743 lines
27 KiB
Python
743 lines
27 KiB
Python
"""Functionality for loading chains."""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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import yaml
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from langchain_core._api import deprecated
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from langchain_core.prompts.loading import (
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_load_output_parser,
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load_prompt,
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load_prompt_from_config,
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)
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from langchain_classic.chains import ReduceDocumentsChain
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from langchain_classic.chains.api.base import APIChain
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from langchain_classic.chains.base import Chain
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from langchain_classic.chains.combine_documents.map_reduce import (
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MapReduceDocumentsChain,
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)
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from langchain_classic.chains.combine_documents.map_rerank import (
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MapRerankDocumentsChain,
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)
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from langchain_classic.chains.combine_documents.refine import RefineDocumentsChain
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from langchain_classic.chains.combine_documents.stuff import StuffDocumentsChain
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from langchain_classic.chains.hyde.base import HypotheticalDocumentEmbedder
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from langchain_classic.chains.llm import LLMChain
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from langchain_classic.chains.llm_checker.base import LLMCheckerChain
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from langchain_classic.chains.llm_math.base import LLMMathChain
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from langchain_classic.chains.qa_with_sources.base import QAWithSourcesChain
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from langchain_classic.chains.qa_with_sources.retrieval import (
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RetrievalQAWithSourcesChain,
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)
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from langchain_classic.chains.qa_with_sources.vector_db import (
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VectorDBQAWithSourcesChain,
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)
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from langchain_classic.chains.retrieval_qa.base import RetrievalQA, VectorDBQA
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if TYPE_CHECKING:
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from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
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from langchain_classic.chains.llm_requests import LLMRequestsChain
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try:
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from langchain_community.llms.loading import load_llm, load_llm_from_config
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except ImportError:
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def load_llm(*_: Any, **__: Any) -> None:
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"""Import error for load_llm."""
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msg = (
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"To use this load_llm functionality you must install the "
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"langchain_community package. "
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"You can install it with `pip install langchain_community`"
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)
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raise ImportError(msg)
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def load_llm_from_config(*_: Any, **__: Any) -> None:
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"""Import error for load_llm_from_config."""
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msg = (
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"To use this load_llm_from_config functionality you must install the "
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"langchain_community package. "
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"You can install it with `pip install langchain_community`"
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)
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raise ImportError(msg)
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URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/chains/"
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def _load_llm_chain(config: dict, **kwargs: Any) -> LLMChain:
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"""Load LLM chain from config dict."""
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if "llm" in config:
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llm_config = config.pop("llm")
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llm = load_llm_from_config(llm_config, **kwargs)
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"), **kwargs)
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else:
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msg = "One of `llm` or `llm_path` must be present."
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raise ValueError(msg)
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if "prompt" in config:
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prompt_config = config.pop("prompt")
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prompt = load_prompt_from_config(prompt_config)
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elif "prompt_path" in config:
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prompt = load_prompt(config.pop("prompt_path"))
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else:
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msg = "One of `prompt` or `prompt_path` must be present."
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raise ValueError(msg)
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_load_output_parser(config)
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return LLMChain(llm=llm, prompt=prompt, **config)
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def _load_hyde_chain(config: dict, **kwargs: Any) -> HypotheticalDocumentEmbedder:
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"""Load hypothetical document embedder chain from config dict."""
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if "llm_chain" in config:
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llm_chain_config = config.pop("llm_chain")
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llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
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else:
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msg = "One of `llm_chain` or `llm_chain_path` must be present."
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raise ValueError(msg)
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if "embeddings" in kwargs:
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embeddings = kwargs.pop("embeddings")
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else:
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msg = "`embeddings` must be present."
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raise ValueError(msg)
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return HypotheticalDocumentEmbedder(
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llm_chain=llm_chain,
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base_embeddings=embeddings,
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**config,
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)
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def _load_stuff_documents_chain(config: dict, **kwargs: Any) -> StuffDocumentsChain:
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if "llm_chain" in config:
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llm_chain_config = config.pop("llm_chain")
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llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
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else:
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msg = "One of `llm_chain` or `llm_chain_path` must be present."
