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docs(docs): Add RecallIO.AI as a memory provider (#32331)
Add requested files to add RecallIO as a memory provider. --------- Co-authored-by: Frey <gfreyburger@gmail.com> Co-authored-by: Mason Daugherty <mason@langchain.dev>
This commit is contained in:
RecallIO
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215
docs/docs/integrations/memory/recallio_memory.ipynb
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215
docs/docs/integrations/memory/recallio_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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"metadata": {},
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"source": [
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"# RecallioMemory + LangChain Integration Demo\n",
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"A minimal notebook to show drop-in usage of RecallioMemory in LangChain (with scoped writes and recall)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install recallio langchain langchain-recallio openai"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup: API Keys & Imports"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_recallio.memory import RecallioMemory\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langchain.prompts import ChatPromptTemplate\n",
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"import os\n",
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"\n",
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"# Set your keys here or use environment variables\n",
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"RECALLIO_API_KEY = os.getenv(\"RECALLIO_API_KEY\", \"YOUR_RECALLIO_API_KEY\")\n",
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"OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\", \"YOUR_OPENAI_API_KEY\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initialize RecallioMemory"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"memory = RecallioMemory(\n",
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" project_id=\"project_abc\",\n",
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" api_key=RECALLIO_API_KEY,\n",
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" session_id=\"demo-session-001\",\n",
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" user_id=\"demo-user-42\",\n",
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" default_tags=[\"test\", \"langchain\"],\n",
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" return_messages=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Build a LangChain ConversationChain with RecallioMemory"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# You can swap in any supported LLM here\n",
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"llm = ChatOpenAI(api_key=OPENAI_API_KEY, temperature=0)\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"The following is a friendly conversation between a human and an AI. \"\n",
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" \"The AI is talkative and provides lots of specific details from its context. \"\n",
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" \"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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" (\"placeholder\", \"{history}\"), # RecallioMemory will fill this slot\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"# LCEL chain that returns an AIMessage\n",
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"base_chain = prompt | llm\n",
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"\n",
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"\n",
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"# Create a stateful chain using RecallioMemory\n",
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"def chat_with_memory(user_input: str):\n",
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" # Load conversation history from memory\n",
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" memory_vars = memory.load_memory_variables({\"input\": user_input})\n",
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"\n",
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" # Run the chain with history and user input\n",
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" response = base_chain.invoke(\n",
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" {\"input\": user_input, \"history\": memory_vars.get(\"history\", \"\")}\n",
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" )\n",
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"\n",
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" # Save the conversation to memory\n",
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" memory.save_context({\"input\": user_input}, {\"output\": response.content})\n",
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"\n",
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" return response"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example: Chat with Memory"
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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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"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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"Bot: Hello Guillaume! It's nice to meet you. How can I assist you today?\n"
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]
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}
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],
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"source": [
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"# First user message – note the AI remembers the name\n",
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"resp1 = chat_with_memory(\"Hi! My name is Guillaume. Remember that.\")\n",
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"print(\"Bot:\", resp1.content)"
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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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"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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"Bot: Your name is Guillaume.\n"
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]
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}
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],
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"source": [
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"# Second user message – AI should recall the name from memory\n",
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"resp2 = chat_with_memory(\"What is my name?\")\n",
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"print(\"Bot:\", resp2.content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## See What Is Stored in Recallio\n",
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"This is for debugging/demo only; in production, you wouldn't do this on every run."
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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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"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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"Current memory variables: {'history': [HumanMessage(content='Name is Guillaume', additional_kwargs={}, response_metadata={})]}\n"
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]
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}
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],
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"source": [
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"print(\"Current memory variables:\", memory.load_memory_variables({}))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Clear Memory (Optional Cleanup - Requires Manager level Key)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# memory.clear()\n",
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"# print(\"Memory cleared.\")"
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]
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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",
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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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"name": "python",
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"version": "3.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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31
docs/docs/integrations/providers/recallio.ipynb
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31
docs/docs/integrations/providers/recallio.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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"metadata": {},
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"source": [
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"# Recallio\n",
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"\n",
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"[Recallio](https://recallio.ai/) is a powerfull API allowing to store, index, and retrieve application “memories” with built-in fact extraction, dynamic summaries, reranked recall, and a full knowledge-graph layer.\n",
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"\n",
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"\n",
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"## Installation\n",
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"\n",
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"```bash\n",
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"pip install langchain-recallio\n",
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"```\n",
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"\n",
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"```python\n",
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"from langchain_recallio.memory import RecallioMemory\n",
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"```"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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