cr

langchain/chains/map/base.py

@@ -1,9 +1,11 @@
 """Chain that generates a list and then maps each output to another chain."""
 
+from typing import Dict, List
+
+from pydantic import BaseModel, Extra, root_validator
+
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from pydantic import BaseModel, Extra, root_validator
-from typing import List, Dict
 
 
 class MapChain(Chain, BaseModel):
@@ -70,4 +72,3 @@ class MapChain(Chain, BaseModel):
             )
         outputs = {self.map_chain.run(text) for text in new_inputs}
         return outputs
-

langchain/chains/pipeline.py

@@ -1,8 +1,10 @@
-"""Simple chain pipeline where the outputs of one step feed directly into next."""
+"""Chain pipeline where the outputs of one step feed directly into next."""
+
+from typing import Dict, List
 
-from langchain.chains.base import Chain
 from pydantic import BaseModel, Extra, root_validator
-from typing import List, Dict
+
+from langchain.chains.base import Chain
 
 
 class Pipeline(Chain, BaseModel):
@@ -36,7 +38,7 @@ class Pipeline(Chain, BaseModel):
 
     @root_validator(pre=True)
     def validate_chains(cls, values: Dict) -> Dict:
-        """Validate that chains are all single input/output."""
+        """Validate that the correct inputs exist for all chains."""
         chains = values["chains"]
         input_variables = values["input_variables"]
         known_variables = set(input_variables)
@@ -46,7 +48,9 @@ class Pipeline(Chain, BaseModel):
                 raise ValueError(f"Missing required input keys: {missing_vars}")
             overlapping_keys = known_variables.intersection(chain.output_keys)
             if overlapping_keys:
-                raise ValueError(f"Chain returned keys that already exist: {overlapping_keys}")
+                raise ValueError(
+                    f"Chain returned keys that already exist: {overlapping_keys}"
+                )
             known_variables |= set(chain.output_keys)
 
         if "output_variables" not in values:
@@ -54,7 +58,9 @@ class Pipeline(Chain, BaseModel):
         else:
             missing_vars = known_variables.difference(values["output_variables"])
             if missing_vars:
-                raise ValueError(f"Expected output variables that were not found: {missing_vars}.")
+                raise ValueError(
+                    f"Expected output variables that were not found: {missing_vars}."
+                )
         return values
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
@@ -63,5 +69,3 @@ class Pipeline(Chain, BaseModel):
             outputs = chain(known_values)
             known_values.update(outputs)
         return {k: known_values[k] for k in self.output_variables}
-
-

langchain/chains/simple_pipeline.py

@@ -1,8 +1,10 @@
 """Simple chain pipeline where the outputs of one step feed directly into next."""
 
-from langchain.chains.base import Chain
+from typing import Dict, List
+
 from pydantic import BaseModel, Extra, root_validator
-from typing import List, Dict
+
+from langchain.chains.base import Chain
 
 
 class SimplePipeline(Chain, BaseModel):
@@ -55,7 +57,5 @@ class SimplePipeline(Chain, BaseModel):
         for chain in self.chains:
             _input = chain.run(_input)
             # Clean the input
-            _input = _input.strip(' \t\n\r')
+            _input = _input.strip()
         return {self.output_key: _input}
-
-

tests/unit_tests/chains/test_pipeline.py

@@ -1,9 +1,10 @@
 from typing import Dict, List
 
-from langchain.chains.pipeline import Pipeline
-from langchain.chains.base import Chain
 from pydantic import BaseModel
 
+from langchain.chains.base import Chain
+from langchain.chains.pipeline import Pipeline
+
 
 class FakeChain(Chain, BaseModel):
 
@@ -30,4 +31,4 @@ def test_pipeline_usage() -> None:
     chain_2 = FakeChain(input_variables=["bar"], output_variables=["baz"])
     pipeline = Pipeline(chains=[chain_1, chain_2], input_variables=["foo"])
     output = pipeline({"foo": "123"})
-    breakpoint()
+    breakpoint()