mirror of
https://github.com/hwchase17/langchain.git
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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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175
libs/community/langchain_community/llms/volcengine_maas.py
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175
libs/community/langchain_community/llms/volcengine_maas.py
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from __future__ import annotations
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from typing import Any, Dict, Iterator, List, 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.outputs import GenerationChunk
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from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class VolcEngineMaasBase(BaseModel):
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"""Base class for VolcEngineMaas models."""
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client: Any
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volc_engine_maas_ak: Optional[str] = None
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"""access key for volc engine"""
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volc_engine_maas_sk: Optional[str] = None
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"""secret key for volc engine"""
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endpoint: Optional[str] = "maas-api.ml-platform-cn-beijing.volces.com"
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"""Endpoint of the VolcEngineMaas LLM."""
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region: Optional[str] = "Region"
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"""Region of the VolcEngineMaas LLM."""
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model: str = "skylark-lite-public"
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"""Model name. you could check this model details here
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https://www.volcengine.com/docs/82379/1133187
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and you could choose other models by change this field"""
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model_version: Optional[str] = None
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"""Model version. Only used in moonshot large language model.
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you could check details here https://www.volcengine.com/docs/82379/1158281"""
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top_p: Optional[float] = 0.8
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"""Total probability mass of tokens to consider at each step."""
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temperature: Optional[float] = 0.95
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"""A non-negative float that tunes the degree of randomness in generation."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""model special arguments, you could check detail on model page"""
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streaming: bool = False
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"""Whether to stream the results."""
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connect_timeout: Optional[int] = 60
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"""Timeout for connect to volc engine maas endpoint. Default is 60 seconds."""
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read_timeout: Optional[int] = 60
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"""Timeout for read response from volc engine maas endpoint.
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Default is 60 seconds."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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ak = get_from_dict_or_env(values, "volc_engine_maas_ak", "VOLC_ACCESSKEY")
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sk = get_from_dict_or_env(values, "volc_engine_maas_sk", "VOLC_SECRETKEY")
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endpoint = values["endpoint"]
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if values["endpoint"] is not None and values["endpoint"] != "":
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endpoint = values["endpoint"]
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try:
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from volcengine.maas import MaasService
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maas = MaasService(
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endpoint,
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values["region"],
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connection_timeout=values["connect_timeout"],
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socket_timeout=values["read_timeout"],
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)
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maas.set_ak(ak)
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values["volc_engine_maas_ak"] = ak
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values["volc_engine_maas_sk"] = sk
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maas.set_sk(sk)
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values["client"] = maas
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except ImportError:
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raise ImportError(
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"volcengine package not found, please install it with "
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"`pip install volcengine`"
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)
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling VolcEngineMaas API."""
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normal_params = {
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"top_p": self.top_p,
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"temperature": self.temperature,
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}
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return {**normal_params, **self.model_kwargs}
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class VolcEngineMaasLLM(LLM, VolcEngineMaasBase):
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"""volc engine maas hosts a plethora of models.
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You can utilize these models through this class.
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To use, you should have the ``volcengine`` python package installed.
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and set access key and secret key by environment variable or direct pass those to
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this class.
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access key, secret key are required parameters which you could get help
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https://www.volcengine.com/docs/6291/65568
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In order to use them, it is necessary to install the 'volcengine' Python package.
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The access key and secret key must be set either via environment variables or
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passed directly to this class.
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access key and secret key are mandatory parameters for which assistance can be
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sought at https://www.volcengine.com/docs/6291/65568.
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Example:
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.. code-block:: python
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from langchain_community.llms import VolcEngineMaasLLM
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model = VolcEngineMaasLLM(model="skylark-lite-public",
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volc_engine_maas_ak="your_ak",
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volc_engine_maas_sk="your_sk")
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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 "volc-engine-maas-llm"
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def _convert_prompt_msg_params(
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self,
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prompt: str,
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**kwargs: Any,
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) -> dict:
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model_req = {
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"model": {
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"name": self.model,
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}
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}
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if self.model_version is not None:
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model_req["model"]["version"] = self.model_version
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return {
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**model_req,
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"messages": [{"role": "user", "content": prompt}],
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"parameters": {**self._default_params, **kwargs},
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}
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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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if self.streaming:
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completion = ""
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for chunk in self._stream(prompt, stop, run_manager, **kwargs):
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completion += chunk.text
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return completion
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params = self._convert_prompt_msg_params(prompt, **kwargs)
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response = self.client.chat(params)
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return response.get("choice", {}).get("message", {}).get("content", "")
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def _stream(
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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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) -> Iterator[GenerationChunk]:
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params = self._convert_prompt_msg_params(prompt, **kwargs)
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for res in self.client.stream_chat(params):
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if res:
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chunk = GenerationChunk(
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text=res.get("choice", {}).get("message", {}).get("content", "")
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)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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