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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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291
libs/community/langchain_community/llms/azureml_endpoint.py
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291
libs/community/langchain_community/llms/azureml_endpoint.py
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import json
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import urllib.request
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import warnings
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from abc import abstractmethod
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from typing import Any, Dict, List, Mapping, Optional
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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.pydantic_v1 import BaseModel, validator
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from langchain_core.utils import get_from_dict_or_env
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class AzureMLEndpointClient(object):
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"""AzureML Managed Endpoint client."""
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def __init__(
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self, endpoint_url: str, endpoint_api_key: str, deployment_name: str = ""
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) -> None:
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"""Initialize the class."""
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if not endpoint_api_key or not endpoint_url:
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raise ValueError(
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"""A key/token and REST endpoint should
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be provided to invoke the endpoint"""
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)
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self.endpoint_url = endpoint_url
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self.endpoint_api_key = endpoint_api_key
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self.deployment_name = deployment_name
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def call(self, body: bytes, **kwargs: Any) -> bytes:
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"""call."""
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# The azureml-model-deployment header will force the request to go to a
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# specific deployment. Remove this header to have the request observe the
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# endpoint traffic rules.
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headers = {
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"Content-Type": "application/json",
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"Authorization": ("Bearer " + self.endpoint_api_key),
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}
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if self.deployment_name != "":
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headers["azureml-model-deployment"] = self.deployment_name
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req = urllib.request.Request(self.endpoint_url, body, headers)
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response = urllib.request.urlopen(req, timeout=kwargs.get("timeout", 50))
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result = response.read()
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return result
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class ContentFormatterBase:
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"""Transform request and response of AzureML endpoint to match with
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required schema.
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"""
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"""
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Example:
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.. code-block:: python
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class ContentFormatter(ContentFormatterBase):
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content_type = "application/json"
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accepts = "application/json"
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def format_request_payload(
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self,
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prompt: str,
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model_kwargs: Dict
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) -> bytes:
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input_str = json.dumps(
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{
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"inputs": {"input_string": [prompt]},
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"parameters": model_kwargs,
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}
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)
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return str.encode(input_str)
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def format_response_payload(self, output: str) -> str:
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response_json = json.loads(output)
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return response_json[0]["0"]
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"""
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content_type: Optional[str] = "application/json"
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"""The MIME type of the input data passed to the endpoint"""
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accepts: Optional[str] = "application/json"
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"""The MIME type of the response data returned from the endpoint"""
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@staticmethod
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def escape_special_characters(prompt: str) -> str:
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"""Escapes any special characters in `prompt`"""
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escape_map = {
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"\\": "\\\\",
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'"': '\\"',
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"\b": "\\b",
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"\f": "\\f",
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"\n": "\\n",
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"\r": "\\r",
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"\t": "\\t",
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}
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# Replace each occurrence of the specified characters with escaped versions
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for escape_sequence, escaped_sequence in escape_map.items():
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prompt = prompt.replace(escape_sequence, escaped_sequence)
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return prompt
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@abstractmethod
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def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
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"""Formats the request body according to the input schema of
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the model. Returns bytes or seekable file like object in the
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format specified in the content_type request header.
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"""
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@abstractmethod
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def format_response_payload(self, output: bytes) -> str:
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"""Formats the response body according to the output
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schema of the model. Returns the data type that is
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received from the response.
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"""
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class GPT2ContentFormatter(ContentFormatterBase):
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"""Content handler for GPT2"""
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def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
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prompt = ContentFormatterBase.escape_special_characters(prompt)
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request_payload = json.dumps(
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{"inputs": {"input_string": [f'"{prompt}"']}, "parameters": model_kwargs}
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)
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return str.encode(request_payload)
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def format_response_payload(self, output: bytes) -> str:
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return json.loads(output)[0]["0"]
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class OSSContentFormatter(GPT2ContentFormatter):
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"""Deprecated: Kept for backwards compatibility
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Content handler for LLMs from the OSS catalog."""
