simple

langchain/experimental/llms/jsonformer_decoder.py

@@ -37,7 +37,7 @@ class JsonFormer(HuggingFacePipeline):
         import_jsonformer()
         return values
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/experimental/llms/rellm_decoder.py

@@ -42,7 +42,7 @@ class RELLM(HuggingFacePipeline):
         import_rellm()
         return values
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/ai21.py

@@ -5,7 +5,7 @@ import requests
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.utils import get_from_dict_or_env
 
 
@@ -20,7 +20,7 @@ class AI21PenaltyData(BaseModel):
     applyToEmojis: bool = True
 
 
-class AI21(LLM):
+class AI21(SimpleLLM):
     """Wrapper around AI21 large language models.
 
     To use, you should have the environment variable ``AI21_API_KEY``
@@ -107,7 +107,7 @@ class AI21(LLM):
         """Return type of llm."""
         return "ai21"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/aleph_alpha.py

@@ -4,12 +4,12 @@ from typing import Any, Dict, List, Optional, Sequence
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class AlephAlpha(LLM):
+class AlephAlpha(SimpleLLM):
     """Wrapper around Aleph Alpha large language models.
 
     To use, you should have the ``aleph_alpha_client`` python package installed, and the
@@ -201,7 +201,7 @@ class AlephAlpha(LLM):
         """Return type of llm."""
         return "alpeh_alpha"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/anthropic.py

@@ -9,7 +9,7 @@ from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.utils import get_from_dict_or_env
 
 
@@ -104,7 +104,7 @@ class _AnthropicCommon(BaseModel):
         return self.count_tokens(text)
 
 
-class Anthropic(LLM, _AnthropicCommon):
+class Anthropic(SimpleLLM, _AnthropicCommon):
     r"""Wrapper around Anthropic's large language models.
 
     To use, you should have the ``anthropic`` python package installed, and the
@@ -162,7 +162,7 @@ class Anthropic(LLM, _AnthropicCommon):
         # As a last resort, wrap the prompt ourselves to emulate instruct-style.
         return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/anyscale.py

@@ -5,12 +5,12 @@ import requests
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class Anyscale(LLM):
+class Anyscale(SimpleLLM):
     """Wrapper around Anyscale Services.
     To use, you should have the environment variable ``ANYSCALE_SERVICE_URL``,
     ``ANYSCALE_SERVICE_ROUTE`` and ``ANYSCALE_SERVICE_TOKEN`` set with your Anyscale
@@ -82,7 +82,7 @@ class Anyscale(LLM):
         """Return type of llm."""
         return "anyscale"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/bananadev.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class Banana(LLM):
+class Banana(SimpleLLM):
     """Wrapper around Banana large language models.
 
     To use, you should have the ``banana-dev`` python package installed,
@@ -81,7 +81,7 @@ class Banana(LLM):
         """Return type of llm."""
         return "banana"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/base.py

@@ -365,14 +365,32 @@ class BaseLLM(BaseLanguageModel, ABC):
             raise ValueError(f"{save_path} must be json or yaml")
 
 
-class LLM(BaseLLM):
+class SimpleLLM(BaseLLM):
     """LLM class that expect subclasses to implement a simpler call method.
 
     The purpose of this class is to expose a simpler interface for working
     with LLMs, rather than expect the user to implement the full _generate method.
     """
 
-    @abstractmethod
+    def _generate_single(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    ) -> str:
+        """Run the LLM on a single input string and return the output as a string."""
+        raise NotImplementedError
+
+    async def _agenerate_single(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    ) -> str:
+        """Run the LLM on a single input string and return the output as a string."""
+        raise NotImplementedError
+
+    # Kept for backwards compatibility
     def _call(
         self,
         prompt: str,
@@ -380,7 +398,9 @@ class LLM(BaseLLM):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
     ) -> str:
         """Run the LLM on the given prompt and input."""
+        return self._generate_single(prompt, stop=stop, run_manager=run_manager)
 
+    # Kept for backwards compatibility
     async def _acall(
         self,
         prompt: str,
@@ -388,7 +408,7 @@ class LLM(BaseLLM):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
     ) -> str:
         """Run the LLM on the given prompt and input."""
-        raise NotImplementedError("Async generation not implemented for this LLM.")
+        return await self._agenerate_single(prompt, stop=stop, run_manager=run_manager)
 
     def _generate(
         self,

langchain/llms/cerebriumai.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class CerebriumAI(LLM):
+class CerebriumAI(SimpleLLM):
     """Wrapper around CerebriumAI large language models.
 
