Harrison/improve cache (#368)

make it so everything goes through generate, which removes the need for
two types of caches

langchain/agents/agent.py

@@ -11,7 +11,7 @@ from langchain.agents.tools import Tool
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.input import get_color_mapping
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.schema import AgentAction
 
@@ -87,7 +87,9 @@ class Agent(Chain, BaseModel, ABC):
         pass
 
     @classmethod
-    def from_llm_and_tools(cls, llm: LLM, tools: List[Tool], **kwargs: Any) -> Agent:
+    def from_llm_and_tools(
+        cls, llm: BaseLLM, tools: List[Tool], **kwargs: Any
+    ) -> Agent:
         """Construct an agent from an LLM and tools."""
         cls._validate_tools(tools)
         llm_chain = LLMChain(llm=llm, prompt=cls.create_prompt(tools))

langchain/agents/loading.py

@@ -6,7 +6,7 @@ from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.react.base import ReActDocstoreAgent
 from langchain.agents.self_ask_with_search.base import SelfAskWithSearchAgent
 from langchain.agents.tools import Tool
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 
 AGENT_TO_CLASS = {
     "zero-shot-react-description": ZeroShotAgent,
@@ -17,7 +17,7 @@ AGENT_TO_CLASS = {
 
 def initialize_agent(
     tools: List[Tool],
-    llm: LLM,
+    llm: BaseLLM,
     agent: str = "zero-shot-react-description",
     **kwargs: Any,
 ) -> Agent:

langchain/agents/mrkl/base.py

@@ -6,7 +6,7 @@ from typing import Any, Callable, List, NamedTuple, Optional, Tuple
 from langchain.agents.agent import Agent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.agents.tools import Tool
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 
 FINAL_ANSWER_ACTION = "Final Answer: "
@@ -116,7 +116,9 @@ class MRKLChain(ZeroShotAgent):
     """
 
     @classmethod
-    def from_chains(cls, llm: LLM, chains: List[ChainConfig], **kwargs: Any) -> Agent:
+    def from_chains(
+        cls, llm: BaseLLM, chains: List[ChainConfig], **kwargs: Any
+    ) -> Agent:
         """User friendly way to initialize the MRKL chain.
 
         This is intended to be an easy way to get up and running with the

langchain/agents/react/base.py

@@ -11,7 +11,7 @@ from langchain.agents.tools import Tool
 from langchain.chains.llm import LLMChain
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
@@ -123,7 +123,7 @@ class ReActChain(ReActDocstoreAgent):
             react = ReAct(llm=OpenAI())
     """
 
-    def __init__(self, llm: LLM, docstore: Docstore, **kwargs: Any):
+    def __init__(self, llm: BaseLLM, docstore: Docstore, **kwargs: Any):
         """Initialize with the LLM and a docstore."""
         docstore_explorer = DocstoreExplorer(docstore)
         tools = [

langchain/agents/self_ask_with_search/base.py

@@ -5,7 +5,7 @@ from langchain.agents.agent import Agent
 from langchain.agents.self_ask_with_search.prompt import PROMPT
 from langchain.agents.tools import Tool
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.serpapi import SerpAPIWrapper
 
@@ -72,7 +72,7 @@ class SelfAskWithSearchChain(SelfAskWithSearchAgent):
             self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
     """
 
-    def __init__(self, llm: LLM, search_chain: SerpAPIWrapper, **kwargs: Any):
+    def __init__(self, llm: BaseLLM, search_chain: SerpAPIWrapper, **kwargs: Any):
         """Initialize with just an LLM and a search chain."""
         search_tool = Tool(name="Intermediate Answer", func=search_chain.run)
         llm_chain = LLMChain(llm=llm, prompt=PROMPT)

langchain/cache.py

@@ -1,6 +1,6 @@
 """Beta Feature: base interface for cache."""
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 from sqlalchemy import Column, Integer, String, create_engine, select
 from sqlalchemy.engine.base import Engine
@@ -9,7 +9,7 @@ from sqlalchemy.orm import Session
 
 from langchain.schema import Generation
 
-RETURN_VAL_TYPE = Union[List[Generation], str]
+RETURN_VAL_TYPE = List[Generation]
 
 
 class BaseCache(ABC):
@@ -43,15 +43,6 @@ class InMemoryCache(BaseCache):
 Base = declarative_base()
 
