mirror of https://github.com/microsoft/autogen.git
speed up notebook in CI and fix CI failures (#2427)
* speed up notebook in CI * improve notebook * comment about filter * bug fix for summary * bump version to 0.2.26 * restrict openai version * news update * add tags in example
This commit is contained in:
Chi Wang
GitHub
parent
442c10bd7b
commit
6636689cb9
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@ -5,7 +5,8 @@
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[
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{
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"model": "gpt-4",
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"api_key": "<your OpenAI API key here>"
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"api_key": "<your OpenAI API key here>",
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"tags": ["gpt-4", "tool"]
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},
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{
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"model": "<your Azure OpenAI deployment name>",
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@ -14,7 +14,7 @@
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<img src="https://github.com/microsoft/autogen/blob/main/website/static/img/flaml.svg" width=200>
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<br>
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</p> -->
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:fire: Mar 26, 2024: Andrew Ng gave a shoutout to AutoGen in [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent.
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:fire: Apr 17, 2024: Andrew Ng cited AutoGen in [The Batch newsletter](https://www.deeplearning.ai/the-batch/issue-245/) and [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent (Mar 26).
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:fire: Mar 3, 2024: What's new in AutoGen? 📰[Blog](https://microsoft.github.io/autogen/blog/2024/03/03/AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).
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@ -1120,19 +1120,18 @@ class ConversableAgent(LLMAgent):
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@staticmethod
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def _last_msg_as_summary(sender, recipient, summary_args) -> str:
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"""Get a chat summary from the last message of the recipient."""
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summary = ""
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try:
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content = recipient.last_message(sender)["content"]
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if isinstance(content, str):
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summary = content.replace("TERMINATE", "")
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elif isinstance(content, list):
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# Remove the `TERMINATE` word in the content list.
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summary = [
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{**x, "text": x["text"].replace("TERMINATE", "")} if isinstance(x, dict) and "text" in x else x
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for x in content
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]
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summary = "\n".join(
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x["text"].replace("TERMINATE", "") for x in content if isinstance(x, dict) and "text" in x
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)
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except (IndexError, AttributeError) as e:
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warnings.warn(f"Cannot extract summary using last_msg: {e}. Using an empty str as summary.", UserWarning)
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summary = ""
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return summary
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@staticmethod
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@ -1 +1 @@
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__version__ = "0.2.25"
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__version__ = "0.2.26"
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@ -32,7 +32,7 @@
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"To gather usage data for a list of agents, we provide an utility function `autogen.gather_usage_summary(agents)` where you pass in a list of agents and gather the usage summary.\n",
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"\n",
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"## Caution when using Azure OpenAI!\n",
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"If you are using azure OpenAI, the model returned from completion doesn't have the version information. The returned model is either 'gpt-35-turbo' or 'gpt-4'. From there, we are calculating the cost based on gpt-3.5-0613: ((0.0015, 0.002) per 1k prompt and completion tokens) and gpt-4-0613: (0.03,0.06). This means the cost is wrong if you are using the 1106 version of the models from azure OpenAI.\n",
