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Update RetrieveChat blog to fix some outdated APIs (#2273)
* Fix rag blog * Update wording
This commit is contained in:
Li Jiang
GitHub
parent
4d688a51ad
commit
46bee1f366
@ -79,13 +79,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_config = {\n",
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" \"timeout\": 60,\n",
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" \"temperature\": 0,\n",
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" \"config_list\": config_list,\n",
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"}\n",
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"\n",
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"\n",
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"def termination_msg(x):\n",
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" return isinstance(x, dict) and \"TERMINATE\" == str(x.get(\"content\", \"\"))[-9:].upper()\n",
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"\n",
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@ -117,31 +110,27 @@
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" description=\"Assistant who has extra content retrieval power for solving difficult problems.\",\n",
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")\n",
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"\n",
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"\n",
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"coder_llm_config = llm_config.copy()\n",
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"coder = AssistantAgent(\n",
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" name=\"Senior_Python_Engineer\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a senior python engineer, you provide python code to answer questions. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config={\"config_list\": config_list},\n",
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" llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0},\n",
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" description=\"Senior Python Engineer who can write code to solve problems and answer questions.\",\n",
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")\n",
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"\n",
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"pm_llm_config = llm_config.copy()\n",
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"pm = autogen.AssistantAgent(\n",
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" name=\"Product_Manager\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a product manager. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config={\"config_list\": config_list},\n",
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" llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0},\n",
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" description=\"Product Manager who can design and plan the project.\",\n",
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")\n",
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"\n",
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"reviewer_llm_config = llm_config.copy()\n",
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"reviewer = autogen.AssistantAgent(\n",
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" name=\"Code_Reviewer\",\n",
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" is_termination_msg=termination_msg,\n",
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" system_message=\"You are a code reviewer. Reply `TERMINATE` in the end when everything is done.\",\n",
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" llm_config={\"config_list\": config_list},\n",
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" llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0},\n",
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" description=\"Code Reviewer who can review the code.\",\n",
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")\n",
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"\n",
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@ -161,7 +150,9 @@
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" groupchat = autogen.GroupChat(\n",
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" agents=[boss_aid, pm, coder, reviewer], messages=[], max_round=12, speaker_selection_method=\"round_robin\"\n",
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" )\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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" manager = autogen.GroupChatManager(\n",
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" groupchat=groupchat, llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0}\n",
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" )\n",
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"\n",
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" # Start chatting with boss_aid as this is the user proxy agent.\n",
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" boss_aid.initiate_chat(\n",
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@ -181,7 +172,9 @@
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" speaker_selection_method=\"auto\",\n",
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" allow_repeat_speaker=False,\n",
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" )\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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" manager = autogen.GroupChatManager(\n",
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" groupchat=groupchat, llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0}\n",
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" )\n",
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"\n",
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" # Start chatting with the boss as this is the user proxy agent.\n",
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" boss.initiate_chat(\n",
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@ -233,7 +226,9 @@
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" allow_repeat_speaker=False,\n",
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" )\n",
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"\n",
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" manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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" manager = autogen.GroupChatManager(\n",
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" groupchat=groupchat, llm_config={\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0}\n",
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" )\n",
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"\n",
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" # Start chatting with the boss as this is the user proxy agent.\n",
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" boss.initiate_chat(\n",
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@ -4,6 +4,8 @@ authors: thinkall
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tags: [LLM, RAG]
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---
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*Last update: April 4, 2024; AutoGen version: v0.2.21*
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**TL;DR:**
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@ -57,10 +59,17 @@ pip install "pyautogen[retrievechat]"
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RetrieveChat can handle various types of documents. By default, it can process
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plain text and PDF files, including formats such as 'txt', 'json', 'csv', 'tsv',
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'md', 'html', 'htm', 'rtf', 'rst', 'jsonl', 'log', 'xml', 'yaml', 'yml' and 'pdf'.
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If you install [unstructured](https://unstructured-io.github.io/unstructured/installation/full_installation.html)
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(`pip install "unstructured[all-docs]"`), additional document types such as 'docx',
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If you install [unstructured](https://unstructured-io.github.io/unstructured/installation/full_installation.html),
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additional document types such as 'docx',
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'doc', 'odt', 'pptx', 'ppt', 'xlsx', 'eml', 'msg', 'epub' will also be supported.
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- Install `unstructured` in ubuntu
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```bash
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sudo apt-get update
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sudo apt-get install -y tesseract-ocr poppler-utils
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pip install unstructured[all-docs]
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```
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You can find a list of all supported document types by using `autogen.retrieve_utils.TEXT_FORMATS`.