 
-class LLMCache(Base):  # type: ignore
-    """SQLite table for simple LLM cache (string only)."""
-
-    __tablename__ = "llm_cache"
-    prompt = Column(String, primary_key=True)
-    llm = Column(String, primary_key=True)
-    response = Column(String)
-
-
 class FullLLMCache(Base):  # type: ignore
     """SQLite table for full LLM Cache (all generations)."""
 
@@ -84,29 +75,16 @@ class SQLAlchemyCache(BaseCache):
                 generations.append(Generation(text=row[0]))
             if len(generations) > 0:
                 return generations
-        stmt = (
-            select(LLMCache.response)
-            .where(LLMCache.prompt == prompt)
-            .where(LLMCache.llm == llm_string)
-        )
-        with Session(self.engine) as session:
-            for row in session.execute(stmt):
-                return row[0]
         return None
 
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
         """Look up based on prompt and llm_string."""
-        if isinstance(return_val, str):
+        for i, generation in enumerate(return_val):
-            item = LLMCache(prompt=prompt, llm=llm_string, response=return_val)
+            item = FullLLMCache(
+                prompt=prompt, llm=llm_string, response=generation.text, idx=i
+            )
             with Session(self.engine) as session, session.begin():
                 session.add(item)
-        else:
-            for i, generation in enumerate(return_val):
-                item = FullLLMCache(
-                    prompt=prompt, llm=llm_string, response=generation.text, idx=i
-                )
-                with Session(self.engine) as session, session.begin():
-                    session.add(item)
 
 
 class SQLiteCache(SQLAlchemyCache):

langchain/chains/api/base.py

@@ -9,7 +9,7 @@ from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.input import print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.requests import RequestsWrapper
 
 
@@ -81,7 +81,7 @@ class APIChain(Chain, BaseModel):
 
     @classmethod
     def from_llm_and_api_docs(
-        cls, llm: LLM, api_docs: str, headers: Optional[dict] = None, **kwargs: Any
+        cls, llm: BaseLLM, api_docs: str, headers: Optional[dict] = None, **kwargs: Any
     ) -> APIChain:
         """Load chain from just an LLM and the api docs."""
         get_request_chain = LLMChain(llm=llm, prompt=API_URL_PROMPT)

langchain/chains/conversation/memory.py

@@ -6,7 +6,7 @@ from pydantic import BaseModel, Field, root_validator
 from langchain.chains.base import Memory
 from langchain.chains.conversation.prompt import SUMMARY_PROMPT
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
@@ -88,7 +88,7 @@ class ConversationSummaryMemory(Memory, BaseModel):
     """Conversation summarizer to memory."""
 
     buffer: str = ""
-    llm: LLM
+    llm: BaseLLM
     prompt: BasePromptTemplate = SUMMARY_PROMPT
     memory_key: str = "history"  #: :meta private:
 

langchain/chains/llm.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Extra
 
 import langchain
 from langchain.chains.base import Chain
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
@@ -25,7 +25,7 @@ class LLMChain(Chain, BaseModel):
 
     prompt: BasePromptTemplate
     """Prompt object to use."""
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     output_key: str = "text"  #: :meta private:
 

langchain/chains/llm_bash/base.py

@@ -7,7 +7,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.prompt import PROMPT
 from langchain.input import print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.utilities.bash import BashProcess
 
 
@@ -21,7 +21,7 @@ class LLMBashChain(Chain, BaseModel):
             llm_bash = LLMBashChain(llm=OpenAI())
     """
 
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     input_key: str = "question"  #: :meta private:
     output_key: str = "answer"  #: :meta private:

langchain/chains/llm_checker/base.py

@@ -14,7 +14,7 @@ from langchain.chains.llm_checker.prompt import (
     REVISED_ANSWER_PROMPT,
 )
 from langchain.chains.sequential import SequentialChain
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 
 
