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feat: Update ConversationalAgent (#5065)
* feat: Update ConversationalAgent * Add Tools * Add test * Change default params * fix tests * Fix circular import error * Update conversational-agent prompt * Add conversational-agent-without-tools to legacy list * Add warning to add tools to conversational agent * Add callable tools * Add example script * Fix linter errors * Update ConversationalAgent depending on the existance of tools * Initialize the base Agent with different arguments when there's tool * Inject memory to the prompt in both cases, update prompts accordingly * Override the add_tools method to prevent adding tools to ConversationalAgent without tools * Update test * Fix linter error * Remove unused import * Update docstrings and api reference * Fix imports and doc string code snippet * docstrings update * Update conversational.py * Mock PromptNode * Prevent circular import error * Add max_steps to the ConversationalAgent * Update resolver description * Add prompt_template as parameter * Change docstring --------- Co-authored-by: Darja Fokina <daria.f93@gmail.com>
This commit is contained in:
Bilge Yücel
GitHub
parent
30fdf2b5df
commit
6a1b6b1ae3
@ -1,7 +1,7 @@
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loaders:
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- type: python
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search_path: [../../../haystack/agents]
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modules: ['base']
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modules: ['base', 'conversational']
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ignore_when_discovered: ['__init__']
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processors:
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- type: filter
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@ -1,17 +1,66 @@
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import os
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from haystack.agents.base import Tool
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from haystack.agents.conversational import ConversationalAgent
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from haystack.nodes import PromptNode
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from haystack.agents.memory import ConversationSummaryMemory
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from haystack.nodes import PromptNode, WebRetriever, PromptTemplate
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from haystack.pipelines import WebQAPipeline
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from haystack.agents.types import Color
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pn = PromptNode("gpt-3.5-turbo", api_key=os.environ.get("OPENAI_API_KEY"), max_length=256)
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agent = ConversationalAgent(pn)
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search_api_key = os.environ.get("SEARCH_API_KEY")
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if not search_api_key:
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raise ValueError("Please set the SEARCH_API_KEY environment variable")
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openai_api_key = os.environ.get("OPENAI_API_KEY")
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if not openai_api_key:
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raise ValueError("Please set the OPENAI_API_KEY environment variable")
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while True:
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user_input = input("Human (type 'exit' or 'quit' to quit, 'memory' for agent's memory): ")
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if user_input.lower() == "exit" or user_input.lower() == "quit":
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break
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if user_input.lower() == "memory":
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print("\nMemory:\n", agent.memory.load())
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else:
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assistant_response = agent.run(user_input)
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print("\nAssistant:", assistant_response)
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web_prompt = """
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Synthesize a comprehensive answer from the following most relevant paragraphs and the given question.
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Provide a clear and concise answer, no longer than 10-20 words.
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\n\n Paragraphs: {documents} \n\n Question: {query} \n\n Answer:
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"""
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web_prompt_node = PromptNode(
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"gpt-3.5-turbo", default_prompt_template=PromptTemplate(prompt=web_prompt), api_key=openai_api_key
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)
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web_retriever = WebRetriever(api_key=search_api_key, top_search_results=3, mode="snippets")
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pipeline = WebQAPipeline(retriever=web_retriever, prompt_node=web_prompt_node)
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web_qa_tool = Tool(
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name="Search",
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pipeline_or_node=pipeline,
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description="useful for when you need to Google questions if you cannot find answers in the the previous conversation",
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output_variable="results",
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logging_color=Color.MAGENTA,
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)
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conversational_agent_prompt_node = PromptNode(
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"gpt-3.5-turbo",
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api_key=openai_api_key,
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max_length=256,
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stop_words=["Observation:"],
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model_kwargs={"temperature": 0.5, "top_p": 0.9},
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)
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memory = ConversationSummaryMemory(conversational_agent_prompt_node, summary_frequency=2)
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conversational_agent = ConversationalAgent(
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prompt_node=conversational_agent_prompt_node, tools=[web_qa_tool], memory=memory
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)
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test = False
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if test:
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questions = [
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"Why was Jamie Foxx recently hospitalized?",
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"Where was he hospitalized?",
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"What movie was he filming at the time?",
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"Who is Jamie's female co-star in the movie he was filing at that time?",
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"Tell me more about her, who is her partner?",
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]
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for question in questions:
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conversational_agent.run(question)
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else:
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while True:
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user_input = input("\nHuman (type 'exit' or 'quit' to quit): ")
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if user_input.lower() == "exit" or user_input.lower() == "quit":
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break
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response = conversational_agent.run(user_input)
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@ -13,7 +13,7 @@ from haystack.agents.memory import Memory, NoMemory
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from haystack.telemetry import send_event
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from haystack.agents.agent_step import AgentStep
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from haystack.agents.types import Color, AgentTokenStreamingHandler
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from haystack.agents.utils import print_text
