add documentation on how to load different chain types (#595)

docs/modules/chains/combine_docs_examples/vector_db_qa.ipynb

@@ -46,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
+    "qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", vectorstore=docsearch)"
    ]
   },
   {
@@ -58,7 +58,7 @@
     {
      "data": {
       "text/plain": [
-       "' The president said that Ketanji Brown Jackson is one of the nation’s top legal minds and that she will continue Justice Breyer’s legacy of excellence.'"
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
       ]
      },
      "execution_count": 4,
@@ -71,6 +71,91 @@
     "qa.run(query)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c28f1f64",
+   "metadata": {},
+   "source": [
+    "## Chain Type\n",
+    "You can easily specify different chain types to load and use in the VectorDBQA chain. For a more detailed walkthrough of these types, please see [this notebook](question_answering.ipynb).\n",
+    "\n",
+    "There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `map_reduce`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "22d2417d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "43204ad1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "qa.run(query)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60368f38",
+   "metadata": {},
+   "source": [
+    "The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](question_answering.ipynb)) and then pass that directly to the the VectorDBQA chain with the `combine_documents_chain` parameter. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7b403f0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
+    "qa = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9e04a9ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "qa.run(query)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "0b8c37f7",
@@ -87,7 +172,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch, return_source_documents=True)"
+    "qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", return_source_documents=True)"
    ]
   },
   {

docs/modules/chains/combine_docs_examples/vector_db_qa_with_sources.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "17d1306e",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "0e745d99",
    "metadata": {},
    "outputs": [],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "f42d79dc",
    "metadata": {},
    "outputs": [],
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "8aa571ae",
    "metadata": {},
    "outputs": [],
@@ -73,26 +73,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "aa859d4c",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain import OpenAI\n",
     "\n",
-    "chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
+    "chain = VectorDBQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", vectorstore=docsearch)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "8ba36fa7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'answer': ' The president thanked Justice Breyer for his service.',\n",
+       "{'answer': ' The president thanked Justice Breyer for his service.\\n',\n",
+       " 'sources': '30-pl'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "718ecbda",
+   "metadata": {},
+   "source": [
+    "## Chain Type\n",
+    "You can easily specify different chain types to load and use in the VectorDBQAWithSourcesChain chain. For a more detailed walkthrough of these types, please see [this notebook](qa_with_sources.ipynb).\n",
+    "\n",
+    "There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `map_reduce`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8b35b30a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = VectorDBQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"map_reduce\", vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "58bd424f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': ' The president honored Justice Stephen Breyer for his service.\\n',\n",
        " 'sources': '30-pl'}"
       ]
      },
@@ -104,11 +147,53 @@
    "source": [
     "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21e14eed",
+   "metadata": {},
+   "source": [
+    "The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](qa_with_sources.ipynb)) and then pass that directly to the the VectorDBQA chain with the `combine_documents_chain` parameter. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "af35f0c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
+    "qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
+    "qa = VectorDBQAWithSourcesChain(combine_document_chain=qa_chain, vectorstore=docsearch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c91fdc8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': ' The president honored Justice Stephen Breyer for his service.\\n',\n",
+       " 'sources': '30-pl'}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.0 64-bit ('llm-env')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -122,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.9"
   },
   "vscode": {
    "interpreter": {

langchain/chains/qa_with_sources/__init__.py

@@ -1,169 +1,4 @@
 """Load question answering with sources chains."""
-from typing import Any, Mapping, Optional, Protocol
+from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
 