@@ -28,7 +28,7 @@ class LLMCheckerChain(Chain, BaseModel):
             checker_chain = LLMCheckerChain(llm=llm)
     """
 
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     create_draft_answer_prompt: PromptTemplate = CREATE_DRAFT_ANSWER_PROMPT
     list_assertions_prompt: PromptTemplate = LIST_ASSERTIONS_PROMPT

langchain/chains/llm_math/base.py

@@ -7,7 +7,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_math.prompt import PROMPT
 from langchain.input import print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.python import PythonREPL
 
 
@@ -21,7 +21,7 @@ class LLMMathChain(Chain, BaseModel):
             llm_math = LLMMathChain(llm=OpenAI())
     """
 
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     input_key: str = "question"  #: :meta private:
     output_key: str = "answer"  #: :meta private:

langchain/chains/mapreduce.py

@@ -15,7 +15,7 @@ from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChai
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.text_splitter import TextSplitter
 
@@ -32,7 +32,7 @@ class MapReduceChain(Chain, BaseModel):
 
     @classmethod
     def from_params(
-        cls, llm: LLM, prompt: BasePromptTemplate, text_splitter: TextSplitter
+        cls, llm: BaseLLM, prompt: BasePromptTemplate, text_splitter: TextSplitter
     ) -> MapReduceChain:
         """Construct a map-reduce chain that uses the chain for map and reduce."""
         llm_chain = LLMChain(llm=llm, prompt=prompt)

langchain/chains/natbot/base.py

@@ -8,7 +8,7 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.natbot.prompt import PROMPT
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.llms.openai import OpenAI
 
 
@@ -22,7 +22,7 @@ class NatBotChain(Chain, BaseModel):
             natbot = NatBotChain(llm=OpenAI(), objective="Buy me a new hat.")
     """
 
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     objective: str
     """Objective that NatBot is tasked with completing."""

langchain/chains/pal/base.py

@@ -13,7 +13,7 @@ from langchain.chains.llm import LLMChain
 from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain.chains.pal.math_prompt import MATH_PROMPT
 from langchain.input import print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 from langchain.python import PythonREPL
 
@@ -21,7 +21,7 @@ from langchain.python import PythonREPL
 class PALChain(Chain, BaseModel):
     """Implements Program-Aided Language Models."""
 
-    llm: LLM
+    llm: BaseLLM
     prompt: BasePromptTemplate
     stop: str = "\n\n"
     get_answer_expr: str = "print(solution())"
@@ -59,7 +59,7 @@ class PALChain(Chain, BaseModel):
         return {self.output_key: res.strip()}
 
     @classmethod
-    def from_math_prompt(cls, llm: LLM, **kwargs: Any) -> PALChain:
+    def from_math_prompt(cls, llm: BaseLLM, **kwargs: Any) -> PALChain:
         """Load PAL from math prompt."""
         return cls(
             llm=llm,
@@ -70,7 +70,7 @@ class PALChain(Chain, BaseModel):
         )
 
     @classmethod
-    def from_colored_object_prompt(cls, llm: LLM, **kwargs: Any) -> PALChain:
+    def from_colored_object_prompt(cls, llm: BaseLLM, **kwargs: Any) -> PALChain:
         """Load PAL from colored object prompt."""
         return cls(
             llm=llm,

langchain/chains/qa_with_sources/__init__.py

@@ -11,19 +11,19 @@ from langchain.chains.qa_with_sources import (
     refine_prompts,
     stuff_prompt,
 )
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
 class LoadingCallable(Protocol):
     """Interface for loading the combine documents chain."""
 
