Harrison/v0050 (#452)

docs/examples/memory.rst

@@ -3,7 +3,9 @@ Memory
 
 The examples here all highlight how to use memory in different ways.
 
-`Adding Memory <memory/adding_memory.ipynb>`_: How to add a memory component to any chain.
+`Adding Memory <memory/adding_memory.ipynb>`_: How to add a memory component to any single input chain.
+
+`Adding Memory to Multi-Input Chain <memory/adding_memory_chain_multiple_inputs.ipynb>`_: How to add a memory component to any multiple input chain.
 
 `Conversational Memory Types <memory/conversational_memory.ipynb>`_: An overview of the different types of conversation memory you can load and use with a conversation-like chain.
 

docs/examples/memory/conversational_memory.ipynb

@@ -312,7 +312,7 @@
    "id": "6eecf9d9",
    "metadata": {},
    "source": [
-    "# ConversationBufferWindowMemory\n",
+    "### ConversationBufferWindowMemory\n",
     "\n",
     "`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large\n",
     "\n",
@@ -504,7 +504,7 @@
    "id": "a6d2569f",
    "metadata": {},
    "source": [
-    "# ConversationSummaryBufferMemory\n",
+    "### ConversationSummaryBufferMemory\n",
     "\n",
     "`ConversationSummaryBufferMemory` combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. Unlike the previous implementation though, it uses token length rather than number of interactions to determine when to flush interactions.\n",
     "\n",

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.0.49"
+version = "0.0.50"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"