt = "What is the capital of the United Kingdom?"
|
||||
response = model(prompt)
|
||||
|
||||
"""
|
||||
url = f"{self.base_url}/generate_stream"
|
||||
params = {"text": prompt, **self._default_params}
|
||||
|
||||
response = requests.post(url, json=params, stream=True)
|
||||
response.encoding = "utf-8"
|
||||
buffer = ""
|
||||
for text in response.iter_content(chunk_size=1, decode_unicode=True):
|
||||
buffer += text
|
||||
if "data:" in buffer:
|
||||
# Remove the first instance of "data:" from the buffer.
|
||||
if buffer.startswith("data:"):
|
||||
buffer = ""
|
||||
if len(buffer.split("data:", 1)) == 2:
|
||||
content, _ = buffer.split("data:", 1)
|
||||
buffer = content.rstrip("\n")
|
||||
# Trim the buffer to only have content after the "data:" part.
|
||||
if buffer: # Ensure that there's content to process.
|
||||
chunk = GenerationChunk(text=buffer)
|
||||
buffer = "" # Reset buffer for the next set of data.
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token=chunk.text)
|
||||
|
||||
# Yield any remaining content in the buffer.
|
||||
if buffer:
|
||||
chunk = GenerationChunk(text=buffer.replace("</s>", ""))
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(token=chunk.text)
|
||||
yield chunk
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {"base_url": self.base_url, **{}, **self._default_params}
|
||||
@@ -0,0 +1,178 @@
|
||||
"""Test Titan Takeoff Embedding wrapper."""
|
||||
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_community.embeddings import TitanTakeoffEmbed
|
||||
from langchain_community.embeddings.titan_takeoff import MissingConsumerGroup
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
def test_titan_takeoff_call(httpx_mock: Any) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
port = 2345
|
||||
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=f"http://localhost:{port}/embed",
|
||||
json={"result": [0.46635, 0.234, -0.8521]},
|
||||
)
|
||||
|
||||
embedding = TitanTakeoffEmbed(port=port)
|
||||
|
||||
output_1 = embedding.embed_documents("What is 2 + 2?", "primary")
|
||||
output_2 = embedding.embed_query("What is 2 + 2?", "primary")
|
||||
|
||||
assert isinstance(output_1, list)
|
||||
assert isinstance(output_2, list)
|
||||
|
||||
assert len(httpx_mock.get_requests()) == 2
|
||||
for n in range(2):
|
||||
assert httpx_mock.get_requests()[n].url == f"http://localhost:{port}/embed"
|
||||
assert (
|
||||
json.loads(httpx_mock.get_requests()[n].content)["text"] == "What is 2 + 2?"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
def test_no_consumer_group_fails(httpx_mock: Any) -> None:
|
||||
"""Test that not specifying a consumer group fails."""
|
||||
port = 2345
|
||||
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=f"http://localhost:{port}/embed",
|
||||
json={"result": [0.46635, 0.234, -0.8521]},
|
||||
)
|
||||
|
||||
embedding = TitanTakeoffEmbed(port=port)
|
||||
|
||||
with pytest.raises(MissingConsumerGroup):
|
||||
embedding.embed_documents("What is 2 + 2?")
|
||||
with pytest.raises(MissingConsumerGroup):
|
||||
embedding.embed_query("What is 2 + 2?")
|
||||
|
||||
# Check specifying a consumer group works
|
||||
embedding.embed_documents("What is 2 + 2?", "primary")
|
||||
embedding.embed_query("What is 2 + 2?", "primary")
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
def test_takeoff_initialization(httpx_mock: Any) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
mgnt_port = 36452
|
||||
inf_port = 46253
|
||||
mgnt_url = f"http://localhost:{mgnt_port}/reader"
|
||||
embed_url = f"http://localhost:{inf_port}/embed"
|
||||
reader_1 = {
|
||||
"model_name": "test",
|
||||
"device": "cpu",
|
||||
"consumer_group": "embed",
|
||||
}
|
||||
reader_2 = reader_1.copy()
|
||||
reader_2["model_name"] = "test2"
|
||||
reader_2["device"] = "cuda"
|
||||
|
||||
httpx_mock.add_response(
|
||||
method="POST", url=mgnt_url, json={"key": "value"}, status_code=201
|
||||
)
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=embed_url,
|
||||
json={"result": [0.34, 0.43, -0.934532]},
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
llm = TitanTakeoffEmbed(
|
||||
port=inf_port, mgmt_port=mgnt_port, models=[reader_1, reader_2]
|
||||
)
|
||||
# Shouldn't need to specify consumer group as there is only one specified during
|
||||
# initialization
|
||||
output_1 = llm.embed_documents("What is 2 + 2?")