-    def __call__(self, llm: LLM, **kwargs: Any) -> BaseCombineDocumentsChain:
+    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
         """Callable to load the combine documents chain."""
 
 
 def _load_stuff_chain(
-    llm: LLM,
+    llm: BaseLLM,
     prompt: BasePromptTemplate = stuff_prompt.PROMPT,
     document_variable_name: str = "summaries",
     **kwargs: Any,
@@ -38,7 +38,7 @@ def _load_stuff_chain(
 
 
 def _load_map_reduce_chain(
-    llm: LLM,
+    llm: BaseLLM,
     question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
     combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
     document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
@@ -72,7 +72,7 @@ def _load_map_reduce_chain(
 
 
 def _load_refine_chain(
-    llm: LLM,
+    llm: BaseLLM,
     question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
     refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
     document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
@@ -93,7 +93,7 @@ def _load_refine_chain(
 
 
 def load_qa_with_sources_chain(
-    llm: LLM, chain_type: str = "stuff", **kwargs: Any
+    llm: BaseLLM, chain_type: str = "stuff", **kwargs: Any
 ) -> BaseCombineDocumentsChain:
     """Load question answering with sources chain.
 

langchain/chains/qa_with_sources/base.py

@@ -18,7 +18,7 @@ from langchain.chains.qa_with_sources.map_reduce_prompt import (
     QUESTION_PROMPT,
 )
 from langchain.docstore.document import Document
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
@@ -35,7 +35,7 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
     @classmethod
     def from_llm(
         cls,
-        llm: LLM,
+        llm: BaseLLM,
         document_prompt: BasePromptTemplate = EXAMPLE_PROMPT,
         question_prompt: BasePromptTemplate = QUESTION_PROMPT,
         combine_prompt: BasePromptTemplate = COMBINE_PROMPT,

langchain/chains/question_answering/__init__.py

@@ -11,19 +11,19 @@ from langchain.chains.question_answering import (
     refine_prompts,
     stuff_prompt,
 )
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
 class LoadingCallable(Protocol):
     """Interface for loading the combine documents chain."""
 
-    def __call__(self, llm: LLM, **kwargs: Any) -> BaseCombineDocumentsChain:
+    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
         """Callable to load the combine documents chain."""
 
 
 def _load_stuff_chain(
-    llm: LLM,
+    llm: BaseLLM,
     prompt: BasePromptTemplate = stuff_prompt.PROMPT,
     document_variable_name: str = "context",
     **kwargs: Any,
@@ -36,7 +36,7 @@ def _load_stuff_chain(
 
 
 def _load_map_reduce_chain(
-    llm: LLM,
+    llm: BaseLLM,
     question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
     combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
     combine_document_variable_name: str = "summaries",
@@ -67,7 +67,7 @@ def _load_map_reduce_chain(
 
 
 def _load_refine_chain(
-    llm: LLM,
+    llm: BaseLLM,
     question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
     refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
     document_variable_name: str = "context_str",
@@ -86,7 +86,7 @@ def _load_refine_chain(
 
 
 def load_qa_chain(
-    llm: LLM, chain_type: str = "stuff", **kwargs: Any
+    llm: BaseLLM, chain_type: str = "stuff", **kwargs: Any
 ) -> BaseCombineDocumentsChain:
     """Load question answering chain.
 

langchain/chains/sql_database/base.py

@@ -7,7 +7,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.sql_database.prompt import PROMPT
 from langchain.input import print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.sql_database import SQLDatabase
 
 
@@ -22,7 +22,7 @@ class SQLDatabaseChain(Chain, BaseModel):
             db_chain = SelfAskWithSearchChain(llm=OpenAI(), database=db)
     """
 
-    llm: LLM
+    llm: BaseLLM
     """LLM wrapper to use."""
     database: SQLDatabase
     """SQL Database to connect to."""

langchain/chains/summarize/__init__.py

@@ -7,19 +7,19 @@ from langchain.chains.combine_documents.refine import RefineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.base import BasePromptTemplate
 
 
 class LoadingCallable(Protocol):
     """Interface for loading the combine documents chain."""
 
