chore: minor clean up / formatting (#233)

to get familiarize with the project

langchain/agents/agent.py

@@ -1,4 +1,6 @@
 """Chain that takes in an input and produces an action and action input."""
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import Any, ClassVar, Dict, List, NamedTuple, Optional, Tuple
 
@@ -91,7 +93,7 @@ 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: LLM, 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/mrkl/base.py

@@ -1,4 +1,6 @@
 """Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
+from __future__ import annotations
+
 from typing import Any, Callable, List, NamedTuple, Optional, Tuple
 
 from langchain.agents.agent import Agent
@@ -114,7 +116,7 @@ class MRKLChain(ZeroShotAgent):
     """
 
     @classmethod
-    def from_chains(cls, llm: LLM, chains: List[ChainConfig], **kwargs: Any) -> "Agent":
+    def from_chains(cls, llm: LLM, 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/self_ask_with_search/base.py

@@ -27,9 +27,6 @@ class SelfAskWithSearchAgent(Agent):
 
     def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
         followup = "Follow up:"
-        if "\n" not in text:
-            last_line = text
-        else:
         last_line = text.split("\n")[-1]
 
         if followup not in last_line:
@@ -38,9 +35,6 @@ class SelfAskWithSearchAgent(Agent):
                 return None
             return "Final Answer", last_line[len(finish_string) :]
 
-        if ":" not in last_line:
-            after_colon = last_line
-        else:
         after_colon = text.split(":")[-1]
 
         if " " == after_colon[0]:
@@ -49,7 +43,7 @@ class SelfAskWithSearchAgent(Agent):
         return "Intermediate Answer", after_colon
 
     def _fix_text(self, text: str) -> str:
-        return text + "\nSo the final answer is:"
+        return f"{text}\nSo the final answer is:"
 
     @property
     def observation_prefix(self) -> str:

langchain/chains/conversation/memory.py

@@ -84,8 +84,8 @@ class ConversationSummaryMemory(Memory, BaseModel):
         prompt_input_key = _get_prompt_input_key(inputs, self.memory_variables)
         if len(outputs) != 1:
             raise ValueError(f"One output key expected, got {outputs.keys()}")
-        human = "Human: " + inputs[prompt_input_key]
+        human = f"Human: {inputs[prompt_input_key]}"
-        ai = "AI: " + list(outputs.values())[0]
+        ai = f"AI: {list(outputs.values())[0]}"
         new_lines = "\n".join([human, ai])
         chain = LLMChain(llm=self.llm, prompt=self.prompt)
         self.buffer = chain.predict(summary=self.buffer, new_lines=new_lines)

langchain/chains/mapreduce.py

@@ -3,6 +3,7 @@
 Splits up a document, sends the smaller parts to the LLM with one prompt,
 then combines the results with another one.
 """
+from __future__ import annotations
 
 from typing import Dict, List
 
@@ -32,7 +33,7 @@ class MapReduceChain(Chain, BaseModel):
     @classmethod
     def from_params(
         cls, llm: LLM, prompt: BasePromptTemplate, text_splitter: TextSplitter
-    ) -> "MapReduceChain":
+    ) -> MapReduceChain:
         """Construct a map-reduce chain that uses the chain for map and reduce."""
         llm_chain = LLMChain(llm=llm, prompt=prompt)
         return cls(map_llm=llm_chain, reduce_llm=llm_chain, text_splitter=text_splitter)

langchain/chains/natbot/base.py

@@ -1,4 +1,6 @@
 """Implement an LLM driven browser."""
+from __future__ import annotations
+
 from typing import Dict, List
 
 from pydantic import BaseModel, Extra
@@ -36,7 +38,7 @@ class NatBotChain(Chain, BaseModel):
         arbitrary_types_allowed = True
 
     @classmethod
-    def from_default(cls, objective: str) -> "NatBotChain":
+    def from_default(cls, objective: str) -> NatBotChain:
         """Load with default LLM."""
         llm = OpenAI(temperature=0.5, best_of=10, n=3, max_tokens=50)
         return cls(llm=llm, objective=objective)

langchain/chains/pal/base.py

@@ -2,6 +2,8 @@
 
 As in https://arxiv.org/pdf/2211.10435.pdf.
 """
+from __future__ import annotations
+
 from typing import Any, Dict, List
 
 from pydantic import BaseModel, Extra
@@ -57,7 +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: LLM, **kwargs: Any) -> PALChain:
         """Load PAL from math prompt."""
         return cls(
             llm=llm,
@@ -68,7 +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: LLM, **kwargs: Any) -> PALChain:
         """Load PAL from colored object prompt."""
         return cls(
             llm=llm,

langchain/chains/qa_with_sources/base.py

@@ -1,5 +1,7 @@
 """Question answering with sources over documents."""
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
@@ -40,7 +42,7 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
         question_prompt: BasePromptTemplate = QUESTION_PROMPT,
         combine_prompt: BasePromptTemplate = COMBINE_PROMPT,
         **kwargs: Any,
-    ) -> "BaseQAWithSourcesChain":
+    ) -> BaseQAWithSourcesChain:
         """Construct the chain from an LLM."""
         llm_question_chain = LLMChain(llm=llm, prompt=question_prompt)
         llm_combine_chain = LLMChain(llm=llm, prompt=combine_prompt)

