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https://github.com/hwchase17/langchain
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de496062b3
- **Description:** update langchain anthropic templates to support Claude 3 (iterative search, chain of note, summarization, and XML response) - **Issue:** issue # N/A. Stability issues and errors encountered when trying to use older langchain and anthropic libraries. - **Dependencies:** - langchain_anthropic version 0.1.4\ - anthropic package version in the range ">=0.17.0,<1" to support langchain_anthropic. - **Twitter handle:** @d_w_b7 - [ x]**Add tests and docs**: If you're adding a new integration, please include 1. used instructions in the README for testing - [ x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_xml
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from langchain.tools import DuckDuckGoSearchRun
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from langchain.tools.render import render_text_description
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from langchain_anthropic import ChatAnthropic
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from langchain_core.messages import AIMessage, HumanMessage
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from langchain_core.pydantic_v1 import BaseModel, Field
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from xml_agent.prompts import conversational_prompt, parse_output
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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model = ChatAnthropic(model="claude-3-sonnet-20240229")
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tools = [DuckDuckGoSearchRun()]
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prompt = conversational_prompt.partial(
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tools=render_text_description(tools),
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tool_names=", ".join([t.name for t in tools]),
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)
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llm_with_stop = model.bind(stop=["</tool_input>"])
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agent = (
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{
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"question": lambda x: x["question"],
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"agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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}
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| prompt
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| llm_with_stop
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| parse_output
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)
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class AgentInput(BaseModel):
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question: str
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chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
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agent_executor = AgentExecutor(
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agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
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).with_types(input_type=AgentInput)
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agent_executor = agent_executor | (lambda x: x["output"])
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