mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-21 00:35:23 -04:00
Liang Zhang
5518f24ec3
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes #3983 Mimicing what we do for saving and loading VectorDBQA chain, I added the logic for RetrievalQA chain. Also added a unit test. I did not find how we test other chains for their saving and loading functionality, so I just added a file with one test case. Let me know if there are recommended ways to test it. #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: @dev2049 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
536 lines
22 KiB
Python
536 lines
22 KiB
Python
"""Functionality for loading chains."""
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import json
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from pathlib import Path
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from typing import Any, Union
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import yaml
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from langchain.chains.api.base import APIChain
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from langchain.chains.base import Chain
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from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
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from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
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from langchain.chains.combine_documents.refine import RefineDocumentsChain
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from langchain.chains.combine_documents.stuff import StuffDocumentsChain
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from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
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from langchain.chains.llm import LLMChain
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from langchain.chains.llm_bash.base import LLMBashChain
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from langchain.chains.llm_checker.base import LLMCheckerChain
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from langchain.chains.llm_math.base import LLMMathChain
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from langchain.chains.llm_requests import LLMRequestsChain
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from langchain.chains.pal.base import PALChain
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from langchain.chains.qa_with_sources.base import QAWithSourcesChain
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from langchain.chains.qa_with_sources.vector_db import VectorDBQAWithSourcesChain
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from langchain.chains.retrieval_qa.base import RetrievalQA, VectorDBQA
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from langchain.chains.sql_database.base import SQLDatabaseChain
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from langchain.llms.loading import load_llm, load_llm_from_config
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from langchain.prompts.loading import load_prompt, load_prompt_from_config
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from langchain.utilities.loading import try_load_from_hub
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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)
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"))
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else:
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raise ValueError("One of `llm` or `llm_path` must be present.")
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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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raise ValueError("One of `prompt` or `prompt_path` must be present.")
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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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
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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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raise ValueError("`embeddings` must be present.")
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return HypotheticalDocumentEmbedder(
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llm_chain=llm_chain, base_embeddings=embeddings, **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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
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if not isinstance(llm_chain, LLMChain):
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raise ValueError(f"Expected LLMChain, got {llm_chain}")
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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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raise ValueError(
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"One of `document_prompt` or `document_prompt_path` must be present."
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)
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return StuffDocumentsChain(
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llm_chain=llm_chain, document_prompt=document_prompt, **config
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)
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def _load_map_reduce_documents_chain(
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config: dict, **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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
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if not isinstance(llm_chain, LLMChain):
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raise ValueError(f"Expected LLMChain, got {llm_chain}")
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if "combine_document_chain" in config:
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combine_document_chain_config = config.pop("combine_document_chain")
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combine_document_chain = load_chain_from_config(combine_document_chain_config)
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elif "combine_document_chain_path" in config:
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combine_document_chain = load_chain(config.pop("combine_document_chain_path"))
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else:
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raise ValueError(
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"One of `combine_document_chain` or "
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"`combine_document_chain_path` must be present."
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)
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if "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_document_chain = None
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else:
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collapse_document_chain = load_chain_from_config(
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collapse_document_chain_config
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)
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elif "collapse_document_chain_path" in config:
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collapse_document_chain = load_chain(config.pop("collapse_document_chain_path"))
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return MapReduceDocumentsChain(
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llm_chain=llm_chain,
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combine_document_chain=combine_document_chain,
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collapse_document_chain=collapse_document_chain,
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**config,
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)
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def _load_llm_bash_chain(config: dict, **kwargs: Any) -> LLMBashChain:
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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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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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)
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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"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
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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 LLMBashChain(llm_chain=llm_chain, prompt=prompt, **config)
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else:
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return LLMBashChain(llm=llm, prompt=prompt, **config)
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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)
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"))
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else:
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raise ValueError("One of `llm` or `llm_path` must be present.")
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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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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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)
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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"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
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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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else:
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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, **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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_config` must be present.")
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return MapRerankDocumentsChain(llm_chain=llm_chain, **config)
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def _load_pal_chain(config: dict, **kwargs: Any) -> PALChain:
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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)
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elif "llm_chain_path" in config:
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llm_chain = load_chain(config.pop("llm_chain_path"))
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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)
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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"))
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else:
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raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
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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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raise ValueError("One of `prompt` or `prompt_path` must be present.")
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if llm_chain:
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return PALChain(llm_chain=llm_chain, prompt=prompt, **config)
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else:
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return PALChain(llm=llm, prompt=prompt, **config)
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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)
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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"))
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else:
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raise ValueError(
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"One of `initial_llm_chain` or `initial_llm_chain_config` must be present."
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)
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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)
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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"))
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else:
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raise ValueError(
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"One of `refine_llm_chain` or `refine_llm_chain_config` must be present."
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)
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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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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(combine_documents_chain_config)
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elif "combine_documents_chain_path" in config:
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combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
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else:
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raise ValueError(
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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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return QAWithSourcesChain(combine_documents_chain=combine_documents_chain, **config)
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def _load_sql_database_chain(config: dict, **kwargs: Any) -> SQLDatabaseChain:
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if "database" in kwargs:
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database = kwargs.pop("database")
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else:
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raise ValueError("`database` must be present.")
