mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-25 16:13:25 +00:00
feat: support generate as an early stopping method for OpenAIFunctionsAgent (#7229)
This PR proposes an implementation to support `generate` as an `early_stopping_method` for the new `OpenAIFunctionsAgent` class. The motivation behind is to facilitate the user to set a maximum number of actions the agent can take with `max_iterations` and force a final response with this new agent (as with the `Agent` class). The following changes were made: - The `OpenAIFunctionsAgent.return_stopped_response` method was overwritten to support `generate` as an `early_stopping_method` - A boolean `with_functions` parameter was added to the `OpenAIFunctionsAgent.plan` method This way the `OpenAIFunctionsAgent.return_stopped_response` method can call the `OpenAIFunctionsAgent.plan` method with `with_function=False` when the `early_stopping_method` is set to `generate`, making a call to the LLM with no functions and forcing a final response from the `"assistant"`. - Relevant maintainer: @hinthornw - Twitter handle: @aledelunap --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Alejandra De Luna
GitHub
parent
1dd4236177
commit
2e4047e5e7
@ -16,7 +16,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "c0a83623",
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"metadata": {},
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"outputs": [],
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@ -38,6 +38,18 @@
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">This initializes the SerpAPIWrapper for search functionality (search).\n"
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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": 2,
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"id": "a2b0a215",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"SERPAPI_API_KEY\"] = \"897780527132b5f31d8d73c40c820d5ef2c2279687efa69f413a61f752027747\""
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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": 3,
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@ -199,10 +211,222 @@
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d31d4c09",
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"metadata": {},
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"source": [
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"## Configuring max iteration behavior\n",
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"\n",
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"To make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output."
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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": 16,
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"id": "9f5f6743",
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"metadata": {},
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"outputs": [],
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"source": [
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"mrkl = initialize_agent(\n",
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" tools, \n",
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" llm, \n",
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" agent=AgentType.OPENAI_FUNCTIONS, \n",
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" verbose=True, \n",
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" max_iterations=2, \n",
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" early_stopping_method=\"generate\"\n",
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")"
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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": 19,
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"id": "4362ebc7",
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"metadata": {
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"scrolled": false
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},
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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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"\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n",
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"\u001b[0m{\n",
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" \"input\": \"What is the weather in NYC today, yesterday, and the day before?\"\n",
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"}\n",
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"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n",
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"\u001b[0m{\n",
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" \"prompts\": [\n",
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" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\"\n",
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" ]\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [1.27s] Exiting LLM run with output:\n",
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"\u001b[0m{\n",
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" \"generations\": [\n",
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" [\n",
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" {\n",
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" \"text\": \"\",\n",
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" \"generation_info\": null,\n",
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" \"message\": {\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \"id\": [\n",
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" \"langchain\",\n",
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" \"schema\",\n",
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" \"messages\",\n",
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" \"AIMessage\"\n",
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" ],\n",
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" \"kwargs\": {\n",
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" \"content\": \"\",\n",
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" \"additional_kwargs\": {\n",
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" \"function_call\": {\n",
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" \"name\": \"Search\",\n",
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" \"arguments\": \"{\\n \\\"query\\\": \\\"weather in NYC today\\\"\\n}\"\n",
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" }\n",
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" }\n",
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" }\n",
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" }\n",
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" }\n",
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" ]\n",
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" ],\n",
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" \"llm_output\": {\n",
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" \"token_usage\": {\n",
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" \"prompt_tokens\": 79,\n",
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" \"completion_tokens\": 17,\n",
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" \"total_tokens\": 96\n",
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" },\n",
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" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
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" },\n",
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" \"run\": null\n",
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"}\n",
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"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:\n",
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"\u001b[0m\"{'query': 'weather in NYC today'}\"\n",
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"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] [3.84s] Exiting Tool run with output:\n",
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"\u001b[0m\"10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
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"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:\n",
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"\u001b[0m{\n",
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" \"prompts\": [\n",
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" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\"\n",
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" ]\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:llm:ChatOpenAI] [1.24s] Exiting LLM run with output:\n",
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"\u001b[0m{\n",
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" \"generations\": [\n",
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" [\n",
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" {\n",
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" \"text\": \"\",\n",
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" \"generation_info\": null,\n",
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" \"message\": {\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \"id\": [\n",
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" \"langchain\",\n",
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" \"schema\",\n",
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" \"messages\",\n",
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" \"AIMessage\"\n",
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" ],\n",
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" \"kwargs\": {\n",
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" \"content\": \"\",\n",
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" \"additional_kwargs\": {\n",
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" \"function_call\": {\n",
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" \"name\": \"Search\",\n",
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" \"arguments\": \"{\\n \\\"query\\\": \\\"weather in NYC yesterday\\\"\\n}\"\n",
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" }\n",
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" }\n",
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" }\n",
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" }\n",
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" }\n",
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" ]\n",
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" ],\n",
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" \"llm_output\": {\n",
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" \"token_usage\": {\n",
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" \"prompt_tokens\": 142,\n",
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" \"completion_tokens\": 17,\n",
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" \"total_tokens\": 159\n",
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" },\n",
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" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
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" },\n",
