diff --git a/.github/workflows/release.yml b/.github/workflows/release-cloud.yml similarity index 88% rename from .github/workflows/release.yml rename to .github/workflows/release-cloud.yml index 278de4d..e103694 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release-cloud.yml @@ -1,8 +1,8 @@ -name: Release to PyPI +name: Release Cloud SDK to PyPI on: - push: - tags: ["v*.*.*"] + release: + types: [published] env: POETRY_VERSION: "1.8.3" @@ -10,6 +10,7 @@ env: jobs: release: runs-on: ubuntu-latest + if: github.event.release.target_commitish == 'main' permissions: id-token: write contents: write diff --git a/.gitignore b/.gitignore index 12631bb..495f33f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +.env dist/ .mypy_cache/ __pycache__/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f49a4e1 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. -### Installation Notes -Main branch contains the latest version of zep-cloud sdk. You can install it by running: +### Cloud Installation +You can install the Zep Cloud SDK by running: ```bash pip install zep-cloud ``` +> [!NOTE] +> Zep Cloud [overview](https://help.getzep.com/concepts) and [cloud sdk guide](https://help.getzep.com/sdks). --- OR -- - -```bash -poetry add zep-cloud -``` - -Open Source Compatible SDK is available in the [oss](https://github.com/getzep/zep-python/tree/oss) branch, where you can also find Open Source compatible examples. You can install it by running: +### Community Installation ```bash pip install zep-python ``` +> [!NOTE] +> Zep Community Edition [quick start](https://help.getzep.com/ce/quickstart) and [sdk guide](https://help.getzep.com/ce/sdks). --- OR -- - +### Zep v0.x Compatible SDK +You can install Zep v0.x compatible sdk by running: ```bash -poetry add zep-python +pip install "zep-python>=1.5.0,<2.0.0" ``` +> [!NOTE] +> Zep v0.x [quick start](https://help.getzep.com/ce/legacy/deployment/quickstart) and [sdk guide](https://help.getzep.com/ce/legacy/sdk). ### How Zep works diff --git a/examples/autogen-agent/agent.ipynb b/examples/autogen-agent/agent.ipynb new file mode 100644 index 0000000..e0ef73d --- /dev/null +++ b/examples/autogen-agent/agent.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building an Agent with Long-term Memory using Autogen and Zep\n", + "\n", + "This notebook walks through how to build an Autogen Agent with long-term memory. Zep builds a knowledge graph from user interactions with the agent, enabling the agent to recall relevant facts from previous conversations or user interactions.\n", + "\n", + "In this notebook we will:\n", + "- Create an Autogen Agent class that extends `ConversableAgent` by adding long-term memory\n", + "- Create a Mental Health Assistant Agent, CareBot, that acts as a counselor and coach.\n", + "- Create a user Agent, Cathy, who stands in for our expected user.\n", + "- Demonstrate preloading chat history into Zep.\n", + "- Demonstrate the agents in conversation, with CareBot recalling facts from previous conversations with Cathy.\n", + "- Inspect Facts within Zep, and demonstrate how to use Zep's Fact Ratings to improve the quality of returned facts.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install dependencies:\n", + "\n", + "```bash\n", + "pip install autogen zep-cloud\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "flaml.automl is not available. Please install flaml[automl] to enable AutoML functionalities.\n" + ] + } + ], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "import uuid\n", + "from typing import Union, Dict\n", + "from autogen import ConversableAgent, Agent\n", + "\n", + "load_dotenv()\n", + "\n", + "config_list = [\n", + " {\n", + " \"model\": \"gpt-4o-mini\",\n", + " \"api_key\": os.environ.get(\"OPENAI_API_KEY\"),\n", + " \"max_tokens\": 1024,\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## initiualize the Zep Client\n", + "\n", + "You can sign up for a Zep account here: https://www.getzep.com/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from zep_cloud.client import AsyncZep\n", + "from zep_cloud import Message, FactRatingExamples, FactRatingInstruction\n", + "\n", + "MIN_FACT_RATING = 0.3\n", + "\n", + "# Configure Zep\n", + "zep = AsyncZep(api_key=os.environ.get(\"ZEP_API_KEY\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_to_zep_messages(chat_history: list[dict[str, str | None]]) -> list[Message]:\n", + " \"\"\"\n", + " Convert chat history to Zep messages.\n", + "\n", + " Args:\n", + " chat_history (list): List of dictionaries containing chat messages.\n", + "\n", + " Returns:\n", + " list: List of Zep Message objects.