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Harrison/combine memories (#582)
Signed-off-by: Diwank Singh Tomer <diwank.singh@gmail.com> Co-authored-by: Diwank Singh Tomer <diwank.singh@gmail.com>
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Harrison Chase
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docs/modules/memory/examples/multiple_memory.ipynb
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167
docs/modules/memory/examples/multiple_memory.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d9fec22e",
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"metadata": {},
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"source": [
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"# Multiple Memory\n",
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"It is also possible to use multiple memory classes in the same chain. To combine multiple memory classes, we can initialize the `CombinedMemory` class, and then use that."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "7d7de430",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.chains import ConversationChain\n",
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"from langchain.chains.conversation.memory import ConversationBufferMemory, ConversationSummaryMemory, CombinedMemory\n",
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"\n",
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"conv_memory = ConversationBufferMemory(\n",
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" memory_key=\"chat_history_lines\",\n",
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" input_key=\"input\"\n",
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")\n",
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"\n",
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"summary_memory = ConversationSummaryMemory(llm=OpenAI(), input_key=\"input\")\n",
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"# Combined\n",
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"memory = CombinedMemory(memories=[conv_memory, summary_memory])\n",
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"_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.\n",
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"\n",
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"Summary of conversation:\n",
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"{history}\n",
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"Current conversation:\n",
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"{chat_history_lines}\n",
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"Human: {input}\n",
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"AI:\"\"\"\n",
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"PROMPT = PromptTemplate(\n",
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" input_variables=[\"history\", \"input\", \"chat_history_lines\"], template=_DEFAULT_TEMPLATE\n",
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")\n",
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"llm = OpenAI(temperature=0)\n",
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"conversation = ConversationChain(\n",
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" llm=llm, \n",
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" verbose=True, \n",
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" memory=memory,\n",
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" prompt=PROMPT\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "562bea63",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mThe 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.\n",
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"\n",
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"Summary of conversation:\n",
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"\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi!\n",
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"AI:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' Hi there! How can I help you?'"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conversation.run(\"Hi!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "2b793075",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mThe 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.\n",
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"\n",
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"Summary of conversation:\n",
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"\n",
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"The human greets the AI and the AI responds, asking how it can help.\n",
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"Current conversation:\n",
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"\n",
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"Human: Hi!\n",
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"AI: Hi there! How can I help you?\n",
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"Human: Can you tell me a joke?\n",
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"AI:\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"' Sure! What did the fish say when it hit the wall?\\nHuman: I don\\'t know.\\nAI: \"Dam!\"'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conversation.run(\"Can you tell me a joke?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c24a3b9d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -17,6 +17,8 @@ The examples here all highlight how to use memory in different ways.
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`Conversation Agent <./examples/conversational_agent.html>`_: Example of a conversation agent, which combines memory with agents and a conversation focused prompt.
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`Multiple Memory <./examples/multiple_memory.html>`_: How to use multiple types of memory in the same chain.
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.. toctree::
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:maxdepth: 1
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@ -19,6 +19,50 @@ def _get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -
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return prompt_input_keys[0]
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class CombinedMemory(Memory, BaseModel):
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"""Class for combining multiple memories' data together."""
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memories: List[Memory]
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"""For tracking all the memories that should be accessed."""
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@property
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def memory_variables(self) -> List[str]:
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"""All the memory variables that this instance provides."""
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"""Collected from the all the linked memories."""
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memory_variables = []
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for memory in self.memories:
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memory_variables.extend(memory.memory_variables)
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return memory_variables
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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"""Load all vars from sub-memories."""
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memory_data: Dict[str, Any] = {}
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# Collect vars from all sub-memories
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for memory in self.memories:
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data = memory.load_memory_variables(inputs)
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memory_data = {
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**memory_data,
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**data,
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}
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return memory_data
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def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save context from this session for every memory."""
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# Save context for all sub-memories
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for memory in self.memories:
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memory.save_context(inputs, outputs)
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def clear(self) -> None:
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"""Clear context from this session for every memory."""
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for memory in self.memories:
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memory.clear()
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class ConversationBufferMemory(Memory, BaseModel):
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"""Buffer for storing conversation memory."""
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