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{
"cells": [
{
"cell_type": "markdown",
"id": "94e33ebe",
"metadata": {},
"source": [
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"# Custom Memory\n",
"\n",
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"Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that."
]
},
{
"cell_type": "markdown",
"id": "bdfd0305",
"metadata": {},
"source": [
"For this notebook, we will add a custom memory type to `ConversationChain`. In order to add a custom memory class, we need to import the base memory class and subclass it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6d787ef2",
"metadata": {},
"outputs": [],
"source": [
"from langchain import OpenAI, ConversationChain\n",
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"from langchain.schema import BaseMemory\n",
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"from pydantic import BaseModel\n",
"from typing import List, Dict, Any"
]
},
{
"cell_type": "markdown",
"id": "9489e5e1",
"metadata": {},
"source": [
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"In this example, we will write a custom memory class that uses spaCy to extract entities and save information about them in a simple hash table. Then, during the conversation, we will look at the input text, extract any entities, and put any information about them into the context.\n",
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"\n",
"* Please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations.\n",
"\n",
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"For this, we will need spaCy."
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]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": null,
"id": "48a5dd13",
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"metadata": {},
"outputs": [],
"source": [
"# !pip install spacy\n",
"# !python -m spacy download en_core_web_lg"
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 5,
2022-11-25 18:00:12 +00:00
"id": "ff065f58",
"metadata": {},
"outputs": [],
"source": [
"import spacy\n",
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"\n",
"nlp = spacy.load(\"en_core_web_lg\")"
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]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 9,
2022-11-25 18:00:12 +00:00
"id": "1d45d429",
"metadata": {},
"outputs": [],
"source": [
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"class SpacyEntityMemory(BaseMemory, BaseModel):\n",
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" \"\"\"Memory class for storing information about entities.\"\"\"\n",
"\n",
" # Define dictionary to store information about entities.\n",
" entities: dict = {}\n",
" # Define key to pass information about entities into prompt.\n",
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" memory_key: str = \"entities\"\n",
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"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
" def clear(self):\n",
" self.entities = {}\n",
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"\n",
" @property\n",
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" def memory_variables(self) -> List[str]:\n",
" \"\"\"Define the variables we are providing to the prompt.\"\"\"\n",
" return [self.memory_key]\n",
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"\n",
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" def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n",
" \"\"\"Load the memory variables, in this case the entity key.\"\"\"\n",
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" # Get the input text and run through spaCy\n",
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" doc = nlp(inputs[list(inputs.keys())[0]])\n",
" # Extract known information about entities, if they exist.\n",
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" entities = [\n",
" self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities\n",
" ]\n",
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" # Return combined information about entities to put into context.\n",
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" return {self.memory_key: \"\\n\".join(entities)}\n",
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"\n",
" def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n",
" \"\"\"Save context from this conversation to buffer.\"\"\"\n",
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" # Get the input text and run through spaCy\n",
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" text = inputs[list(inputs.keys())[0]]\n",
" doc = nlp(text)\n",
" # For each entity that was mentioned, save this information to the dictionary.\n",
" for ent in doc.ents:\n",
" ent_str = str(ent)\n",
" if ent_str in self.entities:\n",
" self.entities[ent_str] += f\"\\n{text}\"\n",
" else:\n",
" self.entities[ent_str] = text"
]
},
{
"cell_type": "markdown",
"id": "429ba264",
"metadata": {},
"source": [
2023-09-03 22:06:20 +00:00
"We now define a prompt that takes in information about entities as well as user input."
2022-11-25 18:00:12 +00:00
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 10,
2022-11-25 18:00:12 +00:00
"id": "c05159b6",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"\n",
"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. You are provided with information about entities the Human mentions, if relevant.\n",
"\n",
"Relevant entity information:\n",
"{entities}\n",
"\n",
"Conversation:\n",
"Human: {input}\n",
"AI:\"\"\"\n",
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"prompt = PromptTemplate(input_variables=[\"entities\", \"input\"], template=template)"
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]
},
{
"cell_type": "markdown",
"id": "db611041",
"metadata": {},
"source": [
"And now we put it all together!"
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 11,
2022-11-25 18:00:12 +00:00
"id": "f08dc8ed",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
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"conversation = ConversationChain(\n",
" llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory()\n",
")"
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]
},
{
"cell_type": "markdown",
"id": "92a5f685",
"metadata": {},
"source": [
"In the first example, with no prior knowledge about Harrison, the \"Relevant entity information\" section is empty."
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 12,
2022-11-25 18:00:12 +00:00
"id": "5b96e836",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
2022-11-25 18:00:12 +00:00
"Prompt after formatting:\n",
"\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. You are provided with information about entities the Human mentions, if relevant.\n",
"\n",
"Relevant entity information:\n",
"\n",
"\n",
"Conversation:\n",
"Human: Harrison likes machine learning\n",
"AI:\u001b[0m\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
2022-11-25 18:00:12 +00:00
]
},
{
"data": {
"text/plain": [
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?\""
2022-11-25 18:00:12 +00:00
]
},
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 12,
2022-11-25 18:00:12 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conversation.predict(input=\"Harrison likes machine learning\")"
]
},
{
"cell_type": "markdown",
"id": "b1faa743",
"metadata": {},
"source": [
"Now in the second example, we can see that it pulls in information about Harrison."
]
},
{
"cell_type": "code",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 13,
2022-11-25 18:00:12 +00:00
"id": "4bca7070",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
2022-11-25 18:00:12 +00:00
"Prompt after formatting:\n",
"\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. You are provided with information about entities the Human mentions, if relevant.\n",
"\n",
"Relevant entity information:\n",
"Harrison likes machine learning\n",
"\n",
"Conversation:\n",
"Human: What do you think Harrison's favorite subject in college was?\n",
"AI:\u001b[0m\n",
"\n",
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"\u001b[1m> Finished ConversationChain chain.\u001b[0m\n"
2022-11-25 18:00:12 +00:00
]
},
{
"data": {
"text/plain": [
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'"
2022-11-25 18:00:12 +00:00
]
},
Docs refactor (#480)
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:
- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.
There is also a full reference section, as well as extra resources
(glossary, gallery, etc)
Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
2023-01-02 16:24:09 +00:00
"execution_count": 13,
2022-11-25 18:00:12 +00:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
2023-06-16 18:52:56 +00:00
"conversation.predict(\n",
" input=\"What do you think Harrison's favorite subject in college was?\"\n",
")"
2022-11-25 18:00:12 +00:00
]
},
{
"cell_type": "markdown",
"id": "58b856e3",
"metadata": {},
"source": [
"Again, please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1994600",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}