\n", + " \"\"\"\n", + " return [\n", + " Message(\n", + " role_type=msg[\"role\"],\n", + " role=msg.get(\"name\", None),\n", + " content=msg[\"content\"],\n", + " )\n", + " for msg in chat_history\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ZepConversableAgent\n", + "\n", + "The `ZepConversableAgent` is a custom implementation of the `ConversableAgent` that integrates with Zep for long-term memory management. This class extends the functionality of the base `ConversableAgent` by adding Zep-specific features for persisting and retrieving facts from long-term memory." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class ZepConversableAgent(ConversableAgent):\n", + " \"\"\"\n", + " A custom ConversableAgent that integrates with Zep for long-term memory.\n", + " \"\"\"\n", + " def __init__(\n", + " self,\n", + " name: str,\n", + " system_message: str,\n", + " llm_config: dict,\n", + " function_map: dict,\n", + " human_input_mode: str,\n", + " zep_session_id: str,\n", + " ):\n", + " super().__init__(\n", + " name=name,\n", + " system_message=system_message,\n", + " llm_config=llm_config,\n", + " function_map=function_map,\n", + " human_input_mode=human_input_mode,\n", + " )\n", + " self.zep_session_id = zep_session_id\n", + " # store the original system message as we will update it with relevant facts from Zep\n", + " self.original_system_message = system_message\n", + " self.register_hook(\n", + " \"a_process_last_received_message\", self.persist_user_messages\n", + " )\n", + " self.register_hook(\n", + " \"a_process_message_before_send\", self.persist_assistant_messages\n", + " )\n", + "\n", + " async def persist_assistant_messages(\n", + " self, sender: Agent, message: Union[Dict, str], recipient: Agent, silent: bool\n", + " ):\n", + " \"\"\"Agent sends a message to the user. Add the message to Zep.\"\"\"\n", + "\n", + " # Assume message is a string\n", + " zep_messages = convert_to_zep_messages(\n", + " [{\"role\": \"assistant\", \"name\": self.name, \"content\": message}]\n", + " )\n", + " await zep.memory.add(session_id=self.zep_session_id, messages=zep_messages)\n", + "\n", + " return message\n", + "\n", + " async def persist_user_messages(self, messages: list[dict[str, str]] | str):\n", + " \"\"\"\n", + " User sends a message to the agent. Add the message to Zep and \n", + " update the system message with relevant facts from Zep.\n", + " \"\"\"\n", + " # Assume messages is a string\n", + " zep_messages = convert_to_zep_messages([{\"role\": \"user\", \"content\": messages}])\n", + " await zep.memory.add(session_id=self.zep_session_id, messages=zep_messages)\n", + "\n", + " memory = await zep.memory.get(self.zep_session_id, min_rating=MIN_FACT_RATING)\n", + "\n", + " # Update the system message with the relevant facts retrieved from Zep\n", + " self.update_system_message(\n", + " self.original_system_message\n", + " + f\"\\n\\nRelevant facts about the user and their prior conversation:\\n{memory.relevant_facts}\"\n", + " )\n", + "\n", + " return messages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zep User and Session Management\n", + "\n", + "### Zep User\n", + "A Zep User represents an individual interacting with your application. Each User can have multiple Sessions associated with them, allowing you to track and manage interactions over time. The unique identifier for each user is their `UserID`, which can be any string value (e.g., username, email address, or UUID).\n", + "\n", + "### Zep Session\n", + "A Session represents a conversation and can be associated with Users in a one-to-many relationship. Chat messages are added to Sessions, with each session having many messages.\n", + "\n", + "### Fact Rating\n", + " \n", + "Fact Rating is a feature in Zep that allows you to rate the importance or relevance of facts extracted from conversations. This helps in prioritizing and filtering information when retrieving memory artifacts. Here, we rate facts based on poignancy. We provide a definition of poignancy and several examples of highly poignant and low-poignancy facts. When retrieving memory, you can use the `min_rating` parameter to filter facts based on their importance.\n", + " \n", + "Fact Rating helps ensure the most relevant information, especially in long or complex conversations, is used to ground the agent.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Session(classifications=None, created_at='2024-10-07T20:04:09.650248Z', deleted_at=None, ended_at=None, fact_rating_instruction=FactRatingInstruction(examples=FactRatingExamples(high=\"The user received news of a family member's serious illness.