mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-09 06:53:59 +00:00
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
This commit is contained in:
Bagatur
GitHub
@@ -0,0 +1,169 @@
|
||||
import json
|
||||
from typing import Any, Dict, List, Optional, cast
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.language_models.chat_models import SimpleChatModel
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
BaseMessage,
|
||||
ChatMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.pydantic_v1 import SecretStr, validator
|
||||
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
|
||||
|
||||
from langchain_community.llms.azureml_endpoint import (
|
||||
AzureMLEndpointClient,
|
||||
ContentFormatterBase,
|
||||
)
|
||||
|
||||
|
||||
class LlamaContentFormatter(ContentFormatterBase):
|
||||
"""Content formatter for `LLaMA`."""
|
||||
|
||||
SUPPORTED_ROLES: List[str] = ["user", "assistant", "system"]
|
||||
|
||||
@staticmethod
|
||||
def _convert_message_to_dict(message: BaseMessage) -> Dict:
|
||||
"""Converts message to a dict according to role"""
|
||||
content = cast(str, message.content)
|
||||
if isinstance(message, HumanMessage):
|
||||
return {
|
||||
"role": "user",
|
||||
"content": ContentFormatterBase.escape_special_characters(content),
|
||||
}
|
||||
elif isinstance(message, AIMessage):
|
||||
return {
|
||||
"role": "assistant",
|
||||
"content": ContentFormatterBase.escape_special_characters(content),
|
||||
}
|
||||
elif isinstance(message, SystemMessage):
|
||||
return {
|
||||
"role": "system",
|
||||
"content": ContentFormatterBase.escape_special_characters(content),
|
||||
}
|
||||
elif (
|
||||
isinstance(message, ChatMessage)
|
||||
and message.role in LlamaContentFormatter.SUPPORTED_ROLES
|
||||
):
|
||||
return {
|
||||
"role": message.role,
|
||||
"content": ContentFormatterBase.escape_special_characters(content),
|
||||
}
|
||||
else:
|
||||
supported = ",".join(
|
||||
[role for role in LlamaContentFormatter.SUPPORTED_ROLES]
|
||||
)
|
||||
raise ValueError(
|
||||
f"""Received unsupported role.
|
||||
Supported roles for the LLaMa Foundation Model: {supported}"""
|
||||
)
|
||||
|
||||
def _format_request_payload(
|
||||
self, messages: List[BaseMessage], model_kwargs: Dict
|
||||
) -> bytes:
|
||||
chat_messages = [
|
||||
LlamaContentFormatter._convert_message_to_dict(message)
|
||||
for message in messages
|
||||
]
|
||||
prompt = json.dumps(
|
||||
{"input_data": {"input_string": chat_messages, "parameters": model_kwargs}}
|
||||
)
|
||||
return self.format_request_payload(prompt=prompt, model_kwargs=model_kwargs)
|
||||
|
||||
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
|
||||
"""Formats the request according to the chosen api"""
|
||||
return str.encode(prompt)
|
||||
|
||||
def format_response_payload(self, output: bytes) -> str:
|
||||
"""Formats response"""
|
||||
return json.loads(output)["output"]
|
||||
|
||||
|
||||
class AzureMLChatOnlineEndpoint(SimpleChatModel):
|
||||
"""`AzureML` Chat models API.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
azure_chat = AzureMLChatOnlineEndpoint(
|
||||
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
|
||||
endpoint_api_key="my-api-key",
|
||||
content_formatter=content_formatter,
|
||||
)
|
||||
"""
|
||||
|
||||
endpoint_url: str = ""
|
||||
"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
|
||||
env var `AZUREML_ENDPOINT_URL`."""
|
||||
|
||||
endpoint_api_key: SecretStr = convert_to_secret_str("")
|
||||
"""Authentication Key for Endpoint. Should be passed to constructor or specified as
|
||||
env var `AZUREML_ENDPOINT_API_KEY`."""
|
||||
|
||||
http_client: Any = None #: :meta private:
|
||||
|
||||
content_formatter: Any = None
|
||||
"""The content formatter that provides an input and output
|
||||
transform function to handle formats between the LLM and
|
||||
the endpoint"""
|
||||
|
||||
model_kwargs: Optional[dict] = None
|
||||
"""Keyword arguments to pass to the model."""
|
||||
|
||||
@validator("http_client", always=True, allow_reuse=True)
|
||||
@classmethod
|
||||
def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
|
||||
"""Validate that api key and python package exist in environment."""
|
||||
values["endpoint_api_key"] = convert_to_secret_str(
|
||||
get_from_dict_or_env(values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY")
|
||||
)
|
||||
endpoint_url = get_from_dict_or_env(
|
||||
values, "endpoint_url", "AZUREML_ENDPOINT_URL"
|
||||
)
|
||||
http_client = AzureMLEndpointClient(
|
||||
endpoint_url, values["endpoint_api_key"].get_secret_value()
|
||||
)
|
||||
return http_client
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
return {
|
||||
**{"model_kwargs": _model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "azureml_chat_endpoint"
|
||||
|
||||
def _call(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Call out to an AzureML Managed Online endpoint.
|
||||
Args:
|
||||
messages: The messages in the conversation with the chat model.
|
||||
stop: Optional list of stop words to use when generating.
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
Example:
|
||||
.. code-block:: python
|
||||
response = azureml_model("Tell me a joke.")
|
||||
"""
|
||||
_model_kwargs = self.model_kwargs or {}
|
||||
|
||||
request_payload = self.content_formatter._format_request_payload(
|
||||
messages, _model_kwargs
|
||||
)
|
||||
response_payload = self.http_client.call(request_payload, **kwargs)
|
||||
generated_text = self.content_formatter.format_response_payload(
|
||||
response_payload
|
||||
)
|
||||
return generated_text
|
||||
Reference in New Issue
Block a user