-from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+__all__ = ["load_qa_with_sources_chain"]
-from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
-from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
-from langchain.chains.combine_documents.refine import RefineDocumentsChain
-from langchain.chains.combine_documents.stuff import StuffDocumentsChain
-from langchain.chains.llm import LLMChain
-from langchain.chains.qa_with_sources import (
-    map_reduce_prompt,
-    refine_prompts,
-    stuff_prompt,
-)
-from langchain.chains.question_answering import map_rerank_prompt
-from langchain.llms.base import BaseLLM
-from langchain.prompts.base import BasePromptTemplate
-
-
-class LoadingCallable(Protocol):
-    """Interface for loading the combine documents chain."""
-
-    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
-        """Callable to load the combine documents chain."""
-
-
-def _load_map_rerank_chain(
-    llm: BaseLLM,
-    prompt: BasePromptTemplate = map_rerank_prompt.PROMPT,
-    verbose: bool = False,
-    document_variable_name: str = "context",
-    rank_key: str = "score",
-    answer_key: str = "answer",
-    **kwargs: Any,
-) -> MapRerankDocumentsChain:
-    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
-    return MapRerankDocumentsChain(
-        llm_chain=llm_chain,
-        rank_key=rank_key,
-        answer_key=answer_key,
-        document_variable_name=document_variable_name,
-        **kwargs,
-    )
-
-
-def _load_stuff_chain(
-    llm: BaseLLM,
-    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
-    document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
-    document_variable_name: str = "summaries",
-    verbose: Optional[bool] = None,
-    **kwargs: Any,
-) -> StuffDocumentsChain:
-    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
-    return StuffDocumentsChain(
-        llm_chain=llm_chain,
-        document_variable_name=document_variable_name,
-        document_prompt=document_prompt,
-        verbose=verbose,
-        **kwargs,
-    )
-
-
-def _load_map_reduce_chain(
-    llm: BaseLLM,
-    question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
-    combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
-    document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
-    combine_document_variable_name: str = "summaries",
-    map_reduce_document_variable_name: str = "context",
-    collapse_prompt: Optional[BasePromptTemplate] = None,
-    reduce_llm: Optional[BaseLLM] = None,
-    collapse_llm: Optional[BaseLLM] = None,
-    verbose: Optional[bool] = None,
-    **kwargs: Any,
-) -> MapReduceDocumentsChain:
-    map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
-    _reduce_llm = reduce_llm or llm
-    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
-    combine_document_chain = StuffDocumentsChain(
-        llm_chain=reduce_chain,
-        document_variable_name=combine_document_variable_name,
-        document_prompt=document_prompt,
-        verbose=verbose,
-    )
-    if collapse_prompt is None:
-        collapse_chain = None
-        if collapse_llm is not None:
-            raise ValueError(
-                "collapse_llm provided, but collapse_prompt was not: please "
-                "provide one or stop providing collapse_llm."
-            )
-    else:
-        _collapse_llm = collapse_llm or llm
-        collapse_chain = StuffDocumentsChain(
-            llm_chain=LLMChain(
-                llm=_collapse_llm,
-                prompt=collapse_prompt,
-                verbose=verbose,
-            ),
-            document_variable_name=combine_document_variable_name,
-            document_prompt=document_prompt,
-        )
-    return MapReduceDocumentsChain(
-        llm_chain=map_chain,
-        combine_document_chain=combine_document_chain,
-        document_variable_name=map_reduce_document_variable_name,
-        collapse_document_chain=collapse_chain,
-        verbose=verbose,
-        **kwargs,
-    )
-
-
-def _load_refine_chain(
-    llm: BaseLLM,
-    question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
-    refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
-    document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
-    document_variable_name: str = "context_str",
-    initial_response_name: str = "existing_answer",
-    refine_llm: Optional[BaseLLM] = None,
-    verbose: Optional[bool] = 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,
-        document_prompt=document_prompt,
-        verbose=verbose,
-        **kwargs,
-    )
-
-
-def load_qa_with_sources_chain(
-    llm: BaseLLM,
-    chain_type: str = "stuff",
-    verbose: Optional[bool] = None,
-    **kwargs: Any,
-) -> BaseCombineDocumentsChain:
-    """Load question answering with sources 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.
-
-    Returns:
-        A chain to use for question answering with sources.
-    """
-    loader_mapping: Mapping[str, LoadingCallable] = {
-        "stuff": _load_stuff_chain,
-        "map_reduce": _load_map_reduce_chain,
-        "refine": _load_refine_chain,
-        "map_rerank": _load_map_rerank_chain,
-    }
-    if chain_type not in loader_mapping:
-        raise ValueError(
-            f"Got unsupported chain type: {chain_type}. "
-            f"Should be one of {loader_mapping.keys()}"
-        )
-    _func: LoadingCallable = loader_mapping[chain_type]
-    return _func(llm, verbose=verbose, **kwargs)

langchain/chains/qa_with_sources/base.py

@@ -12,6 +12,7 @@ from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
+from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
 from langchain.chains.qa_with_sources.map_reduce_prompt import (
     COMBINE_PROMPT,
     EXAMPLE_PROMPT,
@@ -25,7 +26,7 @@ from langchain.prompts.base import BasePromptTemplate
 class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
     """Question answering with sources over documents."""
 