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raise ValueError(msg)
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if not isinstance(llm_chain, LLMChain):
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msg = f"Expected LLMChain, got {llm_chain}"
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raise ValueError(msg) # noqa: TRY004
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if "document_prompt" in config:
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prompt_config = config.pop("document_prompt")
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document_prompt = load_prompt_from_config(prompt_config)
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elif "document_prompt_path" in config:
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document_prompt = load_prompt(config.pop("document_prompt_path"))
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else:
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msg = "One of `document_prompt` or `document_prompt_path` must be present."
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raise ValueError(msg)
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return StuffDocumentsChain(
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llm_chain=llm_chain,
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document_prompt=document_prompt,
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**config,
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)
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def _load_map_reduce_documents_chain(
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config: dict,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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if "llm_chain" in config:
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llm_chain_config = config.pop("llm_chain")
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llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
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else:
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msg = "One of `llm_chain` or `llm_chain_path` must be present."
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raise ValueError(msg)
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if not isinstance(llm_chain, LLMChain):
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msg = f"Expected LLMChain, got {llm_chain}"
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raise ValueError(msg) # noqa: TRY004
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if "reduce_documents_chain" in config:
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reduce_documents_chain = load_chain_from_config(
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config.pop("reduce_documents_chain"),
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**kwargs,
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)
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elif "reduce_documents_chain_path" in config:
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reduce_documents_chain = load_chain(
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config.pop("reduce_documents_chain_path"),
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**kwargs,
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)
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else:
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reduce_documents_chain = _load_reduce_documents_chain(config, **kwargs)
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return MapReduceDocumentsChain(
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llm_chain=llm_chain,
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reduce_documents_chain=reduce_documents_chain,
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**config,
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)
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def _load_reduce_documents_chain(config: dict, **kwargs: Any) -> ReduceDocumentsChain:
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combine_documents_chain = None
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collapse_documents_chain = None
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if "combine_documents_chain" in config:
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combine_document_chain_config = config.pop("combine_documents_chain")
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combine_documents_chain = load_chain_from_config(
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combine_document_chain_config,
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**kwargs,
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)
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elif "combine_document_chain" in config:
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combine_document_chain_config = config.pop("combine_document_chain")
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combine_documents_chain = load_chain_from_config(
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combine_document_chain_config,
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**kwargs,
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)
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elif "combine_documents_chain_path" in config:
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combine_documents_chain = load_chain(
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config.pop("combine_documents_chain_path"),
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**kwargs,
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)
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elif "combine_document_chain_path" in config:
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combine_documents_chain = load_chain(
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config.pop("combine_document_chain_path"),
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**kwargs,
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)
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else:
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msg = (
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"One of `combine_documents_chain` or "
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"`combine_documents_chain_path` must be present."
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)
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raise ValueError(msg)
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if "collapse_documents_chain" in config:
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collapse_document_chain_config = config.pop("collapse_documents_chain")
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if collapse_document_chain_config is None:
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collapse_documents_chain = None
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else:
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collapse_documents_chain = load_chain_from_config(
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collapse_document_chain_config,
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**kwargs,
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)
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elif "collapse_documents_chain_path" in config:
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collapse_documents_chain = load_chain(
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config.pop("collapse_documents_chain_path"),
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**kwargs,
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)
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elif "collapse_document_chain" in config:
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collapse_document_chain_config = config.pop("collapse_document_chain")
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if collapse_document_chain_config is None:
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collapse_documents_chain = None
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else:
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collapse_documents_chain = load_chain_from_config(
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collapse_document_chain_config,
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**kwargs,
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)
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elif "collapse_document_chain_path" in config:
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collapse_documents_chain = load_chain(
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config.pop("collapse_document_chain_path"),
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**kwargs,
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)
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return ReduceDocumentsChain(
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combine_documents_chain=combine_documents_chain,
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collapse_documents_chain=collapse_documents_chain,
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**config,
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)
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def _load_llm_bash_chain(config: dict, **kwargs: Any) -> Any:
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"""Load LLM Bash chain from config dict."""
