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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>
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
from langchain.agents import AgentExecutor
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from langchain_anthropic import ChatAnthropic
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from .agent_scratchpad import format_agent_scratchpad
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from .output_parser import parse_output
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from .prompts import retrieval_prompt
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from .retriever import retriever_description, search
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prompt = ChatPromptTemplate.from_messages(
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[
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("user", retrieval_prompt),
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("ai", "{agent_scratchpad}"),
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]
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)
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prompt = prompt.partial(retriever_description=retriever_description)
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model = ChatAnthropic(
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model="claude-3-sonnet-20240229", temperature=0, max_tokens_to_sample=1000
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)
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chain = (
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RunnablePassthrough.assign(
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agent_scratchpad=lambda x: format_agent_scratchpad(x["intermediate_steps"])
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)
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| prompt
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| model.bind(stop_sequences=["</search_query>"])
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| StrOutputParser()
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)
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agent_chain = (
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RunnableParallel(
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{
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"partial_completion": chain,
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"intermediate_steps": lambda x: x["intermediate_steps"],
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}
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)
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| parse_output
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)
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executor = AgentExecutor(agent=agent_chain, tools=[search], verbose=True)
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