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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>
38 lines
1005 B
Python
38 lines
1005 B
Python
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.pydantic_v1 import BaseModel
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from langchain_core.runnables import ConfigurableField
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from .prompts import answer_prompt
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from .retriever_agent import executor
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prompt = ChatPromptTemplate.from_template(answer_prompt)
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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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{"query": lambda x: x["query"], "information": executor | (lambda x: x["output"])}
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for the inputs to be used in the playground
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class Inputs(BaseModel):
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query: str
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chain = chain.with_types(input_type=Inputs)
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chain = chain.configurable_alternatives(
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ConfigurableField(id="chain"),
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default_key="response",
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# This adds a new option, with name `openai` that is equal to `ChatOpenAI()`
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retrieve=executor,
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)
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