     To use, you should have the ``cerebrium`` python package installed, and the
@@ -82,7 +82,7 @@ class CerebriumAI(LLM):
         """Return type of llm."""
         return "cerebriumai"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/cohere.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Optional
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class Cohere(LLM):
+class Cohere(SimpleLLM):
     """Wrapper around Cohere large language models.
 
     To use, you should have the ``cohere`` python package installed, and the
@@ -101,7 +101,7 @@ class Cohere(LLM):
         """Return type of llm."""
         return "cohere"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/deepinfra.py

@@ -5,14 +5,14 @@ import requests
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 DEFAULT_MODEL_ID = "google/flan-t5-xl"
 
 
-class DeepInfra(LLM):
+class DeepInfra(SimpleLLM):
     """Wrapper around DeepInfra deployed models.
 
     To use, you should have the ``requests`` python package installed, and the
@@ -61,7 +61,7 @@ class DeepInfra(LLM):
         """Return type of llm."""
         return "deepinfra"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/fake.py

@@ -2,10 +2,10 @@
 from typing import Any, List, Mapping, Optional
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 
-class FakeListLLM(LLM):
+class FakeListLLM(SimpleLLM):
     """Fake LLM wrapper for testing purposes."""
 
     responses: List
@@ -16,7 +16,7 @@ class FakeListLLM(LLM):
         """Return type of llm."""
         return "fake-list"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/forefrontai.py

@@ -5,12 +5,12 @@ import requests
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class ForefrontAI(LLM):
+class ForefrontAI(SimpleLLM):
     """Wrapper around ForefrontAI large language models.
 
     To use, you should have the environment variable ``FOREFRONTAI_API_KEY``
@@ -82,7 +82,7 @@ class ForefrontAI(LLM):
         """Return type of llm."""
         return "forefrontai"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/gooseai.py

@@ -5,13 +5,13 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class GooseAI(LLM):
+class GooseAI(SimpleLLM):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` python package installed, and the
@@ -131,7 +131,7 @@ class GooseAI(LLM):
         """Return type of llm."""
         return "gooseai"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/gpt4all.py

@@ -5,11 +5,11 @@ from typing import Any, Dict, List, Mapping, Optional, Set
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 
-class GPT4All(LLM):
+class GPT4All(SimpleLLM):
     r"""Wrapper around GPT4All language models.
 
     To use, you should have the ``gpt4all`` python package installed, the
@@ -167,7 +167,7 @@ class GPT4All(LLM):
         """Return the type of llm."""
         return "gpt4all"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/huggingface_endpoint.py

@@ -5,14 +5,14 @@ import requests
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
 
 
-class HuggingFaceEndpoint(LLM):
+class HuggingFaceEndpoint(SimpleLLM):
     """Wrapper around HuggingFaceHub Inference Endpoints.
 
     To use, you should have the ``huggingface_hub`` python package installed, and the
@@ -91,7 +91,7 @@ class HuggingFaceEndpoint(LLM):
         """Return type of llm."""
         return "huggingface_endpoint"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/huggingface_hub.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
@@ -12,7 +12,7 @@ DEFAULT_REPO_ID = "gpt2"
 VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
 
 
-class HuggingFaceHub(LLM):
+class HuggingFaceHub(SimpleLLM):
     """Wrapper around HuggingFaceHub  models.
 
     To use, you should have the ``huggingface_hub`` python package installed, and the
@@ -86,7 +86,7 @@ class HuggingFaceHub(LLM):
         """Return type of llm."""
         return "huggingface_hub"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/huggingface_pipeline.py

@@ -6,7 +6,7 @@ from typing import Any, List, Mapping, Optional
 from pydantic import Extra
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 DEFAULT_MODEL_ID = "gpt2"
@@ -16,7 +16,7 @@ VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
 logger = logging.getLogger(__name__)
 
 
-class HuggingFacePipeline(LLM):
+class HuggingFacePipeline(SimpleLLM):
     """Wrapper around HuggingFace Pipeline API.
 
     To use, you should have the ``transformers`` python package installed.
@@ -64,7 +64,7 @@ class HuggingFacePipeline(LLM):
         device: int = -1,
         model_kwargs: Optional[dict] = None,
         **kwargs: Any,
-    ) -> LLM:
+    ) -> SimpleLLM:
         """Construct the pipeline object from model_id and task."""
         try:
             from transformers import (
@@ -150,7 +150,7 @@ class HuggingFacePipeline(LLM):
     def _llm_type(self) -> str:
         return "huggingface_pipeline"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/huggingface_text_gen_inference.py

@@ -5,10 +5,10 @@ from typing import Any, Dict, List, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 
-class HuggingFaceTextGenInference(LLM):
+class HuggingFaceTextGenInference(SimpleLLM):
     """
     HuggingFace text generation inference API.
 