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" \"run\": null\n",
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"}\n",
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"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] Entering Tool run with input:\n",
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"\u001b[0m\"{'query': 'weather in NYC yesterday'}\"\n",
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"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:tool:Search] [1.15s] Exiting Tool run with output:\n",
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"\u001b[0m\"New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
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"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] Entering LLM run with input:\n",
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"\u001b[0m{\n",
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" \"prompts\": [\n",
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" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in NYC today, yesterday, and the day before?\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC today\\\"\\\\n}'}\\nFunction: 10:00 am · Feels Like85° · WindSE 4 mph · Humidity78% · UV Index3 of 11 · Cloud Cover81% · Rain Amount0 in ...\\nAI: {'name': 'Search', 'arguments': '{\\\\n \\\"query\\\": \\\"weather in NYC yesterday\\\"\\\\n}'}\\nFunction: New York Temperature Yesterday. Maximum temperature yesterday: 81 °F (at 1:51 pm) Minimum temperature yesterday: 72 °F (at 7:17 pm) Average temperature ...\"\n",
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" ]\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:llm:ChatOpenAI] [2.68s] Exiting LLM run with output:\n",
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"\u001b[0m{\n",
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" \"generations\": [\n",
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" [\n",
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" {\n",
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" \"text\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
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" \"generation_info\": null,\n",
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" \"message\": {\n",
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" \"lc\": 1,\n",
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" \"type\": \"constructor\",\n",
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" \"id\": [\n",
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" \"langchain\",\n",
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" \"schema\",\n",
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" \"messages\",\n",
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" \"AIMessage\"\n",
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" ],\n",
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" \"kwargs\": {\n",
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" \"content\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\",\n",
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" \"additional_kwargs\": {}\n",
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" }\n",
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" }\n",
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" }\n",
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" ]\n",
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" ],\n",
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" \"llm_output\": {\n",
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" \"token_usage\": {\n",
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" \"prompt_tokens\": 160,\n",
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" \"completion_tokens\": 91,\n",
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" \"total_tokens\": 251\n",
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" },\n",
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" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
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" },\n",
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" \"run\": null\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [10.18s] Exiting Chain run with output:\n",
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"\u001b[0m{\n",
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" \"output\": \"Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.\"\n",
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"}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Today in NYC, the weather is currently 85°F with a southeast wind of 4 mph. The humidity is at 78% and there is 81% cloud cover. There is no rain expected today.\\n\\nYesterday in NYC, the maximum temperature was 81°F at 1:51 pm, and the minimum temperature was 72°F at 7:17 pm.\\n\\nFor the day before yesterday, I do not have the specific weather information.'"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mrkl.run(\n",
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" \"What is the weather in NYC today, yesterday, and the day before?\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "067a8d3e",
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"metadata": {},
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"source": [
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"Notice that we never get around to looking up the weather the day before yesterday, due to hitting our max_iterations limit."
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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": null,
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"id": "9f5f6743",
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"id": "c3318a11",
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"metadata": {},
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"outputs": [],
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"source": []
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@ -210,9 +434,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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"name": "venv"
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},
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"language_info": {
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"codemirror_mode": {
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@ -224,7 +448,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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@ -189,6 +189,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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with_functions: bool = True,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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@ -207,8 +208,16 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
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full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
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prompt = self.prompt.format_prompt(**full_inputs)
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messages = prompt.to_messages()
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if with_functions:
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predicted_message = self.llm.predict_messages(
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messages, functions=self.functions, callbacks=callbacks
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messages,
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functions=self.functions,
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callbacks=callbacks,
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)
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else:
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predicted_message = self.llm.predict_messages(
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messages,
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callbacks=callbacks,
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)
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agent_decision = _parse_ai_message(predicted_message)
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return agent_decision
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@ -242,6 +251,35 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
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agent_decision = _parse_ai_message(predicted_message)
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return agent_decision
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def return_stopped_response(
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self,
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early_stopping_method: str,
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intermediate_steps: List[Tuple[AgentAction, str]],
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**kwargs: Any,
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) -> AgentFinish:
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"""Return response when agent has been stopped due to max iterations."""
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if early_stopping_method == "force":
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# `force` just returns a constant string
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return AgentFinish(
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{"output": "Agent stopped due to iteration limit or time limit."}, ""
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)
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elif early_stopping_method == "generate":
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# Generate does one final forward pass
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agent_decision = self.plan(
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intermediate_steps, with_functions=False, **kwargs
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)
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if type(agent_decision) == AgentFinish:
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return agent_decision
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else:
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raise ValueError(
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f"got AgentAction with no functions provided: {agent_decision}"
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)
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else:
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raise ValueError(
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"early_stopping_method should be one of `force` or `generate`, "
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f"got {early_stopping_method}"
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)
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@classmethod
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def create_prompt(
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cls,
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Loading…
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Block a user