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from haystack.agents.utils import print_text, react_parameter_resolver
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from haystack.nodes import PromptNode, BaseRetriever, PromptTemplate
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from haystack.pipelines import (
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BaseStandardPipeline,
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@ -63,6 +63,7 @@ class Tool:
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TranslationWrapperPipeline,
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RetrieverQuestionGenerationPipeline,
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WebQAPipeline,
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Callable[[Any], str],
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],
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description: str,
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output_variable: str = "results",
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@ -85,6 +86,8 @@ class Tool:
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result = self.pipeline_or_node.run(query=tool_input, params=params)
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elif isinstance(self.pipeline_or_node, BaseRetriever):
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result = self.pipeline_or_node.run(query=tool_input, root_node="Query")
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elif callable(self.pipeline_or_node):
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result = self.pipeline_or_node(tool_input)
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else:
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result = self.pipeline_or_node.run(query=tool_input)
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return self._process_result(result)
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@ -266,14 +269,6 @@ class Agent:
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f"Prompt template '{prompt_template}' not found. Please check the spelling of the template name."
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)
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self.prompt_template = resolved_prompt_template
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react_parameter_resolver: Callable[
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[str, Agent, AgentStep, Dict[str, Any]], Dict[str, Any]
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] = lambda query, agent, agent_step, **kwargs: {
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"query": query,
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"tool_names": agent.tm.get_tool_names(),
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"tool_names_with_descriptions": agent.tm.get_tool_names_with_descriptions(),
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"transcript": agent_step.transcript,
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}
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self.prompt_parameters_resolver = (
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prompt_parameters_resolver if prompt_parameters_resolver else react_parameter_resolver
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)
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@ -1,63 +1,110 @@
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from typing import Optional, Callable, Union
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from typing import Optional, List, Union
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import logging
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from haystack.agents import Agent
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from haystack.errors import AgentError
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from haystack.agents.base import Tool, ToolsManager, Agent
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from haystack.agents.memory import Memory, ConversationMemory
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from haystack.nodes import PromptNode, PromptTemplate
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from haystack.agents.utils import conversational_agent_parameter_resolver, agent_without_tools_parameter_resolver
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logger = logging.getLogger(__name__)
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class ConversationalAgent(Agent):
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"""
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A conversational agent that can be used to build a conversational chat applications.
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A ConversationalAgent is an extension of the Agent class that enables the use of tools with several default parameters.
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ConversationalAgent can manage a set of tools and seamlessly integrate them into the conversation.
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If no tools are provided, the agent will be initialized to have a basic chat application.
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Here is an example of how you can create a simple chat application:
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```
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Here is an example how you can create a chat application with tools:
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```python
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import os
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from haystack.agents.base import ConversationalAgent
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from haystack.agents.memory import ConversationSummaryMemory
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from haystack.agents.conversational import ConversationalAgent
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from haystack.nodes import PromptNode
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from haystack.agents.base import ToolsManager, Tool
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pn = PromptNode("gpt-3.5-turbo", api_key=os.environ.get("OPENAI_API_KEY"), max_length=256)
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agent = ConversationalAgent(pn, memory=ConversationSummaryMemory(pn))
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# Initialize a PromptNode and a ToolsManager with the desired tools
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prompt_node = PromptNode("gpt-3.5-turbo", api_key=os.environ.get("OPENAI_API_KEY"), max_length=256)
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tools = [Tool(name="ExampleTool", pipeline_or_node=example_tool_node)]
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# Create the ConversationalAgent instance
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agent = ConversationalAgent(prompt_node, tools=tools)
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# Use the agent in a chat application
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while True:
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user_input = input("Human (type 'exit' or 'quit' to quit): ")
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if user_input.lower() == "exit" or user_input.lower() == "quit":
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break
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elif user_input.lower() == "memory":
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print("\nMemory:\n", agent.memory.load())
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else:
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assistant_response = agent.run(user_input)
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print("\nAssistant:", assistant_response)
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```
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If you don't want to have any tools in your chat app, you can create a ConversationalAgent only with a PromptNode:
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```python
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import os
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from haystack.agents.conversational import ConversationalAgent
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from haystack.nodes import PromptNode
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# Initialize a PromptNode
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prompt_node = PromptNode("gpt-3.5-turbo", api_key=os.environ.get("OPENAI_API_KEY"), max_length=256)
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# Create the ConversationalAgent instance
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agent = ConversationalAgent(prompt_node)
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```
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If you're looking for more customization, check out [Agent](https://docs.haystack.deepset.ai/reference/agent-api).
