initial commit

venv/Lib/site-packages/langchain_classic/chains/summarize/__init__.py

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+from langchain_classic.chains.summarize.chain import (
+    LoadingCallable,
+    load_summarize_chain,
+)
+
+__all__ = ["LoadingCallable", "load_summarize_chain"]

venv/Lib/site-packages/langchain_classic/chains/summarize/chain.py

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+"""Load summarizing chains."""
+
+from collections.abc import Mapping
+from typing import Any, Protocol
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.prompts import BasePromptTemplate
+
+from langchain_classic.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain_classic.chains.combine_documents.map_reduce import (
+    MapReduceDocumentsChain,
+)
+from langchain_classic.chains.combine_documents.reduce import ReduceDocumentsChain
+from langchain_classic.chains.combine_documents.refine import RefineDocumentsChain
+from langchain_classic.chains.combine_documents.stuff import StuffDocumentsChain
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.chains.summarize import (
+    map_reduce_prompt,
+    refine_prompts,
+    stuff_prompt,
+)
+
+
+class LoadingCallable(Protocol):
+    """Interface for loading the combine documents chain."""
+
+    def __call__(
+        self,
+        llm: BaseLanguageModel,
+        **kwargs: Any,
+    ) -> BaseCombineDocumentsChain:
+        """Callable to load the combine documents chain."""
+
+
+def _load_stuff_chain(
+    llm: BaseLanguageModel,
+    *,
+    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
+    document_variable_name: str = "text",
+    verbose: bool | None = None,
+    **kwargs: Any,
+) -> StuffDocumentsChain:
+    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
+    """Load a StuffDocumentsChain for summarization.
+
+    Args:
+        llm: Language Model to use in the chain.
+        prompt: Prompt template that controls how the documents are formatted and
+            passed into the LLM.
+        document_variable_name: Variable name in the prompt template where the
+            document text will be inserted.
+        verbose: Whether to log progress and intermediate steps.
+        **kwargs: Additional keyword arguments passed to the StuffDocumentsChain.
+
+    Returns:
+        A StuffDocumentsChain that takes in documents, formats them with the
+        given prompt, and runs the chain on the provided LLM.
+    """
+    return StuffDocumentsChain(
+        llm_chain=llm_chain,
+        document_variable_name=document_variable_name,
+        verbose=verbose,
+        **kwargs,
+    )
+
+
+def _load_map_reduce_chain(
+    llm: BaseLanguageModel,
+    *,
+    map_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
+    combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
+    combine_document_variable_name: str = "text",
+    map_reduce_document_variable_name: str = "text",
+    collapse_prompt: BasePromptTemplate | None = None,
+    reduce_llm: BaseLanguageModel | None = None,
+    collapse_llm: BaseLanguageModel | None = None,
+    verbose: bool | None = None,
+    token_max: int = 3000,
+    callbacks: Callbacks = None,
+    collapse_max_retries: int | None = None,
+    **kwargs: Any,
+) -> MapReduceDocumentsChain:
+    map_chain = LLMChain(
+        llm=llm,
+        prompt=map_prompt,
+        verbose=verbose,
+        callbacks=callbacks,
+    )
+    _reduce_llm = reduce_llm or llm
+    reduce_chain = LLMChain(
+        llm=_reduce_llm,
+        prompt=combine_prompt,
+        verbose=verbose,
+        callbacks=callbacks,
+    )
+    """Load a MapReduceDocumentsChain for summarization.
+
+    This chain first applies a "map" step to summarize each document,
+    then applies a "reduce" step to combine the summaries into a
+    final result. Optionally, a "collapse" step can be used to handle
+    long intermediate results.
+
+    Args:
+        llm: Language Model to use for map and reduce steps.
+        map_prompt: Prompt used to summarize each document in the map step.
+        combine_prompt: Prompt used to combine summaries in the reduce step.
+        combine_document_variable_name: Variable name in the `combine_prompt` where
+            the mapped summaries are inserted.
+        map_reduce_document_variable_name: Variable name in the `map_prompt`
+            where document text is inserted.
+        collapse_prompt: Optional prompt used to collapse intermediate summaries
+            if they exceed the token limit (`token_max`).
+        reduce_llm: Optional separate LLM for the reduce step.
+            which uses the same model as the map step.
+        collapse_llm: Optional separate LLM for the collapse step.
+            which uses the same model as the map step.
+        verbose: Whether to log progress and intermediate steps.
+        token_max: Token threshold that triggers the collapse step during reduction.
+        callbacks: Optional callbacks for logging and tracing.
+        collapse_max_retries: Maximum retries for the collapse step if it fails.
+
+        **kwargs: Additional keyword arguments passed to the MapReduceDocumentsChain.
+
+    Returns:
+        A MapReduceDocumentsChain that maps each document to a summary,
+        then reduces all summaries into a single cohesive result.
+    """
+    combine_documents_chain = StuffDocumentsChain(
+        llm_chain=reduce_chain,
+        document_variable_name=combine_document_variable_name,
+        verbose=verbose,
+        callbacks=callbacks,
+    )
+    if collapse_prompt is None:
+        collapse_chain = None
+        if collapse_llm is not None:
+            msg = (
+                "collapse_llm provided, but collapse_prompt was not: please "
+                "provide one or stop providing collapse_llm."
+            )
+            raise ValueError(msg)
+    else:
+        _collapse_llm = collapse_llm or llm
+        collapse_chain = StuffDocumentsChain(
+            llm_chain=LLMChain(
+                llm=_collapse_llm,
+                prompt=collapse_prompt,
+                verbose=verbose,
+                callbacks=callbacks,
+            ),
+            document_variable_name=combine_document_variable_name,
+        )
+    reduce_documents_chain = ReduceDocumentsChain(
+        combine_documents_chain=combine_documents_chain,
+        collapse_documents_chain=collapse_chain,
+        token_max=token_max,
+        verbose=verbose,
+        callbacks=callbacks,
+        collapse_max_retries=collapse_max_retries,
+    )
+    return MapReduceDocumentsChain(
+        llm_chain=map_chain,
+        reduce_documents_chain=reduce_documents_chain,
+        document_variable_name=map_reduce_document_variable_name,
+        verbose=verbose,
+        callbacks=callbacks,
+        **kwargs,
+    )
+
+
+def _load_refine_chain(
+    llm: BaseLanguageModel,
+    *,
+    question_prompt: BasePromptTemplate = refine_prompts.PROMPT,
+    refine_prompt: BasePromptTemplate = refine_prompts.REFINE_PROMPT,
+    document_variable_name: str = "text",
+    initial_response_name: str = "existing_answer",
+    refine_llm: BaseLanguageModel | None = None,
+    verbose: bool | None = None,
+    **kwargs: Any,
+) -> RefineDocumentsChain:
+    initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
+    _refine_llm = refine_llm or llm
+    refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose)
+    return RefineDocumentsChain(
+        initial_llm_chain=initial_chain,
+        refine_llm_chain=refine_chain,
+        document_variable_name=document_variable_name,
+        initial_response_name=initial_response_name,
+        verbose=verbose,
+        **kwargs,
+    )
+
+
+def load_summarize_chain(
+    llm: BaseLanguageModel,
+    chain_type: str = "stuff",
+    verbose: bool | None = None,  # noqa: FBT001
+    **kwargs: Any,
+) -> BaseCombineDocumentsChain:
+    """Load summarizing chain.
+
+    Args:
+        llm: Language Model to use in the chain.
+        chain_type: Type of document combining chain to use. Should be one of "stuff",
+            "map_reduce", and "refine".
+        verbose: Whether chains should be run in verbose mode or not. Note that this
+            applies to all chains that make up the final chain.
+        **kwargs: Additional keyword arguments.
+
+    Returns:
+        A chain to use for summarizing.
+    """
+    loader_mapping: Mapping[str, LoadingCallable] = {
+        "stuff": _load_stuff_chain,
+        "map_reduce": _load_map_reduce_chain,
+        "refine": _load_refine_chain,
+    }
+    if chain_type not in loader_mapping:
+        msg = (
+            f"Got unsupported chain type: {chain_type}. "
+            f"Should be one of {loader_mapping.keys()}"
+        )
+        raise ValueError(msg)
+    return loader_mapping[chain_type](llm, verbose=verbose, **kwargs)