-    combine_document_chain: BaseCombineDocumentsChain
+    combine_documents_chain: BaseCombineDocumentsChain
     """Chain to use to combine documents."""
     question_key: str = "question"  #: :meta private:
     input_docs_key: str = "docs"  #: :meta private:
@@ -55,10 +56,18 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
             document_variable_name="context",
         )
         return cls(
-            combine_document_chain=combine_document_chain,
+            combine_documents_chain=combine_document_chain,
             **kwargs,
         )
 
+    @classmethod
+    def from_chain_type(
+        cls, llm: BaseLLM, chain_type: str = "stuff", **kwargs: Any
+    ) -> BaseQAWithSourcesChain:
+        """Load chain from chain type."""
+        combine_document_chain = load_qa_with_sources_chain(llm, chain_type=chain_type)
+        return cls(combine_documents_chain=combine_document_chain, **kwargs)
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -81,11 +90,11 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
         """
         return [self.answer_key, self.sources_answer_key]
 
-    @root_validator()
+    @root_validator(pre=True)
-    def validate_combine_chain_can_be_constructed(cls, values: Dict) -> Dict:
+    def validate_naming(cls, values: Dict) -> Dict:
-        """Validate that the combine chain can be constructed."""
+        """Fix backwards compatability in naming."""
-        # Try to construct the combine documents chains.
+        if "combine_document_chain" in values:
-
+            values["combine_documents_chain"] = values.pop("combine_document_chain")
         return values
 
     @abstractmethod
@@ -94,7 +103,7 @@ class BaseQAWithSourcesChain(Chain, BaseModel, ABC):
 
     def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         docs = self._get_docs(inputs)
-        answer, _ = self.combine_document_chain.combine_docs(docs, **inputs)
+        answer, _ = self.combine_documents_chain.combine_docs(docs, **inputs)
         if "SOURCES: " in answer:
             answer, sources = answer.split("SOURCES: ")
         else:

