mirror of
https://github.com/hwchase17/langchain
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493e474063
- Move the API reference into the vercel build - Update api reference organization and styling
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
from typing import List
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from langchain import hub
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_log_to_str
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from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
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from langchain.callbacks.manager import CallbackManagerForRetrieverRun
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from langchain_community.chat_models.fireworks import ChatFireworks
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from langchain_community.utilities.arxiv import ArxivAPIWrapper
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from langchain_core.documents import Document
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.tools.render import render_text_description
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from langchain_core.tools.retriever import create_retriever_tool
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MODEL_ID = "accounts/fireworks/models/mixtral-8x7b-instruct"
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class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
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"""`Arxiv` retriever.
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It wraps load() to get_relevant_documents().
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It uses all ArxivAPIWrapper arguments without any change.
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"""
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get_full_documents: bool = False
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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try:
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if self.is_arxiv_identifier(query):
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results = self.arxiv_search(
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id_list=query.split(),
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max_results=self.top_k_results,
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).results()
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else:
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results = self.arxiv_search( # type: ignore
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query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results
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).results()
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except self.arxiv_exceptions as ex:
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return [Document(page_content=f"Arxiv exception: {ex}")]
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docs = [
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Document(
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page_content=result.summary,
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metadata={
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"Published": result.updated.date(),
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"Title": result.title,
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"Authors": ", ".join(a.name for a in result.authors),
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},
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)
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for result in results
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]
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return docs
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# Set up tool(s)
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description = (
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"A wrapper around Arxiv.org "
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"Useful for when you need to answer questions about Physics, Mathematics, "
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"Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "
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"Electrical Engineering, and Economics "
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"from scientific articles on arxiv.org. "
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"Input should be a search query."
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)
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arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description)
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tools = [arxiv_tool]
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# Set up LLM
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llm = ChatFireworks(
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model=MODEL_ID,
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model_kwargs={
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"temperature": 0,
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"max_tokens": 2048,
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"top_p": 1,
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},
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cache=True,
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)
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# setup ReAct style prompt
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prompt = hub.pull("hwchase17/react-json")
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prompt = 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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# define the agent
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model_with_stop = llm.bind(stop=["\nObservation"])
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agent = (
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{
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"input": lambda x: x["input"],
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"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
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}
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| prompt
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| model_with_stop
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| ReActJsonSingleInputOutputParser()
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)
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class InputType(BaseModel):
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input: str
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# instantiate AgentExecutor
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agent_executor = AgentExecutor(
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agent=agent,
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tools=tools,
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verbose=True,
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handle_parsing_errors=True,
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).with_types(input_type=InputType)
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