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https://github.com/deepset-ai/haystack.git
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6a1b6b1ae3
* 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>
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
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.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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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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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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