langchain/chains/sql_database/base.py

@@ -54,7 +54,7 @@ class SQLDatabaseChain(Chain, BaseModel):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         llm_chain = LLMChain(llm=self.llm, prompt=PROMPT)
         chained_input = ChainedInput(
-            inputs[self.input_key] + "\nSQLQuery:", verbose=self.verbose
+            f"{inputs[self.input_key]} \nSQLQuery:", verbose=self.verbose
         )
         llm_inputs = {
             "input": chained_input.input,

langchain/model_laboratory.py

@@ -1,4 +1,6 @@
 """Experiment with different models."""
+from __future__ import annotations
+
 from typing import List, Optional, Sequence, Union
 
 from langchain.agents.agent import Agent
@@ -49,7 +51,7 @@ class ModelLaboratory:
     @classmethod
     def from_llms(
         cls, llms: List[LLM], prompt: Optional[PromptTemplate] = None
-    ) -> "ModelLaboratory":
+    ) -> ModelLaboratory:
         """Initialize with LLMs to experiment with and optional prompt.
 
         Args:

langchain/prompts/example_selector/semantic_similarity.py

@@ -1,4 +1,6 @@
 """Example selector that selects examples based on SemanticSimilarity."""
+from __future__ import annotations
+
 from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Extra
@@ -55,7 +57,7 @@ class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
         vectorstore_cls: VectorStore,
         k: int = 4,
         **vectorstore_cls_kwargs: Any,
-    ) -> "SemanticSimilarityExampleSelector":
+    ) -> SemanticSimilarityExampleSelector:
         """Create k-shot example selector using example list and embeddings.
 
         Reshuffles examples dynamically based on query similarity.

langchain/prompts/prompt.py

@@ -1,4 +1,6 @@
 """Prompt schema definition."""
+from __future__ import annotations
+
 from typing import Any, Dict, List
 
 from pydantic import BaseModel, Extra, root_validator
@@ -67,7 +69,7 @@ class PromptTemplate(BasePromptTemplate, BaseModel):
         input_variables: List[str],
         example_separator: str = "\n\n",
         prefix: str = "",
-    ) -> "PromptTemplate":
+    ) -> PromptTemplate:
         """Take examples in list format with prefix and suffix to create a prompt.
 
         Intended be used as a way to dynamically create a prompt from examples.
@@ -92,7 +94,7 @@ class PromptTemplate(BasePromptTemplate, BaseModel):
     @classmethod
     def from_file(
         cls, template_file: str, input_variables: List[str]
-    ) -> "PromptTemplate":
+    ) -> PromptTemplate:
         """Load a prompt from a file.
 
         Args:

langchain/sql_database.py

@@ -1,4 +1,6 @@
 """SQLAlchemy wrapper around a database."""
+from __future__ import annotations
+
 from typing import Any, Iterable, List, Optional
 
 from sqlalchemy import create_engine, inspect
@@ -37,7 +39,7 @@ class SQLDatabase:
                 )
 
     @classmethod
-    def from_uri(cls, database_uri: str, **kwargs: Any) -> "SQLDatabase":
+    def from_uri(cls, database_uri: str, **kwargs: Any) -> SQLDatabase:
         """Construct a SQLAlchemy engine from URI."""
         return cls(create_engine(database_uri), **kwargs)
 

langchain/text_splitter.py

@@ -1,4 +1,6 @@
 """Functionality for splitting text."""
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Iterable, List
 
@@ -46,9 +48,7 @@ class TextSplitter(ABC):
         return docs
 
     @classmethod
-    def from_huggingface_tokenizer(
+    def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
-        cls, tokenizer: Any, **kwargs: Any
-    ) -> "TextSplitter":
         """Text splitter than uses HuggingFace tokenizer to count length."""
         try:
             from transformers import PreTrainedTokenizerBase

langchain/vectorstores/base.py

@@ -1,4 +1,6 @@
 """Interface for vector stores."""
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
 from typing import Any, Iterable, List, Optional
 
@@ -26,6 +28,6 @@ class VectorStore(ABC):
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
-        **kwargs: Any
+        **kwargs: Any,
-    ) -> "VectorStore":
+    ) -> VectorStore:
         """Return VectorStore initialized from texts and embeddings."""

langchain/vectorstores/elastic_vector_search.py

@@ -1,4 +1,6 @@
 """Wrapper around Elasticsearch vector database."""
+from __future__ import annotations
+
 import uuid
 from typing import Any, Callable, Dict, Iterable, List, Optional
 
@@ -117,7 +119,7 @@ class ElasticVectorSearch(VectorStore):
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
-    ) -> "ElasticVectorSearch":
+    ) -> ElasticVectorSearch:
         """Construct ElasticVectorSearch wrapper from raw documents.
 
         This is a user-friendly interface that:

langchain/vectorstores/faiss.py

@@ -1,4 +1,6 @@
 """Wrapper around FAISS vector database."""
+from __future__ import annotations
+
 import uuid
 from typing import Any, Callable, Dict, Iterable, List, Optional
 
@@ -96,7 +98,7 @@ class FAISS(VectorStore):
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
-    ) -> "FAISS":
+    ) -> FAISS:
         """Construct FAISS wrapper from raw documents.
 
         This is a user friendly interface that:

tests/unit_tests/chains/test_sequential.py

@@ -28,7 +28,7 @@ class FakeChain(Chain, BaseModel):
         outputs = {}
         for var in self.output_variables:
             variables = [inputs[k] for k in self.input_variables]
-            outputs[var] = " ".join(variables) + "foo"
+            outputs[var] = f"{' '.join(variables)}foo"
         return outputs