move output parsing (#1605)

docs/modules/indexes/chain_examples/qa_with_sources.ipynb

@@ -635,7 +635,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts.base import RegexParser\n",
+    "from langchain.output_parsers import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",
@@ -732,4 +732,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

docs/modules/indexes/chain_examples/question_answering.ipynb

@@ -635,7 +635,7 @@
     }
    ],
    "source": [
-    "from langchain.prompts.base import RegexParser\n",
+    "from langchain.output_parsers import RegexParser\n",
     "\n",
     "output_parser = RegexParser(\n",
     "    regex=r\"(.*?)\\nScore: (.*)\",\n",

langchain/chains/combine_documents/map_rerank.py

@@ -9,7 +9,7 @@ from pydantic import BaseModel, Extra, root_validator
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
-from langchain.prompts.base import RegexParser
+from langchain.output_parsers.regex import RegexParser
 
 
 class MapRerankDocumentsChain(BaseCombineDocumentsChain, BaseModel):

langchain/chains/question_answering/map_rerank_prompt.py

@@ -1,6 +1,6 @@
 # flake8: noqa
 from langchain.prompts import PromptTemplate
-from langchain.prompts.base import RegexParser
+from langchain.output_parsers.regex import RegexParser
 
 output_parser = RegexParser(
     regex=r"(.*?)\nScore: (.*)",

langchain/chains/sql_database/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.prompts.base import CommaSeparatedListOutputParser
+from langchain.output_parsers.list import CommaSeparatedListOutputParser
 from langchain.prompts.prompt import PromptTemplate
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.

langchain/evaluation/qa/generate_prompt.py

@@ -1,6 +1,6 @@
 # flake8: noqa
 from langchain.prompts import PromptTemplate
-from langchain.prompts.base import RegexParser
+from langchain.output_parsers.regex import RegexParser
 
 template = """You are a teacher coming up with questions to ask on a quiz. 
 Given the following document, please generate a question and answer based on that document.

langchain/output_parsers/__init__.py

@@ -0,0 +1,13 @@
+from langchain.output_parsers.base import BaseOutputParser
+from langchain.output_parsers.list import (
+    CommaSeparatedListOutputParser,
+    ListOutputParser,
+)
+from langchain.output_parsers.regex import RegexParser
+
+__all__ = [
+    "RegexParser",
+    "ListOutputParser",
+    "CommaSeparatedListOutputParser",
+    "BaseOutputParser",
+]

langchain/output_parsers/base.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict
+
+from pydantic import BaseModel
+
+
+class BaseOutputParser(BaseModel, ABC):
+    """Class to parse the output of an LLM call."""
+
+    @abstractmethod
+    def parse(self, text: str) -> Any:
+        """Parse the output of an LLM call."""
+
+    @property
+    def _type(self) -> str:
+        """Return the type key."""
+        raise NotImplementedError
+
+    def dict(self, **kwargs: Any) -> Dict:
+        """Return dictionary representation of output parser."""
+        output_parser_dict = super().dict()
+        output_parser_dict["_type"] = self._type
+        return output_parser_dict

langchain/output_parsers/list.py

@@ -0,0 +1,22 @@
+from __future__ import annotations
+
+from abc import abstractmethod
+from typing import List
+
+from langchain.output_parsers.base import BaseOutputParser
+
+
+class ListOutputParser(BaseOutputParser):
+    """Class to parse the output of an LLM call to a list."""
+
+    @abstractmethod
+    def parse(self, text: str) -> List[str]:
+        """Parse the output of an LLM call."""
+
+
+class CommaSeparatedListOutputParser(ListOutputParser):
+    """Parse out comma separated lists."""
+
+    def parse(self, text: str) -> List[str]:
+        """Parse the output of an LLM call."""
+        return text.strip().split(", ")

langchain/output_parsers/loading.py

@@ -0,0 +1,15 @@
+from langchain.output_parsers.regex import RegexParser
+
+
+def load_output_parser(config: dict) -> dict:
+    """Load output parser."""
+    if "output_parsers" in config:
+        if config["output_parsers"] is not None:
+            _config = config["output_parsers"]
+            output_parser_type = _config["_type"]
+            if output_parser_type == "regex_parser":
+                output_parser = RegexParser(**_config)
+            else:
+                raise ValueError(f"Unsupported output parser {output_parser_type}")
+            config["output_parsers"] = output_parser
+    return config