-    def __call__(self, llm: LLM, **kwargs: Any) -> BaseCombineDocumentsChain:
+    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
         """Callable to load the combine documents chain."""
 
 
 def _load_stuff_chain(
-    llm: LLM,
+    llm: BaseLLM,
     prompt: BasePromptTemplate = stuff_prompt.PROMPT,
     document_variable_name: str = "text",
     **kwargs: Any,
@@ -32,7 +32,7 @@ def _load_stuff_chain(
 
 
 def _load_map_reduce_chain(
-    llm: LLM,
+    llm: BaseLLM,
     map_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
     combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
     combine_document_variable_name: str = "text",
@@ -63,7 +63,7 @@ def _load_map_reduce_chain(
 
 
 def _load_refine_chain(
-    llm: LLM,
+    llm: BaseLLM,
     question_prompt: BasePromptTemplate = refine_prompts.PROMPT,
     refine_prompt: BasePromptTemplate = refine_prompts.REFINE_PROMPT,
     document_variable_name: str = "text",
@@ -82,7 +82,7 @@ def _load_refine_chain(
 
 
 def load_summarize_chain(
-    llm: LLM, chain_type: str = "stuff", **kwargs: Any
+    llm: BaseLLM, chain_type: str = "stuff", **kwargs: Any
 ) -> BaseCombineDocumentsChain:
     """Load summarizing chain.
 

langchain/chains/vector_db_qa/base.py

@@ -10,7 +10,7 @@ from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.chains.vector_db_qa.prompt import PROMPT
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
 from langchain.vectorstores.base import VectorStore
 
@@ -84,7 +84,7 @@ class VectorDBQA(Chain, BaseModel):
 
     @classmethod
     def from_llm(
-        cls, llm: LLM, prompt: PromptTemplate = PROMPT, **kwargs: Any
+        cls, llm: BaseLLM, prompt: PromptTemplate = PROMPT, **kwargs: Any
     ) -> VectorDBQA:
         """Initialize from LLM."""
         llm_chain = LLMChain(llm=llm, prompt=prompt)

langchain/example_generator.py

@@ -2,7 +2,7 @@
 from typing import List
 
 from langchain.chains.llm import LLMChain
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
 
@@ -10,7 +10,7 @@ TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
 
 
 def generate_example(
-    examples: List[dict], llm: LLM, prompt_template: PromptTemplate
+    examples: List[dict], llm: BaseLLM, prompt_template: PromptTemplate
 ) -> str:
     """Return another example given a list of examples for a prompt."""
     prompt = FewShotPromptTemplate(

langchain/llms/__init__.py

@@ -2,7 +2,7 @@
 from typing import Dict, Type
 
 from langchain.llms.ai21 import AI21
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.llms.cohere import Cohere
 from langchain.llms.huggingface_hub import HuggingFaceHub
 from langchain.llms.huggingface_pipeline import HuggingFacePipeline
@@ -18,7 +18,7 @@ __all__ = [
     "AI21",
 ]
 
-type_to_cls_dict: Dict[str, Type[LLM]] = {
+type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
     "ai21": AI21,
     "cohere": Cohere,
     "huggingface_hub": HuggingFaceHub,

langchain/llms/base.py

@@ -21,7 +21,7 @@ class LLMResult(NamedTuple):
     """For arbitrary LLM provider specific output."""
 
 
-class LLM(BaseModel, ABC):
+class BaseLLM(BaseModel, ABC):
     """LLM wrapper should take in a prompt and return a string."""
 
     class Config:
@@ -29,16 +29,11 @@ class LLM(BaseModel, ABC):
 
         extra = Extra.forbid
 
+    @abstractmethod
     def _generate(
         self, prompts: List[str], stop: Optional[List[str]] = None
     ) -> LLMResult:
-        """Run the LLM on the given prompt and input."""
+        """Run the LLM on the given prompts."""
-        # TODO: add caching here.
-        generations = []
-        for prompt in prompts:
-            text = self(prompt, stop=stop)
-            generations.append([Generation(text=text)])
-        return LLMResult(generations=generations)
 
     def generate(
         self, prompts: List[str], stop: Optional[List[str]] = None
@@ -88,28 +83,9 @@ class LLM(BaseModel, ABC):
         # calculate the number of tokens in the tokenized text
         return len(tokenized_text)
 