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"If you are using azure OpenAI, the model returned from completion doesn't have the version information. The returned model is either 'gpt-35-turbo' or 'gpt-4'. From there, we are calculating the cost based on gpt-3.5-turbo-0125: (0.0005, 0.0015) per 1k prompt and completion tokens and gpt-4-0613: (0.03, 0.06). This means the cost can be wrong if you are using a different version from azure OpenAI.\n",
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"\n",
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"This will be improved in the future. However, the token count summary is accurate. You can use the token count to calculate the cost yourself.\n",
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"\n",
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@ -55,25 +55,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import autogen\n",
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"from autogen import AssistantAgent, OpenAIWrapper, UserProxyAgent, gather_usage_summary\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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"# filter_dict={\n",
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"# \"model\": [\"gpt-3.5-turbo\", \"gpt-4-1106-preview\"],\n",
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"# },\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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" # filter_dict={\n",
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" # \"model\": [\"gpt-3.5-turbo\", \"gpt-35-turbo\"],\n",
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" # },\n",
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" filter_dict={\n",
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" \"tags\": [\"gpt-3.5-turbo\", \"gpt-3.5-turbo-16k\"], # comment out to get all\n",
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" },\n",
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")"
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]
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},
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@ -81,21 +74,23 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n",
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"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by tags (you can filter by other keys as well).\n",
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"\n",
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"The config list looks like the following:\n",
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"```python\n",
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"config_list = [\n",
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" {\n",
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" \"model\": \"gpt-4\",\n",
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" \"model\": \"gpt-3.5-turbo\",\n",
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" \"api_key\": \"<your OpenAI API key>\",\n",
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" }, # OpenAI API endpoint for gpt-4\n",
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" \"tags\": [\"gpt-3.5-turbo\"],\n",
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" }, # OpenAI API endpoint for gpt-3.5-turbo\n",
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" {\n",
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" \"model\": \"gpt-35-turbo-0613\", # 0613 or newer is needed to use functions\n",
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" \"base_url\": \"<your Azure OpenAI API base>\", \n",
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" \"api_type\": \"azure\", \n",
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" \"api_version\": \"2024-02-15-preview\", # 2023-07-01-preview or newer is needed to use functions\n",
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" \"api_key\": \"<your Azure OpenAI API key>\"\n",
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" \"api_key\": \"<your Azure OpenAI API key>\",\n",
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" \"tags\": [\"gpt-3.5-turbo\", \"0613\"],\n",
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" }\n",
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"]\n",
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"```\n",
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@ -112,14 +107,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.00861\n"
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"0.00020600000000000002\n"
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]
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}
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],
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@ -128,7 +123,7 @@
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"messages = [\n",
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" {\"role\": \"user\", \"content\": \"Can you give me 3 useful tips on learning Python? Keep it simple and short.\"},\n",
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"]\n",
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"response = client.create(messages=messages, model=\"gpt-3.5-turbo\", cache_seed=None)\n",
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"response = client.create(messages=messages, cache_seed=None)\n",
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"print(response.cost)"
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]
