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

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+"""Chain that carries on a conversation from a prompt plus history."""

venv/Lib/site-packages/langchain_classic/chains/conversation/base.py

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+"""Chain that carries on a conversation and calls an LLM."""
+
+from langchain_core._api import deprecated
+from langchain_core.prompts import BasePromptTemplate
+from pydantic import ConfigDict, Field, model_validator
+from typing_extensions import Self, override
+
+from langchain_classic.base_memory import BaseMemory
+from langchain_classic.chains.conversation.prompt import PROMPT
+from langchain_classic.chains.llm import LLMChain
+from langchain_classic.memory.buffer import ConversationBufferMemory
+
+
+@deprecated(
+    since="0.2.7",
+    alternative="langchain_core.runnables.history.RunnableWithMessageHistory",
+    removal="1.0",
+)
+class ConversationChain(LLMChain):
+    """Chain to have a conversation and load context from memory.
+
+    This class is deprecated in favor of `RunnableWithMessageHistory`. Please refer
+    to this tutorial for more detail: https://python.langchain.com/docs/tutorials/chatbot/
+
+    `RunnableWithMessageHistory` offers several benefits, including:
+
+    - Stream, batch, and async support;
+    - More flexible memory handling, including the ability to manage memory
+        outside the chain;
+    - Support for multiple threads.
+
+    Below is a minimal implementation, analogous to using `ConversationChain` with
+    the default `ConversationBufferMemory`:
+
+        ```python
+        from langchain_core.chat_history import InMemoryChatMessageHistory
+        from langchain_core.runnables.history import RunnableWithMessageHistory
+        from langchain_openai import ChatOpenAI
+
+
+        store = {}  # memory is maintained outside the chain
+
+
+        def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
+            if session_id not in store:
+                store[session_id] = InMemoryChatMessageHistory()
+            return store[session_id]
+
+
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+        chain = RunnableWithMessageHistory(model, get_session_history)
+        chain.invoke(
+            "Hi I'm Bob.",
+            config={"configurable": {"session_id": "1"}},
+        )  # session_id determines thread
+        ```
+
+    Memory objects can also be incorporated into the `get_session_history` callable:
+
+        ```python
+        from langchain_classic.memory import ConversationBufferWindowMemory
+        from langchain_core.chat_history import InMemoryChatMessageHistory
+        from langchain_core.runnables.history import RunnableWithMessageHistory
+        from langchain_openai import ChatOpenAI
+
+
+        store = {}  # memory is maintained outside the chain
+
+
+        def get_session_history(session_id: str) -> InMemoryChatMessageHistory:
+            if session_id not in store:
+                store[session_id] = InMemoryChatMessageHistory()
+                return store[session_id]
+
+            memory = ConversationBufferWindowMemory(
+                chat_memory=store[session_id],
+                k=3,
+                return_messages=True,
+            )
+            assert len(memory.memory_variables) == 1
+            key = memory.memory_variables[0]
+            messages = memory.load_memory_variables({})[key]
+            store[session_id] = InMemoryChatMessageHistory(messages=messages)
+            return store[session_id]
+
+
+        model = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+        chain = RunnableWithMessageHistory(model, get_session_history)
+        chain.invoke(
+            "Hi I'm Bob.",
+            config={"configurable": {"session_id": "1"}},
+        )  # session_id determines thread
+        ```
+
+    Example:
+        ```python
+        from langchain_classic.chains import ConversationChain
+        from langchain_openai import OpenAI
+
+        conversation = ConversationChain(llm=OpenAI())
+        ```
+    """
+
+    memory: BaseMemory = Field(default_factory=ConversationBufferMemory)
+    """Default memory store."""
+    prompt: BasePromptTemplate = PROMPT
+    """Default conversation prompt to use."""
+
+    input_key: str = "input"
+    output_key: str = "response"
+
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+    )
+
+    @classmethod
+    @override
+    def is_lc_serializable(cls) -> bool:
+        return False
+
+    @property
+    def input_keys(self) -> list[str]:
+        """Use this since so some prompt vars come from history."""
+        return [self.input_key]
+
+    @model_validator(mode="after")
+    def validate_prompt_input_variables(self) -> Self:
+        """Validate that prompt input variables are consistent."""
+        memory_keys = self.memory.memory_variables
+        input_key = self.input_key
+        if input_key in memory_keys:
+            msg = (
+                f"The input key {input_key} was also found in the memory keys "
+                f"({memory_keys}) - please provide keys that don't overlap."
+            )
+            raise ValueError(msg)
+        prompt_variables = self.prompt.input_variables
+        expected_keys = [*memory_keys, input_key]
+        if set(expected_keys) != set(prompt_variables):
+            msg = (
+                "Got unexpected prompt input variables. The prompt expects "
+                f"{prompt_variables}, but got {memory_keys} as inputs from "
+                f"memory, and {input_key} as the normal input key."
+            )
+            raise ValueError(msg)
+        return self

venv/Lib/site-packages/langchain_classic/chains/conversation/memory.py

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+"""Memory modules for conversation prompts."""
+
+from typing import TYPE_CHECKING, Any
+
+from langchain_classic._api import create_importer
+from langchain_classic.memory.buffer import (
+    ConversationBufferMemory,
+    ConversationStringBufferMemory,
+)
+from langchain_classic.memory.buffer_window import ConversationBufferWindowMemory
+from langchain_classic.memory.combined import CombinedMemory
+from langchain_classic.memory.entity import ConversationEntityMemory
+from langchain_classic.memory.summary import ConversationSummaryMemory
+from langchain_classic.memory.summary_buffer import ConversationSummaryBufferMemory
+
+if TYPE_CHECKING:
+    from langchain_community.memory.kg import ConversationKGMemory
+
+# Create a way to dynamically look up deprecated imports.
+# Used to consolidate logic for raising deprecation warnings and
+# handling optional imports.
+DEPRECATED_LOOKUP = {
+    "ConversationKGMemory": "langchain_community.memory.kg",
+}
+
+_importer = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
+
+
+def __getattr__(name: str) -> Any:
+    """Look up attributes dynamically."""
+    return _importer(name)
+
+
+# This is only for backwards compatibility.
+
+__all__ = [
+    "CombinedMemory",
+    "ConversationBufferMemory",
+    "ConversationBufferWindowMemory",
+    "ConversationEntityMemory",
+    "ConversationKGMemory",
+    "ConversationStringBufferMemory",
+    "ConversationSummaryBufferMemory",
+    "ConversationSummaryMemory",
+]

venv/Lib/site-packages/langchain_classic/chains/conversation/prompt.py

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+from langchain_core.prompts.prompt import PromptTemplate
+
+from langchain_classic.memory.prompt import (
+    ENTITY_EXTRACTION_PROMPT,
+    ENTITY_MEMORY_CONVERSATION_TEMPLATE,
+    ENTITY_SUMMARIZATION_PROMPT,
+    KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT,
+    SUMMARY_PROMPT,
+)
+
+DEFAULT_TEMPLATE = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
+
+Current conversation:
+{history}
+Human: {input}
+AI:"""  # noqa: E501
+PROMPT = PromptTemplate(input_variables=["history", "input"], template=DEFAULT_TEMPLATE)
+
+# Only for backwards compatibility
+
+__all__ = [
+    "ENTITY_EXTRACTION_PROMPT",
+    "ENTITY_MEMORY_CONVERSATION_TEMPLATE",
+    "ENTITY_SUMMARIZATION_PROMPT",
+    "KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT",
+    "PROMPT",
+    "SUMMARY_PROMPT",
+]