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msg = (
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"LLMBash Chain is not available through LangChain anymore. "
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"The relevant code can be found in langchain_experimental, "
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"but it is not appropriate for production usage due to security "
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"concerns. Please refer to langchain-experimental repository for more details."
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)
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raise NotImplementedError(msg)
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def _load_llm_checker_chain(config: dict, **kwargs: Any) -> LLMCheckerChain:
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if "llm" in config:
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llm_config = config.pop("llm")
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llm = load_llm_from_config(llm_config, **kwargs)
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"), **kwargs)
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else:
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msg = "One of `llm` or `llm_path` must be present."
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raise ValueError(msg)
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if "create_draft_answer_prompt" in config:
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create_draft_answer_prompt_config = config.pop("create_draft_answer_prompt")
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create_draft_answer_prompt = load_prompt_from_config(
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create_draft_answer_prompt_config,
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)
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elif "create_draft_answer_prompt_path" in config:
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create_draft_answer_prompt = load_prompt(
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config.pop("create_draft_answer_prompt_path"),
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)
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if "list_assertions_prompt" in config:
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list_assertions_prompt_config = config.pop("list_assertions_prompt")
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list_assertions_prompt = load_prompt_from_config(list_assertions_prompt_config)
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elif "list_assertions_prompt_path" in config:
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list_assertions_prompt = load_prompt(config.pop("list_assertions_prompt_path"))
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if "check_assertions_prompt" in config:
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check_assertions_prompt_config = config.pop("check_assertions_prompt")
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check_assertions_prompt = load_prompt_from_config(
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check_assertions_prompt_config,
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)
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elif "check_assertions_prompt_path" in config:
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check_assertions_prompt = load_prompt(
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config.pop("check_assertions_prompt_path"),
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)
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if "revised_answer_prompt" in config:
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revised_answer_prompt_config = config.pop("revised_answer_prompt")
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revised_answer_prompt = load_prompt_from_config(revised_answer_prompt_config)
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elif "revised_answer_prompt_path" in config:
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revised_answer_prompt = load_prompt(config.pop("revised_answer_prompt_path"))
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return LLMCheckerChain(
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llm=llm,
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create_draft_answer_prompt=create_draft_answer_prompt,
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list_assertions_prompt=list_assertions_prompt,
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check_assertions_prompt=check_assertions_prompt,
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revised_answer_prompt=revised_answer_prompt,
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**config,
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)
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def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
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llm_chain = None
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if "llm_chain" in config:
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llm_chain_config = config.pop("llm_chain")
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llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
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# llm attribute is deprecated in favor of llm_chain, here to support old configs
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elif "llm" in config:
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llm_config = config.pop("llm")
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llm = load_llm_from_config(llm_config, **kwargs)
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# llm_path attribute is deprecated in favor of llm_chain_path,
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# its to support old configs
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"), **kwargs)
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else:
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msg = "One of `llm_chain` or `llm_chain_path` must be present."
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raise ValueError(msg)
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if "prompt" in config:
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prompt_config = config.pop("prompt")
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prompt = load_prompt_from_config(prompt_config)
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elif "prompt_path" in config:
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prompt = load_prompt(config.pop("prompt_path"))
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if llm_chain:
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return LLMMathChain(llm_chain=llm_chain, prompt=prompt, **config)
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return LLMMathChain(llm=llm, prompt=prompt, **config)
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def _load_map_rerank_documents_chain(
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config: dict,
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**kwargs: Any,
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) -> MapRerankDocumentsChain:
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if "llm_chain" in config:
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llm_chain_config = config.pop("llm_chain")
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llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
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else:
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msg = "One of `llm_chain` or `llm_chain_path` must be present."