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},
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@ -143,7 +138,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -164,7 +159,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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@ -172,19 +167,21 @@
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"output_type": "stream",
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"text": [
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"----------------------------------------------------------------------------------------------------\n",
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"No actual cost incurred (all completions are using cache).\n",
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"Usage summary excluding cached usage: \n",
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"Total cost: 0.00023\n",
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"* Model 'gpt-35-turbo': cost: 0.00023, prompt_tokens: 25, completion_tokens: 142, total_tokens: 167\n",
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"\n",
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"Usage summary including cached usage: \n",
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"Total cost: 0.01059\n",
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"* Model 'gpt-4': cost: 0.01059, prompt_tokens: 25, completion_tokens: 164, total_tokens: 189\n",
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"All completions are non-cached: the total cost with cached completions is the same as actual cost.\n",
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"----------------------------------------------------------------------------------------------------\n",
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"----------------------------------------------------------------------------------------------------\n",
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"No actual cost incurred (all completions are using cache).\n",
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"Usage summary excluding cached usage: \n",
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"Total cost: 0.00023\n",
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"* Model 'gpt-35-turbo': cost: 0.00023, prompt_tokens: 25, completion_tokens: 142, total_tokens: 167\n",
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"----------------------------------------------------------------------------------------------------\n",
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"----------------------------------------------------------------------------------------------------\n",
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"Usage summary including cached usage: \n",
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"Total cost: 0.01059\n",
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"* Model 'gpt-4': cost: 0.01059, prompt_tokens: 25, completion_tokens: 164, total_tokens: 189\n",
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"Total cost: 0.00023\n",
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"* Model 'gpt-35-turbo': cost: 0.00023, prompt_tokens: 25, completion_tokens: 142, total_tokens: 167\n",
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"----------------------------------------------------------------------------------------------------\n"
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]
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}
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@ -192,7 +189,7 @@
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"source": [
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"# The first creation\n",
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"# By default, cache_seed is set to 41 and enabled. If you don't want to use cache, set cache_seed to None.\n",
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"response = client.create(messages=messages, model=\"gpt-35-turbo-1106\", cache_seed=41)\n",
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"response = client.create(messages=messages, cache_seed=41)\n",
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"client.print_usage_summary() # default to [\"actual\", \"total\"]\n",
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"client.print_usage_summary(mode=\"actual\") # print actual usage summary\n",
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"client.print_usage_summary(mode=\"total\") # print total usage summary"
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@ -200,15 +197,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"None\n",
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"{'total_cost': 0.01059, 'gpt-4': {'cost': 0.01059, 'prompt_tokens': 25, 'completion_tokens': 164, 'total_tokens': 189}}\n"
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"{'total_cost': 0.0002255, 'gpt-35-turbo': {'cost': 0.0002255, 'prompt_tokens': 25, 'completion_tokens': 142, 'total_tokens': 167}}\n",
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"{'total_cost': 0.0002255, 'gpt-35-turbo': {'cost': 0.0002255, 'prompt_tokens': 25, 'completion_tokens': 142, 'total_tokens': 167}}\n"
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]
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}
