2023-11-01 15:10:07 +00:00
|
|
|
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
|
2024-01-02 20:32:16 +00:00
|
|
|
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'
```
2023-12-12 00:49:10 +00:00
|
|
|
from langchain_core.pydantic_v1 import BaseModel
|
2023-11-01 15:10:07 +00:00
|
|
|
|
|
|
|
|
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"])
|