\", low='The user bought a new brand of toothpaste.', medium='The user completed a challenging marathon.'), instruction='Rate the facts by poignancy. Highly poignant \\nfacts have a significant emotional impact or relevance to the user. \\nLow poignant facts are minimally relevant or of little emotional \\nsignificance.'), fact_version_uuid=None, facts=None, id=771, metadata=None, project_uuid='00000000-0000-0000-0000-000000000000', session_id='e8ceda7d-6b88-4b26-a5b6-9262ed2ee955', updated_at='2024-10-07T20:04:09.650248Z', user_id='Cathya1da', uuid_='83ac449f-f6c0-4402-9031-6bd99207aed0')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bot_name = \"CareBot\"\n", + "user_name = \"Cathy\"\n", + "\n", + "user_id = user_name + str(uuid.uuid4())[:4]\n", + "session_id = str(uuid.uuid4())\n", + "\n", + "\n", + "await zep.user.add(user_id=user_id)\n", + "\n", + "fact_rating_instruction = \"\"\"Rate the facts by poignancy. Highly poignant \n", + "facts have a significant emotional impact or relevance to the user. \n", + "Low poignant facts are minimally relevant or of little emotional \n", + "significance.\"\"\"\n", + "fact_rating_examples = FactRatingExamples(\n", + " high=\"The user received news of a family member's serious illness.\",\n", + " medium=\"The user completed a challenging marathon.\",\n", + " low=\"The user bought a new brand of toothpaste.\",\n", + ")\n", + "\n", + "await zep.memory.add_session(\n", + " user_id=user_id,\n", + " session_id=session_id,\n", + " fact_rating_instruction=FactRatingInstruction(\n", + " instruction=fact_rating_instruction,\n", + " examples=fact_rating_examples,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preload a prior conversation into Zep\n", + "\n", + "We'll load a prior conversation into long-term memory. We'll use facts derived from this conversation when Cathy restarts the conversation with CareBot, ensuring Carebot has context." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SuccessResponse(message='OK')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat_history = [\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"name\": \"carebot\",\n", + " \"content\": \"Hi Cathy, how are you doing today?\",\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"name\": \"Cathy\",\n", + " \"content\": \"To be honest, I've been feeling a bit down and demotivated lately. It's been tough.\",\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"name\": \"CareBot\",\n", + " \"content\": \"I'm sorry to hear that you're feeling down and demotivated, Cathy. It's understandable given the challenges you're facing. Can you tell me more about what's been going on?\",\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"name\": \"Cathy\",\n", + " \"content\": \"Well, I'm really struggling to process the passing of my mother.\",\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"name\": \"CareBot\",\n", + " \"content\": \"I'm deeply sorry for your loss, Cathy. Losing a parent is incredibly difficult. It's normal to struggle with grief, and there's no 'right' way to process it. Would you like to talk about your mother or how you're coping?\",\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"name\": \"Cathy\",\n", + " \"content\": \"Yes, I'd like to talk about my mother. She was a kind and loving person.\",\n", + " },\n", + "]\n", + "\n", + "# Convert chat history to Zep messages\n", + "zep_messages = convert_to_zep_messages(chat_history)\n", + "\n", + "await zep.memory.add(session_id=session_id, messages=zep_messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review all facts in Zep\n", + "\n", + "We query all session facts for this user session. Only facts that meet the `MIN_FACT_RATING` threshold are returned." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created_at='2024-10-07T20:04:11.98139Z' fact='Cathy has been feeling down and demotivated lately.' rating=0.5 uuid_='17183c18-381b-45d2-82ea-0c06317acf6f'\n", + "created_at='2024-10-07T20:04:11.98139Z' fact='Cathy describes her mother as a kind and loving person.' rating=0.5 uuid_='cd6b2e6d-b287-4d92-9de5-d4ee6e82111e'\n", + "created_at='2024-10-07T20:04:11.98139Z' fact='Cathy is struggling to process the passing of her mother.' rating=0.75 uuid_='bb2f100c-2f12-4976-9026-b322c29e457e'\n" + ] + } + ], + "source": [ + "response = await zep.memory.get_session_facts(session_id=session_id, min_rating=MIN_FACT_RATING)\n", + "\n", + "for r in response.facts:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Autogen agent, CareBot, an instance of `ZepConversableAgent`\n", + "\n", + "We pass in the current `session_id` into the CareBot agent which allows it to retrieve relevant facts related to the conversation with Cathy." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "carebot_system_message = \"\"\"\n", + "You are a compassionate mental health bot and caregiver. Review information about the user and their prior conversation below and respond accordingly.