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],
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@ -220,7 +217,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -228,11 +225,13 @@
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"output_type": "stream",
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"text": [
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"----------------------------------------------------------------------------------------------------\n",
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"No actual cost incurred (all completions are using cache).\n",
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"Usage summary excluding cached usage: \n",
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"Total cost: 0.00023\n",
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"* Model 'gpt-35-turbo': cost: 0.00023, prompt_tokens: 25, completion_tokens: 142, total_tokens: 167\n",
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"\n",
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"Usage summary including cached usage: \n",
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"Total cost: 0.02118\n",
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"* Model 'gpt-4': cost: 0.02118, prompt_tokens: 50, completion_tokens: 328, total_tokens: 378\n",
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"Total cost: 0.00045\n",
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"* Model 'gpt-35-turbo': cost: 0.00045, prompt_tokens: 50, completion_tokens: 284, total_tokens: 334\n",
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"----------------------------------------------------------------------------------------------------\n"
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]
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}
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@ -240,13 +239,13 @@
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"source": [
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"# Since cache is enabled, the same completion will be returned from cache, which will not incur any actual cost.\n",
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"# So actual cost doesn't change but total cost doubles.\n",
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"response = client.create(messages=messages, model=\"gpt-35-turbo-1106\", cache_seed=41)\n",
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"response = client.create(messages=messages, cache_seed=41)\n",
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"client.print_usage_summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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@ -265,7 +264,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@ -276,15 +275,15 @@
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"No actual cost incurred (all completions are using cache).\n",
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"\n",
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"Usage summary including cached usage: \n",
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"Total cost: 0.01059\n",
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"* Model 'gpt-4': cost: 0.01059, prompt_tokens: 25, completion_tokens: 164, total_tokens: 189\n",
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"Total cost: 0.00023\n",
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"* Model 'gpt-35-turbo': cost: 0.00023, prompt_tokens: 25, completion_tokens: 142, total_tokens: 167\n",
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"----------------------------------------------------------------------------------------------------\n"
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]
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}
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],
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"source": [
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"# all completions are returned from cache, so no actual cost incurred.\n",
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"response = client.create(messages=messages, model=\"gpt-35-turbo-1106\", cache_seed=41)\n",
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"response = client.create(messages=messages, cache_seed=41)\n",
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"client.print_usage_summary()"
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]
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},
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@ -302,7 +301,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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@ -313,32 +312,22 @@
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"\n",
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"$x^3=125$. What is x?\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"\u001b[33massistant\u001b[0m (to ai_user):\n",
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"\n",
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"To find the value of $x$ when $x^3 = 125$, you can find the cube root of 125. The cube root of a number is a value that, when multiplied by itself three times, gives the original number.\n",
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"To find x, we need to take the cube root of 125. The cube root of a number is the number that, when multiplied by itself three times, gives the original number.\n",