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raise ValueError(msg)
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return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
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def _load_pal_chain(config: dict, **kwargs: Any) -> Any:
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msg = (
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"PALChain is not available through LangChain anymore. "
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"The relevant code can be found in langchain_experimental, "
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"but it is not appropriate for production usage due to security "
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"concerns. Please refer to langchain-experimental repository for more details."
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)
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raise NotImplementedError(msg)
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def _load_refine_documents_chain(config: dict, **kwargs: Any) -> RefineDocumentsChain:
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if "initial_llm_chain" in config:
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initial_llm_chain_config = config.pop("initial_llm_chain")
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initial_llm_chain = load_chain_from_config(initial_llm_chain_config, **kwargs)
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elif "initial_llm_chain_path" in config:
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initial_llm_chain = load_chain(config.pop("initial_llm_chain_path"), **kwargs)
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else:
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msg = "One of `initial_llm_chain` or `initial_llm_chain_path` must be present."
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raise ValueError(msg)
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if "refine_llm_chain" in config:
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refine_llm_chain_config = config.pop("refine_llm_chain")
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refine_llm_chain = load_chain_from_config(refine_llm_chain_config, **kwargs)
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elif "refine_llm_chain_path" in config:
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refine_llm_chain = load_chain(config.pop("refine_llm_chain_path"), **kwargs)
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else:
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msg = "One of `refine_llm_chain` or `refine_llm_chain_path` must be present."
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raise ValueError(msg)
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if "document_prompt" in config:
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prompt_config = config.pop("document_prompt")
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document_prompt = load_prompt_from_config(prompt_config)
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elif "document_prompt_path" in config:
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document_prompt = load_prompt(config.pop("document_prompt_path"))
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return RefineDocumentsChain(
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initial_llm_chain=initial_llm_chain,
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refine_llm_chain=refine_llm_chain,
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document_prompt=document_prompt,
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**config,
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)
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|
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def _load_qa_with_sources_chain(config: dict, **kwargs: Any) -> QAWithSourcesChain:
|
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if "combine_documents_chain" in config:
|
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combine_documents_chain_config = config.pop("combine_documents_chain")
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combine_documents_chain = load_chain_from_config(
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combine_documents_chain_config,
|
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**kwargs,
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)
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elif "combine_documents_chain_path" in config:
|
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combine_documents_chain = load_chain(
|
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config.pop("combine_documents_chain_path"),
|
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**kwargs,
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)
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else:
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msg = (
|
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"One of `combine_documents_chain` or "
|
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"`combine_documents_chain_path` must be present."
|
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)
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raise ValueError(msg)
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return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
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|
|
|
|
|
def _load_sql_database_chain(config: dict, **kwargs: Any) -> Any:
|
|
"""Load SQL Database chain from config dict."""
|
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msg = (
|
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"SQLDatabaseChain is not available through LangChain anymore. "
|
|
"The relevant code can be found in langchain_experimental, "
|
|
"but it is not appropriate for production usage due to security "
|
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"concerns. Please refer to langchain-experimental repository for more details, "
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"or refer to this tutorial for best practices: "
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"https://python.langchain.com/docs/tutorials/sql_qa/"
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)
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raise NotImplementedError(msg)
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|
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def _load_vector_db_qa_with_sources_chain(
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config: dict,
|
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**kwargs: Any,
|
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) -> VectorDBQAWithSourcesChain:
|
|
if "vectorstore" in kwargs:
|
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vectorstore = kwargs.pop("vectorstore")
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else:
|
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msg = "`vectorstore` must be present."