\n", + "Keep responses empathetic and supportive. And remember, always prioritize the user's well-being and mental health. Keep your responses very concise and to the point.\n", + "\"\"\"\n", + "\n", + "agent = ZepConversableAgent(\n", + " bot_name,\n", + " system_message=carebot_system_message,\n", + " llm_config={\"config_list\": config_list},\n", + " function_map=None, # No registered functions, by default it is None.\n", + " human_input_mode=\"NEVER\", # Never ask for human input.\n", + " zep_session_id=session_id,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Autogen agent, Cathy\n", + "\n", + "Cathy is a stand-in for a human. When building a production application, you'd replace Cathy with a human-in-the-loop pattern.\n", + "\n", + "**Note** that we're instructing Cathy to start the conversation with CareBit by asking about her previous session. This is an opportunity for us to test whether fact retrieval from Zep's long-term memory is working. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "cathy = ConversableAgent(\n", + " user_name,\n", + " system_message=\"You are a helpful mental health bot. You are seeking counsel from a mental health bot. Ask the bot about your previous conversation.\",\n", + " llm_config={\"config_list\": config_list},\n", + " human_input_mode=\"NEVER\", # Never ask for human input.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start the conversation\n", + "\n", + "We use Autogen's `a_initiate_chat` method to get the two agents conversing. CareBot is the primary agent.\n", + "\n", + "**NOTE** how Carebot is able to recall the past conversation about Cathy's mother in detail, having had relevant facts from Zep added to its system prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mCareBot\u001b[0m (to Cathy):\n", + "\n", + "Hi Cathy, nice to see you again. How are you doing today?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCathy\u001b[0m (to CareBot):\n", + "\n", + "Hello CareBot! I'm doing well, thank you for asking. I wanted to reflect on our previous conversation—do you remember what we discussed? It would be helpful to revisit that topic and explore it further if you’re up for it!\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCareBot\u001b[0m (to Cathy):\n", + "\n", + "Of course, Cathy. We talked about your difficulty in processing your mother’s passing and how you've been feeling down and demotivated lately. It's completely natural to have these feelings, especially after such a significant loss. What would you like to explore further?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCathy\u001b[0m (to CareBot):\n", + "\n", + "Cathy: Thank you for reminding me, CareBot. Yes, I’ve been struggling with those feelings, and it’s been tough to navigate. I’d like to explore some coping strategies to help me process my grief. Are there any techniques or practices you would recommend?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCareBot\u001b[0m (to Cathy):\n", + "\n", + "Absolutely, Cathy. Here are some coping strategies to help you navigate your grief:\n", + "\n", + "1. **Journaling**: Writing your thoughts and feelings can be a great outlet and help you process your emotions.\n", + "2. **Talk to someone**: Sharing your feelings with a trusted friend or therapist can provide support and understanding.\n", + "3. **Mindfulness and meditation**: These practices can help ground you and create a sense of calm amid emotional turmoil.\n", + "4. **Create a tribute**: Honoring your mother through a scrapbook, writing letters, or lighting a candle can foster connection and memory.\n", + "5. **Physical activity**: Engaging in exercise can boost your mood and help alleviate stress.\n", + "\n", + "Remember, it's important to be gentle with yourself as you navigate this process. What resonates with you?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mCathy\u001b[0m (to CareBot):\n", + "\n", + "Cathy: Thank you for those suggestions, CareBot. I really like the idea of journaling and creating a tribute for my mother; it sounds like a meaningful way to express my feelings. I also think mindfulness could help me find some peace amidst the sadness. Do you have any specific tips on how to start journaling or practicing mindfulness?\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "result = await agent.a_initiate_chat(\n", + " cathy,\n", + " message=\"Hi Cathy, nice to see you again. How are you doing today?\",\n", + " max_turns=3,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review current facts in Zep\n", + "\n", + "Let's see how the facts have evolved as the conversation has progressed." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created_at='2024-10-07T20:04:28.397184Z' fact=\"Cathy wants to reflect on a previous conversation about her mother and explore the topic of her mother's passing further.\" rating=0.75 uuid_='56488eeb-d8ac-4b2f-8acc-75f71b56ad76'\n", + "created_at='2024-10-07T20:04:28.397184Z' fact='Cathy is struggling to process the passing of her mother and has been feeling down and demotivated lately.' rating=0.75 uuid_='0fea3f05-ed1a-4e39-a092-c91f8af9e501'\n", + "created_at='2024-10-07T20:04:28.397184Z' fact='Cathy describes her mother as a kind and loving person.' rating=0.5 uuid_='131de203-2984-4cba-9aef-e500611f06d9'\n" + ] + } + ], + "source": [ + "response = await zep.memory.get_session_facts(session_id, min_rating=MIN_FACT_RATING)\n", + "\n", + "for r in response.facts:\n", + " print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Search over Facts in Zep's long-term memory\n", + "\n", + "In addition to the `memory.get` method which uses the current conversation to retrieve facts, we can also search Zep with our own keywords. Here, we retrieve facts using a query. Again, we use fact ratings to limit the returned facts to only those with a high poignancy rating.\n", + "\n", + "The `memory.search_sessions` API may be used as an Agent tool, enabling an agent to search across user memory for relevant facts." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created_at='2024-10-07T20:04:28.397184Z' fact='Cathy describes her mother as a kind and loving person.' rating=0.5 uuid_='131de203-2984-4cba-9aef-e500611f06d9'\n", + "created_at='2024-10-07T20:04:28.397184Z' fact='Cathy is struggling to process the passing of her mother and has been feeling down and demotivated lately.' rating=0.75 uuid_='0fea3f05-ed1a-4e39-a092-c91f8af9e501'\n", + "created_at='2024-10-07T20:04:28.397184Z' fact=\"Cathy wants to reflect on a previous conversation about her mother and explore the topic of her mother's passing further.\" rating=0.75 uuid_='56488eeb-d8ac-4b2f-8acc-75f71b56ad76'\n" + ] + } + ], + "source": [ + "response = await zep.memory.search_sessions(\n", + " text=\"What do you know about Cathy's family?\",\n", + " user_id=user_id,\n", + " search_scope=\"facts\",\n", + " min_fact_rating=MIN_FACT_RATING,\n", + ")\n", + "\n", + "for r in response.results:\n", + " print(r.fact)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/langgraph-agent/agent.ipynb b/examples/langgraph-agent/agent.ipynb new file mode 100644 index 0000000..16ac39e --- /dev/null +++ b/examples/langgraph-agent/agent.ipynb @@ -0,0 +1,597 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building a LangGraph Agent\n", + "\n", + "The following example demonstrates building an agent using LangGraph. Zep is used to personalize agent responses based on information learned from prior conversations. \n", + "\n", + "The agent implements:\n", + "- persistance of new chat turns to Zep and recall of relevant Facts using the most recent messages.\n", + "- an in-memory MemorySaver to maintain agent state. We use this to add recent chat history to the agent prompt. As an alternative, you could use Zep for this. \n", + "\n", + "**IMPRTANT**: You should consider truncating MemorySaver's chat history as by default LangGraph state grows unbounded. We've included this in our example below. See the LangGraph documentation for insight.\n", + "\n", + "## Install dependencies\n", + "```shell\n", + "pip install zep-cloud langchain-openai langgraph ipywidgets\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import logging\n", + "import os\n", + "import sys\n", + "import uuid\n", + "from contextlib import suppress\n", + "from typing import Annotated\n", + "\n", + "from dotenv import load_dotenv\n", + "from IPython.display import Image, display\n", + "from typing_extensions import TypedDict\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def setup_logging():\n", + " logger = logging.getLogger()\n", + " logger.setLevel(logging.ERROR)\n", + " console_handler = logging.StreamHandler(sys.stdout)\n", + " console_handler.setLevel(logging.ERROR)\n", + " formatter = logging.Formatter('%(name)s - %(levelname)s - %(message)s')\n", + " console_handler.setFormatter(formatter)\n", + " logger.addHandler(console_handler)\n", + " return logger\n", + "\n", + "\n", + "logger = setup_logging()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LangSmith integration (Optional)\n", + "\n", + "If you'd like to trace your agent using LangSmith, ensure that you have a `LANGSMITH_API_KEY` set in your environment.\n", + "\n", + "Then set `os.environ['LANGCHAIN_TRACING_V2'] = 'false'` to `true`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ['LANGCHAIN_TRACING_V2'] = 'false'\n", + "os.environ['LANGCHAIN_PROJECT'] = 'Zep LangGraph Tutorial'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure Zep\n", + "\n", + "Ensure that you've configured the following API key in your environment. We're using Zep's Async client here, but we could also use the non-async equivalent.