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"\n",
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"The cube root of 125 can be written as $125^{1/3}$ or $\\sqrt[3]{125}$. Since $5 \\times 5 \\times 5 = 125$, it follows that:\n",
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"\n",
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"$$x = \\sqrt[3]{125} = 5$$\n",
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"\n",
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"Therefore, $x = 5$.\n",
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"In this case, the cube root of 125 is 5 since 5 * 5 * 5 = 125. Therefore, x = 5.\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33mai_user\u001b[0m (to assistant):\n",
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"\n",
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"Your calculation is correct. The value of $x$ when $x^3 = 125$ is indeed $x = 5$. Great job!\n",
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"That's correct! Well done. The value of x is indeed 5, as you correctly found by taking the cube root of 125. Keep up the good work!\n",
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"\n",
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"--------------------------------------------------------------------------------\n",
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"\u001b[33massistant\u001b[0m (to ai_user):\n",
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"\n",
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"Thank you for the confirmation! I'm glad the answer was helpful. If you have any more questions or need assistance with anything else, feel free to ask!\n",
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"Thank you! I'm glad I could help. If you have any more questions, feel free to ask!\n",
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"\n",
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"--------------------------------------------------------------------------------\n"
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]
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@ -346,10 +335,10 @@
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{
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"data": {
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"text/plain": [
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"ChatResult(chat_history=[{'content': '$x^3=125$. What is x?', 'role': 'assistant'}, {'content': 'To find the value of $x$ when $x^3 = 125$, you can find the cube root of 125. The cube root of a number is a value that, when multiplied by itself three times, gives the original number.\\n\\nThe cube root of 125 can be written as $125^{1/3}$ or $\\\\sqrt[3]{125}$. Since $5 \\\\times 5 \\\\times 5 = 125$, it follows that:\\n\\n$$x = \\\\sqrt[3]{125} = 5$$\\n\\nTherefore, $x = 5$.', 'role': 'user'}, {'content': 'Your calculation is correct. The value of $x$ when $x^3 = 125$ is indeed $x = 5$. Great job!', 'role': 'assistant'}, {'content': \"Thank you for the confirmation! I'm glad the answer was helpful. If you have any more questions or need assistance with anything else, feel free to ask!\", 'role': 'user'}], summary=\"Thank you for the confirmation! I'm glad the answer was helpful. If you have any more questions or need assistance with anything else, feel free to ask!\", cost=({'total_cost': 0.022019999999999998, 'gpt-4': {'cost': 0.022019999999999998, 'prompt_tokens': 372, 'completion_tokens': 181, 'total_tokens': 553}}, {'total_cost': 0.022019999999999998, 'gpt-4': {'cost': 0.022019999999999998, 'prompt_tokens': 372, 'completion_tokens': 181, 'total_tokens': 553}}), human_input=[])"
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"ChatResult(chat_id=None, chat_history=[{'content': '$x^3=125$. What is x?', 'role': 'assistant'}, {'content': 'To find x, we need to take the cube root of 125. The cube root of a number is the number that, when multiplied by itself three times, gives the original number.\\n\\nIn this case, the cube root of 125 is 5 since 5 * 5 * 5 = 125. Therefore, x = 5.', 'role': 'user'}, {'content': \"That's correct! Well done. The value of x is indeed 5, as you correctly found by taking the cube root of 125. Keep up the good work!\", 'role': 'assistant'}, {'content': \"Thank you! I'm glad I could help. If you have any more questions, feel free to ask!\", 'role': 'user'}], summary=\"Thank you! I'm glad I could help. If you have any more questions, feel free to ask!\", cost={'usage_including_cached_inference': {'total_cost': 0.000333, 'gpt-35-turbo': {'cost': 0.000333, 'prompt_tokens': 282, 'completion_tokens': 128, 'total_tokens': 410}}, 'usage_excluding_cached_inference': {'total_cost': 0.000333, 'gpt-35-turbo': {'cost': 0.000333, 'prompt_tokens': 282, 'completion_tokens': 128, 'total_tokens': 410}}}, human_input=[])"
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]
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},
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"execution_count": 22,
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"execution_count": 9,
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"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -387,7 +376,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -397,8 +386,8 @@