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"""
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def __init__(
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self,
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prompt_node: PromptNode,
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prompt_template: Optional[Union[str, PromptTemplate]] = None,
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tools: Optional[List[Tool]] = None,
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memory: Optional[Memory] = None,
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prompt_parameters_resolver: Optional[Callable] = None,
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max_steps: Optional[int] = None,
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):
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"""
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Creates a new ConversationalAgent instance
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Creates a new ConversationalAgent instance.
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:param prompt_node: A PromptNode used to communicate with LLM.
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:param prompt_template: A string or PromptTemplate instance to use as the prompt template. If no prompt_template
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is provided, the agent will use the "conversational-agent" template.
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:param memory: A memory instance for storing conversation history and other relevant data, defaults to
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ConversationMemory.
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:param prompt_parameters_resolver: An optional callable for resolving prompt template parameters,
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defaults to a callable that returns a dictionary with the query and the conversation history.
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:param prompt_node: A PromptNode used by Agent to decide which tool to use and what input to provide to it
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in each iteration. If there are no tools added, the model specified with PromptNode will be used for chatting.
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:param prompt_template: A new PromptTemplate or the name of an existing PromptTemplate for the PromptNode. It's
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used for keeping the chat history, generating thoughts and choosing tools (if provided) to answer queries. It defaults to
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to "conversational-agent" if there is at least one tool provided and "conversational-agent-without-tools" otherwise.
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:param tools: A list of tools to use in the Agent. Each tool must have a unique name.
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:param memory: A memory object for storing conversation history and other relevant data, defaults to
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ConversationMemory if no memory is provided.
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:param max_steps: The number of times the Agent can run a tool +1 to let it infer it knows the final answer. It defaults to 5 if there is at least one tool provided and 2 otherwise.
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"""
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super().__init__(
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prompt_node=prompt_node,
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prompt_template=prompt_template or "conversational-agent",
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max_steps=2,
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memory=memory if memory else ConversationMemory(),
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prompt_parameters_resolver=prompt_parameters_resolver
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if prompt_parameters_resolver
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else lambda query, agent, **kwargs: {"query": query, "history": agent.memory.load()},
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final_answer_pattern=r"^([\s\S]+)$",
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)
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if tools:
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super().__init__(
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prompt_node=prompt_node,
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memory=memory if memory else ConversationMemory(),
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tools_manager=ToolsManager(tools=tools),
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max_steps=max_steps if max_steps else 5,
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prompt_template=prompt_template if prompt_template else "conversational-agent",
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final_answer_pattern=r"Final Answer\s*:\s*(.*)",
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prompt_parameters_resolver=conversational_agent_parameter_resolver,
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)
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else:
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logger.warning("ConversationalAgent is created without tools")
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super().__init__(
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prompt_node=prompt_node,
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memory=memory if memory else ConversationMemory(),
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max_steps=max_steps if max_steps else 2,
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prompt_template=prompt_template if prompt_template else "conversational-agent-without-tools",
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final_answer_pattern=r"^([\s\S]+)$",
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prompt_parameters_resolver=agent_without_tools_parameter_resolver,
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)
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def add_tool(self, tool: Tool):
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if len(self.tm.tools) == 0:
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raise AgentError(
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"You cannot add tools after initializing the ConversationalAgent without any tools. If you want to add tools, reinitailize the ConversationalAgent and provide `tools`."
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)
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return super().add_tool(tool)
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@ -1,6 +1,10 @@
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from typing import Optional
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from typing import Optional, TYPE_CHECKING, Dict, Any
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from haystack.agents.types import Color
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from haystack.agents.agent_step import AgentStep
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if TYPE_CHECKING:
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from haystack.agents import Agent
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def print_text(text: str, end="", color: Optional[Color] = None) -> None:
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@ -14,3 +18,39 @@ def print_text(text: str, end="", color: Optional[Color] = None) -> None:
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print(f"{color.value}{text}{Color.RESET.value}", end=end, flush=True)
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else:
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print(text, end=end, flush=True)
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def react_parameter_resolver(query: str, agent: "Agent", agent_step: AgentStep, **kwargs) -> Dict[str, Any]:
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"""
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A parameter resolver for ReAct-based agents that returns the query, the tool names, the tool names
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with descriptions, and the transcript (internal monologue).
