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Remove dependency on RetrieveAssistantAgent for RetrieveChat (#3320)
* Remove deps on RetrieveAssistantAgent for getting human input * Terminate when no more context * Add deprecation warning message * Clean up RetrieveAssistantAgent, part 1 * Update version * Clean up docs and notebooks
This commit is contained in:
Li Jiang
@@ -10,7 +10,7 @@
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"AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"RetrieveChat is a conversational system for retrieval-augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
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"RetrieveChat is a conversational system for retrieval-augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `AssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
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"\n",
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"## Table of Contents\n",
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"We'll demonstrate six examples of using RetrieveChat for code generation and question answering:\n",
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@@ -92,29 +92,13 @@
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"from sentence_transformers import SentenceTransformer\n",
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"\n",
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"import autogen\n",
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"from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent\n",
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"from autogen import AssistantAgent\n",
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"from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n",
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"\n",
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"# Accepted file formats for that can be stored in\n",
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"# a vector database instance\n",
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"from autogen.retrieve_utils import TEXT_FORMATS\n",
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"\n",
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"config_list = [\n",
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" {\n",
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" \"model\": \"Meta-Llama-3-8B-Instruct-imatrix\",\n",
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" \"api_key\": \"YOUR_API_KEY\",\n",
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" \"base_url\": \"http://localhost:8080/v1\",\n",
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" \"api_type\": \"openai\",\n",
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" },\n",
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" {\"model\": \"gpt-3.5-turbo-0125\", \"api_key\": \"YOUR_API_KEY\", \"api_type\": \"openai\"},\n",
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" {\n",
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" \"model\": \"gpt-35-turbo\",\n",
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" \"base_url\": \"...\",\n",
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" \"api_type\": \"azure\",\n",
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" \"api_version\": \"2023-07-01-preview\",\n",
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" \"api_key\": \"...\",\n",
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" },\n",
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"]\n",
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"config_list = autogen.config_list_from_json(\n",
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" \"OAI_CONFIG_LIST\",\n",
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" file_location=\".\",\n",
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@@ -136,7 +120,7 @@
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"\n",
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"## Construct agents for RetrieveChat\n",
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"\n",
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"We start by initializing the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`. The system message needs to be set to \"You are a helpful assistant.\" for RetrieveAssistantAgent. The detailed instructions are given in the user message. Later we will use the `RetrieveUserProxyAgent.message_generator` to combine the instructions and a retrieval augmented generation task for an initial prompt to be sent to the LLM assistant."
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"We start by initializing the `AssistantAgent` and `RetrieveUserProxyAgent`. The system message needs to be set to \"You are a helpful assistant.\" for AssistantAgent. The detailed instructions are given in the user message. Later we will use the `RetrieveUserProxyAgent.message_generator` to combine the instructions and a retrieval augmented generation task for an initial prompt to be sent to the LLM assistant."
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]
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},
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{
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@@ -173,8 +157,8 @@
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}
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],
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"source": [
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"# 1. create an RetrieveAssistantAgent instance named \"assistant\"\n",
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"assistant = RetrieveAssistantAgent(\n",
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"# 1. create an AssistantAgent instance named \"assistant\"\n",
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"assistant = AssistantAgent(\n",
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" name=\"assistant\",\n",
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" system_message=\"You are a helpful assistant. You must always reply with some form of text.\",\n",
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" llm_config={\n",
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@@ -191,15 +175,9 @@
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"sentence_transformer_ef = SentenceTransformer(\"all-distilroberta-v1\").encode\n",
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"\n",
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"# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
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"# By default, the human_input_mode is \"ALWAYS\", which means the agent will ask for human input at every step. We set it to \"NEVER\" here.\n",
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"# `docs_path` is the path to the docs directory. It can also be the path to a single file, or the url to a single file. By default,\n",
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"# it is set to None, which works only if the collection is already created.\n",
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"# `task` indicates the kind of task we're working on. In this example, it's a `code` task.\n",
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"# `chunk_token_size` is the chunk token size for the retrieve chat. By default, it is set to `max_tokens * 0.6`, here we set it to 2000.\n",
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"# `custom_text_types` is a list of file types to be processed. Default is `autogen.retrieve_utils.TEXT_FORMATS`.\n",
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"# This only applies to files under the directories in `docs_path`. Explicitly included files and urls will be chunked regardless of their types.\n",
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"# In this example, we set it to [\"non-existent-type\"] to only process markdown files. Since no \"non-existent-type\" files are included in the `websit/docs`,\n",
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"# no files there will be processed. However, the explicitly included urls will still be processed.\n",
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"# Refer to https://microsoft.github.io/autogen/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
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"# and https://microsoft.github.io/autogen/docs/reference/agentchat/contrib/vectordb/pgvectordb\n",
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"# for more information on the RetrieveUserProxyAgent and PGVectorDB\n",
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"ragproxyagent = RetrieveUserProxyAgent(\n",
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" name=\"ragproxyagent\",\n",
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" human_input_mode=\"NEVER\",\n",
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@@ -209,9 +187,7 @@
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" \"docs_path\": [\n",
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" \"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md\",\n",
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" \"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Research.md\",\n",
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" os.path.join(os.path.abspath(\"\"), \"..\", \"website\", \"docs\"),\n",
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" ],\n",
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" \"custom_text_types\": [\"non-existent-type\"],\n",
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" \"chunk_token_size\": 2000,\n",
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" \"model\": config_list[0][\"model\"],\n",
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" \"vector_db\": \"pgvector\", # PGVector database\n",
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