venv/Lib/site-packages/langchain_classic/chains/summarize/map_reduce_prompt.py

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+from langchain_core.prompts import PromptTemplate
+
+prompt_template = """Write a concise summary of the following:
+
+
+"{text}"
+
+
+CONCISE SUMMARY:"""
+PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])

venv/Lib/site-packages/langchain_classic/chains/summarize/refine_prompts.py

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+from langchain_core.prompts import PromptTemplate
+
+REFINE_PROMPT_TMPL = """\
+Your job is to produce a final summary.
+We have provided an existing summary up to a certain point: {existing_answer}
+We have the opportunity to refine the existing summary (only if needed) with some more context below.
+------------
+{text}
+------------
+Given the new context, refine the original summary.
+If the context isn't useful, return the original summary.\
+"""  # noqa: E501
+REFINE_PROMPT = PromptTemplate.from_template(REFINE_PROMPT_TMPL)
+
+
+prompt_template = """Write a concise summary of the following:
+
+
+"{text}"
+
+
+CONCISE SUMMARY:"""
+PROMPT = PromptTemplate.from_template(prompt_template)

venv/Lib/site-packages/langchain_classic/chains/summarize/stuff_prompt.py

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+from langchain_core.prompts import PromptTemplate
+
+prompt_template = """Write a concise summary of the following:
+
+
+"{text}"
+
+
+CONCISE SUMMARY:"""
+PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])