@@ -108,7 +108,7 @@ class HuggingFaceTextGenInference(LLM):
         """Return type of llm."""
         return "hf_textgen_inference"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/human.py

@@ -3,7 +3,7 @@ from typing import Any, Callable, List, Mapping, Optional
 from pydantic import Field
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 
@@ -32,7 +32,7 @@ def _collect_user_input(
     return multi_line_input
 
 
-class HumanInputLLM(LLM):
+class HumanInputLLM(SimpleLLM):
     """
     A LLM wrapper which returns user input as the response.
     """
@@ -55,7 +55,7 @@ class HumanInputLLM(LLM):
         """Returns the type of LLM."""
         return "human-input"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/llamacpp.py

@@ -5,12 +5,12 @@ from typing import Any, Dict, Generator, List, Optional
 from pydantic import Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 logger = logging.getLogger(__name__)
 
 
-class LlamaCpp(LLM):
+class LlamaCpp(SimpleLLM):
     """Wrapper around the llama.cpp model.
 
     To use, you should have the llama-cpp-python library installed, and provide the
@@ -195,7 +195,7 @@ class LlamaCpp(LLM):
 
         return params
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/manifest.py

@@ -4,10 +4,10 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 
-class ManifestWrapper(LLM):
+class ManifestWrapper(SimpleLLM):
     """Wrapper around HazyResearch's Manifest library."""
 
     client: Any  #: :meta private:
@@ -43,7 +43,7 @@ class ManifestWrapper(LLM):
         """Return type of llm."""
         return "manifest"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/modal.py

@@ -6,13 +6,13 @@ import requests
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 logger = logging.getLogger(__name__)
 
 
-class Modal(LLM):
+class Modal(SimpleLLM):
     """Wrapper around Modal large language models.
 
     To use, you should have the ``modal-client`` python package installed.
@@ -70,7 +70,7 @@ class Modal(LLM):
         """Return type of llm."""
         return "modal"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/nlpcloud.py

@@ -4,11 +4,11 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.utils import get_from_dict_or_env
 
 
-class NLPCloud(LLM):
+class NLPCloud(SimpleLLM):
     """Wrapper around NLPCloud large language models.
 
     To use, you should have the ``nlpcloud`` python package installed, and the
@@ -112,7 +112,7 @@ class NLPCloud(LLM):
         """Return type of llm."""
         return "nlpcloud"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/petals.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class Petals(LLM):
+class Petals(SimpleLLM):
     """Wrapper around Petals Bloom models.
 
     To use, you should have the ``petals`` python package installed, and the
@@ -131,7 +131,7 @@ class Petals(LLM):
         """Return type of llm."""
         return "petals"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/pipelineai.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class PipelineAI(LLM, BaseModel):
+class PipelineAI(SimpleLLM, BaseModel):
     """Wrapper around PipelineAI large language models.
 
     To use, you should have the ``pipeline-ai`` python package installed,
@@ -81,7 +81,7 @@ class PipelineAI(LLM, BaseModel):
         """Return type of llm."""
         return "pipeline_ai"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/predictionguard.py

@@ -5,14 +5,14 @@ from typing import Any, Dict, List, Optional
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class PredictionGuard(LLM):
+class PredictionGuard(SimpleLLM):
     """Wrapper around Prediction Guard large language models.
     To use, you should have the ``predictionguard`` python package installed, and the
     environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass
@@ -74,7 +74,7 @@ class PredictionGuard(LLM):
         """Return type of llm."""
         return "predictionguard"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/replicate.py

@@ -5,13 +5,13 @@ from typing import Any, Dict, List, Mapping, Optional
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class Replicate(LLM):
+class Replicate(SimpleLLM):
     """Wrapper around Replicate models.
 
     To use, you should have the ``replicate`` python package installed,
@@ -79,7 +79,7 @@ class Replicate(LLM):
         """Return type of model."""
         return "replicate"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/rwkv.py

@@ -8,11 +8,11 @@ from typing import Any, Dict, List, Mapping, Optional, Set
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 
-class RWKV(LLM, BaseModel):
+class RWKV(SimpleLLM, BaseModel):
     r"""Wrapper around RWKV language models.
 
     To use, you should have the ``rwkv`` python package installed, the
@@ -205,7 +205,7 @@ class RWKV(LLM, BaseModel):
 
         return decoded
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/sagemaker_endpoint.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]])
@@ -61,7 +61,7 @@ class LLMContentHandler(ContentHandlerBase[str, str]):
     """Content handler for LLM class."""
 
 
-class SagemakerEndpoint(LLM):
+class SagemakerEndpoint(SimpleLLM):
     """Wrapper around custom Sagemaker Inference Endpoints.
 