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content_formatter: Any = None
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def __init__(self) -> None:
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super().__init__()
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warnings.warn(
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"""`OSSContentFormatter` will be deprecated in the future.
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Please use `GPT2ContentFormatter` instead.
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"""
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)
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class HFContentFormatter(ContentFormatterBase):
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"""Content handler for LLMs from the HuggingFace catalog."""
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def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
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ContentFormatterBase.escape_special_characters(prompt)
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request_payload = json.dumps(
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{"inputs": [f'"{prompt}"'], "parameters": model_kwargs}
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)
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return str.encode(request_payload)
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def format_response_payload(self, output: bytes) -> str:
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return json.loads(output)[0]["generated_text"]
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class DollyContentFormatter(ContentFormatterBase):
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"""Content handler for the Dolly-v2-12b model"""
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def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
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prompt = ContentFormatterBase.escape_special_characters(prompt)
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request_payload = json.dumps(
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{
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"input_data": {"input_string": [f'"{prompt}"']},
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"parameters": model_kwargs,
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}
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)
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return str.encode(request_payload)
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def format_response_payload(self, output: bytes) -> str:
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return json.loads(output)[0]
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class LlamaContentFormatter(ContentFormatterBase):
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"""Content formatter for LLaMa"""
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def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
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"""Formats the request according to the chosen api"""
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prompt = ContentFormatterBase.escape_special_characters(prompt)
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request_payload = json.dumps(
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{
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"input_data": {
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"input_string": [f'"{prompt}"'],
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"parameters": model_kwargs,
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}
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}
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)
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return str.encode(request_payload)
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def format_response_payload(self, output: bytes) -> str:
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"""Formats response"""
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return json.loads(output)[0]["0"]
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class AzureMLOnlineEndpoint(LLM, BaseModel):
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"""Azure ML Online Endpoint models.
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Example:
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.. code-block:: python
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azure_llm = AzureMLOnlineEndpoint(
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endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
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endpoint_api_key="my-api-key",
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content_formatter=content_formatter,
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)
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""" # noqa: E501
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endpoint_url: str = ""
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"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
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env var `AZUREML_ENDPOINT_URL`."""
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endpoint_api_key: str = ""
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"""Authentication Key for Endpoint. Should be passed to constructor or specified as
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env var `AZUREML_ENDPOINT_API_KEY`."""
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deployment_name: str = ""
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"""Deployment Name for Endpoint. NOT REQUIRED to call endpoint. Should be passed
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to constructor or specified as env var `AZUREML_DEPLOYMENT_NAME`."""
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http_client: Any = None #: :meta private:
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content_formatter: Any = None
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"""The content formatter that provides an input and output
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transform function to handle formats between the LLM and
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the endpoint"""
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model_kwargs: Optional[dict] = None
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"""Keyword arguments to pass to the model."""
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@validator("http_client", always=True, allow_reuse=True)
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@classmethod
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def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
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"""Validate that api key and python package exists in environment."""
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endpoint_key = get_from_dict_or_env(
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values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY"
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)
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endpoint_url = get_from_dict_or_env(
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values, "endpoint_url", "AZUREML_ENDPOINT_URL"
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)
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deployment_name = get_from_dict_or_env(
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values, "deployment_name", "AZUREML_DEPLOYMENT_NAME", ""
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)
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http_client = AzureMLEndpointClient(endpoint_url, endpoint_key, deployment_name)
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return http_client
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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_model_kwargs = self.model_kwargs or {}
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return {
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**{"deployment_name": self.deployment_name},
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**{"model_kwargs": _model_kwargs},
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "azureml_endpoint"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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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 an AzureML Managed Online 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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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = azureml_model("Tell me a joke.")
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"""
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_model_kwargs = self.model_kwargs or {}
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request_payload = self.content_formatter.format_request_payload(
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prompt, _model_kwargs
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)
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response_payload = self.http_client.call(request_payload, **kwargs)
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generated_text = self.content_formatter.format_response_payload(
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response_payload
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)
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return generated_text
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