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python3-langchain-cohere-0.4.4-1.lbn36.noarch: Langchain-Cohere This package contains the LangChain integrations for Cohere. Cohere empowers every developer and enterprise to build amazing products and capture true business value with language AI. Installation Install the langchain-cohere package: pip install langchain-cohere Get a Cohere API key and set it as an environment variable (COHERE_API_KEY) Migration from langchain-community Cohere's integrations used to be part of the langchain-community package, but since version 0.0.30 the integration in langchain-community has been deprecated in favour langchain-cohere. The two steps to migrate are: Import from langchain_cohere instead of langchain_community, for example: from langchain_community.chat_models import ChatCohere -> from langchain_cohere import ChatCohere from langchain_community.retrievers import CohereRagRetriever -> from langchain_cohere import CohereRagRetriever from langchain.embeddings import CohereEmbeddings -> from langchain_cohere import CohereEmbeddings

python3-langchain-aws-0.2.22-1.lbn36.noarch: langchain-aws This package contains the LangChain integrations with AWS. Installation pip install -U langchain-aws All integrations in this package assume that you have the credentials setup to connect with AWS services. Chat Models ChatBedrock class exposes chat models from Bedrock. from langchain_aws import ChatBedrock llm = ChatBedrock() llm.invoke("Sing a ballad of LangChain.") Embeddings BedrockEmbeddings class exposes embeddings from Bedrock. from langchain_aws import BedrockEmbeddings embeddings = BedrockEmbeddings() embeddings.embed_query("What is the meaning of life?") LLMs BedrockLLM class exposes LLMs from Bedrock. from langchain_aws import BedrockLLM llm = BedrockLLM() llm.invoke("The meaning of life is") Retrievers AmazonKendraRetriever class provides a retriever to connect with Amazon Kendra. from langchain_aws import AmazonKendraRetriever retriever = AmazonKendraRetriever( index_id="561be2b6d-9804c7e7-f6a0fbb8-5ccd350" ) retriever.get_relevant_documents(quer

python3-langchain-astradb-0.6.0-2.lbn36.noarch: langchain-astradb This package contains the LangChain integrations for using DataStax Astra DB. DataStax Astra DB is a serverless vector-capable database built on Apache Cassandra and made conveniently available through an easy-to-use JSON API.

python3-langchain-anthropic-0.3.12-1.lbn36.noarch: 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 here. 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 Francisc

python3-langchain+openai-0.3.26-1.lbn36.noarch: This is a metapackage bringing in openai extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+google-vertexai-0.3.26-1.lbn36.noarch: This is a metapackage bringing in google-vertexai extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+fireworks-0.3.26-1.lbn36.noarch: This is a metapackage bringing in fireworks extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+community-0.3.26-1.lbn36.noarch: This is a metapackage bringing in community extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+cohere-0.3.26-1.lbn36.noarch: This is a metapackage bringing in cohere extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+azure-ai-0.3.26-1.lbn36.noarch: This is a metapackage bringing in azure-ai extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+anthropic-0.3.26-1.lbn36.noarch: This is a metapackage bringing in anthropic extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-joblib-1.4.2-5.lbn36.noarch: Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: * transparent disk-caching of the output values and lazy re-evaluation (memorize pattern) * easy simple parallel computing * logging and tracing of the execution

python3-inkex-1.4.1-1.lbn36.noarch: This package supports Inkscape extensions. It provides - a simplification layer for SVG manipulation through lxml - base classes for common types of Inkscape extensions - simplified testing of those extensions - a user interface library based on GTK3 At its core, Inkscape extensions take in a file, and output a file. - For effect extensions, those two files are SVG files. - For input extensions, the input file may be any arbitrary file and the output is an SVG. - For output extensions, the input is an SVG file while the output is an arbitrary file. - Some extensions (e.g. the extensions manager) don't manipulate files. This folder also contains the stock Inkscape extensions, i.e. the scripts that implement some commands that you can use from within Inkscape. Most of these commands are in the Extensions menu, or in the Open / Save dialogs.

python3-ibm-watsonx-ai-1.3.1-1.lbn36.noarch: Welcome to ibm-watsonx-ai ibm-watsonx-ai is a library that allows to work with watsonx.ai service on IBM Cloud and IBM Cloud for Data. Train, test and deploy your models as APIs for application development, share with colleagues using this python library. Package documentation ==========================================

python3-ibm-cos-sdk-s3transfer-2.14.0-1.lbn36.noarch: s3transfer - An IBM COS Transfer Manager for Pythons3transfer is a Python library for managing IBM COS transfers.

python3-ibm-cos-sdk-core-2.14.0-1.lbn36.noarch: ibm-cos-sdk-python-coreA low-level interface to IBM Cloud Object Storage based on the ibm_botocore package. This core package is the foundation for the [ibm- cos-sdk-python]( package. DocumentationDocumentation for ibm-cos-sdk-python- core can be found [here]( Getting HelpFeel free to use GitHub issues for tracking bugs and feature requests, but for help please use one of the following...

python3-ibm-cos-sdk-2.14.0-1.lbn36.noarch: IBM Cloud Object Storage - Python SDKThis package allows Python developers to write software that interacts with [IBM Cloud Object Storage]( It is a fork of the [boto3 library]( and can stand as a drop-in replacement if the application needs to connect to object storage using an S3-like API and does not make use of other AWS services. NoticeIBM has added a [Language Support Policy](language-...

python3-huggingface-hub-0.30.2-4.lbn36.noarch: The huggingface_hub library allows you to interact with the Hugging Face Hub, a machine learning platform for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the hundreds of machine learning apps hosted on the Hub. You can also create and share your own models and datasets with the community. The huggingface_hub library provides a simple way to do all these things with Python.

python3-groq-0.24.0-1.lbn36.noarch: Groq Python API library The Groq Python library provides convenient access to the Groq REST API from any Python 3.8+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx. It is generated with Stainless. Documentation The REST API documentation can be found on console.groq.com. The full API of this library can be found in api.md. Usage The full API of this library can be found in api.md. import os from groq import Groq client = Groq( api_key=os.environ.get("GROQ_API_KEY"), # This is the default and can be omitted ) chat_completion = client.chat.completions.create( messages=[ { "role": "user", "content": "Explain the importance of low latency LLMs", } ], model="llama3-8b-8192", ) print(chat_completion.choices[0].message.content)

python3-griffe-1.7.3-1.lbn36.noarch: Griffe Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API. Griffe, pronounced "grif" (/ɡʁif/), is a french word that means "claw", but also "signature" in a familiar way. "On reconnaît bien là sa griffe." Installation With pip: pip install griffe With pipx: python3.8 -m pip install --user pipx pipx install griffe Usage On the command line, pass the names of packages to the griffe dump command: $ griffe dump httpx fastapi { "httpx": { "name": "httpx", ... }, "fastapi": { "name": "fastapi", ... } } See the Dumping data section for more examples. Or pass a relative path to the griffe check command: $ griffe check mypackage --verbose mypackage/mymodule.py:10: MyClass.mymethod(myparam): Parameter kind was changed: Old: positional or keyword New: keyword-only For src layouts: $ griffe check --search src mypackage --verbose src/mypackage/

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