|
||||
output_2 = llm.embed_query("What is 2 + 2?")
|
||||
|
||||
assert isinstance(output_1, list)
|
||||
assert isinstance(output_2, list)
|
||||
# Ensure the management api was called to create the reader
|
||||
assert len(httpx_mock.get_requests()) == 4
|
||||
for key, value in reader_1.items():
|
||||
assert json.loads(httpx_mock.get_requests()[0].content)[key] == value
|
||||
assert httpx_mock.get_requests()[0].url == mgnt_url
|
||||
# Also second call should be made to spin uo reader 2
|
||||
for key, value in reader_2.items():
|
||||
assert json.loads(httpx_mock.get_requests()[1].content)[key] == value
|
||||
assert httpx_mock.get_requests()[1].url == mgnt_url
|
||||
# Ensure the third call is to generate endpoint to inference
|
||||
for n in range(2, 4):
|
||||
assert httpx_mock.get_requests()[n].url == embed_url
|
||||
assert (
|
||||
json.loads(httpx_mock.get_requests()[n].content)["text"] == "What is 2 + 2?"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
def test_takeoff_initialization_with_more_than_one_consumer_group(
|
||||
httpx_mock: Any,
|
||||
) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
mgnt_port = 36452
|
||||
inf_port = 46253
|
||||
mgnt_url = f"http://localhost:{mgnt_port}/reader"
|
||||
embed_url = f"http://localhost:{inf_port}/embed"
|
||||
reader_1 = {
|
||||
"model_name": "test",
|
||||
"device": "cpu",
|
||||
"consumer_group": "embed",
|
||||
}
|
||||
reader_2 = reader_1.copy()
|
||||
reader_2["model_name"] = "test2"
|
||||
reader_2["device"] = "cuda"
|
||||
reader_2["consumer_group"] = "embed2"
|
||||
|
||||
httpx_mock.add_response(
|
||||
method="POST", url=mgnt_url, json={"key": "value"}, status_code=201
|
||||
)
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=embed_url,
|
||||
json={"result": [0.34, 0.43, -0.934532]},
|
||||
status_code=200,
|
||||
)
|
||||
|
||||
llm = TitanTakeoffEmbed(
|
||||
port=inf_port, mgmt_port=mgnt_port, models=[reader_1, reader_2]
|
||||
)
|
||||
# There was more than one consumer group specified during initialization so we
|
||||
# need to specify which one to use
|
||||
with pytest.raises(MissingConsumerGroup):
|
||||
llm.embed_documents("What is 2 + 2?")
|
||||
with pytest.raises(MissingConsumerGroup):
|
||||
llm.embed_query("What is 2 + 2?")
|
||||
|
||||
output_1 = llm.embed_documents("What is 2 + 2?", "embed")
|
||||
output_2 = llm.embed_query("What is 2 + 2?", "embed2")
|
||||
|
||||
assert isinstance(output_1, list)
|
||||
assert isinstance(output_2, list)
|
||||
# Ensure the management api was called to create the reader
|
||||
assert len(httpx_mock.get_requests()) == 4
|
||||
for key, value in reader_1.items():
|
||||
assert json.loads(httpx_mock.get_requests()[0].content)[key] == value
|
||||
assert httpx_mock.get_requests()[0].url == mgnt_url
|
||||
# Also second call should be made to spin uo reader 2
|
||||
for key, value in reader_2.items():
|
||||
assert json.loads(httpx_mock.get_requests()[1].content)[key] == value
|
||||
assert httpx_mock.get_requests()[1].url == mgnt_url
|
||||
# Ensure the third call is to generate endpoint to inference
|
||||
for n in range(2, 4):
|
||||
assert httpx_mock.get_requests()[n].url == embed_url
|
||||
assert (
|
||||
json.loads(httpx_mock.get_requests()[n].content)["text"] == "What is 2 + 2?"