-    @abstractmethod
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        """Run the LLM on the given prompt and input."""
-
     def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
         """Check Cache and run the LLM on the given prompt and input."""
-        if langchain.llm_cache is None:
+        return self.generate([prompt], stop=stop).generations[0][0].text
-            return self._call(prompt, stop=stop)
-        params = self._llm_dict()
-        params["stop"] = stop
-        llm_string = str(sorted([(k, v) for k, v in params.items()]))
-        if langchain.cache is not None:
-            cache_val = langchain.llm_cache.lookup(prompt, llm_string)
-            if cache_val is not None:
-                if isinstance(cache_val, str):
-                    return cache_val
-                else:
-                    return cache_val[0].text
-        return_val = self._call(prompt, stop=stop)
-        if langchain.cache is not None:
-            langchain.llm_cache.update(prompt, llm_string, return_val)
-        return return_val
 
     @property
     def _identifying_params(self) -> Mapping[str, Any]:
@@ -163,3 +139,26 @@ class LLM(BaseModel, ABC):
                 yaml.dump(prompt_dict, f, default_flow_style=False)
         else:
             raise ValueError(f"{save_path} must be json or yaml")
+
+
+class LLM(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 _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        """Run the LLM on the given prompt and input."""
+
+    def _generate(
+        self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        """Run the LLM on the given prompt and input."""
+        # TODO: add caching here.
+        generations = []
+        for prompt in prompts:
+            text = self._call(prompt, stop=stop)
+            generations.append([Generation(text=text)])
+        return LLMResult(generations=generations)

langchain/llms/loading.py

@@ -6,10 +6,10 @@ from typing import Union
 import yaml
 
 from langchain.llms import type_to_cls_dict
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 
 
-def load_llm_from_config(config: dict) -> LLM:
+def load_llm_from_config(config: dict) -> BaseLLM:
     """Load LLM from Config Dict."""
     if "_type" not in config:
         raise ValueError("Must specify an LLM Type in config")
@@ -22,7 +22,7 @@ def load_llm_from_config(config: dict) -> LLM:
     return llm_cls(**config)
 
 
-def load_llm(file: Union[str, Path]) -> LLM:
+def load_llm(file: Union[str, Path]) -> BaseLLM:
     """Load LLM from file."""
     # Convert file to Path object.
     if isinstance(file, str):

langchain/llms/openai.py

@@ -4,12 +4,12 @@ from typing import Any, Dict, Generator, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra, Field, root_validator
 
-from langchain.llms.base import LLM, LLMResult
+from langchain.llms.base import BaseLLM, LLMResult
 from langchain.schema import Generation
 from langchain.utils import get_from_dict_or_env
 
 
-class OpenAI(LLM, BaseModel):
+class OpenAI(BaseLLM, BaseModel):
     """Wrapper around OpenAI large language models.
 
     To use, you should have the ``openai`` python package installed, and the
@@ -197,23 +197,6 @@ class OpenAI(LLM, BaseModel):
         """Return type of llm."""
         return "openai"
 
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        """Call out to OpenAI's create 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
-
-                response = openai("Tell me a joke.")
-        """
-        return self.generate([prompt], stop=stop).generations[0][0].text
-
     def get_num_tokens(self, text: str) -> int:
         """Calculate num tokens with tiktoken package."""
         # tiktoken NOT supported for Python 3.8 or below

langchain/model_laboratory.py

@@ -6,7 +6,7 @@ from typing import List, Optional, Sequence
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.input import get_color_mapping, print_text
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 from langchain.prompts.prompt import PromptTemplate
 
 
@@ -46,7 +46,7 @@ class ModelLaboratory:
 
     @classmethod
     def from_llms(
-        cls, llms: List[LLM], prompt: Optional[PromptTemplate] = None
+        cls, llms: List[BaseLLM], prompt: Optional[PromptTemplate] = None
     ) -> ModelLaboratory:
         """Initialize with LLMs to experiment with and optional prompt.
 

tests/integration_tests/llms/utils.py

@@ -1,9 +1,9 @@
 """Utils for LLM Tests."""
 
-from langchain.llms.base import LLM
+from langchain.llms.base import BaseLLM
 
 
-def assert_llm_equality(llm: LLM, loaded_llm: LLM) -> None:
+def assert_llm_equality(llm: BaseLLM, loaded_llm: BaseLLM) -> None:
     """Assert LLM Equality for tests."""
     # Check that they are the same type.
     assert type(llm) == type(loaded_llm)