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f624ad489a
**TL;DR much of the provided `Makefile` targets were broken, and any time I wanted to preview changes locally I either had to refer to a command Chester gave me or try waiting on a Vercel preview deployment. With this PR, everything should behave like normal.** Significant updates to the `Makefile` and documentation files, focusing on improving usability, adding clear messaging, and fixing/enhancing documentation workflows. ### Updates to `Makefile`: #### Enhanced build and cleaning processes: - Added informative messages (e.g., "📚 Building LangChain documentation...") to makefile targets like `docs_build`, `docs_clean`, and `api_docs_build` for better user feedback during execution. - Introduced a `clean-cache` target to the `docs` `Makefile` to clear cached dependencies and ensure clean builds. #### Improved dependency handling: - Modified `install-py-deps` to create a `.venv/deps_installed` marker, preventing redundant/duplicate dependency installations and improving efficiency. #### Streamlined file generation and infrastructure setup: - Added caching for the LangServe README download and parallelized feature table generation - Added user-friendly completion messages for targets like `copy-infra` and `render`. #### Documentation server updates: - Enhanced the `start` target with messages indicating server start and URL for local documentation viewing. --- ### Documentation Improvements: #### Content clarity and consistency: - Standardized section titles for consistency across documentation files. [[1]](diffhunk://#diff-9b1a85ea8a9dcf79f58246c88692cd7a36316665d7e05a69141cfdc50794c82aL1-R1) [[2]](diffhunk://#diff-944008ad3a79d8a312183618401fcfa71da0e69c75803eff09b779fc8e03183dL1-R1) - Refined phrasing and formatting in sections like "Dependency management" and "Formatting and linting" for better readability. [[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L6-R6) [[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L84-R82) #### Enhanced workflows: - Updated instructions for building and viewing documentation locally, including tips for specifying server ports and handling API reference previews. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L60-R94) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126) - Expanded guidance on cleaning documentation artifacts and using linting tools effectively. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L82-R126) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142) #### API reference documentation: - Improved instructions for generating and formatting in-code documentation, highlighting best practices for docstring writing. [[1]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L107-R142) [[2]](diffhunk://#diff-048deddcfd44b242e5b23aed9f2e9ec73afc672244ce14df2a0a316d95840c87L144-R186) --- ### Minor Changes: - Added support for a new package name (`langchain_v1`) in the API documentation generation script. - Fixed minor capitalization and formatting issues in documentation files. [[1]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L40-R40) [[2]](diffhunk://#diff-2069d4f956ab606ae6d51b191439283798adaf3a6648542c409d258131617059L166-R160) --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
langchain-anthropic
This package contains the LangChain integration for Anthropic's generative models.
Installation
pip install -U langchain-anthropic
Chat Models
Anthropic recommends using their chat models over text completions.
You can see their recommended models in the Anthropic docs.
To use, you should have an Anthropic API key configured. Initialize the model as:
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import AIMessage, HumanMessage
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0, max_tokens=1024)
Define the input message
message = HumanMessage(content="What is the capital of France?")
Generate a response using the model
response = model.invoke([message])
For a more detailed walkthrough see here.
LLMs (Legacy)
You can use the Claude 2 models for text completions.
from langchain_anthropic import AnthropicLLM
model = AnthropicLLM(model="claude-2.1", temperature=0, max_tokens=1024)
response = model.invoke("The best restaurant in San Francisco is: ")