|
|||
"Agent 'ai_user':\n",
|
||||
"----------------------------------------------------------------------------------------------------\n",
|
||||
"Usage summary excluding cached usage: \n",
|
||||
"Total cost: 0.00669\n",
|
||||
"* Model 'gpt-4': cost: 0.00669, prompt_tokens: 161, completion_tokens: 31, total_tokens: 192\n",
|
||||
"Total cost: 0.00011\n",
|
||||
"* Model 'gpt-35-turbo': cost: 0.00011, prompt_tokens: 114, completion_tokens: 35, total_tokens: 149\n",
|
||||
"\n",
|
||||
"All completions are non-cached: the total cost with cached completions is the same as actual cost.\n",
|
||||
"----------------------------------------------------------------------------------------------------\n",
|
||||
|
|
@ -406,8 +395,8 @@
|
|||
"Agent 'assistant':\n",
|
||||
"----------------------------------------------------------------------------------------------------\n",
|
||||
"Usage summary excluding cached usage: \n",
|
||||
"Total cost: 0.01533\n",
|
||||
"* Model 'gpt-4': cost: 0.01533, prompt_tokens: 211, completion_tokens: 150, total_tokens: 361\n",
|
||||
"Total cost: 0.00022\n",
|
||||
"* Model 'gpt-35-turbo': cost: 0.00022, prompt_tokens: 168, completion_tokens: 93, total_tokens: 261\n",
|
||||
"\n",
|
||||
"All completions are non-cached: the total cost with cached completions is the same as actual cost.\n",
|
||||
"----------------------------------------------------------------------------------------------------\n"
|
||||
|
|
@ -422,7 +411,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -446,17 +435,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Actual usage summary for assistant (excluding completion from cache): {'total_cost': 0.01533, 'gpt-4': {'cost': 0.01533, 'prompt_tokens': 211, 'completion_tokens': 150, 'total_tokens': 361}}\n",
|
||||
"Total usage summary for assistant (including completion from cache): {'total_cost': 0.01533, 'gpt-4': {'cost': 0.01533, 'prompt_tokens': 211, 'completion_tokens': 150, 'total_tokens': 361}}\n",
|
||||
"Actual usage summary for ai_user_proxy: {'total_cost': 0.00669, 'gpt-4': {'cost': 0.00669, 'prompt_tokens': 161, 'completion_tokens': 31, 'total_tokens': 192}}\n",
|
||||
"Total usage summary for ai_user_proxy: {'total_cost': 0.00669, 'gpt-4': {'cost': 0.00669, 'prompt_tokens': 161, 'completion_tokens': 31, 'total_tokens': 192}}\n",
|
||||
"Actual usage summary for assistant (excluding completion from cache): {'total_cost': 0.0002235, 'gpt-35-turbo': {'cost': 0.0002235, 'prompt_tokens': 168, 'completion_tokens': 93, 'total_tokens': 261}}\n",
|
||||
"Total usage summary for assistant (including completion from cache): {'total_cost': 0.0002235, 'gpt-35-turbo': {'cost': 0.0002235, 'prompt_tokens': 168, 'completion_tokens': 93, 'total_tokens': 261}}\n",
|
||||
"Actual usage summary for ai_user_proxy: {'total_cost': 0.0001095, 'gpt-35-turbo': {'cost': 0.0001095, 'prompt_tokens': 114, 'completion_tokens': 35, 'total_tokens': 149}}\n",
|
||||
"Total usage summary for ai_user_proxy: {'total_cost': 0.0001095, 'gpt-35-turbo': {'cost': 0.0001095, 'prompt_tokens': 114, 'completion_tokens': 35, 'total_tokens': 149}}\n",
|
||||
"Actual usage summary for user_proxy: None\n",
|
||||
"Total usage summary for user_proxy: None\n"
|
||||
]
|
||||
|
|
@ -475,20 +464,20 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'total_cost': 0.022019999999999998,\n",
|
||||
" 'gpt-4': {'cost': 0.022019999999999998,\n",
|
||||
" 'prompt_tokens': 372,\n",
|
||||
" 'completion_tokens': 181,\n",
|
||||
" 'total_tokens': 553}}"
|
||||
"{'total_cost': 0.000333,\n",
|
||||
" 'gpt-35-turbo': {'cost': 0.000333,\n",
|
||||
" 'prompt_tokens': 282,\n",
|
||||
" 'completion_tokens': 128,\n",
|
||||
" 'total_tokens': 410}}"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -29,7 +29,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 14,
|
||||
"id": "2b803c17",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -50,7 +50,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 15,
|
||||
"id": "dca301a4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -65,9 +65,7 @@
|
|||
"\n",
|
||||
"config_list = autogen.config_list_from_json(\n",
|
||||
" \"OAI_CONFIG_LIST\",\n",
|
||||
" filter_dict={\n",
|
||||
" \"model\": [\"gpt-4\", \"gpt-3.5-turbo\", \"gpt-3.5-turbo-16k\"],\n",
|
||||
" },\n",
|
||||
" filter_dict={\"tags\": [\"3.5-tool\"]}, # comment out to get all\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
|
|
@ -77,14 +75,15 @@
|
|||
"id": "92fde41f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well). Only the models with matching names are kept in the list based on the filter condition.\n",
|
||||
"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by tags (you can filter by other keys as well). Only the configs with matching tags are kept in the list based on the filter condition.\n",
|
||||
"\n",
|
||||
"The config list looks like the following:\n",
|
||||
"```python\n",
|
||||
"config_list = [\n",
|
||||
" {\n",
|
||||
" 'model': 'gpt-4',\n",
|
||||
" 'model': 'gpt-3.5-turbo',\n",
|
||||
" 'api_key': '<your OpenAI API key here>',\n",
|
||||
" 'tags': ['tool', '3.5-tool'],\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" 'model': 'gpt-3.5-turbo',\n",
|
||||
|
|
@ -92,6 +91,7 @@
|
|||
" 'base_url': '<your Azure OpenAI API base here>',\n",
|
||||
" 'api_type': 'azure',\n",
