remove code (langchain-ai#8425)

libs/experimental/langchain_experimental/pal_chain/base.py

@@ -8,16 +8,14 @@
 from __future__ import annotations
 
 import ast
-import warnings
 from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.schema import BasePromptTemplate
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities import PythonREPL
-from pydantic import Extra, Field, root_validator
+from pydantic import Extra, Field
 
 from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
@@ -95,10 +93,6 @@ class PALChain(Chain):
     """
 
     llm_chain: LLMChain
-    llm: Optional[BaseLanguageModel] = None
-    """[Deprecated]"""
-    prompt: BasePromptTemplate = MATH_PROMPT
-    """[Deprecated]"""
     stop: str = "\n\n"
     """Stop token to use when generating code."""
     get_answer_expr: str = "print(solution())"
@@ -121,26 +115,13 @@ class Config:
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    @root_validator(pre=True)
-    def raise_deprecation(cls, values: Dict) -> Dict:
-        if "llm" in values:
-            warnings.warn(
-                "Directly instantiating a PALChain with an llm is deprecated. "
-                "Please instantiate with llm_chain argument or using one of "
-                "the class method constructors from_math_prompt, "
-                "from_colored_object_prompt."
-            )
-            if "llm_chain" not in values and values["llm"] is not None:
-                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=MATH_PROMPT)
-        return values
-
     @property
     def input_keys(self) -> List[str]:
         """Return the singular input key.
 
         :meta private:
         """
-        return self.prompt.input_variables
+        return self.llm_chain.prompt.input_variables
 
     @property
     def output_keys(self) -> List[str]:

libs/langchain/tests/unit_tests/chains/test_pal.py → libs/experimental/tests/unit_tests/test_pal.py

@@ -1,10 +1,10 @@
 """Test LLM PAL functionality."""
 import pytest
 
-from langchain.chains.pal.base import PALChain, PALValidation
-from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
-from langchain.chains.pal.math_prompt import MATH_PROMPT
-from tests.unit_tests.llms.fake_llm import FakeLLM
+from langchain_experimental.pal_chain.base import PALChain, PALValidation
+from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
+from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
+from tests.unit_tests.fake_llm import FakeLLM
 
 _MATH_SOLUTION_1 = """
 def solution():

libs/langchain/langchain/__init__.py

@@ -11,9 +11,7 @@
     LLMChain,
     LLMCheckerChain,
     LLMMathChain,
-    PALChain,
     QAWithSourcesChain,
-    SQLDatabaseChain,
     VectorDBQA,
     VectorDBQAWithSourcesChain,
 )
@@ -106,7 +104,6 @@
     "SagemakerEndpoint",
     "HuggingFacePipeline",
     "SQLDatabase",
-    "SQLDatabaseChain",
     "PowerBIDataset",
     "FAISS",
     "MRKLChain",
@@ -116,7 +113,6 @@
     "ConversationChain",
     "VectorDBQAWithSourcesChain",
     "QAWithSourcesChain",
-    "PALChain",
     "LlamaCpp",
     "HuggingFaceTextGenInference",
 ]

libs/langchain/langchain/agents/load_tools.py

@@ -11,7 +11,6 @@
 from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
 from langchain.chains.api.base import APIChain
 from langchain.chains.llm_math.base import LLMMathChain
-from langchain.chains.pal.base import PALChain
 from langchain.utilities.requests import TextRequestsWrapper
 from langchain.tools.arxiv.tool import ArxivQueryRun
 from langchain.tools.golden_query.tool import GoldenQueryRun
@@ -105,22 +104,6 @@ def _get_sleep() -> BaseTool:
 }
 
 
-def _get_pal_math(llm: BaseLanguageModel) -> BaseTool:
-    return Tool(
-        name="PAL-MATH",
-        description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",
-        func=PALChain.from_math_prompt(llm).run,
-    )
-
-
-def _get_pal_colored_objects(llm: BaseLanguageModel) -> BaseTool:
-    return Tool(
-        name="PAL-COLOR-OBJ",
-        description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.",
-        func=PALChain.from_colored_object_prompt(llm).run,
-    )
-
-
 def _get_llm_math(llm: BaseLanguageModel) -> BaseTool:
     return Tool(
         name="Calculator",
@@ -140,8 +123,6 @@ def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
 