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"""
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return {
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"query": query,
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"tool_names": agent.tm.get_tool_names(),
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"tool_names_with_descriptions": agent.tm.get_tool_names_with_descriptions(),
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"transcript": agent_step.transcript,
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}
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def agent_without_tools_parameter_resolver(query: str, agent: "Agent", **kwargs) -> Dict[str, Any]:
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"""
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A parameter resolver for simple chat agents without tools that returns the query and the history.
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"""
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return {"query": query, "history": agent.memory.load()}
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def conversational_agent_parameter_resolver(
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query: str, agent: "Agent", agent_step: AgentStep, **kwargs
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) -> Dict[str, Any]:
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"""
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A parameter resolver for ReAct-based conversational agent that returns the query, the tool names, the tool names
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with descriptions, the history of the conversation, and the transcript (internal monologue).
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"""
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return {
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"query": query,
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"tool_names": agent.tm.get_tool_names(),
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"tool_names_with_descriptions": agent.tm.get_tool_names_with_descriptions(),
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"transcript": agent_step.transcript,
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"history": agent.memory.load(),
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}
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@ -149,11 +149,29 @@ LEGACY_DEFAULT_TEMPLATES: Dict[str, Dict] = {
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"Thought: Let's think step-by-step, I first need to {transcript}"
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},
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"conversational-agent": {
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"prompt": "The following is a conversation between a human and an AI.\n{history}\nHuman: {query}\nAI:"
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"prompt": "In the following conversation, a human user interacts with an AI Agent. The human user poses questions, and the AI Agent goes through several steps to provide well-informed answers.\n"
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"If the AI Agent knows the answer, the response begins with `Final Answer:` on a new line.\n"
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"If the AI Agent is uncertain or concerned that the information may be outdated or inaccurate, it must use the available tools to find the most up-to-date information. The AI has access to these tools:\n"
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"{tool_names_with_descriptions}\n"
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"The following is the previous conversation between a human and an AI:\n"
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"{history}\n"
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"AI Agent responses must start with one of the following:\n"
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"Thought: [AI Agent's reasoning process]\n"
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"Tool: [{tool_names}] (on a new line) Tool Input: [input for the selected tool WITHOUT quotation marks and on a new line] (These must always be provided together and on separate lines.)\n"
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"Final Answer: [final answer to the human user's question]\n"
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"When selecting a tool, the AI Agent must provide both the `Tool:` and `Tool Input:` pair in the same response, but on separate lines. `Observation:` marks the beginning of a tool's result, and the AI Agent trusts these results.\n"
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"The AI Agent should not ask the human user for additional information, clarification, or context.\n"
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"If the AI Agent cannot find a specific answer after exhausting available tools and approaches, it answers with Final Answer: inconclusive\n"
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"Question: {query}\n"
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"Thought:\n"
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"{transcript}\n"
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},
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"conversational-summary": {
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"prompt": "Condense the following chat transcript by shortening and summarizing the content without losing important information:\n{chat_transcript}\nCondensed Transcript:"
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},
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"conversational-agent-without-tools": {
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"prompt": "The following is a conversation between a human and an AI.\n{history}\nHuman: {query}\nAI:"
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},
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# DO NOT ADD ANY NEW TEMPLATE IN HERE!