|
||||
" 'api_version': '2024-02-15-preview',\n",
|
||||
" 'tags': ['tool', '3.5-tool'],\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" 'model': 'gpt-3.5-turbo-16k',\n",
|
||||
|
|
@ -99,6 +99,7 @@
|
|||
" 'base_url': '<your Azure OpenAI API base here>',\n",
|
||||
" 'api_type': 'azure',\n",
|
||||
" 'api_version': '2024-02-15-preview',\n",
|
||||
" 'tags': ['tool', '3.5-tool'],\n",
|
||||
" },\n",
|
||||
"]\n",
|
||||
"```\n",
|
||||
|
|
@ -119,7 +120,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 16,
|
||||
"id": "9fb85afb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -179,7 +180,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 17,
|
||||
"id": "3e52bbfe",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -203,7 +204,7 @@
|
|||
" 'required': ['base_amount']}}}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -228,7 +229,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 18,
|
||||
"id": "bd943369",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -246,7 +247,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 19,
|
||||
"id": "d5518947",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -258,12 +259,22 @@
|
|||
"\n",
|
||||
"How much is 123.45 USD in EUR?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Suggested tool Call (call_Ak49uR4cwLWyPKs5T2gK9bMg): currency_calculator *****\u001b[0m\n",
|
||||
"\u001b[32m***** Suggested tool call (call_9ogJS4d40BT1rXfMn7YJb151): currency_calculator *****\u001b[0m\n",
|
||||
"Arguments: \n",
|
||||
"{\"base_amount\":123.45}\n",
|
||||
"{\n",
|
||||
" \"base_amount\": 123.45,\n",
|
||||
" \"base_currency\": \"USD\",\n",
|
||||
" \"quote_currency\": \"EUR\"\n",
|
||||
"}\n",
|
||||
"\u001b[32m************************************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
|
|
@ -273,14 +284,14 @@
|
|||
"\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Response from calling tool \"call_Ak49uR4cwLWyPKs5T2gK9bMg\" *****\u001b[0m\n",
|
||||
"\u001b[32m***** Response from calling tool (call_9ogJS4d40BT1rXfMn7YJb151) *****\u001b[0m\n",
|
||||
"112.22727272727272 EUR\n",
|
||||
"\u001b[32m**********************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"123.45 USD is approximately 112.23 EUR.\n",
|
||||
"123.45 USD is equivalent to 112.23 EUR.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
|
|
@ -306,7 +317,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 20,
|
||||
"id": "4b5a0edc",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -314,7 +325,7 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chat summary: 123.45 USD is equivalent to approximately 112.23 EUR.\n"
|
||||
"Chat summary: 123.45 USD is equivalent to 112.23 EUR.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -340,7 +351,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 21,
|
||||
"id": "7b3d8b58",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -389,7 +400,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 22,
|
||||
"id": "971ed0d5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -420,7 +431,7 @@
|
|||
" 'required': ['base']}}}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -431,7 +442,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 23,
|
||||
"id": "ab081090",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -443,12 +454,24 @@
|
|||
"\n",
|
||||
"How much is 112.23 Euros in US Dollars?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Suggested tool Call (call_G64JQKQBT2rI4vnuA4iz1vmE): currency_calculator *****\u001b[0m\n",
|
||||
"\u001b[32m***** Suggested tool call (call_BQkSmdFHsrKvmtDWCk0mY5sF): currency_calculator *****\u001b[0m\n",
|
||||
"Arguments: \n",
|
||||
"{\"base\":{\"currency\":\"EUR\",\"amount\":112.23},\"quote_currency\":\"USD\"}\n",
|
||||
"{\n",
|
||||
" \"base\": {\n",
|
||||
" \"currency\": \"EUR\",\n",
|
||||
" \"amount\": 112.23\n",
|
||||
" },\n",
|
||||
" \"quote_currency\": \"USD\"\n",
|
||||
"}\n",
|
||||
"\u001b[32m************************************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
|
|
@ -458,23 +481,14 @@
|
|||
"\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Response from calling tool \"call_G64JQKQBT2rI4vnuA4iz1vmE\" *****\u001b[0m\n",
|
||||
"\u001b[32m***** Response from calling tool (call_BQkSmdFHsrKvmtDWCk0mY5sF) *****\u001b[0m\n",
|
||||
"{\"currency\":\"USD\",\"amount\":123.45300000000002}\n",
|
||||
"\u001b[32m**********************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"112.23 Euros is equivalent to approximately 123.45 US Dollars.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"112.23 Euros is equivalent to 123.45 US Dollars.\n",
|
||||
"TERMINATE\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
|
|
@ -491,7 +505,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 24,
|
||||
"id": "4799f60c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -499,7 +513,7 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chat summary: 112.23 Euros is approximately 123.45 US Dollars.\n"
|
||||
"Chat summary: 112.23 Euros is equivalent to 123.45 US Dollars.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -509,7 +523,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 25,
|
||||
"id": "0064d9cd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -521,12 +535,24 @@
|
|||
"\n",
|
||||