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raise ValueError(msg)
|
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if "combine_documents_chain" in config:
|
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combine_documents_chain_config = config.pop("combine_documents_chain")
|
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combine_documents_chain = load_chain_from_config(
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combine_documents_chain_config,
|
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**kwargs,
|
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)
|
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elif "combine_documents_chain_path" in config:
|
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combine_documents_chain = load_chain(
|
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config.pop("combine_documents_chain_path"),
|
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**kwargs,
|
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)
|
|
else:
|
|
msg = (
|
|
"One of `combine_documents_chain` or "
|
|
"`combine_documents_chain_path` must be present."
|
|
)
|
|
raise ValueError(msg)
|
|
return VectorDBQAWithSourcesChain(
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combine_documents_chain=combine_documents_chain,
|
|
vectorstore=vectorstore,
|
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**config,
|
|
)
|
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|
|
|
|
def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
|
|
if "retriever" in kwargs:
|
|
retriever = kwargs.pop("retriever")
|
|
else:
|
|
msg = "`retriever` must be present."
|
|
raise ValueError(msg)
|
|
if "combine_documents_chain" in config:
|
|
combine_documents_chain_config = config.pop("combine_documents_chain")
|
|
combine_documents_chain = load_chain_from_config(
|
|
combine_documents_chain_config,
|
|
**kwargs,
|
|
)
|
|
elif "combine_documents_chain_path" in config:
|
|
combine_documents_chain = load_chain(
|
|
config.pop("combine_documents_chain_path"),
|
|
**kwargs,
|
|
)
|
|
else:
|
|
msg = (
|
|
"One of `combine_documents_chain` or "
|
|
"`combine_documents_chain_path` must be present."
|
|
)
|
|
raise ValueError(msg)
|
|
return RetrievalQA(
|
|
combine_documents_chain=combine_documents_chain,
|
|
retriever=retriever,
|
|
**config,
|
|
)
|
|
|
|
|
|
def _load_retrieval_qa_with_sources_chain(
|
|
config: dict,
|
|
**kwargs: Any,
|
|
) -> RetrievalQAWithSourcesChain:
|
|
if "retriever" in kwargs:
|
|
retriever = kwargs.pop("retriever")
|
|
else:
|
|
msg = "`retriever` must be present."
|
|
raise ValueError(msg)
|
|
if "combine_documents_chain" in config:
|
|
combine_documents_chain_config = config.pop("combine_documents_chain")
|
|
combine_documents_chain = load_chain_from_config(
|
|
combine_documents_chain_config,
|
|
**kwargs,
|
|
)
|
|
elif "combine_documents_chain_path" in config:
|
|
combine_documents_chain = load_chain(
|
|
config.pop("combine_documents_chain_path"),
|
|
**kwargs,
|
|
)
|
|
else:
|
|
msg = (
|
|
"One of `combine_documents_chain` or "
|
|
"`combine_documents_chain_path` must be present."
|
|
)
|
|
raise ValueError(msg)
|
|
return RetrievalQAWithSourcesChain(
|
|
combine_documents_chain=combine_documents_chain,
|
|
retriever=retriever,
|
|
**config,
|
|
)
|
|
|
|
|
|
def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
|
|
if "vectorstore" in kwargs:
|
|
vectorstore = kwargs.pop("vectorstore")
|
|
else:
|
|
msg = "`vectorstore` must be present."
|
|
raise ValueError(msg)
|
|
if "combine_documents_chain" in config:
|
|
combine_documents_chain_config = config.pop("combine_documents_chain")
|
|
combine_documents_chain = load_chain_from_config(
|
|
combine_documents_chain_config,
|
|
**kwargs,
|
|
)
|
|
elif "combine_documents_chain_path" in config:
|
|
combine_documents_chain = load_chain(
|
|
config.pop("combine_documents_chain_path"),
|
|
**kwargs,
|
|
)
|
|
else:
|
|
msg = (
|
|
"One of `combine_documents_chain` or "
|
|
"`combine_documents_chain_path` must be present."
|
|
)
|
|
raise ValueError(msg)
|
|
return VectorDBQA(
|
|
combine_documents_chain=combine_documents_chain,
|
|
vectorstore=vectorstore,
|
|
**config,
|
|
)
|
|
|
|
|
|
def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
|
|
if "graph" in kwargs:
|
|
graph = kwargs.pop("graph")
|
|
else:
|
|
msg = "`graph` must be present."