 
 _LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] = {
-    "pal-math": _get_pal_math,
-    "pal-colored-objects": _get_pal_colored_objects,
     "llm-math": _get_llm_math,
     "open-meteo-api": _get_open_meteo_api,
 }

libs/langchain/langchain/chains/__init__.py

@@ -55,7 +55,6 @@
     create_tagging_chain,
     create_tagging_chain_pydantic,
 )
-from langchain.chains.pal.base import PALChain
 from langchain.chains.qa_generation.base import QAGenerationChain
 from langchain.chains.qa_with_sources.base import QAWithSourcesChain
 from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
@@ -69,10 +68,6 @@
     RouterChain,
 )
 from langchain.chains.sequential import SequentialChain, SimpleSequentialChain
-from langchain.chains.sql_database.base import (
-    SQLDatabaseChain,
-    SQLDatabaseSequentialChain,
-)
 from langchain.chains.sql_database.query import create_sql_query_chain
 from langchain.chains.transform import TransformChain
 
@@ -108,16 +103,13 @@
     "NebulaGraphQAChain",
     "OpenAIModerationChain",
     "OpenAPIEndpointChain",
-    "PALChain",
     "QAGenerationChain",
     "QAWithSourcesChain",
     "ReduceDocumentsChain",
     "RefineDocumentsChain",
     "RetrievalQA",
     "RetrievalQAWithSourcesChain",
     "RouterChain",
-    "SQLDatabaseChain",
-    "SQLDatabaseSequentialChain",
     "SequentialChain",
     "SimpleSequentialChain",
     "StuffDocumentsChain",

libs/langchain/langchain/chains/loading.py

@@ -19,11 +19,9 @@
 from langchain.chains.llm_checker.base import LLMCheckerChain
 from langchain.chains.llm_math.base import LLMMathChain
 from langchain.chains.llm_requests import LLMRequestsChain
-from langchain.chains.pal.base import PALChain
 from langchain.chains.qa_with_sources.base import QAWithSourcesChain
 from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
 from langchain.chains.retrieval_qa.base import RetrievalQA, VectorDBQA
-from langchain.chains.sql_database.base import SQLDatabaseChain
 from langchain.llms.loading import load_llm, load_llm_from_config
 from langchain.prompts.loading import (
     _load_output_parser,
@@ -266,34 +264,17 @@ def _load_map_rerank_documents_chain(
     return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
 
 
-def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
-    llm_chain = None
+def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
+    from langchain_experimental.pal_chain import PALChain
+
     if "llm_chain" in config:
         llm_chain_config = config.pop("llm_chain")
         llm_chain = load_chain_from_config(llm_chain_config)
     elif "llm_chain_path" in config:
         llm_chain = load_chain(config.pop("llm_chain_path"))
-    # llm attribute is deprecated in favor of llm_chain, here to support old configs
-    elif "llm" in config:
-        llm_config = config.pop("llm")
-        llm = load_llm_from_config(llm_config)
-    # llm_path attribute is deprecated in favor of llm_chain_path,
-    # its to support old configs
-    elif "llm_path" in config:
-        llm = load_llm(config.pop("llm_path"))
     else:
         raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
-    if "prompt" in config:
-        prompt_config = config.pop("prompt")
-        prompt = load_prompt_from_config(prompt_config)
-    elif "prompt_path" in config:
-        prompt = load_prompt(config.pop("prompt_path"))
-    else:
-        raise ValueError("One of `prompt` or `prompt_path` must be present.")
-    if llm_chain:
-        return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
-    else:
-        return PALChain(llm=llm, prompt=prompt, **config)
+    return PALChain(llm_chain=llm_chain, **config)
 
 
 def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
@@ -342,7 +323,7 @@ def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesCha
     return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
 
 
-def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain:
+def _load_sql_database_chain(config: dict, **kwargs: Any) -> Any:
     if "database" in kwargs:
         database = kwargs.pop("database")
     else:
@@ -359,6 +340,8 @@ def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain:
         prompt = load_prompt_from_config(prompt_config)
     else:
         prompt = None
+    from langchain_experimental.sql import SQLDatabaseChain
+
     return SQLDatabaseChain.from_llm(llm, database, prompt=prompt, **config)