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}
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@ -1,14 +1,22 @@
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import pytest
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from unittest.mock import MagicMock, Mock
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from unittest.mock import MagicMock, patch
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from haystack.errors import AgentError
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from haystack.agents.base import Tool
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from haystack.agents.conversational import ConversationalAgent
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from haystack.agents.memory import ConversationSummaryMemory, ConversationMemory, NoMemory
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from test.conftest import MockPromptNode
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from haystack.nodes import PromptNode
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@pytest.fixture
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@patch("haystack.nodes.prompt.prompt_node.PromptModel")
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def prompt_node(mock_model):
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prompt_node = PromptNode()
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return prompt_node
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@pytest.mark.unit
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def test_init():
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prompt_node = MockPromptNode()
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def test_init_without_tools(prompt_node):
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agent = ConversationalAgent(prompt_node)
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# Test normal case
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@ -16,34 +24,82 @@ def test_init():
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assert callable(agent.prompt_parameters_resolver)
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assert agent.max_steps == 2
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assert agent.final_answer_pattern == r"^([\s\S]+)$"
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assert agent.prompt_template.name == "conversational-agent-without-tools"
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# ConversationalAgent doesn't have tools
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assert not agent.tm.tools
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@pytest.mark.unit
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def test_init_with_summary_memory():
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def test_init_with_tools(prompt_node):
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agent = ConversationalAgent(prompt_node, tools=[Tool("ExampleTool", lambda x: x, description="Example tool")])
|
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|
||||
# Test normal case
|
||||
assert isinstance(agent.memory, ConversationMemory)
|
||||
assert callable(agent.prompt_parameters_resolver)
|
||||
assert agent.max_steps == 5
|
||||
assert agent.final_answer_pattern == r"Final Answer\s*:\s*(.*)"
|
||||
assert agent.prompt_template.name == "conversational-agent"
|
||||
assert agent.has_tool("ExampleTool")
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_init_with_summary_memory(prompt_node):
|
||||
# Test with summary memory
|
||||
prompt_node = MockPromptNode()
|
||||
agent = ConversationalAgent(prompt_node, memory=ConversationSummaryMemory(prompt_node))
|
||||
assert isinstance(agent.memory, ConversationSummaryMemory)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_init_with_no_memory():
|
||||
prompt_node = MockPromptNode()
|
||||
def test_init_with_no_memory(prompt_node):
|
||||
# Test with no memory
|
||||
agent = ConversationalAgent(prompt_node, memory=NoMemory())
|
||||
assert isinstance(agent.memory, NoMemory)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_run():
|
||||
prompt_node = MockPromptNode()
|
||||
def test_init_with_custom_max_steps(prompt_node):
|
||||
# Test with custom max step
|
||||
agent = ConversationalAgent(prompt_node, max_steps=8)
|
||||
assert agent.max_steps == 8
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_init_with_custom_prompt_template(prompt_node):
|
||||
# Test with custom prompt template
|
||||
agent = ConversationalAgent(prompt_node, prompt_template="translation")
|
||||
assert agent.prompt_template.name == "translation"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_run(prompt_node):
|
||||
agent = ConversationalAgent(prompt_node)
|
||||
|
||||
# Mock the Agent run method
|
||||
result = agent.run("query")
|
||||
agent.run = MagicMock(return_value="Hello")
|
||||
assert agent.run("query") == "Hello"
|
||||
agent.run.assert_called_once_with("query")
|
||||
|
||||
# empty answer
|
||||
assert result["answers"][0].answer == ""
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_add_tool(prompt_node):
|
||||
agent = ConversationalAgent(prompt_node, tools=[Tool("ExampleTool", lambda x: x, description="Example tool")])
|
||||
# ConversationalAgent has tools
|
||||
assert len(agent.tm.tools) == 1
|
||||
|
||||
# and add more tools if ConversationalAgent is initialized with tools
|
||||
agent.add_tool(Tool("AnotherTool", lambda x: x, description="Example tool"))
|
||||
assert len(agent.tm.tools) == 2
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_add_tool_not_allowed(prompt_node):
|
||||
agent = ConversationalAgent(prompt_node)
|
||||
# ConversationalAgent has no tools
|
||||
assert not agent.tm.tools
|
||||
|
||||
# and can't add tools when a ConversationalAgent is initialized without tools
|
||||
with pytest.raises(
|
||||
AgentError, match="You cannot add tools after initializing the ConversationalAgent without any tools."
|
||||
):
|
||||
agent.add_tool(Tool("ExampleTool", lambda x: x, description="Example tool"))
|
||||
|
||||
@ -17,6 +17,14 @@ def tools_manager():
|
||||
return ToolsManager(tools=tools)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_using_callable_as_tool():
|
||||
# test that we can also pass a callable as a tool
|
||||
tool_input = "Haystack"
|
||||
tool = Tool(name="ToolA", pipeline_or_node=lambda x: x + x, description="Tool A Description")
|
||||
assert tool.run(tool_input) == tool_input + tool_input
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
def test_add_tool(tools_manager):
|
||||
new_tool = Tool(name="ToolC", pipeline_or_node=mock.Mock(), description="Tool C Description")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user