     To use, you must supply the endpoint name from your deployed
@@ -202,7 +202,7 @@ class SagemakerEndpoint(LLM):
         """Return type of llm."""
         return "sagemaker_endpoint"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/self_hosted.py

@@ -7,7 +7,7 @@ from typing import Any, Callable, List, Mapping, Optional
 from pydantic import Extra
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 
 logger = logging.getLogger(__name__)
@@ -62,7 +62,7 @@ def _send_pipeline_to_device(pipeline: Any, device: int) -> Any:
     return pipeline
 
 
-class SelfHostedPipeline(LLM):
+class SelfHostedPipeline(SimpleLLM):
     """Run model inference on self-hosted remote hardware.
 
     Supported hardware includes auto-launched instances on AWS, GCP, Azure,
@@ -178,7 +178,7 @@ class SelfHostedPipeline(LLM):
         model_reqs: Optional[List[str]] = None,
         device: int = 0,
         **kwargs: Any,
-    ) -> LLM:
+    ) -> SimpleLLM:
         """Init the SelfHostedPipeline from a pipeline object or string."""
         if not isinstance(pipeline, str):
             logger.warning(
@@ -209,7 +209,7 @@ class SelfHostedPipeline(LLM):
     def _llm_type(self) -> str:
         return "self_hosted_llm"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/self_hosted_hugging_face.py

@@ -202,7 +202,7 @@ class SelfHostedHuggingFaceLLM(SelfHostedPipeline):
     def _llm_type(self) -> str:
         return "selfhosted_huggingface_pipeline"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/stochasticai.py

@@ -7,14 +7,14 @@ import requests
 from pydantic import Extra, Field, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 logger = logging.getLogger(__name__)
 
 
-class StochasticAI(LLM):
+class StochasticAI(SimpleLLM):
     """Wrapper around StochasticAI large language models.
 
     To use, you should have the environment variable ``STOCHASTICAI_API_KEY``
@@ -81,7 +81,7 @@ class StochasticAI(LLM):
         """Return type of llm."""
         return "stochasticai"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

langchain/llms/writer.py

@@ -5,12 +5,12 @@ import requests
 from pydantic import Extra, root_validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.llms.utils import enforce_stop_tokens
 from langchain.utils import get_from_dict_or_env
 
 
-class Writer(LLM):
+class Writer(SimpleLLM):
     """Wrapper around Writer large language models.
 
     To use, you should have the environment variable ``WRITER_API_KEY`` and
@@ -113,7 +113,7 @@ class Writer(LLM):
         """Return type of llm."""
         return "writer"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

tests/unit_tests/agents/test_agent.py

@@ -5,17 +5,17 @@ from typing import Any, List, Mapping, Optional
 from langchain.agents import AgentExecutor, AgentType, initialize_agent
 from langchain.agents.tools import Tool
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
-class FakeListLLM(LLM):
+class FakeListLLM(SimpleLLM):
     """Fake LLM for testing that outputs elements of a list."""
 
     responses: List[str]
     i: int = -1
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

tests/unit_tests/agents/test_react.py

@@ -7,7 +7,7 @@ from langchain.agents.tools import Tool
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import AgentAction
 
@@ -22,7 +22,7 @@ Made in 2022."""
 _FAKE_PROMPT = PromptTemplate(input_variables=["input"], template="{input}")
 
 
-class FakeListLLM(LLM):
+class FakeListLLM(SimpleLLM):
     """Fake LLM for testing that outputs elements of a list."""
 
     responses: List[str]
@@ -33,7 +33,7 @@ class FakeListLLM(LLM):
         """Return type of llm."""
         return "fake_list"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

tests/unit_tests/chains/test_natbot.py

@@ -4,13 +4,13 @@ from typing import Any, List, Mapping, Optional
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chains.natbot.base import NatBotChain
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 
-class FakeLLM(LLM):
+class FakeLLM(SimpleLLM):
     """Fake LLM wrapper for testing purposes."""
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,

tests/unit_tests/llms/fake_llm.py

@@ -4,10 +4,10 @@ from typing import Any, List, Mapping, Optional, cast
 from pydantic import validator
 
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.base import LLM
+from langchain.llms.base import SimpleLLM
 
 
-class FakeLLM(LLM):
+class FakeLLM(SimpleLLM):
     """Fake LLM wrapper for testing purposes."""
 
     queries: Optional[Mapping] = None
@@ -29,7 +29,7 @@ class FakeLLM(LLM):
         """Return type of llm."""
         return "fake"
 
-    def _call(
+    def _generate_single(
         self,
         prompt: str,
         stop: Optional[List[str]] = None,