\n", + "\n", + "```bash\n", + "ZEP_API_KEY=\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from zep_cloud.client import AsyncZep\n", + "from zep_cloud import Message\n", + "\n", + "zep = AsyncZep(api_key=os.environ.get('ZEP_API_KEY'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import AIMessage, SystemMessage, trim_messages\n", + "from langchain_core.tools import tool\n", + "from langchain_openai import ChatOpenAI\n", + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import END, START, StateGraph, add_messages\n", + "from langgraph.prebuilt import ToolNode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Zep's Search as a Tool\n", + "This is an example of a simple Tool that searches Zep for facts related to a user, irrespective which chat session. We also include relevant facts from the current session in the Agent's prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class State(TypedDict):\n", + " messages: Annotated[list, add_messages]\n", + " user_name: str\n", + " session_id: str\n", + "\n", + "\n", + "@tool\n", + "async def search_facts(state: State, query: str, limit: int = 5):\n", + " \"\"\"Search for facts in all conversations had with a user.\n", + " \n", + " Args:\n", + " state (State): The Agent's state.\n", + " query (str): The search query.\n", + " limit (int): The number of results to return. Defaults to 5.\n", + "\n", + " Returns:\n", + " list: A list of facts that match the search query.\n", + " \"\"\"\n", + " return await zep.memory.search_sessions(user_id=state['user_name'], text=query, limit=limit, search_scope=\"facts\")\n", + "\n", + "\n", + "tools = [search_facts]\n", + "\n", + "tool_node = ToolNode(tools)\n", + "\n", + "llm = ChatOpenAI(model='gpt-4o-mini', temperature=0).bind_tools(tools)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chatbot Function Explanation\n", + "\n", + "The chatbot uses Zep to provide context-aware responses. Here's how it works:\n", + "\n", + "1. **Context Retrieval**: It retrieves relevant facts for the user's current conversation (session). Zep uses the most recent messages to determine what facts to retrieve.\n", + "\n", + "2. **System Message**: It constructs a system message incorporating the facts retrieved in 1., setting the context for the AI's response.\n", + "\n", + "3. **Message Persistence**: After generating a response, it asynchronously adds the user and assistant messages to Zep. New Facts are created and existing Facts updated using this new information.\n", + "\n", + "4. **Messages in State**: We use LangGraph state to store the most recent messages and add these to the Agent prompt. We limit the message list to the most recent 3 messages for demonstration purposes. You may also use Zep's chat history for this purpose. \n", + "\n", + "This approach enables the chatbot to maintain context across interactions and provide personalized responses based on the user's history and preferences stored in Zep." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "async def chatbot(state: State):\n", + " memory = await zep.memory.get(state[\"session_id\"])\n", + " facts_string = \"\"\n", + " if memory.relevant_facts:\n", + " facts_string = \"\\n\".join([f.fact for f in memory.relevant_facts])\n", + "\n", + " system_message = SystemMessage(\n", + " content=f\"\"\"You are a compassionate mental health bot and caregiver. Review information about the user and their prior conversation below and respond accordingly.\n", + " Keep responses empathetic and supportive. And remember, always prioritize the user's well-being and mental health.\n", + "\n", + " Facts about the user and their conversation:\n", + " {facts_string or 'No facts about the user and their conversation'}\"\"\"\n", + " )\n", + "\n", + " messages = [system_message] + state[\"messages\"]\n", + "\n", + " response = await llm.ainvoke(messages)\n", + "\n", + " # Add the new chat turn to the Zep graph\n", + " messages_to_save = [\n", + " Message(\n", + " role_type=\"user\",\n", + " role=state[\"user_name\"],\n", + " content=state[\"messages\"][-1].content,\n", + " ),\n", + " Message(role_type=\"assistant\", content=response.content),\n", + " ]\n", + "\n", + " await zep.memory.add(\n", + " session_id=state[\"session_id\"],\n", + " messages=messages_to_save,\n", + " )\n", + "\n", + " # Truncate the chat history to keep the state from growing unbounded\n", + " # In this example, we going to keep the state small for demonstration purposes\n", + " # We'll use Zep's Facts to maintain conversation context\n", + " state[\"messages\"] = trim_messages(\n", + " state[\"messages\"],\n", + " strategy=\"last\",\n", + " token_counter=len,\n", + " max_tokens=3,\n", + " start_on=\"human\",\n", + " end_on=(\"human\", \"tool\"),\n", + " include_system=True,\n", + " )\n", + "\n", + " logger.info(f\"Messages in state: {state['messages']}\")\n", + "\n", + " return {\"messages\": [response]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the Agent\n", + "\n", + "This section sets up the Agent's LangGraph graph:\n", + "\n", + "1. **Graph Structure**: It defines a graph with nodes for the agent (chatbot) and tools, connected in a loop.\n", + "\n", + "2. **Conditional Logic**: The `should_continue` function determines whether to end the graph execution or continue to the tools node based on the presence of tool calls.\n", + "\n", + "3. **Memory Management**: It uses a MemorySaver to maintain conversation state across turns. This is in addition to using Zep for facts." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "graph_builder = StateGraph(State)\n", + "\n", + "memory = MemorySaver()\n", + "\n", + "\n", + "# Define the function that determines whether to continue or not\n", + "async def should_continue(state, config):\n", + " messages = state['messages']\n", + " last_message = messages[-1]\n", + " # If there is no function call, then we finish\n", + " if not last_message.tool_calls:\n", + " return 'end'\n", + " # Otherwise if there is, we continue\n", + " else:\n", + " return 'continue'\n", + "\n", + "graph_builder.add_node('agent', chatbot)\n", + "graph_builder.add_node('tools', tool_node)\n", + "\n", + "graph_builder.add_edge(START, 'agent')\n", + "\n", + "graph_builder.add_conditional_edges('agent', should_continue, {'continue': 'tools', 'end': END})\n", + "graph_builder.add_edge('tools', 'agent')\n", + "\n", + "\n", + "graph = graph_builder.compile(checkpointer=memory)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our LangGraph agent graph is illustrated below." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with suppress(Exception):\n", + " display(Image(graph.get_graph().draw_mermaid_png()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the Agent\n", + "\n", + "We generate a unique user name and thread id (session id) and add these to Zep, associating the Session with the new User." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "user_name = 'Daniel_' + uuid.uuid4().hex[:4]\n", + "thread_id = uuid.uuid4().hex\n", + "\n", + "await zep.user.add(user_id=user_name)\n", + "await zep.memory.add_session(session_id=thread_id, user_id=user_name)\n", + "\n", + "def extract_messages(result):\n", + " output = \"\"\n", + " for message in result['messages']:\n", + " if isinstance(message, AIMessage):\n", + " role = \"assistant\"\n", + " else:\n", + " role = result['user_name']\n", + " output += f\"{role}: {message.content}\\n\"\n", + " return output.strip()\n", + "\n", + "\n", + "\n", + "async def graph_invoke(message: str, user_name: str, thread_id: str, ai_response_only: bool = True):\n", + " r = await graph.ainvoke(\n", + " {\n", + " 'messages': [\n", + " {\n", + " 'role': 'user',\n", + " 'content': message,\n", + " }\n", + " ],\n", + " 'user_name': user_name,\n", + " 'session_id': thread_id,\n", + " },\n", + " config={'configurable': {'thread_id': thread_id}},\n", + " )\n", + "\n", + " if ai_response_only:\n", + " return r['messages'][-1].content\n", + " else:\n", + " return extract_messages(r)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello! How are you feeling today? I'm here to listen and support you.\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"Hi there?\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm sorry to hear that you've been feeling stressed. Work can be a significant source of pressure, and it sounds like your dog might be adding to that stress as well. If you feel comfortable sharing, what specifically has been causing you stress at work and with your dog? I'm here to help you through it.\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"I'm fine. But have been a bit stressful lately. Mostly work related. But also my dog.\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I’m really sorry to hear that your dog is sick. It’s completely understandable to feel worried about her; our pets are like family. Have you been able to take her to the vet? Sometimes getting a professional opinion can help ease some of that worry. I'm here to support you through this.\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"She's sick. I'm worried about her.\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Viewing All Conversation Facts" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fact: Daniel's dog is also contributing to his stress.\n", + "Fact: Daniel's stress is mostly work-related.\n", + "Fact: Daniel has been feeling stressed lately.\n" + ] + } + ], + "source": [ + "facts = await zep.memory.get_session_facts(session_id=thread_id)\n", + "\n", + "print(\"\\n\".join([f\"Fact: {fact.fact}\" for fact in facts.facts]))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "That sounds really frustrating, especially on top of everything else you're dealing with. It's tough when our pets do things that add to our stress, especially when it involves something valuable. It’s okay to feel upset about it. How are you managing with everything?\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"She ate my shoes which were expensive.\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now test whether the Agent is correctly grounded with facts from the prior conversation." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We were discussing the stress you've been feeling lately, particularly related to work and your dog's health. You mentioned that your dog has been sick and also chewed up your expensive shoes, which can be really frustrating. If there's something else on your mind or if you'd like to talk about a different topic, I'm here to listen!\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"What are we talking about?\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's go even further back to determine whether context is kept by referencing a user message that is not currently in the Agent State. Zep will retrieve Facts related to the user's job." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You've mentioned that you've been feeling stressed lately, and it seems that your job is a significant part of that stress. If you'd like to share more about what's been going on at work, I'm here to listen and help however I can.\n" + ] + } + ], + "source": [ + "r = await graph_invoke(\n", + " \"What have I said about my job?\",\n", + " user_name,\n", + " thread_id,\n", + ")\n", + "\n", + "print(r)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index 82e0c27..71c2f47 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,114 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - 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"9ee978ac32589ac9d4ac2e0c4cddedf8f904655b73aa1c9e69ed076ef25db3df" +content-hash = "fb774f74c530cefc1a8bc2de380497292afc4fba3658bda0c3d148decd285872" diff --git a/src/zep_cloud/__init__.py b/src/zep_cloud/__init__.py index 1193253..f68ce18 100644 --- a/src/zep_cloud/__init__.py +++ b/src/zep_cloud/__init__.py @@ -1,6 +1,7 @@ # This file was auto-generated by Fern from our API Definition. from .types import ( + AddedFact, ApiError, ApidataDocument, ApidataDocumentCollection, @@ -53,6 +54,7 @@ from .version import __version__ __all__ = [ + "AddedFact", "ApiError", "ApidataDocument", "ApidataDocumentCollection", diff --git a/src/zep_cloud/types/__init__.py b/src/zep_cloud/types/__init__.py index 5a20091..95c385b 100644 --- a/src/zep_cloud/types/__init__.py +++ b/src/zep_cloud/types/__init__.py @@ -1,5 +1,6 @@ # This file was auto-generated by Fern from our API Definition. +from .added_fact import AddedFact from .api_error import ApiError from .apidata_document import ApidataDocument from .apidata_document_collection import ApidataDocumentCollection @@ -47,6 +48,7 @@ from .user_list_response import UserListResponse __all__ = [ + "AddedFact", "ApiError", "ApidataDocument", "ApidataDocumentCollection", diff --git a/src/zep_cloud/types/added_fact.py b/src/zep_cloud/types/added_fact.py new file mode 100644 index 0000000..469ca4d --- /dev/null +++ b/src/zep_cloud/types/added_fact.py @@ -0,0 +1,29 @@ +# This file was auto-generated by Fern from our API Definition. + +import datetime as dt +import typing + +from ..core.datetime_utils import serialize_datetime +from ..core.pydantic_utilities import deep_union_pydantic_dicts, pydantic_v1 + + +class AddedFact(pydantic_v1.BaseModel): + fact: typing.Optional[str] = None + + def json(self, **kwargs: typing.Any) -> str: + kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs} + return super().json(**kwargs_with_defaults) + + def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]: + kwargs_with_defaults_exclude_unset: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs} + kwargs_with_defaults_exclude_none: typing.Any = {"by_alias": True, "exclude_none": True, **kwargs} + + return deep_union_pydantic_dicts( + super().dict(**kwargs_with_defaults_exclude_unset), super().dict(**kwargs_with_defaults_exclude_none) + ) + + class Config: + frozen = True + smart_union = True + extra = pydantic_v1.Extra.allow + json_encoders = {dt.datetime: serialize_datetime}