forked from Archives/langchain
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@ -1,9 +1,11 @@
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"""Chain that generates a list and then maps each output to another chain."""
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from typing import Dict, List
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from pydantic import BaseModel, Extra, root_validator
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from pydantic import BaseModel, Extra, root_validator
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from typing import List, Dict
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class MapChain(Chain, BaseModel):
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@ -70,4 +72,3 @@ class MapChain(Chain, BaseModel):
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)
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outputs = {self.map_chain.run(text) for text in new_inputs}
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return outputs
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@ -1,8 +1,10 @@
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"""Simple chain pipeline where the outputs of one step feed directly into next."""
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"""Chain pipeline where the outputs of one step feed directly into next."""
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from typing import Dict, List
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from pydantic import BaseModel, Extra, root_validator
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from langchain.chains.base import Chain
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from pydantic import BaseModel, Extra, root_validator
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from typing import List, Dict
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class Pipeline(Chain, BaseModel):
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@ -36,7 +38,7 @@ class Pipeline(Chain, BaseModel):
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@root_validator(pre=True)
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def validate_chains(cls, values: Dict) -> Dict:
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"""Validate that chains are all single input/output."""
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"""Validate that the correct inputs exist for all chains."""
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chains = values["chains"]
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input_variables = values["input_variables"]
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known_variables = set(input_variables)
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@ -46,7 +48,9 @@ class Pipeline(Chain, BaseModel):
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raise ValueError(f"Missing required input keys: {missing_vars}")
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overlapping_keys = known_variables.intersection(chain.output_keys)
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if overlapping_keys:
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raise ValueError(f"Chain returned keys that already exist: {overlapping_keys}")
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raise ValueError(
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f"Chain returned keys that already exist: {overlapping_keys}"
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)
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known_variables |= set(chain.output_keys)
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if "output_variables" not in values:
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@ -54,7 +58,9 @@ class Pipeline(Chain, BaseModel):
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else:
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missing_vars = known_variables.difference(values["output_variables"])
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if missing_vars:
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raise ValueError(f"Expected output variables that were not found: {missing_vars}.")
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raise ValueError(
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f"Expected output variables that were not found: {missing_vars}."
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)
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return values
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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@ -63,5 +69,3 @@ class Pipeline(Chain, BaseModel):
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outputs = chain(known_values)
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known_values.update(outputs)
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return {k: known_values[k] for k in self.output_variables}
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"""Simple chain pipeline where the outputs of one step feed directly into next."""
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from langchain.chains.base import Chain
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from typing import Dict, List
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from pydantic import BaseModel, Extra, root_validator
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from typing import List, Dict
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from langchain.chains.base import Chain
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class SimplePipeline(Chain, BaseModel):
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@ -55,7 +57,5 @@ class SimplePipeline(Chain, BaseModel):
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for chain in self.chains:
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_input = chain.run(_input)
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# Clean the input
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_input = _input.strip(' \t\n\r')
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_input = _input.strip()
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return {self.output_key: _input}
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@ -1,9 +1,10 @@
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from typing import Dict, List
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from langchain.chains.pipeline import Pipeline
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from langchain.chains.base import Chain
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from pydantic import BaseModel
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from langchain.chains.base import Chain
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from langchain.chains.pipeline import Pipeline
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class FakeChain(Chain, BaseModel):
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