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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)
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elif "llm_path" in config:
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llm = load_llm(config.pop("llm_path"))
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else:
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raise ValueError("One of `llm` or `llm_path` must be present.")
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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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else:
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prompt = None
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return SQLDatabaseChain.from_llm(llm, database, prompt=prompt, **config)
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def _load_vector_db_qa_with_sources_chain(
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config: dict, **kwargs: Any
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) -> VectorDBQAWithSourcesChain:
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if "vectorstore" in kwargs:
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vectorstore = kwargs.pop("vectorstore")
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else:
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raise ValueError("`vectorstore` must be present.")
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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(combine_documents_chain_config)
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elif "combine_documents_chain_path" in config:
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combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
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else:
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raise ValueError(
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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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return VectorDBQAWithSourcesChain(
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combine_documents_chain=combine_documents_chain,
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vectorstore=vectorstore,
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**config,
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)
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def _load_retrieval_qa(config: dict, **kwargs: Any) -> RetrievalQA:
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if "retriever" in kwargs:
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retriever = kwargs.pop("retriever")
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else:
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raise ValueError("`retriever` must be present.")
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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(combine_documents_chain_config)
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elif "combine_documents_chain_path" in config:
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combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
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else:
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raise ValueError(
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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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return RetrievalQA(
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combine_documents_chain=combine_documents_chain,
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retriever=retriever,
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**config,
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)
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def _load_vector_db_qa(config: dict, **kwargs: Any) -> VectorDBQA:
|
|
if "vectorstore" in kwargs:
|
|
vectorstore = kwargs.pop("vectorstore")
|
|
else:
|
|
raise ValueError("`vectorstore` must be present.")
|
|
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)
|
|
elif "combine_documents_chain_path" in config:
|
|
combine_documents_chain = load_chain(config.pop("combine_documents_chain_path"))
|
|
else:
|
|
raise ValueError(
|
|
"One of `combine_documents_chain` or "
|
|
"`combine_documents_chain_path` must be present."
|
|
)
|
|
return VectorDBQA(
|
|
combine_documents_chain=combine_documents_chain,
|
|
vectorstore=vectorstore,
|
|
**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)
|
|
elif "api_request_chain_path" in config:
|
|
api_request_chain = load_chain(config.pop("api_request_chain_path"))
|
|
else:
|
|
raise ValueError(
|
|
"One of `api_request_chain` or `api_request_chain_path` must be present."
|
|
)
|
|
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)
|
|
elif "api_answer_chain_path" in config:
|
|
api_answer_chain = load_chain(config.pop("api_answer_chain_path"))
|
|
else:
|
|
raise ValueError(
|
|
"One of `api_answer_chain` or `api_answer_chain_path` must be present."
|
|
)
|
|
if "requests_wrapper" in kwargs:
|
|
requests_wrapper = kwargs.pop("requests_wrapper")
|
|
else:
|
|
raise ValueError("`requests_wrapper` must be present.")
|
|
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:
|
|
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"))
|
|
else:
|
|
raise ValueError("One of `llm_chain` or `llm_chain_path` must be present.")
|
|
if "requests_wrapper" in kwargs:
|
|
requests_wrapper = kwargs.pop("requests_wrapper")
|
|
return LLMRequestsChain(
|
|
llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config
|
|
)
|
|
else:
|
|
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,
|
|
"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,
|
|
}
|
|
|
|
|
|
def load_chain_from_config(config: dict, **kwargs: Any) -> Chain:
|
|
"""Load chain from Config Dict."""
|
|
if "_type" not in config:
|
|
raise ValueError("Must specify a chain Type in config")
|
|
config_type = config.pop("_type")
|
|
|
|
if config_type not in type_to_loader_dict:
|
|
raise ValueError(f"Loading {config_type} chain not supported")
|
|
|
|
chain_loader = type_to_loader_dict[config_type]
|
|
return chain_loader(config, **kwargs)
|
|
|
|
|
|
def load_chain(path: Union[str, Path], **kwargs: Any) -> Chain:
|
|
"""Unified method for loading a chain from LangChainHub or local fs."""
|
|
if hub_result := try_load_from_hub(
|
|
path, _load_chain_from_file, "chains", {"json", "yaml"}, **kwargs
|
|
):
|
|
return hub_result
|
|
else:
|
|
return _load_chain_from_file(path, **kwargs)
|
|
|
|
|
|
def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain:
|
|
"""Load chain from file."""
|
|
# Convert file to Path object.
|
|
if isinstance(file, str):
|
|
file_path = Path(file)
|
|
else:
|
|
file_path = file
|
|
# Load from either json or yaml.
|
|
if file_path.suffix == ".json":
|
|
with open(file_path) as f:
|
|
config = json.load(f)
|
|
elif file_path.suffix == ".yaml":
|
|
with open(file_path, "r") as f:
|
|
config = yaml.safe_load(f)
|
|
else:
|
|
raise ValueError("File type must be json or yaml")
|
|
|
|
# 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)
|