|
|
raise ValueError(msg)
|
|
if "cypher_generation_chain" in config:
|
|
cypher_generation_chain_config = config.pop("cypher_generation_chain")
|
|
cypher_generation_chain = load_chain_from_config(
|
|
cypher_generation_chain_config,
|
|
**kwargs,
|
|
)
|
|
else:
|
|
msg = "`cypher_generation_chain` must be present."
|
|
raise ValueError(msg)
|
|
if "qa_chain" in config:
|
|
qa_chain_config = config.pop("qa_chain")
|
|
qa_chain = load_chain_from_config(qa_chain_config, **kwargs)
|
|
else:
|
|
msg = "`qa_chain` must be present."
|
|
raise ValueError(msg)
|
|
|
|
try:
|
|
from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
|
|
except ImportError as e:
|
|
msg = (
|
|
"To use this GraphCypherQAChain functionality you must install the "
|
|
"langchain_community package. "
|
|
"You can install it with `pip install langchain_community`"
|
|
)
|
|
raise ImportError(msg) from e
|
|
return GraphCypherQAChain(
|
|
graph=graph,
|
|
cypher_generation_chain=cypher_generation_chain,
|
|
qa_chain=qa_chain,
|
|
**config,
|
|
)
|
|
|
|
|
|
def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
|
|
if "api_request_chain" in config:
|
|
api_request_chain_config = config.pop("api_request_chain")
|
|
api_request_chain = load_chain_from_config(api_request_chain_config, **kwargs)
|
|
elif "api_request_chain_path" in config:
|
|
api_request_chain = load_chain(config.pop("api_request_chain_path"))
|
|
else:
|
|
msg = "One of `api_request_chain` or `api_request_chain_path` must be present."
|
|
raise ValueError(msg)
|
|
if "api_answer_chain" in config:
|
|
api_answer_chain_config = config.pop("api_answer_chain")
|
|
api_answer_chain = load_chain_from_config(api_answer_chain_config, **kwargs)
|
|
elif "api_answer_chain_path" in config:
|
|
api_answer_chain = load_chain(config.pop("api_answer_chain_path"), **kwargs)
|
|
else:
|
|
msg = "One of `api_answer_chain` or `api_answer_chain_path` must be present."
|
|
raise ValueError(msg)
|
|
if "requests_wrapper" in kwargs:
|
|
requests_wrapper = kwargs.pop("requests_wrapper")
|
|
else:
|
|
msg = "`requests_wrapper` must be present."
|
|
raise ValueError(msg)
|
|
return APIChain(
|
|
api_request_chain=api_request_chain,
|
|
api_answer_chain=api_answer_chain,
|
|
requests_wrapper=requests_wrapper,
|
|
**config,
|
|
)
|
|
|
|
|
|
def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
|
|
try:
|
|
from langchain_classic.chains.llm_requests import LLMRequestsChain
|
|
except ImportError as e:
|
|
msg = (
|
|
"To use this LLMRequestsChain functionality you must install the "
|
|
"langchain package. "
|
|
"You can install it with `pip install langchain`"
|
|
)
|
|
raise ImportError(msg) from e
|
|
|
|
if "llm_chain" in config:
|
|
llm_chain_config = config.pop("llm_chain")
|
|
llm_chain = load_chain_from_config(llm_chain_config, **kwargs)
|
|
elif "llm_chain_path" in config:
|
|
llm_chain = load_chain(config.pop("llm_chain_path"), **kwargs)
|
|
else:
|
|
msg = "One of `llm_chain` or `llm_chain_path` must be present."