langchain/chains/qa_with_sources/loading.py

@@ -0,0 +1,169 @@
+"""Load question answering with sources chains."""
+from typing import Any, Mapping, Optional, Protocol
+
+from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
+from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
+from langchain.chains.combine_documents.refine import RefineDocumentsChain
+from langchain.chains.combine_documents.stuff import StuffDocumentsChain
+from langchain.chains.llm import LLMChain
+from langchain.chains.qa_with_sources import (
+    map_reduce_prompt,
+    refine_prompts,
+    stuff_prompt,
+)
+from langchain.chains.question_answering import map_rerank_prompt
+from langchain.llms.base import BaseLLM
+from langchain.prompts.base import BasePromptTemplate
+
+
+class LoadingCallable(Protocol):
+    """Interface for loading the combine documents chain."""
+
+    def __call__(self, llm: BaseLLM, **kwargs: Any) -> BaseCombineDocumentsChain:
+        """Callable to load the combine documents chain."""
+
+
+def _load_map_rerank_chain(
+    llm: BaseLLM,
+    prompt: BasePromptTemplate = map_rerank_prompt.PROMPT,
+    verbose: bool = False,
+    document_variable_name: str = "context",
+    rank_key: str = "score",
+    answer_key: str = "answer",
+    **kwargs: Any,
+) -> MapRerankDocumentsChain:
+    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
+    return MapRerankDocumentsChain(
+        llm_chain=llm_chain,
+        rank_key=rank_key,
+        answer_key=answer_key,
+        document_variable_name=document_variable_name,
+        **kwargs,
+    )
+
+
+def _load_stuff_chain(
+    llm: BaseLLM,
+    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
+    document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
+    document_variable_name: str = "summaries",
+    verbose: Optional[bool] = None,
+    **kwargs: Any,
+) -> StuffDocumentsChain:
+    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
+    return StuffDocumentsChain(
+        llm_chain=llm_chain,
+        document_variable_name=document_variable_name,
+        document_prompt=document_prompt,
+        verbose=verbose,
+        **kwargs,
+    )
+
+
+def _load_map_reduce_chain(
+    llm: BaseLLM,
+    question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
+    combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
+    document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
+    combine_document_variable_name: str = "summaries",
+    map_reduce_document_variable_name: str = "context",
+    collapse_prompt: Optional[BasePromptTemplate] = None,
+    reduce_llm: Optional[BaseLLM] = None,
+    collapse_llm: Optional[BaseLLM] = None,
+    verbose: Optional[bool] = None,
+    **kwargs: Any,
+) -> MapReduceDocumentsChain:
+    map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
+    _reduce_llm = reduce_llm or llm
+    reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
+    combine_document_chain = StuffDocumentsChain(
+        llm_chain=reduce_chain,
+        document_variable_name=combine_document_variable_name,
+        document_prompt=document_prompt,
+        verbose=verbose,
+    )
+    if collapse_prompt is None:
+        collapse_chain = None
+        if collapse_llm is not None:
+            raise ValueError(
+                "collapse_llm provided, but collapse_prompt was not: please "
+                "provide one or stop providing collapse_llm."
+            )
+    else:
+        _collapse_llm = collapse_llm or llm
+        collapse_chain = StuffDocumentsChain(
+            llm_chain=LLMChain(
+                llm=_collapse_llm,
+                prompt=collapse_prompt,
+                verbose=verbose,
+            ),
+            document_variable_name=combine_document_variable_name,
+            document_prompt=document_prompt,
+        )
+    return MapReduceDocumentsChain(
+        llm_chain=map_chain,
+        combine_document_chain=combine_document_chain,
+        document_variable_name=map_reduce_document_variable_name,
+        collapse_document_chain=collapse_chain,
+        verbose=verbose,
+        **kwargs,
+    )
+
+
+def _load_refine_chain(
+    llm: BaseLLM,
+    question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
+    refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
+    document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
+    document_variable_name: str = "context_str",
+    initial_response_name: str = "existing_answer",
+    refine_llm: Optional[BaseLLM] = None,
+    verbose: Optional[bool] = 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,
+        document_prompt=document_prompt,
+        verbose=verbose,
+        **kwargs,
+    )
+
+
+def load_qa_with_sources_chain(
+    llm: BaseLLM,
+    chain_type: str = "stuff",
+    verbose: Optional[bool] = None,
+    **kwargs: Any,
+) -> BaseCombineDocumentsChain:
+    """Load question answering with sources 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.
+
+    Returns:
+        A chain to use for question answering with sources.
+    """
+    loader_mapping: Mapping[str, LoadingCallable] = {
+        "stuff": _load_stuff_chain,
+        "map_reduce": _load_map_reduce_chain,
+        "refine": _load_refine_chain,
+        "map_rerank": _load_map_rerank_chain,
+    }
+    if chain_type not in loader_mapping:
+        raise ValueError(
+            f"Got unsupported chain type: {chain_type}. "
+            f"Should be one of {loader_mapping.keys()}"
+        )
+    _func: LoadingCallable = loader_mapping[chain_type]
+    return _func(llm, verbose=verbose, **kwargs)

langchain/chains/vector_db_qa/base.py

@@ -9,6 +9,7 @@ from langchain.chains.base import Chain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
+from langchain.chains.question_answering import load_qa_chain
 from langchain.chains.vector_db_qa.prompt import PROMPT
 from langchain.llms.base import BaseLLM
 from langchain.prompts import PromptTemplate
@@ -104,19 +105,24 @@ class VectorDBQA(Chain, BaseModel):
 
         return cls(combine_documents_chain=combine_documents_chain, **kwargs)
 
-    def _call(
+    @classmethod
-        self,
+    def from_chain_type(
-        inputs: Dict[str, str],
+        cls, llm: BaseLLM, chain_type: str = "stuff", **kwargs: Any
-    ) -> Dict[str, Any]:
+    ) -> VectorDBQA:
+        """Load chain from chain type."""
+        combine_documents_chain = load_qa_chain(llm, chain_type=chain_type)
+        return cls(combine_documents_chain=combine_documents_chain, **kwargs)
+
+    def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         """Run similarity search and llm on input query.
 
-        If inputs contains 'return_source_documents' as 'True', returns
+        If chain has 'return_source_documents' as 'True', returns
         the retrieved documents as well under the key 'source_documents'.
 
         Example:
         .. code-block:: python
 
-        res = vectordbqa({'query': 'This is my query', 'return_source_documents': True})
+        res = vectordbqa({'query': 'This is my query'})
         answer, docs = res['result'], res['source_documents']
         """
         question = inputs[self.input_key]