langchain/output_parsers/regex.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+import re
+from typing import Dict, List, Optional
+
+from pydantic import BaseModel
+
+from langchain.output_parsers.base import BaseOutputParser
+
+
+class RegexParser(BaseOutputParser, BaseModel):
+    """Class to parse the output into a dictionary."""
+
+    regex: str
+    output_keys: List[str]
+    default_output_key: Optional[str] = None
+
+    @property
+    def _type(self) -> str:
+        """Return the type key."""
+        return "regex_parser"
+
+    def parse(self, text: str) -> Dict[str, str]:
+        """Parse the output of an LLM call."""
+        match = re.search(self.regex, text)
+        if match:
+            return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
+        else:
+            if self.default_output_key is None:
+                raise ValueError(f"Could not parse output: {text}")
+            else:
+                return {
+                    key: text if key == self.default_output_key else ""
+                    for key in self.output_keys
+                }

langchain/prompts/base.py

@@ -2,7 +2,6 @@
 from __future__ import annotations
 
 import json
-import re
 from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Mapping, Optional, Union
@@ -11,6 +10,12 @@ import yaml
 from pydantic import BaseModel, Extra, Field, root_validator
 
 from langchain.formatting import formatter
+from langchain.output_parsers.base import BaseOutputParser
+from langchain.output_parsers.list import (  # noqa: F401
+    CommaSeparatedListOutputParser,
+    ListOutputParser,
+)
+from langchain.output_parsers.regex import RegexParser  # noqa: F401
 from langchain.schema import BaseMessage, HumanMessage, PromptValue
 
 
@@ -54,68 +59,6 @@ def check_valid_template(
         )
 
 
-class BaseOutputParser(BaseModel, ABC):
-    """Class to parse the output of an LLM call."""
-
-    @abstractmethod
-    def parse(self, text: str) -> Union[str, List[str], Dict[str, str]]:
-        """Parse the output of an LLM call."""
-
-    @property
-    def _type(self) -> str:
-        """Return the type key."""
-        raise NotImplementedError
-
-    def dict(self, **kwargs: Any) -> Dict:
-        """Return dictionary representation of output parser."""
-        output_parser_dict = super().dict()
-        output_parser_dict["_type"] = self._type
-        return output_parser_dict
-
-
-class ListOutputParser(BaseOutputParser):
-    """Class to parse the output of an LLM call to a list."""
-
-    @abstractmethod
-    def parse(self, text: str) -> List[str]:
-        """Parse the output of an LLM call."""
-
-
-class CommaSeparatedListOutputParser(ListOutputParser):
-    """Parse out comma separated lists."""
-
-    def parse(self, text: str) -> List[str]:
-        """Parse the output of an LLM call."""
-        return text.strip().split(", ")
-
-
-class RegexParser(BaseOutputParser, BaseModel):
-    """Class to parse the output into a dictionary."""
-
-    regex: str
-    output_keys: List[str]
-    default_output_key: Optional[str] = None
-
-    @property
-    def _type(self) -> str:
-        """Return the type key."""
-        return "regex_parser"
-
-    def parse(self, text: str) -> Dict[str, str]:
-        """Parse the output of an LLM call."""
-        match = re.search(self.regex, text)
-        if match:
-            return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
-        else:
-            if self.default_output_key is None:
-                raise ValueError(f"Could not parse output: {text}")
-            else:
-                return {
-                    key: text if key == self.default_output_key else ""
-                    for key in self.output_keys
-                }
-
-
 class StringPromptValue(PromptValue):
     text: str
 

langchain/prompts/loading.py

@@ -7,7 +7,8 @@ from typing import Union
 
 import yaml
 
-from langchain.prompts.base import BasePromptTemplate, RegexParser
+from langchain.output_parsers.regex import RegexParser
+from langchain.prompts.base import BasePromptTemplate
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
 from langchain.utilities.loading import try_load_from_hub
@@ -73,15 +74,15 @@ def _load_examples(config: dict) -> dict:
 
 def _load_output_parser(config: dict) -> dict:
     """Load output parser."""
-    if "output_parser" in config:
-        if config["output_parser"] is not None:
-            _config = config["output_parser"]
+    if "output_parsers" in config:
+        if config["output_parsers"] is not None:
+            _config = config["output_parsers"]
             output_parser_type = _config["_type"]
             if output_parser_type == "regex_parser":
                 output_parser = RegexParser(**_config)
             else:
                 raise ValueError(f"Unsupported output parser {output_parser_type}")
-            config["output_parser"] = output_parser
+            config["output_parsers"] = output_parser
     return config
 
 

tests/unit_tests/chains/test_llm.py

@@ -7,7 +7,7 @@ import pytest
 
 from langchain.chains.llm import LLMChain
 from langchain.chains.loading import load_chain
-from langchain.prompts.base import BaseOutputParser
+from langchain.output_parsers.base import BaseOutputParser
 from langchain.prompts.prompt import PromptTemplate
 from tests.unit_tests.llms.fake_llm import FakeLLM