|
|
raise ValueError(msg)
|
|
if "requests_wrapper" in kwargs:
|
|
requests_wrapper = kwargs.pop("requests_wrapper")
|
|
return LLMRequestsChain(
|
|
llm_chain=llm_chain,
|
|
requests_wrapper=requests_wrapper,
|
|
**config,
|
|
)
|
|
return LLMRequestsChain(llm_chain=llm_chain, **config)
|
|
|
|
|
|
type_to_loader_dict = {
|
|
"api_chain": _load_api_chain,
|
|
"hyde_chain": _load_hyde_chain,
|
|
"llm_chain": _load_llm_chain,
|
|
"llm_bash_chain": _load_llm_bash_chain,
|
|
"llm_checker_chain": _load_llm_checker_chain,
|
|
"llm_math_chain": _load_llm_math_chain,
|
|
"llm_requests_chain": _load_llm_requests_chain,
|
|
"pal_chain": _load_pal_chain,
|
|
"qa_with_sources_chain": _load_qa_with_sources_chain,
|
|
"stuff_documents_chain": _load_stuff_documents_chain,
|
|
"map_reduce_documents_chain": _load_map_reduce_documents_chain,
|
|
"reduce_documents_chain": _load_reduce_documents_chain,
|
|
"map_rerank_documents_chain": _load_map_rerank_documents_chain,
|
|
"refine_documents_chain": _load_refine_documents_chain,
|
|
"sql_database_chain": _load_sql_database_chain,
|
|
"vector_db_qa_with_sources_chain": _load_vector_db_qa_with_sources_chain,
|
|
"vector_db_qa": _load_vector_db_qa,
|
|
"retrieval_qa": _load_retrieval_qa,
|
|
"retrieval_qa_with_sources_chain": _load_retrieval_qa_with_sources_chain,
|
|
"graph_cypher_chain": _load_graph_cypher_chain,
|
|
}
|
|
|
|
|
|
@deprecated(
|
|
since="0.2.13",
|
|
message=(
|
|
"This function is deprecated and will be removed in langchain 1.0. "
|
|
"At that point chains must be imported from their respective modules."
|
|
),
|
|
removal="1.0",
|
|
)
|
|
def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
|
|
"""Load chain from Config Dict."""
|
|
if "_type" not in config:
|
|
msg = "Must specify a chain Type in config"
|
|
raise ValueError(msg)
|
|
config_type = config.pop("_type")
|
|
|
|
if config_type not in type_to_loader_dict:
|
|
msg = f"Loading {config_type} chain not supported"
|
|
raise ValueError(msg)
|
|
|
|
chain_loader = type_to_loader_dict[config_type]
|
|
return chain_loader(config, **kwargs)
|
|
|
|
|
|
@deprecated(
|
|
since="0.2.13",
|
|
message=(
|
|
"This function is deprecated and will be removed in langchain 1.0. "
|
|
"At that point chains must be imported from their respective modules."
|
|
),
|
|
removal="1.0",
|
|
)
|
|
def load_chain(path: str | Path, **kwargs: Any) -> Chain:
|
|
"""Unified method for loading a chain from LangChainHub or local fs."""
|
|
if isinstance(path, str) and path.startswith("lc://"):
|
|
msg = (
|
|
"Loading from the deprecated github-based Hub is no longer supported. "
|
|
"Please use the new LangChain Hub at https://smith.langchain.com/hub "
|
|
"instead."
|
|
)
|
|
raise RuntimeError(msg)
|
|
return _load_chain_from_file(path, **kwargs)
|
|
|
|
|
|
def _load_chain_from_file(file: str | Path, **kwargs: Any) -> Chain:
|
|
"""Load chain from file."""
|
|
# Convert file to Path object.
|
|
file_path = Path(file) if isinstance(file, str) else file
|
|
# Load from either json or yaml.
|
|
if file_path.suffix == ".json":
|
|
with file_path.open() as f:
|
|
config = json.load(f)
|
|
elif file_path.suffix.endswith((".yaml", ".yml")):
|
|
with file_path.open() as f:
|
|
config = yaml.safe_load(f)
|
|
else:
|
|
msg = "File type must be json or yaml"
|
|
raise ValueError(msg)
|
|
|
|
# Override default 'verbose' and 'memory' for the chain
|
|
if "verbose" in kwargs:
|
|
config["verbose"] = kwargs.pop("verbose")
|
|
if "memory" in kwargs:
|
|
config["memory"] = kwargs.pop("memory")
|
|
|
|
# Load the chain from the config now.
|
|
return load_chain_from_config(config, **kwargs)
|