Patch Chat History Formatting (#3236)

While we work on solidifying the memory interfaces, handle common chat
history formats.

This may break linting on anyone who has been passing in
`get_chat_history` .

Somewhat handles #3077

Alternative to #3078 that updates the typing

langchain/chains/conversational_retrieval/base.py

@@ -15,16 +15,32 @@ from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts.base import BasePromptTemplate
-from langchain.schema import BaseLanguageModel, BaseRetriever, Document
+from langchain.schema import BaseLanguageModel, BaseMessage, BaseRetriever, Document
 from langchain.vectorstores.base import VectorStore
 
+# Depending on the memory type and configuration, the chat history format may differ.
+# This needs to be consolidated.
+CHAT_TURN_TYPE = Union[Tuple[str, str], BaseMessage]
 
-def _get_chat_history(chat_history: List[Tuple[str, str]]) -> str:
+
+_ROLE_MAP = {"human": "Human: ", "ai": "Assistant: "}
+
+
+def _get_chat_history(chat_history: List[CHAT_TURN_TYPE]) -> str:
     buffer = ""
-    for human_s, ai_s in chat_history:
-        human = "Human: " + human_s
-        ai = "Assistant: " + ai_s
-        buffer += "\n" + "\n".join([human, ai])
+    for dialogue_turn in chat_history:
+        if isinstance(dialogue_turn, BaseMessage):
+            role_prefix = _ROLE_MAP.get(dialogue_turn.type, f"{dialogue_turn.type}: ")
+            buffer += f"\n{role_prefix}{dialogue_turn.content}"
+        elif isinstance(dialogue_turn, tuple):
+            human = "Human: " + dialogue_turn[0]
+            ai = "Assistant: " + dialogue_turn[1]
+            buffer += "\n" + "\n".join([human, ai])
+        else:
+            raise ValueError(
+                f"Unsupported chat history format: {type(dialogue_turn)}."
+                f" Full chat history: {chat_history} "
+            )
     return buffer
 
 
@@ -35,7 +51,7 @@ class BaseConversationalRetrievalChain(Chain):
     question_generator: LLMChain
     output_key: str = "answer"
     return_source_documents: bool = False
-    get_chat_history: Optional[Callable[[Tuple[str, str]], str]] = None
+    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
     """Return the source documents."""
 
     class Config: