From 9fac8991e38ebd4868ad1fbc694d36ea155eab81 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Sun, 1 Dec 2024 12:17:30 -0800 Subject: [PATCH 01/21] gmagent v.1 --- recipes/use_cases/gmail_agent/LICENSE | 21 ++ recipes/use_cases/gmail_agent/README.md | 327 ++++++++++++++++++ .../use_cases/gmail_agent/examples_log.txt | 230 ++++++++++++ .../use_cases/gmail_agent/functions_prompt.py | 241 +++++++++++++ recipes/use_cases/gmail_agent/main.py | 32 ++ .../use_cases/gmail_agent/requirements.txt | 9 + 6 files changed, 860 insertions(+) create mode 100644 recipes/use_cases/gmail_agent/LICENSE create mode 100644 recipes/use_cases/gmail_agent/README.md create mode 100644 recipes/use_cases/gmail_agent/examples_log.txt create mode 100644 recipes/use_cases/gmail_agent/functions_prompt.py create mode 100644 recipes/use_cases/gmail_agent/main.py create mode 100644 recipes/use_cases/gmail_agent/requirements.txt diff --git a/recipes/use_cases/gmail_agent/LICENSE b/recipes/use_cases/gmail_agent/LICENSE new file mode 100644 index 000000000..fd3a6d2f7 --- /dev/null +++ b/recipes/use_cases/gmail_agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Jeff Tang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md new file mode 100644 index 000000000..2d564af38 --- /dev/null +++ b/recipes/use_cases/gmail_agent/README.md @@ -0,0 +1,327 @@ +# Gmagent - A Llama Powered Gmail Agent + +This Gmagent app shows how to build a Gmail agent app powered by Llama 3.1 8B running locally via Ollama (for privacy concern since Gamgent is about your Gmail). We'll start with building from scratch a basic agent with custom tool calling natively supported in Llama 3.1. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your Gmail, with great user experience: intuitive, engaging, efficient and reliable. + +Currently implemented features of Gmagent include: +* search for emails and attachments +* get email detail +* reply to a specific email +* forward an email +* get summary of a PDF attachment +* draft and send an email + +# Overview + +Email is an essential and one top killer app people use every day. A recent [State of AI Agents](https://www.langchain.com/stateofaiagents) survey by LangChain finds that "The top use cases for agents include performing research and summarization (58%), followed by streamlining tasks for personal productivity or assistance (53.5%)." + +Andrew Ng wrote a 5-part [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) in March 2024 predicting "AI agent workflows will drive massive AI progress this year". + +Deloitte published in November 2024 a report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) stating that "Through their ability to reason, plan, remember and act, AI agents address key limitations of typical language models." and "Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation." + +In the Thanksgiving week, a new startup [/dev/agent](https://sdsa.ai/) building the next-gen OS for AI agents was on a spotlight. + +So what exactly is an AI agent and how to start building an agent app? + +## What is an agent? + +The concept of agent is not new - in the 2010 3rd edition of Russell and Norvig's classic book Artificial Intelligence: A Modern Approach ("Modern" by 2010, two years before the deep learning revolution that started the truly modern AI), an agent is defined as "anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators". These days, AI agent basically means LLM-powered agent - well, if we treat natural language understanding as a type of sensor, LLM agent is still a sub-category of the traditional agent. + +Lilian Weng in her popular June 2023 blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) defines LLM-powered agent system to have four key components: + * Planning and Reflection: can break down large tasks into smaller ones; can do self-reflection over past actions and self improve; + * Memory: can use contextual info and recall info over extended periods (for other components to use); + * Tool Use: can understand what external APIs to use for info or action not built into LLMs; + * Action: can actually run the tools. + +Andrew Ng describes four [agentic design patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) as: +* Reflection +* Planning +* Tool calling +* Multi-agent collaboration, where "memory" is mentioned: Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technology: how can an agent remember enough of its past interactions to perform better on upcoming ones?) + +In Deloitte's [report](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf), AI agents are reasoning engines that can understand context, plan workflows, connect to external tools and data, and execute actions to achieve a defined goal. + +In a November 2024 blog by Letta [The AI agents stack](https://www.letta.com/blog/ai-agents-stack), LLM powered agent is described as the combination of tools use, autonomous execution, and memory. + +In addition, Harrison Chase defines agent in the blog [What is an AI agent](https://blog.langchain.dev/what-is-an-agent/) as "a system that uses an LLM to decide the control flow of an application." + +Yet another simple [summary](https://www.felicis.com/insight/the-agentic-web) by Felicis of what an agent does is that an agent expands LLMs to go from chat to act: an agent can pair with LLMs with external data, multi-step reasoning and planning, and act on user's behalf. + +All in all (see [Resources](#resources) for even more info), agents are systems that take a high-level task, use an LLM as a reasoning and planning engine, with the help of contextual info and long-term memory if needed, to decide what actions to take, reflect and improve on the actions, and eventually execute those actions to accomplish the task. + +It's time to see an agent app in action and enjoy some coding. Below is a preview of the questions or requests one may ask Gmagent: + +# Example Asks to Gmagent + +* do i have any emails with attachments? +* what's the content of the email from LangSmith +* do i have emails with attachment larger than 1mb? +* what kind of attachments for the email with subject papers to read? +* give me a summary of the pdf thinking_llm.pdf +* Draft an email to gmagent_test1@gmail.com saying working on it and will keep you updated. thanks for your patience. +* send the draft +* do i have any emails with attachment larger than 10mb? +* how about 5mb +* reply to the email saying thanks for sharing! +* forward the email to gmagent_test2@gmail.com +* how many emails do i have from xxx@gmail.com? +* how about from yyy@gmail.com? + +[Here](./examples_log.txt) is a complete example interaction log with Gmagent. + +# Setup and Installation + +If you feel intimated by the steps of the following Enable Gmail API section, you may want to check again the example asks (to see what you can ask to the agent) and the example log (to see the whole conversation with gmagent) - the devil's in the detail and all the glorious description of a powerful trendy agent may not mention the little details one has to deal with to build it. + +## Enable Gmail API +1. Go to the [Google Cloud Console](https://console.cloud.google.com/). +2. Create a new project by clicking the dropdown on the top left then click NEW PROJECT. +3. Enter a Project name then click CREATE. +4. Under "APIs & Services" > "Enabled APIs & services", search for "gmail" and then Enable the "Gmail API" for your project. +5. Under "APIs & Services" > "OAuth consent screen", click "GO TO NEW EXPERIENCE", then click "GET STARTED", enter App name, select your gmail as User support email, choose External under Audience, enter your gmail again as Contact Information, and finally check the I agree to the Google API Services under Finish and click Continue - Create. +5. Again under "APIs & Services", go to Credentials. Click on + CREATE CREDENTIALS, then choose OAuth client ID (NOT API key). +Select Desktop App (NOT Web application, because you're assumed to want to start your Gmail agent locally first) as the application type and name it. Click Create to generate your client ID and client secret. +6. Click Download JSON and rename the downloaded file as credentials.json. This file will be used in your Python script for authentication. + +## Install Ollama with Llama 3.1 8B + +Download Ollama (available for macOS, Linux, and Windows) [here](https://ollama.com/). Then download and test run the Llama 3.1 8B model by running on a Terminal: +``` +ollama run llama3.1 +``` + +This will download a quantized version of Llama 3.1 of the size 4.7GB. + +## Install required packages +First, create a Conda or virtual env: + +``` +conda create -n gmagent python=3.10 +conda activate gmagent +``` +or +``` +python -m venv gmagent +source gmagent/bin/activate # on Linux, macOS: +source gmagent\Scripts\activate # on Windows +``` + +Then install the required Python libraries: +``` +git clone https://github.com/jeffxtang/gmagent +cd gmagent +pip install -r requirements.txt +``` + +# Build and Run Gament + +To run Gmagent, you need to first copy the `credentials.json` file downloaded and renamed above in Step 6 of Enable Gmail API to the gmagent folder, then run: +``` +python main.py --user_email +``` + +The first time you run it, you'll get a prompt like this; +``` +Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=xxxx +Enter the authorization code: +``` + +You need to copy the URL above and open it in a browser - if you Sign in with Google using the same Gmail you enabled for the Gmail API, then you'll see "You’ve been given access to an app that’s currently being tested. You should only continue if you know the developer that invited you.", otherwise if you sign in with another Gmail, you'll see "Gmail Agent App has not completed the Google verification process. The app is currently being tested, and can only be accessed by developer-approved testers. If you think you should have access, contact the developer." + +In the latter case, go to APIs & Services > OAuth consent screen > Test users, and click the + ADD USERS button, and you'll see this message: While publishing status is set to "Testing", only test users are able to access the app. Allowed user cap prior to app verification is 100, and is counted over the entire lifetime of the app. + +After clicking Continue, check the Select all checkbox to enable both settings required for running Gmagent: +``` +View your email messages and settings. +Manage drafts and send emails. +``` + +Finally, copy the Authorization code and paste it to the Terminal, hit Enter and you'll see Gmagent's initial greeting (which will likely differ because the default temperature value 0.8 is used here - see [Ollama's model file](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) for detail) such as: +``` +Hello! I'm Gmagent, here to help you manage your Gmail account with ease. + +What would you like to do today? Do you want me to: + +Check and respond to new emails +Compose a new email +Organize your inbox with filters or labels +Delete unwanted emails +Something else? + +Let me know how I can assist you! + +Your ask: +``` + +If you cancel here and run the command `python main.py --user_email ` again you should see the Gmagent greeting right away without the need to enter an authorization code, unless you enter a different Gmail address for the first time - in fact, for each authorized (added as a test user) Gmail address, a file `token_xxxx@gmail.com.pickle` will be created which contains the authorized token. + +See the example asks and interaction log above for the types of asks you may enter. + +# Implementation Notes +Notes here mainly cover how custom functions are defined, how Gmail API based functions are implemented, and how an Agent class is defined to handle memory for contextual chat and perform pre- and post-processing on the tool calling. + +## Available Custom Functions (Tools) Definition +The `functions_prompt.py` defines the following six custom functions, as part of the system prompt (along with examples for each function call spec that Llama should return): + +* list_emails_function +* get_email_function +* send_email_function (new, reply, forward) +* get_pdf_summary_function +* create_draft_function +* send_draft_function + +Below is an example function call spec in JSON format, for the user asks such as "do i have emails with attachments larger than 5mb", "any attachments larger than 5mb" or "let me know if i have large attachments over 5mb": +``` +{"name": "list_emails", "parameters": {"query": "has:attachment larger:5mb"}} +``` + +Before LLMs, it'd be a REAL pain to cover ALL the possible user natural language inputs that can be and should be all translated into the same semantic representation (if you've done Amazon Alex Skill or Google Assistant development or any pre-LLM NLU work before, you'd know that the JSON format is the same as intent-slots representation). Now LLMs such as Llama do the most heavy lifting in translating a natural language open input into its semantic representation. + +But still, if you look at how the `list_emails_function` (which is used to search for emails based on a user query) is defined below, you'd see a lot of work would be needed to convert the user's asks to the filter values the Gmail API can accept: + +``` +list_emails_function = """ +{ + "type": "function", + "function": { + "name": "list_emails", + "description": "Return a list of emails matching an optionally specified query.", + "parameters": { + "type": "dic", + "properties": [ + { + "maxResults": { + "type": "integer", + "description": "The default maximum number of emails to return is 100; the maximum allowed value for this field is 500." + } + }, + { + "query": { + "type": "string", + "description": "One or more keywords in the email subject and body, or one or more filters. There can be 6 types of filters: 1) Field-specific Filters: from, to, cc, bcc, subject; 2) Date Filters: before, after, older than, newer than); 3) Status Filters: read, unread, starred, importatant; 4) Attachment Filters: has, filename or type; 5) Size Filters: larger, smaller; 6) logical operators (or, and, not)." + } + } + ], + "required": [] + } + } +} +""" +``` +In fact, even though many hours of pre-processing work has been done to cover some test examples, not all of the examples in `functions_prompt.py`,have been covered and tested. + +## Actual Function Call Implementation + +For each defined custom function call, its implementation using the Gmail API is present in `gmagent.py`. For example, the `list_emails` is defined as follows: + +``` +def list_emails(query='', max_results=100): + emails = [] + next_page_token = None + + while True: + response = service.users().messages().list( + userId=user_id, + maxResults=max_results, + pageToken=next_page_token, + q=query + ).execute() + + if 'messages' in response: + for msg in response['messages']: + sender, subject, received_time = get_email_info(msg['id']) + emails.append( + { + "message_id": msg['id'], + "sender": sender, + "subject": subject, + "received_time": received_time + } + ) + + next_page_token = response.get('nextPageToken') + + if not next_page_token: + break + + return emails +``` + +The function will be called by our agent after a user ask such as "do i have emails with attachments larger than 5mb" gets Llama's response below: +``` +{"name": "list_emails", "parameters": {"query": "has:attachment larger:5mb"}} + ``` + +## The Agent class +Implemented also in `gmagent.py`, the Agent class uses 3 instance members to allow for contextual aware asks to Gmagent, making it have short-term memory: +1. `messages`: this list holds all the previous user asks and the function call results based on Llama's response to the user asks, making Llama able to answer follow-up questions such as "how about 5mb" (after initial ask "attachments larger than 10mb") or "how about from yyy@gmail.com" (after ask "any emails from xxx@gmail.com). +2. `emails`: this list holds a list of emails that matches the user query, so follow-up questions such as "what kind of attachments for the email with subject xxx" can be answered. +3. `draft_id`: this is used to handle the ask "send the draft" after an initial ask such as "draft an email to xxx". + +The `__call__` method of `Agent` includes the call to Llama with the `messages` and parses the Llama response if it's a tool calling spec JSON result, or if Llama doesn't return a tool calling spec, it means it doesn't find a custom tool for the user ask so the Llama response is returned directly: +``` + try: + res = json.loads(result.split("<|python_tag|>")[-1]) + function_name = res['name'] + parameters = res['parameters'] + return {"function_name": function_name, + "parameters": parameters} + except: + return result +``` + +Also implemented there are both pre-processing logic, mainly to convert some parameter values from Llama's responses to what Gmail APIs can accept to make the API calls happy, and post-processing logic to convert function call results to user-friendly natural language. + +``` +function_name = result["function_name"] +func = globals()[function_name] +parameters = result["parameters"] +... +result = func(**parameters) +... +``` + +When you try out Gmagent, you'll likely find that further pre- and post-processing still needed to make it production ready. In a great video on [Vertical LLM Agents](https://www.youtube.com/watch?v=eBVi_sLaYsc), Jake Heller said "after passes frankly even like 100 tests the odds that it will do on any random distribution of user inputs of the next 100,000, 100% accurately is very high" and "by the time you've dealt with like all the edge cases... there might be dozens of things you build into your application to actually make it work well and then you get to the prompting piece and writing out tests and very specific prompts and the strategy for how you break down a big problem into step by step by step thinking and how you feed in the information how you format that information the right way". That's what all the business logic is about. We'll cover decomposing a complicated ask and multi-step reasoning in a future version of Gmagent, and continue to explore the best possible way to streamline the pre- and post-processing. + +## Debugging output + +When running Gmagent, the detailed Llama returns, pre-processed tool call specs and the actual tool calling results are inside the `-------------------------` block, e.g.: + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'subject:papers to read has:attachment'}}. + +Calling tool to access Gmail API: list_emails, {'query': 'subject:papers to read has:attachment'}... + +Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang ', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}] + +------------------------- + + +# TODOs + +1. Port the app to using [Llama Stack](https://github.com/meta-llama/llama-stack) Agents API. +2. Improve the search, reply, forward, create email draft, and query about attachments to cover all listed and other examples in `functions_prompt.py`. +3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. +4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete. +5. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods. +6. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta) +7. Implement reflection - on the tool calling spec and results. +8. Introduce multiple-agent collaboration. +9. Support any and all types of asks a user may have to Gmagent. +10. Implement the agent observability. +11. Compare different agent frameworks using Gmagent as the case study. +12. Productionize Gmagent. + + +# Resources +1. Lilien Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) +2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) +3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents) +4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) +5. Letta's blog [The AI agents stack](https://www.letta.com/blog/ai-agents-stack) +6. Microsoft's multi-agent system [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks) +7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/) +8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). +9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) +10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. \ No newline at end of file diff --git a/recipes/use_cases/gmail_agent/examples_log.txt b/recipes/use_cases/gmail_agent/examples_log.txt new file mode 100644 index 000000000..efccc2cd6 --- /dev/null +++ b/recipes/use_cases/gmail_agent/examples_log.txt @@ -0,0 +1,230 @@ +python main.py +Hello! I'm Gmagent, your friendly Gmail assistant. I'm here to help you with any Gmail-related tasks. What can I do for you today? Do you need help checking email, composing a new message, or perhaps something else? + +Your ask: do i have any emails with attachments? + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment'}}. + +Calling tool to access Gmail API: list_emails, {'query': 'has:attachment'}... + +Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang ', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}, {'message_id': '18fa7f38f83197d3', 'sender': 'LangSmith ', 'subject': 'New Data Retention policy for LangSmith', 'received_time': '2024-05-23 17:14:22 PDT'}] + +------------------------- + +Gmagent: I found 3 emails matching your query: +1. From: gmagent_tester1@gmail.com, Subject: Fwd: papers to read, Received on: 2024-11-27 10:51:51 PST +2. From: Jeff Tang , Subject: papers to read, Received on: 2024-11-26 18:44:19 PST +3. From: LangSmith , Subject: New Data Retention policy for LangSmith, Received on: 2024-05-23 17:14:22 PDT + + +Your ask: what's the content of the email from LangSmith + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'get_email_detail', 'parameters': {'detail': 'body', 'which': 'from '}}. + +Calling tool to access Gmail API: get_email_detail, {'detail': 'body', 'which': '18fa7f38f83197d3'}... + +Tool calling returned: New Data Retention policy for LangSmith +Hi Jeff, +You are receiving this email because you have an active account on LangSmith. We are transitioning to a new data retention strategy that limits the length of time trace data is stored in LangSmith. +Effective with traces received on or after Wednesday, May 22, all traces stored in LangSmith now have a maximum retention of 400 days from the time of ingest. +For more information about this change please visit the LangSmith Docs. +If after visiting the docs you still have additional questions, please reach out to LangSmith Support via support@langchain.dev or, if you are an Enterprise plan customer, your dedicated Slack channel. + +------------------------- + +Gmagent: New Data Retention policy for LangSmith +Hi Jeff, +You are receiving this email because you have an active account on LangSmith. We are transitioning to a new data retention strategy that limits the length of time trace data is stored in LangSmith. +Effective with traces received on or after Wednesday, May 22, all traces stored in LangSmith now have a maximum retention of 400 days from the time of ingest. +For more information about this change please visit the LangSmith Docs. +If after visiting the docs you still have additional questions, please reach out to LangSmith Support via support@langchain.dev or, if you are an Enterprise plan customer, your dedicated Slack channel. + + + + + + + + +Unsubscribe + +Your ask: do i have emails with attachment larger than 1mb? + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:1mb'}}. + +Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:1mb'}... + +Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang ', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}] + +------------------------- + +Gmagent: I found 2 emails matching your query: +1. From: gmagent_tester1@gmail.com, Subject: Fwd: papers to read, Received on: 2024-11-27 10:51:51 PST +2. From: Jeff Tang , Subject: papers to read, Received on: 2024-11-26 18:44:19 PST + + +Your ask: what kind of attachments for the email with subject papers to read? + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'get_email_detail', 'parameters': {'detail': 'attachment', 'which': 'subject papers to read'}}. + +Calling tool to access Gmail API: get_email_detail, {'detail': 'attachment', 'which': '1936b819706a4923'}... + +Tool calling returned: thinking_llm.pdf - 1115430 bytes +logic-of-thought.pdf - 1719126 bytes + + +------------------------- + +Gmagent: thinking_llm.pdf - 1115430 bytes +logic-of-thought.pdf - 1719126 bytes + + +Your ask: give me a summary of the pdf thinking_llm.pdf + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'get_pdf_summary', 'parameters': {'file_name': 'thinking_llm.pdf'}}. + +Calling tool to access Gmail API: get_pdf_summary, {'file_name': 'thinking_llm.pdf'}... + +PDF text length: 80464 + +Calling Llama 31 to generate a summary... + +Tool calling returned: It appears that you've provided several examples of responses generated by a model, along with some analysis and comparisons. + +From what I can see, these examples illustrate various aspects of model performance, including: + +1. **Inconsistent or incorrect outputs**: In some cases, the model generates responses that are not accurate or consistent (e.g., Examples 17-19). +2. **Self-correction in math tasks**: The model sometimes makes mistakes but then corrects them based on further thought and analysis (e.g., Example 20). +3. **Improved performance with specific prompts**: Using specific prompts can help the model generate more accurate or helpful responses, especially in cases where it initially made a mistake (e.g., Examples 17-19). +4. **Comparison to direct models**: The TPO model's ability to correct its own mistakes and generate more accurate responses is highlighted through comparisons with direct models (e.g., Example 20). + +These examples are likely from the report you mentioned, which explores the performance of a sequence-to-sequence model for generating natural language responses. + +If you'd like to discuss any specific points or ask questions about these examples, I'm here to help! + +------------------------- + +Gmagent: It appears that you've provided several examples of responses generated by a model, along with some analysis and comparisons. + +From what I can see, these examples illustrate various aspects of model performance, including: + +1. **Inconsistent or incorrect outputs**: In some cases, the model generates responses that are not accurate or consistent (e.g., Examples 17-19). +2. **Self-correction in math tasks**: The model sometimes makes mistakes but then corrects them based on further thought and analysis (e.g., Example 20). +3. **Improved performance with specific prompts**: Using specific prompts can help the model generate more accurate or helpful responses, especially in cases where it initially made a mistake (e.g., Examples 17-19). +4. **Comparison to direct models**: The TPO model's ability to correct its own mistakes and generate more accurate responses is highlighted through comparisons with direct models (e.g., Example 20). + +These examples are likely from the report you mentioned, which explores the performance of a sequence-to-sequence model for generating natural language responses. + +If you'd like to discuss any specific points or ask questions about these examples, I'm here to help! + +Your ask: Draft an email to jeffxtang@meta.com saying working on it and will keep you updated. thanks for your patience. + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'create_draft', 'parameters': {'action': 'new', 'to': 'jeffxtang@meta.com', 'subject': 'Update on [Your Project]', 'body': 'Hi Jeff,\\n\\nI wanted to touch base with you regarding [Your Project]. I am currently working on it and will keep you updated. Thank you for your patience.\\n\\nBest regards,'}}. + +Calling tool to access Gmail API: create_draft, {'action': 'new', 'to': 'jeffxtang@meta.com', 'subject': 'Update on [Your Project]', 'body': 'Hi Jeff,\\n\\nI wanted to touch base with you regarding [Your Project]. I am currently working on it and will keep you updated. Thank you for your patience.\\n\\nBest regards,'}... +Draft created with ID: r5930232887125763038 + +Tool calling returned: r5930232887125763038 + +------------------------- + +Gmagent: Draft created. + +Your ask: send the draft + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'send_draft', 'parameters': {'id': '...'}}. + +Calling tool to access Gmail API: send_draft, {'id': 'r5930232887125763038'}... + +Tool calling returned: Draft sent with email ID: 1936fc3db1993629 + +------------------------- + +Gmagent: Draft sent with email ID: 1936fc3db1993629 + +Your ask: do i have any emails with attachment larger than 10mb? + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:10mb'}}. + +Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:10mb'}... + +Tool calling returned: [] + +------------------------- + +Gmagent: I couldn't find any such emails. What else would you like to do? + +Your ask: how about 5mb + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'has:attachment larger:5mb'}}. + +Calling tool to access Gmail API: list_emails, {'query': 'has:attachment larger:5mb'}... + +Tool calling returned: [{'message_id': '1936fce85e88a3ef', 'sender': 'Jeff Tang ', 'subject': 'this is an interesting paper', 'received_time': '2024-11-27 14:46:50 PST'}] + +------------------------- + +Gmagent: I found 1 email matching your query: +1. From: Jeff Tang , Subject: this is an interesting paper, Received on: 2024-11-27 14:46:50 PST + + +Your ask: reply to the email saying thanks for sharing! + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'send_email', 'parameters': {'action': 'reply', 'to': 'gmagent_tester2@gmail.com', 'subject': 'Re: this is an interesting paper', 'body': 'Thanks for sharing!', 'email_id': '1936fce85e88a3ef'}}. + +Calling tool to access Gmail API: send_email, {'action': 'reply', 'to': 'gmagent_tester2@gmail.com', 'subject': 'Re: this is an interesting paper', 'body': 'Thanks for sharing!', 'email_id': '1936fce85e88a3ef'}... +Reply sent. Message ID: 1936fd65f9f24e70 + +Tool calling returned: None + +------------------------- + +Gmagent: Email sent. + +Your ask: forward the email to jeffxtang@meta.com + +------------------------- +Calling Llama... + +Llama returned: {'function_name': 'send_email', 'parameters': {'action': 'forward', 'to': 'jeffxtang@meta.com', 'subject': 'this is an interesting paper', 'body': '', 'email_id': '1936fce85e88a3ef'}}. + +Calling tool to access Gmail API: send_email, {'action': 'forward', 'to': 'jeffxtang@meta.com', 'subject': 'this is an interesting paper', 'body': '', 'email_id': '1936fce85e88a3ef'}... +Message forwarded successfully! Message ID: 1936fdd039c68451 + +Tool calling returned: None + +------------------------- + +Gmagent: Email sent. + +Your ask: diff --git a/recipes/use_cases/gmail_agent/functions_prompt.py b/recipes/use_cases/gmail_agent/functions_prompt.py new file mode 100644 index 000000000..eebbc2997 --- /dev/null +++ b/recipes/use_cases/gmail_agent/functions_prompt.py @@ -0,0 +1,241 @@ +list_emails_function = """ +{ + "type": "function", + "function": { + "name": "list_emails", + "description": "Return a list of emails matching an optionally specified query.", + "parameters": { + "type": "dic", + "properties": [ + { + "maxResults": { + "type": "integer", + "description": "The default maximum number of emails to return is 100; the maximum allowed value for this field is 500." + } + }, + { + "query": { + "type": "string", + "description": "One or more keywords in the email subject and body, or one or more filters. There can be 6 types of filters: 1) Field-specific Filters: from, to, cc, bcc, subject; 2) Date Filters: before, after, older than, newer than); 3) Status Filters: read, unread, starred, importatant; 4) Attachment Filters: has, filename or type; 5) Size Filters: larger, smaller; 6) logical operators (or, and, not)." + } + } + ], + "required": [] + } + } +} +""" + +get_email_function = """ +{ + "type": "function", + "function": { + "name": "get_email_detail", + "description": "Get detailed info about a specific email", + "parameters": { + "type": "dict", + "properties": [ + { + "detail": { + "type": "string", + "description": "what detail the user wants to know about - two possible values: body or attachment" + } + }, + { + "which": { + "type": "string", + "description": "which email to get detail about - possible values include: 'first', 'second', ..., 'last', 'from ...', and 'subject ...'" + } + }, + ], + "required": ["detail", "which"] + } + } +} +""" + +send_email_function = """ +{ + "type": "function", + "function": { + "name": "send_email", + "description": "Compose, reply, or forward email", + "parameters": { + "type": "dict", + "properties": [ + { + "action": { + "type": "string", + "description": "Whether to compose, reply, or forward an email" + } + }, + { + "to": { + "type": "string", + "description": "The recipient of the email" + } + }, + { + "subject": { + "type": "string", + "description": "The email subject" + } + }, + { + "body": { + "type": "string", + "description": "The email content" + } + }, + { + "email_id": { + "type": "string", + "description": "the email id to reply or forward to" + } + } + ], + "required": ["action", "to", "subject", "body"] + } + } +} +""" + +get_pdf_summary_function = """ +{ + "type": "function", + "function": { + "name": "get_pdf_summary", + "description": "get a summary of a PDF attachment", + "parameters": { + "type": "dict", + "properties": [ + { + "file_name": { + "type": "string", + "description": "The name of the PDF file" + } + }, + ], + "required": ["file_name"] + } + } +} +""" + +create_draft_function = """ +{ + "type": "function", + "function": { + "name": "create_draft", + "description": "Create a new, reply, or forward email draft", + "parameters": { + "type": "dict", + "properties": [ + { + "action": { + "type": "string", + "description": "Whether to draft a new, reply, or forward an email" + } + }, + { + "to": { + "type": "string", + "description": "The recipient of the email" + } + }, + { + "subject": { + "type": "string", + "description": "The email subject" + } + }, + { + "body": { + "type": "string", + "description": "The email content" + } + }, + { + "email_id": { + "type": "string", + "description": "the email id to reply or forward to, or empty if draft a new email." + } + } + ], + "required": ["action", "to", "subject", "body", "email_id"] + } + } +} +""" + +# for now, only allow for one draft email to be saved in a session +# to support for multiple drafts, cf how get_email_detail after list_emails is implemented. +send_draft_function = """ +{ + "type": "function", + "function": { + "name": "send_draft", + "description": "Send a draft email", + "parameters": { + "type": "dict", + "properties": [ + { + "id": { + "type": "string", + "description": "draft id" + } + }, + ], + "required": ["id"] + } + } +} +""" + +examples = """ +{"name": "list_emails", "parameters": {"query": "has:attachment larger:5mb"}} +{"name": "list_emails", "parameters": {"query": "has:attachment"}} +{"name": "list_emails", "parameters": {"query": "newer_than:1d"}} +{"name": "list_emails", "parameters": {"query": "older_than:1d"}} +{"name": "list_emails", "parameters": {"query": "is:unread"}} +{"name": "list_emails", "parameters": {"query": " is:unread"}} +{"name": "list_emails", "parameters": {"query": " is:read"}} +{"name": "get_email_detail", "parameters": {"detail": "body", "which": "first"}} +{"name": "get_email_detail", "parameters": {"detail": "body", "which": "last"}} +{"name": "get_email_detail", "parameters": {"detail": "body", "which": "second"}} +{"name": "get_email_detail", "parameters": {"detail": "body", "which": "subject "}} +{"name": "get_email_detail", "parameters": {"detail": "attachment", "which": "from "}} +{"name": "get_email_detail", "parameters": {"detail": "attachment", "which": "first"}} +{"name": "get_email_detail", "parameters": {"detail": "attachment", "which": "last"}} +{"name": "get_email_detail", "parameters": {"detail": "attachment", "which": ""}} +{"name": "send_email", "parameters": {"action": "compose", "to": "jeffxtang@meta.com", "subject": "xxxxx", "body": "xxxxx"}} +{"name": "send_email", "parameters": {"action": "reply", "to": "", "subject": "xxxxx", "body": "xxxxx", "email_id": "xxxxx"}} +{"name": "send_email", "parameters": {"action": "forward", "to": "jeffxtang@meta.com", "subject": "xxxxx", "body": "xxxxx", "email_id": "xxxxx"}} +{"name": "create_draft", "parameters": {"action": "new", "to": "jeffxtang@meta.com", "subject": "xxxxx", "body": "xxxxx", "email_id": ""}} +{"name": "create_draft", "parameters": {"action": "reply", "to": "", "subject": "xxxxx", "body": "xxxxx", "email_id": "xxxxx"}} +{"name": "create_draft", "parameters": {"action": "forward", "to": "jeffxtang@meta.com", "subject": "xxxxx", "body": "xxxxx", "email_id": "xxxxx"}} +{"name": "send_draft", "parameters": {"id": "..."}} +{"name": "get_pdf_summary", "parameters": {"file_name": "..."}} +""" + +system_prompt = f""" +Environment: ipython +Cutting Knowledge Date: December 2023 +Today Date: 1 December 2024 + +Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user. +Respond to the user's ask by making use of the following functions if needed. +If no available functions can be used, just say "I don't know" and don't make up facts. +Here is a list of available functions in JSON format: + +{list_emails_function} +{get_email_function} +{send_email_function} +{get_pdf_summary_function} +{create_draft_function} +{send_draft_function} + +Example responses: +{examples} + +""" diff --git a/recipes/use_cases/gmail_agent/main.py b/recipes/use_cases/gmail_agent/main.py new file mode 100644 index 000000000..9a1e0aeda --- /dev/null +++ b/recipes/use_cases/gmail_agent/main.py @@ -0,0 +1,32 @@ +import argparse +import gmagent +from gmagent import * +from functions_prompt import system_prompt + + +def main(): + parser = argparse.ArgumentParser(description="Set email address") + parser.add_argument("--user_email", type=str, required=True, help="Your Gmail address") + args = parser.parse_args() + + gmagent.set_email_service(args.user_email) + + greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.") + agent_response = f"{greeting}\n\nYour ask: " + agent = Agent(system_prompt) + + while True: + ask = input(agent_response) + if ask == "bye": + print(llama31("bye")) + break + print("\n-------------------------\nCalling Llama...") + agent(ask) + agent_response = "Your ask: " + + +if __name__ == "__main__": + main() + + + diff --git a/recipes/use_cases/gmail_agent/requirements.txt b/recipes/use_cases/gmail_agent/requirements.txt new file mode 100644 index 000000000..d400c9252 --- /dev/null +++ b/recipes/use_cases/gmail_agent/requirements.txt @@ -0,0 +1,9 @@ + +google-auth==2.27.0 +google-auth-oauthlib==0.4.6 +google-auth-httplib2==0.1.0 +google-api-python-client==2.34.0 +pytz +beautifulsoup4 +ollama +pypdf \ No newline at end of file From 39b0443a96909b3b3ca2037ad9baaa9afa732ef4 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Sun, 1 Dec 2024 12:18:01 -0800 Subject: [PATCH 02/21] lic --- recipes/use_cases/gmail_agent/LICENSE | 21 --------------------- 1 file changed, 21 deletions(-) delete mode 100644 recipes/use_cases/gmail_agent/LICENSE diff --git a/recipes/use_cases/gmail_agent/LICENSE b/recipes/use_cases/gmail_agent/LICENSE deleted file mode 100644 index fd3a6d2f7..000000000 --- a/recipes/use_cases/gmail_agent/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2024 Jeff Tang - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. From 469c66836baf7ca070ca0f4153cea6d95b218be7 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Sun, 1 Dec 2024 12:22:02 -0800 Subject: [PATCH 03/21] added gmagent.py --- recipes/use_cases/gmail_agent/gmagent.py | 624 +++++++++++++++++++++++ 1 file changed, 624 insertions(+) create mode 100644 recipes/use_cases/gmail_agent/gmagent.py diff --git a/recipes/use_cases/gmail_agent/gmagent.py b/recipes/use_cases/gmail_agent/gmagent.py new file mode 100644 index 000000000..44bd8b3ee --- /dev/null +++ b/recipes/use_cases/gmail_agent/gmagent.py @@ -0,0 +1,624 @@ +from google.auth.transport.requests import Request +from google_auth_oauthlib.flow import InstalledAppFlow +from googleapiclient.discovery import build +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.mime.base import MIMEBase +from email import encoders + +from bs4 import BeautifulSoup +import os +import pytz +import base64 +import pickle +from datetime import datetime, timezone +import json +import ollama +from pypdf import PdfReader +from pathlib import Path + +SCOPES = ['https://www.googleapis.com/auth/gmail.readonly', 'https://www.googleapis.com/auth/gmail.compose'] + +def authenticate_gmail(user_email): + creds = None + token_file = f'token_{user_email}.pickle' # Unique token file for each user + + # Load the user's token if it exists + if os.path.exists(token_file): + with open(token_file, 'rb') as token: + creds = pickle.load(token) + + # If no valid credentials, prompt the user to log in + if not creds or not creds.valid: + if creds and creds.expired and creds.refresh_token: + creds.refresh(Request()) + else: + flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES) + creds = flow.run_console() + + # Save the new credentials to a user-specific token file + with open(token_file, 'wb') as token: + pickle.dump(creds, token) + + # Build the Gmail API service + service = build('gmail', 'v1', credentials=creds) + return service + + +def num_of_emails(query=''): + response = service.users().messages().list( + userId='me', + q=query, + maxResults=1).execute() + return response.get('resultSizeEstimate', 0) + + +def list_emails(query='', max_results=100): + emails = [] + next_page_token = None + + while True: + response = service.users().messages().list( + userId=user_id, + maxResults=max_results, + pageToken=next_page_token, + q=query + ).execute() + + if 'messages' in response: + for msg in response['messages']: + sender, subject, received_time = get_email_info(msg['id']) + emails.append( + { + "message_id": msg['id'], + "sender": sender, + "subject": subject, + "received_time": received_time + } + ) + + next_page_token = response.get('nextPageToken') + + if not next_page_token: + break + + return emails + +def get_email_detail(detail, which=''): + if detail == 'body': + return get_email_body(which) + elif detail == 'attachment': + return get_email_attachments(which) + + +def get_email_body(message_id): + try: + message = service.users().messages().get( + userId=user_id, + id=message_id, + format='full').execute() + + # Recursive function to extract the parts + def extract_parts(payload): + text_body = "" + if 'parts' in payload: + for part in payload['parts']: + return extract_parts(part) + else: + mime_type = payload.get('mimeType') + body = payload.get('body', {}).get('data') + if mime_type == 'text/html': + decoded_body = base64.urlsafe_b64decode(body).decode('utf-8') + soup = BeautifulSoup(decoded_body, 'html.parser') + text_body = soup.get_text().strip() + elif mime_type == 'text/plain': + decoded_body = base64.urlsafe_b64decode(body).decode('utf-8') + text_body = decoded_body + + return text_body + + return extract_parts(message['payload']) + + except Exception as e: + print(f"An error occurred: {e}") + return None + + +def parse_message(message): + payload = message['payload'] + headers = payload.get("headers") + + subject = None + sender = None + for header in headers: + if header['name'] == 'Subject': + subject = header['value'] + elif header['name'] == 'From': + sender = header['value'] + + internal_date = message.get('internalDate') + utc_time = datetime.fromtimestamp(int(internal_date) / 1000, tz=timezone.utc) + + # Convert UTC to the specified timezone + local_timezone = pytz.timezone("America/Los_Angeles") + local_time = utc_time.astimezone(local_timezone) + + # Format the local time as a string + received_time = local_time.strftime('%Y-%m-%d %H:%M:%S %Z') + + # Check if the email is plain text or multipart + if 'parts' in payload: + # Multipart message - find the text/plain or text/html part + for part in payload['parts']: + if part['mimeType'] == 'text/plain' or part['mimeType'] == 'text/html': # You can also look for 'text/html' + data = part['body']['data'] + body = base64.urlsafe_b64decode(data).decode('utf-8') + return sender, subject, received_time, body + elif part['mimeType'] in ['multipart/related', 'multipart/mixed', 'multipart/alternative']: + return sender, subject, received_time, get_email_body(message.get('id')) + else: + # Single part message + data = payload['body']['data'] + body = base64.urlsafe_b64decode(data).decode('utf-8') + return sender, subject, received_time, body + + +def get_email_info(msg_id): + message = service.users().messages().get( + userId=user_id, + id=msg_id, + format='full').execute() + + sender, subject, received_time, body = parse_message(message) + + return sender, subject, received_time + + +def reply_email(message_id, reply_text): + # Fetch the original message + original_message = service.users().messages().get( + userId=user_id, + id=message_id, + format='full').execute() + + # Get headers + headers = original_message['payload']['headers'] + subject = None + to = None + for header in headers: + if header['name'] == 'Subject': + subject = header['value'] + if header['name'] == 'From': + to = header['value'] + + # Create the reply subject + if not subject.startswith("Re: "): + subject = "Re: " + subject + + # Compose the reply message + reply_message = MIMEText(reply_text) + reply_message['to'] = to + reply_message['from'] = user_id + reply_message['subject'] = subject + reply_message['In-Reply-To'] = message_id + + # Encode and send the message + raw_message = base64.urlsafe_b64encode(reply_message.as_bytes()).decode("utf-8") + body = {'raw': raw_message, + 'threadId': original_message['threadId']} + sent_message = service.users().messages().send( + userId=user_id, + body=body).execute() + print("Reply sent. Message ID:", sent_message['id']) + + +def forward_email(message_id, forward_to, email_body=None): + """ + Forwards an email, preserving the original MIME type, including multipart/related. + """ + # Get the original message in 'full' format + original_message = service.users().messages().get( + userId=user_id, + id=message_id, + format='full').execute() + + # Extract the payload and headers + payload = original_message.get('payload', {}) + headers = payload.get('headers', []) + parts = payload.get('parts', []) + # Get the Subject + subject = next((header['value'] for header in headers if header['name'].lower() == 'subject'), 'No Subject') + + # Create a new MIME message for forwarding + mime_message = MIMEMultipart(payload.get('mimeType', 'mixed').split('/')[-1]) + mime_message['To'] = forward_to + mime_message['Subject'] = f"Fwd: {subject}" + + # Add the optional custom email body + if email_body: + mime_message.attach(MIMEText(email_body, 'plain')) + + # Function to fetch attachment data by attachmentId + def fetch_attachment_data(attachment_id, message_id): + attachment = service.users().messages().attachments().get( + userId=user_id, messageId=message_id, id=attachment_id + ).execute() + return base64.urlsafe_b64decode(attachment['data']) + + # Rebuild MIME structure + def rebuild_parts(parts): + """ + Recursively rebuild MIME parts. + """ + if not parts: + return None + + for part in parts: + part_mime_type = part.get('mimeType', 'text/plain') + part_body = part.get('body', {}) + part_data = part_body.get('data', '') + part_parts = part.get('parts', []) # Sub-parts for multipart types + filename = part.get('filename') + attachment_id = part_body.get('attachmentId') + + if part_mime_type.startswith('multipart/'): + # Rebuild nested multipart + sub_multipart = MIMEMultipart(part_mime_type.split('/')[-1]) + sub_parts = rebuild_parts(part_parts) + if sub_parts: + for sub_part in sub_parts: + sub_multipart.attach(sub_part) + yield sub_multipart + elif filename and attachment_id: + # Handle attachments + decoded_data = fetch_attachment_data(attachment_id, message_id) + attachment = MIMEBase(*part_mime_type.split('/')) + attachment.set_payload(decoded_data) + encoders.encode_base64(attachment) + attachment.add_header('Content-Disposition', f'attachment; filename="{filename}"') + yield attachment + else: + if part_data: + # Decode and attach non-multipart parts + decoded_data = base64.urlsafe_b64decode(part_data) + + if part_mime_type == 'text/plain': + yield MIMEText(decoded_data.decode('utf-8'), 'plain') + elif part_mime_type == 'text/html': + yield MIMEText(decoded_data.decode('utf-8'), 'html') + + # Rebuild the main MIME structure + rebuilt_parts = rebuild_parts(parts) + if rebuilt_parts: + for rebuilt_part in rebuilt_parts: + mime_message.attach(rebuilt_part) + + # Encode the MIME message to base64 + raw = base64.urlsafe_b64encode(mime_message.as_bytes()).decode('utf-8') + + # Send the email + forward_body = {'raw': raw} + sent_message = service.users().messages().send(userId=user_id, body=forward_body).execute() + + print(f"Message forwarded successfully! Message ID: {sent_message['id']}") + + +def send_email(action, to, subject, body="", email_id=""): + if action == "compose": + message = MIMEText(body) + message['to'] = to + message['from'] = user_id + message['subject'] = subject + + # Encode and send the message + raw_message = base64.urlsafe_b64encode(message.as_bytes()).decode("utf-8") + body = {'raw': raw_message} + sent_message = service.users().messages().send( + userId=user_id, + body=body).execute() + return sent_message['id'] + elif action == "reply": # reply or forward; a message id is needed + reply_email(email_id, body) + elif action == "forward": + forward_email(email_id, to, body) + + +def create_draft(action, to, subject, body="", email_id=""): + if action == "new": + message = MIMEText(body) + message['to'] = to + message['from'] = user_id + message['subject'] = subject + + encoded_message = base64.urlsafe_b64encode(message.as_bytes()).decode() + draft_body = {'message': {'raw': encoded_message}} + draft = service.users().drafts().create( + userId=user_id, + body=draft_body).execute() + print(f"Draft created with ID: {draft['id']}") + return draft['id'] + elif action == "reply": + return create_reply_draft(email_id, body) + elif action == "forward": + return create_forward_draft(email_id, to, body) + else: + return + + + +def create_reply_draft(message_id, reply_text): + # Fetch the original message + original_message = service.users().messages().get( + userId=user_id, + id=message_id, + format='full').execute() + + # Get headers + headers = original_message['payload']['headers'] + subject = None + to = None + for header in headers: + if header['name'] == 'Subject': + subject = header['value'] + if header['name'] == 'From': + to = header['value'] + + # Create the reply subject + if not subject.startswith("Re: "): + subject = "Re: " + subject + + # Compose the reply message + reply_message = MIMEText(reply_text) + reply_message['to'] = to + reply_message['from'] = user_id + reply_message['subject'] = subject + reply_message['In-Reply-To'] = message_id + + encoded_message = base64.urlsafe_b64encode(reply_message.as_bytes()).decode() + draft_body = {'message': {'raw': encoded_message, 'threadId': original_message['threadId']}} + draft = service.users().drafts().create(userId=user_id, body=draft_body).execute() + return draft['id'] + + +def create_forward_draft(message_id, recipient_email, custom_message=None): + # Get the original message + original_message = service.users().messages().get( + userId=user_id, + id=message_id, + format='raw').execute() + + # Decode the raw message + raw_message = base64.urlsafe_b64decode(original_message['raw'].encode('utf-8')) + + # Prepare the forward header and optional custom message + forward_header = f"----- Forwarded message -----\nFrom: {recipient_email}\n\n" + if custom_message: + forward_header += f"{custom_message}\n\n" + + # Combine the forward header with the original message + new_message = forward_header + raw_message.decode('utf-8') + + # Encode the combined message into base64 format + encoded_message = base64.urlsafe_b64encode(new_message.encode('utf-8')).decode('utf-8') + + draft_body = {'message': {'raw': encoded_message, 'threadId': original_message['threadId']}} + draft = service.users().drafts().create(userId=user_id, body=draft_body).execute() + print(f"Forward draft created with ID: {draft['id']}") + return draft['id'] + + +def send_draft(id): + sent_message = service.users().drafts().send( + userId=user_id, + body={'id': id} + ).execute() + return f"Draft sent with email ID: {sent_message['id']}" + + +def get_pdf_summary(file_name): + text = pdf2text(file_name) + print("Calling Llama to generate a summary...") + response = llama31(text, "Generate a summary of the input text in 5 sentences.") + return response + + +def get_email_attachments(message_id, mime_type='application/pdf'): + attachments = [] + + # Helper function to process email parts + def process_parts(parts): + for part in parts: + if part['mimeType'] in ['multipart/related', 'multipart/mixed', 'multipart/alternative']: + # Recursively process nested parts + if 'parts' in part: + process_parts(part['parts']) + elif 'filename' in part and part['filename']: + if part['mimeType'] == mime_type: # Check for the desired MIME type + attachment_id = part['body'].get('attachmentId') + if attachment_id: + # Get the attachment data + attachment = service.users().messages().attachments().get( + userId=user_id, + messageId=message_id, + id=attachment_id + ).execute() + + # Decode the attachment content + file_data = base64.urlsafe_b64decode(attachment['data'].encode('UTF-8')) + + with open(part['filename'], "wb") as f: + f.write(file_data) + + # Save the attachment information + attachments.append( + {'filename': part['filename'], + 'data': file_data, + 'size': attachment.get('size', 0) + }) + + # Retrieve the email message + message = service.users().messages().get( + userId=user_id, + id=message_id, + format='full').execute() + payload = message['payload'] + + # Start processing the parts + if 'parts' in payload: + process_parts(payload['parts']) + + rslt = "" + for a in attachments: + rslt += f"{a['filename']} - {a['size']} bytes\n" + return rslt #attachments + + +def pdf2text(file): + text = '' + try: + with Path(file).open("rb") as f: + reader = PdfReader(f) + text = "\n\n".join([page.extract_text() for page in reader.pages]) + except Exception as e: + raise f"Error reading the PDF file: {str(e)}" + + print(f"\nPDF text length: {len(text)}\n") + + return text + + +user_email = None +service = None +user_id = 'me' + +def set_email_service(email): + global user_email + global service + + user_email = email + service = authenticate_gmail(user_email) + +class Agent: + def __init__(self, system_prompt=""): + self.system_prompt = system_prompt + self.messages = [] + + # Gmagent-specific short term memory, used to answer follow up questions AFTER a list of emails is found matching user's query + self.emails = [] + self.draft_id = None + + if self.system_prompt: + self.messages.append({"role": "system", "content": system_prompt}) + + def __call__(self, user_prompt_or_tool_result, is_tool_call=False): + # if it's tool call result, use "ipython" instead of "user" for the role + self.messages.append({"role": ("ipython" if is_tool_call else "user"), "content": user_prompt_or_tool_result}) + result = self.llama() + print(f"\nLlama returned: {result}.") + if type(result) == dict: # result is a dict only if it's a tool call spec + function_name = result["function_name"] + func = globals()[function_name] + parameters = result["parameters"] + if function_name == "get_email_detail": + # TODO: parse which - valid values are first, second, + # third, fourth, last, from xxx + if 'id' in parameters.keys(): + parameters['which'] = parameters['id'] + del parameters['id'] # per the function spec + elif 'which' in parameters.keys(): + if 'from ' in parameters['which']: + sender = parameters['which'].split('from ')[-1] + for email in self.emails: + if email['sender'].find(sender) != -1: + parameters['which'] = email['message_id'] + break + if 'subject ' in parameters['which']: + subject = parameters['which'].split('subject ')[-1] + # exact match beats substring + for email in self.emails: + if email['subject'].upper() == subject.upper(): + parameters['which'] = email['message_id'] + break + elif email['subject'].upper().find(subject.upper()) != -1: + parameters['which'] = email['message_id'] + + elif 'id_' in parameters['which']: + parameters['which'] = parameters['which'].split('id_')[-1] + else: + parameters['which'] = self.emails[-1]['message_id'] + elif function_name == "send_draft": + parameters['id'] = self.draft_id + + print(f"\nCalling tool to access Gmail API: {function_name}, {parameters}...") + result = func(**parameters) + print(f"\nTool calling returned: {result}") + + # convert function calling result to concise summary, offering interactive follow ups, + # for smooth and user friendly experience + if function_name == 'list_emails': + self.emails = result + num = len(result) + if num == 0: + output = "I couldn't find any such emails. What else would you like to do?" + elif num <= 5: + output = f"I found {num} email{'s' if num > 1 else ''} matching your query:\n" + for i, email in enumerate(result, start=1): + output += f"{i}. From: {email['sender']}, Subject: {email['subject']}, Received on: {email['received_time']}\n" + else: + output = f"I found {num} emails matching your query. Here are the first 5 emails:\n" + for i in range(1, 6): + output += f"{i}. From: {result[i-1]['sender']}, Subject: {result[i-1]['subject']}, Received on: {result[i-1]['received_time']}\n" + elif function_name == "get_email_detail": + output = result + elif function_name == "get_pdf_summary": + output = result + elif function_name == "send_email": + output = "Email sent." + elif function_name == "create_draft": + output = "Draft created." + self.draft_id = result + elif function_name == "send_draft": + output = result + + print(f"\n-------------------------\n\nGmagent: {output}\n") + else: + output = result # direct text, not JSON, response by Llama + + # adding this may cause Llama to hallucinate when answering + # follow up questions. e.g. "do i have emails with attachments + # larger than 20mb" got right tool calling response, then + # follow up "larger than 10mb" got hallucinated response. + # self.messages.append({"role": "assistant", "content": output}) + + # this mitigates the hallucination + self.messages.append({"role": "assistant", "content": str(result)}) + + return output + + def llama(self): + response = ollama.chat(model='llama3.1', + messages = self.messages, + options = { + "temperature": 0.0 + } + ) + result = response['message']['content'] + + try: + res = json.loads(result.split("<|python_tag|>")[-1]) + function_name = res['name'] + parameters = res['parameters'] + return {"function_name": function_name, + "parameters": parameters} + except: + return result + + +def llama31(user_prompt: str, system_prompt = ""): + response = ollama.chat(model='llama3.1', + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + ) + return response['message']['content'] From a13e6d98fcc8e788010222b2c99b218a57802c28 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Mon, 2 Dec 2024 08:56:16 -0800 Subject: [PATCH 04/21] README typo --- recipes/use_cases/gmail_agent/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index 2d564af38..f0b9b4b27 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -112,7 +112,7 @@ cd gmagent pip install -r requirements.txt ``` -# Build and Run Gament +# Run Gmagent To run Gmagent, you need to first copy the `credentials.json` file downloaded and renamed above in Step 6 of Enable Gmail API to the gmagent folder, then run: ``` @@ -324,4 +324,4 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/) 8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) -10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. \ No newline at end of file +10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. From 50fb2403639ddb6c337d1801a23b0ad7b80960c7 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Mon, 2 Dec 2024 08:58:17 -0800 Subject: [PATCH 05/21] README typo --- recipes/use_cases/gmail_agent/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index f0b9b4b27..1843f4cd1 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -315,7 +315,7 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te # Resources -1. Lilien Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) +1. Lilian Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) 2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) 3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents) 4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) From 2ecd6e004133b74eb33f0bdf6c5949239c7371e8 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Mon, 2 Dec 2024 15:25:32 -0800 Subject: [PATCH 06/21] README TODO update --- recipes/use_cases/gmail_agent/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index 1843f4cd1..33cd0efa3 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -304,11 +304,11 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 2. Improve the search, reply, forward, create email draft, and query about attachments to cover all listed and other examples in `functions_prompt.py`. 3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. 4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete. -5. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods. -6. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta) -7. Implement reflection - on the tool calling spec and results. -8. Introduce multiple-agent collaboration. -9. Support any and all types of asks a user may have to Gmagent. +5. Implement the async behavior of Gmagent - schedule an email to be sent later. +6. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods. +7. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta) +8. Implement reflection - on the tool calling spec and results. +9. Introduce multiple-agent collaboration. 10. Implement the agent observability. 11. Compare different agent frameworks using Gmagent as the case study. 12. Productionize Gmagent. @@ -324,4 +324,4 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/) 8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) -10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. +10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. \ No newline at end of file From b62f2d5ccdd032d4501d6e590e589a91ca66a3f7 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Wed, 4 Dec 2024 14:54:04 -0800 Subject: [PATCH 07/21] README update --- recipes/use_cases/gmail_agent/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index 33cd0efa3..b606000e8 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -44,7 +44,7 @@ In a November 2024 blog by Letta [The AI agents stack](https://www.letta.com/blo In addition, Harrison Chase defines agent in the blog [What is an AI agent](https://blog.langchain.dev/what-is-an-agent/) as "a system that uses an LLM to decide the control flow of an application." -Yet another simple [summary](https://www.felicis.com/insight/the-agentic-web) by Felicis of what an agent does is that an agent expands LLMs to go from chat to act: an agent can pair with LLMs with external data, multi-step reasoning and planning, and act on user's behalf. +Yet another simple [summary](https://www.felicis.com/insight/the-agentic-web) by Felicis of what an agent does is that an agent expands LLMs to go from chat to act: an agent can pair LLMs with external data, multi-step reasoning and planning, and act on the user's behalf. All in all (see [Resources](#resources) for even more info), agents are systems that take a high-level task, use an LLM as a reasoning and planning engine, with the help of contextual info and long-term memory if needed, to decide what actions to take, reflect and improve on the actions, and eventually execute those actions to accomplish the task. @@ -324,4 +324,4 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/) 8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) -10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. \ No newline at end of file +10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. From 171401395e3f2867214ea2896166de43756e45ea Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Wed, 4 Dec 2024 15:06:04 -0800 Subject: [PATCH 08/21] README update --- recipes/use_cases/gmail_agent/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index b606000e8..a243234d2 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -16,9 +16,9 @@ Email is an essential and one top killer app people use every day. A recent [Sta Andrew Ng wrote a 5-part [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) in March 2024 predicting "AI agent workflows will drive massive AI progress this year". -Deloitte published in November 2024 a report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) stating that "Through their ability to reason, plan, remember and act, AI agents address key limitations of typical language models." and "Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation." +Deloitte published in November 2024 a report on [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) stating that "Through their ability to reason, plan, remember and act, AI agents address key limitations of typical language models." and "Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation." -In the Thanksgiving week, a new startup [/dev/agent](https://sdsa.ai/) building the next-gen OS for AI agents was on a spotlight. +In the Thanksgiving week, a new startup [/dev/agent](https://sdsa.ai/) building the next-gen OS for AI agents was in the spotlight. So what exactly is an AI agent and how to start building an agent app? From 2739ee001d1afb522a38cc4e3a637e0905c0e4c8 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Fri, 6 Dec 2024 17:23:26 -0800 Subject: [PATCH 09/21] some code and README update based on PR --- recipes/use_cases/gmail_agent/README.md | 8 ++++---- recipes/use_cases/gmail_agent/gmagent.py | 4 ++-- recipes/use_cases/gmail_agent/main.py | 6 ++++-- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index a243234d2..38a70d6dc 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -301,7 +301,7 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te # TODOs 1. Port the app to using [Llama Stack](https://github.com/meta-llama/llama-stack) Agents API. -2. Improve the search, reply, forward, create email draft, and query about attachments to cover all listed and other examples in `functions_prompt.py`. +2. Improve the search, reply, forward, create email draft, and query about types of attachments. 3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. 4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete. 5. Implement the async behavior of Gmagent - schedule an email to be sent later. @@ -311,17 +311,17 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 9. Introduce multiple-agent collaboration. 10. Implement the agent observability. 11. Compare different agent frameworks using Gmagent as the case study. -12. Productionize Gmagent. +12. Add and implement a test plan and productionize Gmagent. # Resources 1. Lilian Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) -2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) +2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) with basic [implementations from scratch](https://github.com/neural-maze/agentic_patterns). 3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents) 4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) 5. Letta's blog [The AI agents stack](https://www.letta.com/blog/ai-agents-stack) 6. Microsoft's multi-agent system [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks) 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/) -8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). +8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen, Letta) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) 10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. diff --git a/recipes/use_cases/gmail_agent/gmagent.py b/recipes/use_cases/gmail_agent/gmagent.py index 44bd8b3ee..69603a8c7 100644 --- a/recipes/use_cases/gmail_agent/gmagent.py +++ b/recipes/use_cases/gmail_agent/gmagent.py @@ -491,11 +491,11 @@ def pdf2text(file): service = None user_id = 'me' -def set_email_service(email): +def set_email_service(gmail): global user_email global service - user_email = email + user_email = gmail service = authenticate_gmail(user_email) class Agent: diff --git a/recipes/use_cases/gmail_agent/main.py b/recipes/use_cases/gmail_agent/main.py index 9a1e0aeda..781937de6 100644 --- a/recipes/use_cases/gmail_agent/main.py +++ b/recipes/use_cases/gmail_agent/main.py @@ -6,10 +6,12 @@ def main(): parser = argparse.ArgumentParser(description="Set email address") - parser.add_argument("--user_email", type=str, required=True, help="Your Gmail address") + parser.add_argument("--gmail", type=str, required=True, help="Your Gmail address") args = parser.parse_args() - gmagent.set_email_service(args.user_email) + print(f"{args.gmail=}") + + gmagent.set_email_service(args.gmail) greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.") agent_response = f"{greeting}\n\nYour ask: " From 83d300fd8d732745415ffab53dcebd2fe803231d Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Fri, 6 Dec 2024 17:24:42 -0800 Subject: [PATCH 10/21] cleanup --- recipes/use_cases/gmail_agent/main.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/recipes/use_cases/gmail_agent/main.py b/recipes/use_cases/gmail_agent/main.py index 781937de6..0366933e7 100644 --- a/recipes/use_cases/gmail_agent/main.py +++ b/recipes/use_cases/gmail_agent/main.py @@ -9,8 +9,6 @@ def main(): parser.add_argument("--gmail", type=str, required=True, help="Your Gmail address") args = parser.parse_args() - print(f"{args.gmail=}") - gmagent.set_email_service(args.gmail) greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.") 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a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index 0fe8e5769..d5b095ab9 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -20,6 +20,8 @@ Deloitte published in November 2024 a report on [AI agents and multiagent system In the Thanksgiving week, a new startup [/dev/agent](https://sdsa.ai/) building the next-gen OS for AI agents was in the spotlight. +In December, Sequoia posted [here](https://www.linkedin.com/posts/konstantinebuhler_the-ai-landscape-is-shifting-from-simple-activity-7270111755710672897-ZHnr/) saying 2024 has been the year of agents (an agent is an AI that can complete tasks, not only tells you how to do it but also does it for you directly), and 2025 will be the year of networks of AI agents. + So what exactly is an AI agent and how to start building an agent app? ## What is an agent? @@ -330,3 +332,4 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen, Letta) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web) 10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though. +11. Sequoia's [post](https://www.linkedin.com/posts/konstantinebuhler_the-ai-landscape-is-shifting-from-simple-activity-7270111755710672897-ZHnr/) on 2024 being the year of AI agents and 2025 networks of AI agents. From 5919e294e0d102464bdee5e4d11313a8deacddbb Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Tue, 10 Dec 2024 10:10:14 -0800 Subject: [PATCH 13/21] README update --- recipes/use_cases/gmail_agent/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index d5b095ab9..854f8e200 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -114,8 +114,8 @@ source gmagent\Scripts\activate # on Windows Then install the required Python libraries: ``` -git clone https://github.com/jeffxtang/gmagent -cd gmagent +git clone https://github.com/meta-llama/llama-recipes +cd llama-recipes/recipes/use_cases/gmail_agent pip install -r requirements.txt ``` From 411cad4b4831d55beaed4432e74f36fc820d1f77 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Tue, 10 Dec 2024 15:07:34 -0800 Subject: [PATCH 14/21] added flowchart --- recipes/use_cases/gmail_agent/README.md | 2 ++ recipes/use_cases/gmail_agent/gmagent.png | Bin 0 -> 594400 bytes 2 files changed, 2 insertions(+) create mode 100644 recipes/use_cases/gmail_agent/gmagent.png diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md index 854f8e200..eb99d7e8e 100644 --- a/recipes/use_cases/gmail_agent/README.md +++ b/recipes/use_cases/gmail_agent/README.md @@ -10,6 +10,8 @@ Currently implemented features of Gmagent include: * get summary of a PDF attachment * draft and send an email +![](gmagent.png) + # Overview Email is an essential and one top killer app people use every day. 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z!@aEFHEC4I_ZMQVR^J&kcujJX`({fo#PM0(;6$zpoqhTIv?JP(g+`V5f2>&W-#nEJ zG=t($y0v&^0?StasTuu|1M&%d_Ag;84XI$PxlDE670k=?$!_}z$>a?Qe#;JB*I`9p z*P;ILhT`(``yFWXb$zqoH&~)ZTl$#$=ua#_0I>(ym8{@3n%J|S5iMm}#i}1gpZjLZ zTGa6sA2))l@*y4FU_vL!Z~DX%*4I6iXa;4O)q0?M9h7?!Tm6?1iU0Dcge)|61Y~sZ zlE-~{t+5Q>y_eNSUhbON#9TT$Z_G2MN7CR8MbGsZ)AKduOk#w*F+sL8WQwwj&~?+Y zMvIXzq0~tbU_{$`>GF-!t~TG8wfMrnWc?O9%MlQ#^CqMQE(ql|*T8mGxDX|k_V-H4 zImDA&yuL)$&YghNu?LZYKBwD`@4oosxSx+4>i=teF>Ur=ZLOQeR(4?n=D>fty9~9G IHIJVCU)xmnp#T5? literal 0 HcmV?d00001 From fbc64706aa5e89efb7c6008699c5c3f64c792ab6 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Tue, 10 Dec 2024 18:27:53 -0800 Subject: [PATCH 15/21] rename folder --- .../use_cases/{gmail_agent => email_agent}/1.png | Bin .../use_cases/{gmail_agent => email_agent}/2.png | Bin .../use_cases/{gmail_agent => email_agent}/3.png | Bin .../{gmail_agent => email_agent}/README.md | 0 .../{gmail_agent => email_agent}/examples_log.txt | 0 .../functions_prompt.py | 0 .../{gmail_agent => email_agent}/gmagent.png | Bin .../{gmail_agent => email_agent}/gmagent.py | 0 .../use_cases/{gmail_agent => email_agent}/main.py | 0 .../{gmail_agent => email_agent}/requirements.txt | 0 10 files changed, 0 insertions(+), 0 deletions(-) rename recipes/use_cases/{gmail_agent => email_agent}/1.png (100%) rename recipes/use_cases/{gmail_agent => email_agent}/2.png (100%) rename recipes/use_cases/{gmail_agent => email_agent}/3.png (100%) rename recipes/use_cases/{gmail_agent => email_agent}/README.md (100%) rename recipes/use_cases/{gmail_agent => email_agent}/examples_log.txt (100%) rename recipes/use_cases/{gmail_agent => email_agent}/functions_prompt.py (100%) rename recipes/use_cases/{gmail_agent => email_agent}/gmagent.png (100%) rename recipes/use_cases/{gmail_agent => email_agent}/gmagent.py (100%) rename recipes/use_cases/{gmail_agent => email_agent}/main.py (100%) rename recipes/use_cases/{gmail_agent => email_agent}/requirements.txt (100%) diff --git a/recipes/use_cases/gmail_agent/1.png b/recipes/use_cases/email_agent/1.png similarity index 100% rename from recipes/use_cases/gmail_agent/1.png rename to recipes/use_cases/email_agent/1.png diff --git a/recipes/use_cases/gmail_agent/2.png b/recipes/use_cases/email_agent/2.png similarity index 100% rename from recipes/use_cases/gmail_agent/2.png rename to recipes/use_cases/email_agent/2.png diff --git a/recipes/use_cases/gmail_agent/3.png b/recipes/use_cases/email_agent/3.png similarity index 100% rename from recipes/use_cases/gmail_agent/3.png rename to recipes/use_cases/email_agent/3.png diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/email_agent/README.md similarity index 100% rename from recipes/use_cases/gmail_agent/README.md rename to recipes/use_cases/email_agent/README.md diff --git a/recipes/use_cases/gmail_agent/examples_log.txt b/recipes/use_cases/email_agent/examples_log.txt similarity index 100% rename from recipes/use_cases/gmail_agent/examples_log.txt rename to recipes/use_cases/email_agent/examples_log.txt diff --git a/recipes/use_cases/gmail_agent/functions_prompt.py b/recipes/use_cases/email_agent/functions_prompt.py similarity index 100% rename from recipes/use_cases/gmail_agent/functions_prompt.py rename to recipes/use_cases/email_agent/functions_prompt.py diff --git a/recipes/use_cases/gmail_agent/gmagent.png b/recipes/use_cases/email_agent/gmagent.png similarity index 100% rename from recipes/use_cases/gmail_agent/gmagent.png rename to recipes/use_cases/email_agent/gmagent.png diff --git a/recipes/use_cases/gmail_agent/gmagent.py b/recipes/use_cases/email_agent/gmagent.py similarity index 100% rename from recipes/use_cases/gmail_agent/gmagent.py rename to recipes/use_cases/email_agent/gmagent.py diff --git a/recipes/use_cases/gmail_agent/main.py b/recipes/use_cases/email_agent/main.py similarity index 100% rename from recipes/use_cases/gmail_agent/main.py rename to recipes/use_cases/email_agent/main.py diff --git a/recipes/use_cases/gmail_agent/requirements.txt b/recipes/use_cases/email_agent/requirements.txt similarity index 100% rename from recipes/use_cases/gmail_agent/requirements.txt rename to recipes/use_cases/email_agent/requirements.txt From 1a7fe9c81f6f7caaf06c0f176f46827eeb4e7c35 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Tue, 10 Dec 2024 19:15:07 -0800 Subject: [PATCH 16/21] README update --- recipes/use_cases/email_agent/README.md | 35 +++++++++++-------------- 1 file changed, 15 insertions(+), 20 deletions(-) diff --git a/recipes/use_cases/email_agent/README.md b/recipes/use_cases/email_agent/README.md index eb99d7e8e..73a895e7a 100644 --- a/recipes/use_cases/email_agent/README.md +++ b/recipes/use_cases/email_agent/README.md @@ -1,8 +1,8 @@ -# Gmagent - A Llama Powered Gmail Agent +# Emagent - A Llama Powered Email Agent -This Gmagent app shows how to build a Gmail agent app powered by Llama 3.1 8B running locally via Ollama (for privacy concern since Gamgent is about your Gmail). We'll start with building from scratch a basic agent with custom tool calling natively supported in Llama 3.1. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your Gmail, with great user experience: intuitive, engaging, efficient and reliable. +This Emagent app shows how to build a Email agent app powered by Llama 3.1 8B running locally via Ollama (for privacy concern since Emagent is about your email). We'll start with building from scratch a basic agent with custom tool calling natively supported in Llama 3.1. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your email, with great user experience: intuitive, engaging, efficient and reliable. We'll use Gmail as an example but any email client API's can be used instead. -Currently implemented features of Gmagent include: +Currently implemented features of Emagent include: * search for emails and attachments * get email detail * reply to a specific email @@ -52,9 +52,9 @@ Yet another simple [summary](https://www.felicis.com/insight/the-agentic-web) by All in all (see [Resources](#resources) for even more info), agents are systems that take a high-level task, use an LLM as a reasoning and planning engine, with the help of contextual info and long-term memory if needed, to decide what actions to take, reflect and improve on the actions, and eventually execute those actions to accomplish the task. -It's time to see an agent app in action and enjoy some coding. Below is a preview of the questions or requests one may ask Gmagent: +It's time to see an agent app in action and enjoy some coding. Below is a preview of the questions or requests one may ask Emagent: -# Example Asks to Gmagent +# Example Asks to Emagent * do i have any emails with attachments? * what's the content of the email from LangSmith @@ -70,12 +70,7 @@ It's time to see an agent app in action and enjoy some coding. Below is a previe * how many emails do i have from xxx@gmail.com? * how about from yyy@gmail.com? -[Here](./examples_log.txt) is an example interaction log with Gmagent, with some screenshots of the interaction below (the user inputs and Gmagent outputs are after Your ask: and Gmagent: ; what's between ---- are Llama outputs and tool calling results): - -![](1.png) -![](2.png) -![](3.png) - +[Here](./examples_log.txt) is an example interaction log with Emagent. # Setup and Installation @@ -104,28 +99,28 @@ This will download a quantized version of Llama 3.1 of the size 4.7GB. First, create a Conda or virtual env: ``` -conda create -n gmagent python=3.10 -conda activate gmagent +conda create -n emagent python=3.10 +conda activate emagent ``` or ``` -python -m venv gmagent -source gmagent/bin/activate # on Linux, macOS: -source gmagent\Scripts\activate # on Windows +python -m venv emagent +source emagent/bin/activate # on Linux, macOS: +source emagent\Scripts\activate # on Windows ``` Then install the required Python libraries: ``` git clone https://github.com/meta-llama/llama-recipes -cd llama-recipes/recipes/use_cases/gmail_agent +cd llama-recipes/recipes/use_cases/email_agent pip install -r requirements.txt ``` -# Run Gmagent +# Run Emagent -To run Gmagent, you need to first copy the `credentials.json` file downloaded and renamed above in Step 6 of Enable Gmail API to the gmagent folder, then run: +To run Emagent, you need to first copy the `credentials.json` file downloaded and renamed above in Step 6 of Enable Gmail API to the emagent folder, then run: ``` -python main.py --user_email +python main.py --gmail ``` The first time you run it, you'll get a prompt like this; From fa06774e83659bbcfeee73f6d1c4f17e6bb947c5 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Wed, 11 Dec 2024 10:51:13 -0800 Subject: [PATCH 17/21] code and readme updated --- recipes/use_cases/email_agent/README.md | 38 +++++++++--------- recipes/use_cases/email_agent/email_agent.png | Bin 0 -> 155365 bytes .../{gmagent.py => email_agent.py} | 4 +- recipes/use_cases/email_agent/gmagent.png | Bin 594400 -> 0 bytes recipes/use_cases/email_agent/main.py | 10 ++--- 5 files changed, 26 insertions(+), 26 deletions(-) create mode 100644 recipes/use_cases/email_agent/email_agent.png rename recipes/use_cases/email_agent/{gmagent.py => email_agent.py} (99%) delete mode 100644 recipes/use_cases/email_agent/gmagent.png diff --git a/recipes/use_cases/email_agent/README.md b/recipes/use_cases/email_agent/README.md index 73a895e7a..e5ce90471 100644 --- a/recipes/use_cases/email_agent/README.md +++ b/recipes/use_cases/email_agent/README.md @@ -1,8 +1,8 @@ -# Emagent - A Llama Powered Email Agent +# Building A Llama Powered Email Agent -This Emagent app shows how to build a Email agent app powered by Llama 3.1 8B running locally via Ollama (for privacy concern since Emagent is about your email). We'll start with building from scratch a basic agent with custom tool calling natively supported in Llama 3.1. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your email, with great user experience: intuitive, engaging, efficient and reliable. We'll use Gmail as an example but any email client API's can be used instead. +This app shows how to build an email agent powered by Llama 3.1 8B running locally via Ollama. We'll start with building from scratch a basic agent with custom tool calling natively supported in Llama 3.1. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your email, with great user experience: intuitive, engaging, efficient and reliable. We'll use Gmail as an example but any email client API's can be used instead. -Currently implemented features of Emagent include: +Currently implemented features include: * search for emails and attachments * get email detail * reply to a specific email @@ -10,7 +10,7 @@ Currently implemented features of Emagent include: * get summary of a PDF attachment * draft and send an email -![](gmagent.png) +![](email_agent.png) # Overview @@ -61,12 +61,12 @@ It's time to see an agent app in action and enjoy some coding. Below is a previe * do i have emails with attachment larger than 1mb? * what kind of attachments for the email with subject papers to read? * give me a summary of the pdf thinking_llm.pdf -* Draft an email to gmagent_test1@gmail.com saying working on it and will keep you updated. thanks for your patience. +* Draft an email to xxx@gmail.com saying working on it and will keep you updated. thanks for your patience. * send the draft * do i have any emails with attachment larger than 10mb? * how about 5mb * reply to the email saying thanks for sharing! -* forward the email to gmagent_test2@gmail.com +* forward the email to xxx@gmail.com * how many emails do i have from xxx@gmail.com? * how about from yyy@gmail.com? @@ -74,7 +74,7 @@ It's time to see an agent app in action and enjoy some coding. Below is a previe # Setup and Installation -If you feel intimated by the steps of the following Enable Gmail API section, you may want to check again the example asks (to see what you can ask to the agent) and the example log (to see the whole conversation with gmagent) - the devil's in the detail and all the glorious description of a powerful trendy agent may not mention the little details one has to deal with to build it. +If you feel intimated by the steps of the following Enable Gmail API section, you may want to check again the example asks (to see what you can ask to the agent) and the example log (to see the whole conversation with the agent) - the devil's in the detail and all the glorious description of a powerful trendy agent may not mention the little details one has to deal with to build it. ## Enable Gmail API 1. Go to the [Google Cloud Console](https://console.cloud.google.com/). @@ -133,15 +133,15 @@ You need to copy the URL above and open it in a browser - if you Sign in with Go In the latter case, go to APIs & Services > OAuth consent screen > Test users, and click the + ADD USERS button, and you'll see this message: While publishing status is set to "Testing", only test users are able to access the app. Allowed user cap prior to app verification is 100, and is counted over the entire lifetime of the app. -After clicking Continue, check the Select all checkbox to enable both settings required for running Gmagent: +After clicking Continue, check the Select all checkbox to enable both settings required for running the agent: ``` View your email messages and settings. Manage drafts and send emails. ``` -Finally, copy the Authorization code and paste it to the Terminal, hit Enter and you'll see Gmagent's initial greeting (which will likely differ because the default temperature value 0.8 is used here - see [Ollama's model file](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) for detail) such as: +Finally, copy the Authorization code and paste it to the Terminal, hit Enter and you'll see the agent's initial greeting (which will likely differ because the default temperature value 0.8 is used here - see [Ollama's model file](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) for detail) such as: ``` -Hello! I'm Gmagent, here to help you manage your Gmail account with ease. +Hello! I'm Email Agent, here to help you manage your email account with ease. What would you like to do today? Do you want me to: @@ -156,7 +156,7 @@ Let me know how I can assist you! Your ask: ``` -If you cancel here and run the command `python main.py --user_email ` again you should see the Gmagent greeting right away without the need to enter an authorization code, unless you enter a different Gmail address for the first time - in fact, for each authorized (added as a test user) Gmail address, a file `token_xxxx@gmail.com.pickle` will be created which contains the authorized token. +If you cancel here and run the command `python main.py --email ` again you should see the agent greeting right away without the need to enter an authorization code, unless you enter a different Gmail address for the first time - in fact, for each authorized (added as a test user) Gmail address, a file `token_xxxx@gmail.com.pickle` will be created which contains the authorized token. See the example asks and interaction log above for the types of asks you may enter. @@ -215,7 +215,7 @@ In fact, even though many hours of pre-processing work has been done to cover so ## Actual Function Call Implementation -For each defined custom function call, its implementation using the Gmail API is present in `gmagent.py`. For example, the `list_emails` is defined as follows: +For each defined custom function call, its implementation using the Gmail API is present in `email_agent.py`. For example, the `list_emails` is defined as follows: ``` def list_emails(query='', max_results=100): @@ -256,7 +256,7 @@ The function will be called by our agent after a user ask such as "do i have ema ``` ## The Agent class -Implemented also in `gmagent.py`, the Agent class uses 3 instance members to allow for contextual aware asks to Gmagent, making it have short-term memory: +Implemented also in `email_agent.py`, the Agent class uses 3 instance members to allow for contextual aware asks to the agent, making it have short-term memory: 1. `messages`: this list holds all the previous user asks and the function call results based on Llama's response to the user asks, making Llama able to answer follow-up questions such as "how about 5mb" (after initial ask "attachments larger than 10mb") or "how about from yyy@gmail.com" (after ask "any emails from xxx@gmail.com). 2. `emails`: this list holds a list of emails that matches the user query, so follow-up questions such as "what kind of attachments for the email with subject xxx" can be answered. 3. `draft_id`: this is used to handle the ask "send the draft" after an initial ask such as "draft an email to xxx". @@ -284,11 +284,11 @@ result = func(**parameters) ... ``` -When you try out Gmagent, you'll likely find that further pre- and post-processing still needed to make it production ready. In a great video on [Vertical LLM Agents](https://www.youtube.com/watch?v=eBVi_sLaYsc), Jake Heller said "after passes frankly even like 100 tests the odds that it will do on any random distribution of user inputs of the next 100,000, 100% accurately is very high" and "by the time you've dealt with like all the edge cases... there might be dozens of things you build into your application to actually make it work well and then you get to the prompting piece and writing out tests and very specific prompts and the strategy for how you break down a big problem into step by step by step thinking and how you feed in the information how you format that information the right way". That's what all the business logic is about. We'll cover decomposing a complicated ask and multi-step reasoning in a future version of Gmagent, and continue to explore the best possible way to streamline the pre- and post-processing. +When you try out the app, you'll likely find that further pre- and post-processing still needed to make it production ready. In a great video on [Vertical LLM Agents](https://www.youtube.com/watch?v=eBVi_sLaYsc), Jake Heller said "after passes frankly even like 100 tests the odds that it will do on any random distribution of user inputs of the next 100,000, 100% accurately is very high" and "by the time you've dealt with like all the edge cases... there might be dozens of things you build into your application to actually make it work well and then you get to the prompting piece and writing out tests and very specific prompts and the strategy for how you break down a big problem into step by step by step thinking and how you feed in the information how you format that information the right way". That's what all the business logic is about. We'll cover decomposing a complicated ask and multi-step reasoning in a future version of the app, and continue to explore the best possible way to streamline the pre- and post-processing. ## Debugging output -When running Gmagent, the detailed Llama returns, pre-processed tool call specs and the actual tool calling results are inside the `-------------------------` block, e.g.: +When running the app, the detailed Llama returns, pre-processed tool call specs and the actual tool calling results are inside the `-------------------------` block, e.g.: ------------------------- Calling Llama... @@ -297,7 +297,7 @@ Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'subjec Calling tool to access Gmail API: list_emails, {'query': 'subject:papers to read has:attachment'}... -Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang ', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}] +Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'xxx@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang ', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}] ------------------------- @@ -308,14 +308,14 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te 2. Improve the search, reply, forward, create email draft, and query about types of attachments. 3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. 4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete. -5. Implement the async behavior of Gmagent - schedule an email to be sent later. +5. Implement the async behavior of the agent - schedule an email to be sent later. 6. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods. 7. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta) 8. Implement reflection - on the tool calling spec and results. 9. Introduce multiple-agent collaboration. 10. Implement the agent observability. -11. Compare different agent frameworks using Gmagent as the case study. -12. Add and implement a test plan and productionize Gmagent. +11. Compare different agent frameworks using the app as the case study. +12. 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recipes/use_cases/email_agent/email_agent.py index 69603a8c7..8936ea217 100644 --- a/recipes/use_cases/email_agent/gmagent.py +++ b/recipes/use_cases/email_agent/email_agent.py @@ -503,7 +503,7 @@ def __init__(self, system_prompt=""): self.system_prompt = system_prompt self.messages = [] - # Gmagent-specific short term memory, used to answer follow up questions AFTER a list of emails is found matching user's query + # agent-specific short term memory, used to answer follow up questions AFTER a list of emails is found matching user's query self.emails = [] self.draft_id = None @@ -580,7 +580,7 @@ def __call__(self, user_prompt_or_tool_result, is_tool_call=False): elif function_name == "send_draft": output = result - print(f"\n-------------------------\n\nGmagent: {output}\n") + print(f"\n-------------------------\n\nAgent: {output}\n") else: output = result # direct text, not JSON, response by Llama diff --git a/recipes/use_cases/email_agent/gmagent.png 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help="Your Gmail address") + parser.add_argument("--email", type=str, required=True, help="Your Gmail address") args = parser.parse_args() - gmagent.set_email_service(args.gmail) + email_agent.set_email_service(args.email) - greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.") + greeting = llama31("hello", "Your name is Email Agent, an assistant that can perform all email related tasks for your user.") agent_response = f"{greeting}\n\nYour ask: " agent = Agent(system_prompt) From 9ff907e393fb4daeb8c677ef484427a259c42002 Mon Sep 17 00:00:00 2001 From: Jeff Tang Date: Wed, 11 Dec 2024 11:22:36 -0800 Subject: [PATCH 18/21] example run screenshots update --- recipes/use_cases/email_agent/1.png | Bin 749822 -> 444068 bytes recipes/use_cases/email_agent/2.png | Bin 752925 -> 504412 bytes recipes/use_cases/email_agent/3.png | Bin 701992 -> 537177 bytes recipes/use_cases/email_agent/README.md | 23 +- 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