langchain/templates/solo-performance-prompting-.../solo_performance_prompting_.../agent.py

from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_xml
from langchain.tools import DuckDuckGoSearchRun
from langchain.tools.render import render_text_description
from langchain_community.llms import OpenAI
from langchain_core.pydantic_v1 import BaseModel

from solo_performance_prompting_agent.parser import parse_output
from solo_performance_prompting_agent.prompts import conversational_prompt

_model = OpenAI()
_tools = [DuckDuckGoSearchRun()]
_prompt = conversational_prompt.partial(
    tools=render_text_description(_tools),
    tool_names=", ".join([t.name for t in _tools]),
)
_llm_with_stop = _model.bind(stop=["</tool_input>", "</final_answer>"])

agent = (
    {
        "question": lambda x: x["question"],
        "agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),
    }
    | _prompt
    | _llm_with_stop
    | parse_output
)


class AgentInput(BaseModel):
    question: str


agent_executor = AgentExecutor(
    agent=agent, tools=_tools, verbose=True, handle_parsing_errors=True
).with_types(input_type=AgentInput)

agent_executor = agent_executor | (lambda x: x["output"])
Adding a template for Solo Performance Prompting Agent (#12627) Description: This template creates an agent that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. Tag maintainer: @hwchase17 --------- Co-authored-by: Sayandip Sarkar <sayandip.sarkar@skypointcloud.com> Co-authored-by: Erick Friis <erick@langchain.dev> 11 months ago			`from langchain.agents import AgentExecutor`
			`from langchain.agents.format_scratchpad import format_xml`
			`from langchain.tools import DuckDuckGoSearchRun`
			`from langchain.tools.render import render_text_description`
docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412) …tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' \| xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' \| xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' \| xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ``` 9 months ago			`from langchain_community.llms import OpenAI`
docs[patch], templates[patch]: Import from core (#14575) Update imports to use core for the low-hanging fruit changes. Ran following ```bash git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} \| xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g' git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} \| xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g' git grep -l 'langchain.schema.messages' {docs,templates,cookbook} \| xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g' git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} \| xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g' git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} \| xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g' git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g' git grep -l 'from langchain.tools.base' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g' git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g' git grep -l 'from langchain.llms.base' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g' git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g' git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g' git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g' git grep -l 'from langchain.schema.output' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g' git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g' git grep -l 'from langchain.schema.document' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g' git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g' git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g' git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} \| xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g' ``` 9 months ago			`from langchain_core.pydantic_v1 import BaseModel`
Adding a template for Solo Performance Prompting Agent (#12627) Description: This template creates an agent that transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. Tag maintainer: @hwchase17 --------- Co-authored-by: Sayandip Sarkar <sayandip.sarkar@skypointcloud.com> Co-authored-by: Erick Friis <erick@langchain.dev> 11 months ago
			`from solo_performance_prompting_agent.parser import parse_output`
			`from solo_performance_prompting_agent.prompts import conversational_prompt`

			`_model = OpenAI()`
			`_tools = [DuckDuckGoSearchRun()]`
			`_prompt = conversational_prompt.partial(`
			`tools=render_text_description(_tools),`
			`tool_names=", ".join([t.name for t in _tools]),`
			`)`
			`_llm_with_stop = _model.bind(stop=["</tool_input>", "</final_answer>"])`

			`agent = (`
			`{`
			`"question": lambda x: x["question"],`
			`"agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),`
			`}`
			`\| _prompt`
			`\| _llm_with_stop`
			`\| parse_output`
			`)`


			`class AgentInput(BaseModel):`
			`question: str`


			`agent_executor = AgentExecutor(`
			`agent=agent, tools=_tools, verbose=True, handle_parsing_errors=True`
			`).with_types(input_type=AgentInput)`

			`agent_executor = agent_executor \| (lambda x: x["output"])`