"How much is 123.45 US Dollars in Euros?\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"--------------------------------------------------------------------------------\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Suggested tool Call (call_qv2SwJHpKrG73btxNzUnYBoR): currency_calculator *****\u001b[0m\n",
|
||||
"\u001b[32m***** Suggested tool call (call_Xxol42xTswZHGX60OjvIQRG1): currency_calculator *****\u001b[0m\n",
|
||||
"Arguments: \n",
|
||||
"{\"base\":{\"currency\":\"USD\",\"amount\":123.45},\"quote_currency\":\"EUR\"}\n",
|
||||
"{\n",
|
||||
" \"base\": {\n",
|
||||
" \"currency\": \"USD\",\n",
|
||||
" \"amount\": 123.45\n",
|
||||
" },\n",
|
||||
" \"quote_currency\": \"EUR\"\n",
|
||||
"}\n",
|
||||
"\u001b[32m************************************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
|
|
@ -536,14 +562,14 @@
|
|||
"\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
"\n",
|
||||
"\u001b[32m***** Response from calling tool \"call_qv2SwJHpKrG73btxNzUnYBoR\" *****\u001b[0m\n",
|
||||
"\u001b[32m***** Response from calling tool (call_Xxol42xTswZHGX60OjvIQRG1) *****\u001b[0m\n",
|
||||
"{\"currency\":\"EUR\",\"amount\":112.22727272727272}\n",
|
||||
"\u001b[32m**********************************************************************\u001b[0m\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
|
||||
"\n",
|
||||
"123.45 US Dollars is approximately 112.23 Euros.\n",
|
||||
"123.45 US Dollars is equivalent to 112.23 Euros.\n",
|
||||
"\n",
|
||||
"--------------------------------------------------------------------------------\n",
|
||||
"\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
|
||||
|
|
@ -571,7 +597,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 26,
|
||||
"id": "80b2b42c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -579,7 +605,7 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chat history: [{'content': 'How much is 123.45 US Dollars in Euros?', 'role': 'assistant'}, {'tool_calls': [{'id': 'call_qv2SwJHpKrG73btxNzUnYBoR', 'function': {'arguments': '{\"base\":{\"currency\":\"USD\",\"amount\":123.45},\"quote_currency\":\"EUR\"}', 'name': 'currency_calculator'}, 'type': 'function'}], 'content': None, 'role': 'assistant'}, {'content': '{\"currency\":\"EUR\",\"amount\":112.22727272727272}', 'tool_responses': [{'tool_call_id': 'call_qv2SwJHpKrG73btxNzUnYBoR', 'role': 'tool', 'content': '{\"currency\":\"EUR\",\"amount\":112.22727272727272}'}], 'role': 'tool'}, {'content': '123.45 US Dollars is approximately 112.23 Euros.', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'TERMINATE', 'role': 'user'}]\n"
|
||||
"Chat history: [{'content': 'How much is 123.45 US Dollars in Euros?', 'role': 'assistant'}, {'tool_calls': [{'id': 'call_Xxol42xTswZHGX60OjvIQRG1', 'function': {'arguments': '{\\n \"base\": {\\n \"currency\": \"USD\",\\n \"amount\": 123.45\\n },\\n \"quote_currency\": \"EUR\"\\n}', 'name': 'currency_calculator'}, 'type': 'function'}], 'content': None, 'role': 'assistant'}, {'content': '{\"currency\":\"EUR\",\"amount\":112.22727272727272}', 'tool_responses': [{'tool_call_id': 'call_Xxol42xTswZHGX60OjvIQRG1', 'role': 'tool', 'content': '{\"currency\":\"EUR\",\"amount\":112.22727272727272}'}], 'role': 'tool'}, {'content': '123.45 US Dollars is equivalent to 112.23 Euros.', 'role': 'user'}, {'content': '', 'role': 'assistant'}, {'content': 'TERMINATE', 'role': 'user'}]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -589,30 +615,30 @@
|
|||
}
|
||||
],
|
||||
"metadata": {
|
||||
"front_matter": {
|
||||
"description": "Learn how to register function calls using AssistantAgent and UserProxyAgent in AutoGen.",
|
||||
"tags": [
|
||||
"function call",
|
||||
"tool use"
|
||||
]
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "flaml_dev",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.13"
|
||||
}
|
||||
"front_matter": {
|
||||
"description": "Learn how to register function calls using AssistantAgent and UserProxyAgent in AutoGen.",
|
||||
"tags": [
|
||||
"function call",
|
||||
"tool use"
|
||||
]
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "flaml_dev",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
2
setup.py
2
setup.py
|
|
@ -14,7 +14,7 @@ with open(os.path.join(here, "autogen/version.py")) as fp:
|
|||
__version__ = version["__version__"]
|
||||
|
||||
install_requires = [
|
||||
"openai>=1.3",
|
||||
"openai>=1.3,<1.21",
|
||||
"diskcache",
|
||||
"termcolor",
|
||||
"flaml",
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ if not skip_openai:
|
|||
filter_dict={
|
||||
"api_type": ["openai"],
|
||||
"model": [
|
||||
"gpt-4-turbo",
|
||||
"gpt-4-turbo-preview",
|
||||
"gpt-4-0125-preview",
|
||||
"gpt-4-1106-preview",
|
||||
|
|
@ -640,10 +641,10 @@ def test_gpt_reflection_with_llm() -> None:
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_gpt_assistant_chat()
|
||||
test_get_assistant_instructions()
|
||||
test_gpt_assistant_instructions_overwrite()
|
||||
test_gpt_assistant_existing_no_instructions()
|
||||
# test_gpt_assistant_chat()
|
||||
# test_get_assistant_instructions()
|
||||
# test_gpt_assistant_instructions_overwrite()
|
||||
# test_gpt_assistant_existing_no_instructions()
|
||||
test_get_assistant_files()
|
||||
test_assistant_mismatch_retrieval()
|
||||
test_gpt_assistant_tools_overwrite()
|
||||
# test_assistant_mismatch_retrieval()
|
||||
# test_gpt_assistant_tools_overwrite()
|
||||
|
|
|
|||
Loading…
Reference in New Issue