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1. Import Agents
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@ -90,7 +99,7 @@ ragproxyagent = RetrieveUserProxyAgent(
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3. Initialize Chat and ask a question
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```python
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assistant.reset()
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ragproxyagent.initiate_chat(assistant, problem="What is autogen?")
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ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem="What is autogen?")
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```
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Output is like:
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@ -283,28 +292,6 @@ However, you may want to initialize the chat with another agent in some cases. T
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you'll need to call it from a function.
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```python
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llm_config = {
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"functions": [
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{
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"name": "retrieve_content",
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"description": "retrieve content for code generation and question answering.",
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"parameters": {
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"type": "object",
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"properties": {
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"message": {
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"type": "string",
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"description": "Refined message which keeps the original meaning and can be used to retrieve content for code generation and question answering.",
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}
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},
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"required": ["message"],
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},
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},
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],
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"config_list": config_list,
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"timeout": 60,
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"seed": 42,
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}
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boss = autogen.UserProxyAgent(
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name="Boss",
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is_termination_msg=termination_msg,
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@ -328,46 +315,58 @@ coder = AssistantAgent(
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name="Senior_Python_Engineer",
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is_termination_msg=termination_msg,
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system_message="You are a senior python engineer. Reply `TERMINATE` in the end when everything is done.",
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llm_config=llm_config,
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llm_config={"config_list": config_list, "timeout": 60, "temperature": 0},
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)
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pm = autogen.AssistantAgent(
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name="Product_Manager",
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is_termination_msg=termination_msg,
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system_message="You are a product manager. Reply `TERMINATE` in the end when everything is done.",
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llm_config=llm_config,
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llm_config={"config_list": config_list, "timeout": 60, "temperature": 0},
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)
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reviewer = autogen.AssistantAgent(
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name="Code_Reviewer",
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is_termination_msg=termination_msg,
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system_message="You are a code reviewer. Reply `TERMINATE` in the end when everything is done.",
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llm_config=llm_config,
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llm_config={"config_list": config_list, "timeout": 60, "temperature": 0},
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)
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def retrieve_content(message, n_results=3):
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boss_aid.n_results = n_results # Set the number of results to be retrieved.
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# Check if we need to update the context.
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update_context_case1, update_context_case2 = boss_aid._check_update_context(message)
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if (update_context_case1 or update_context_case2) and boss_aid.update_context:
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boss_aid.problem = message if not hasattr(boss_aid, "problem") else boss_aid.problem
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_, ret_msg = boss_aid._generate_retrieve_user_reply(message)
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else:
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_context = {"problem": message, "n_results": n_results}
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ret_msg = boss_aid.message_generator(boss_aid, None, _context)
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return ret_msg if ret_msg else message
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def retrieve_content(
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message: Annotated[
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str,
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"Refined message which keeps the original meaning and can be used to retrieve content for code generation and question answering.",
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],
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n_results: Annotated[int, "number of results"] = 3,
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) -> str:
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boss_aid.n_results = n_results # Set the number of results to be retrieved.
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# Check if we need to update the context.
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update_context_case1, update_context_case2 = boss_aid._check_update_context(message)
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if (update_context_case1 or update_context_case2) and boss_aid.update_context:
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boss_aid.problem = message if not hasattr(boss_aid, "problem") else boss_aid.problem
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_, ret_msg = boss_aid._generate_retrieve_user_reply(message)
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else:
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_context = {"problem": message, "n_results": n_results}
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ret_msg = boss_aid.message_generator(boss_aid, None, _context)
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return ret_msg if ret_msg else message
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for agent in [boss, coder, pm, reviewer]:
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# register functions for all agents.
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agent.register_function(
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function_map={
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"retrieve_content": retrieve_content,
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}
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)
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for caller in [pm, coder, reviewer]:
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d_retrieve_content = caller.register_for_llm(
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description="retrieve content for code generation and question answering.", api_style="function"
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)(retrieve_content)
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for executor in [boss, pm]:
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executor.register_for_execution()(d_retrieve_content)
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groupchat = autogen.GroupChat(
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agents=[boss, coder, pm, reviewer], messages=[], max_round=12
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agents=[boss, pm, coder, reviewer],
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messages=[],
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max_round=12,
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speaker_selection_method="round_robin",
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allow_repeat_speaker=False,
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)
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llm_config = {"config_list": config_list, "timeout": 60, "temperature": 0}
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manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
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# Start chatting with the boss as this is the user proxy agent.
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