|
||||
)
|
||||
@@ -1,18 +1,141 @@
|
||||
"""Test Titan Takeoff wrapper."""
|
||||
import json
|
||||
from typing import Any, Union
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain_community.llms import TitanTakeoff, TitanTakeoffPro
|
||||
|
||||
|
||||
import responses
|
||||
|
||||
from langchain_community.llms.titan_takeoff import TitanTakeoff
|
||||
|
||||
|
||||
@responses.activate
|
||||
def test_titan_takeoff_call() -> None:
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.parametrize("streaming", [True, False])
|
||||
@pytest.mark.parametrize("takeoff_object", [TitanTakeoff, TitanTakeoffPro])
|
||||
def test_titan_takeoff_call(
|
||||
httpx_mock: Any,
|
||||
streaming: bool,
|
||||
takeoff_object: Union[TitanTakeoff, TitanTakeoffPro],
|
||||
) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
url = "http://localhost:8000/generate"
|
||||
responses.add(responses.POST, url, json={"message": "2 + 2 is 4"}, status=200)
|
||||
from pytest_httpx import IteratorStream
|
||||
|
||||
# response = requests.post(url)
|
||||
llm = TitanTakeoff()
|
||||
port = 2345
|
||||
url = (
|
||||
f"http://localhost:{port}/generate_stream"
|
||||
if streaming
|
||||
else f"http://localhost:{port}/generate"
|
||||
)
|
||||
|
||||
if streaming:
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=url,
|
||||
stream=IteratorStream([b"data: ask someone else\n\n"]),
|
||||
)
|
||||
else:
|
||||
httpx_mock.add_response(
|
||||
method="POST",
|
||||
url=url,
|
||||
json={"text": "ask someone else"},
|
||||
)
|
||||
|
||||
llm = takeoff_object(port=port, streaming=streaming)
|
||||
number_of_calls = 0
|
||||
for function_call in [llm, llm.invoke]:
|
||||
number_of_calls += 1
|
||||
output = function_call("What is 2 + 2?")
|
||||
assert isinstance(output, str)
|
||||
assert len(httpx_mock.get_requests()) == number_of_calls
|
||||
assert httpx_mock.get_requests()[0].url == url
|
||||
assert (
|
||||
json.loads(httpx_mock.get_requests()[0].content)["text"] == "What is 2 + 2?"
|
||||
)
|
||||
|
||||
if streaming:
|
||||
output = llm._stream("What is 2 + 2?")
|
||||
for chunk in output:
|
||||
assert isinstance(chunk.text, str)
|
||||
assert len(httpx_mock.get_requests()) == number_of_calls + 1
|
||||
assert httpx_mock.get_requests()[0].url == url
|
||||
assert (
|
||||
json.loads(httpx_mock.get_requests()[0].content)["text"] == "What is 2 + 2?"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
@pytest.mark.parametrize("streaming", [True, False])
|
||||
@pytest.mark.parametrize("takeoff_object", [TitanTakeoff, TitanTakeoffPro])
|
||||
def test_titan_takeoff_bad_call(
|
||||
httpx_mock: Any,
|
||||
streaming: bool,
|
||||
takeoff_object: Union[TitanTakeoff, TitanTakeoffPro],
|
||||
) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
from takeoff_client import TakeoffException
|
||||
|
||||
url = (
|
||||
"http://localhost:3000/generate"
|
||||
if not streaming
|
||||
else "http://localhost:3000/generate_stream"
|
||||
)
|
||||
httpx_mock.add_response(
|
||||
method="POST", url=url, json={"text": "bad things"}, status_code=400
|
||||
)
|
||||
|
||||
llm = takeoff_object(streaming=streaming)
|
||||
with pytest.raises(TakeoffException):
|
||||
llm("What is 2 + 2?")
|
||||
assert len(httpx_mock.get_requests()) == 1
|
||||
assert httpx_mock.get_requests()[0].url == url
|
||||
assert json.loads(httpx_mock.get_requests()[0].content)["text"] == "What is 2 + 2?"
|
||||
|
||||
|
||||
@pytest.mark.requires("pytest_httpx")
|
||||
@pytest.mark.requires("takeoff_client")
|
||||
@pytest.mark.parametrize("takeoff_object", [TitanTakeoff, TitanTakeoffPro])
|
||||
def test_titan_takeoff_model_initialisation(
|
||||
httpx_mock: Any,
|
||||
takeoff_object: Union[TitanTakeoff, TitanTakeoffPro],
|
||||
) -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
mgnt_port = 36452
|
||||
inf_port = 46253
|
||||
mgnt_url = f"http://localhost:{mgnt_port}/reader"
|
||||
gen_url = f"http://localhost:{inf_port}/generate"
|
||||
reader_1 = {
|
||||
"model_name": "test",
|
||||
"device": "cpu",
|
||||
"consumer_group": "primary",
|
||||
"max_sequence_length": 512,
|
||||
"max_batch_size": 4,
|
||||
"tensor_parallel": 3,
|
||||
}
|
||||
reader_2 = reader_1.copy()
|
||||
reader_2["model_name"] = "test2"
|
||||
|
||||
httpx_mock.add_response(
|
||||
method="POST", url=mgnt_url, json={"key": "value"}, status_code=201
|
||||
)
|
||||
httpx_mock.add_response(
|
||||
method="POST", url=gen_url, json={"text": "value"}, status_code=200
|
||||
)
|
||||
|
||||
llm = takeoff_object(
|
||||
port=inf_port, mgmt_port=mgnt_port, models=[reader_1, reader_2]
|
||||
)
|
||||
output = llm("What is 2 + 2?")
|
||||
|
||||
assert isinstance(output, str)
|
||||
# Ensure the management api was called to create the reader
|
||||
assert len(httpx_mock.get_requests()) == 3
|
||||
for key, value in reader_1.items():
|
||||
assert json.loads(httpx_mock.get_requests()[0].content)[key] == value
|
||||
assert httpx_mock.get_requests()[0].url == mgnt_url
|
||||
# Also second call should be made to spin uo reader 2
|
||||
for key, value in reader_2.items():
|
||||
assert json.loads(httpx_mock.get_requests()[1].content)[key] == value
|
||||
assert httpx_mock.get_requests()[1].url == mgnt_url
|
||||
# Ensure the third call is to generate endpoint to inference
|
||||
assert httpx_mock.get_requests()[2].url == gen_url
|
||||
assert json.loads(httpx_mock.get_requests()[2].content)["text"] == "What is 2 + 2?"
|
||||
|
||||
@@ -1,18 +0,0 @@
|
||||
"""Test Titan Takeoff wrapper."""
|
||||
|
||||
|
||||
import responses
|
||||
|
||||
from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro
|
||||
|
||||
|
||||
@responses.activate
|
||||
def test_titan_takeoff_pro_call() -> None:
|
||||
"""Test valid call to Titan Takeoff."""
|
||||
url = "http://localhost:3000/generate"
|
||||
responses.add(responses.POST, url, json={"message": "2 + 2 is 4"}, status=200)
|
||||
|
||||
# response = requests.post(url)
|
||||
llm = TitanTakeoffPro()
|
||||
output = llm("What is 2 + 2?")
|
||||
assert isinstance(output, str)
|
||||
@@ -66,6 +66,7 @@ EXPECTED_ALL = [
|
||||
"QuantizedBiEncoderEmbeddings",
|
||||
"NeMoEmbeddings",
|
||||
"SparkLLMTextEmbeddings",
|
||||
"TitanTakeoffEmbed",
|
||||
"QuantizedBgeEmbeddings",
|
||||
"PremAIEmbeddings",
|
||||
"YandexGPTEmbeddings",
|
||||
|
||||
@@ -469,9 +469,9 @@ def _import_titan_takeoff() -> Any:
|
||||
|
||||
|
||||
def _import_titan_takeoff_pro() -> Any:
|
||||
from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro
|
||||
from langchain_community.llms.titan_takeoff import TitanTakeoff
|
||||
|
||||
return TitanTakeoffPro
|
||||
return TitanTakeoff
|
||||
|
||||
|
||||
def _import_together() -> Any:
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
from langchain_community.llms.titan_takeoff_pro import TitanTakeoffPro
|
||||
from langchain_community.llms.titan_takeoff import TitanTakeoff as TitanTakeoffPro
|
||||
|
||||
__all__ = ["TitanTakeoffPro"]
|
||||
|
||||
Reference in New Issue
Block a user