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python3-langchain-fireworks-0.3.0-1.lbn36.noarch: LangChain-Fireworks This is the partner package for tying Fireworks.ai and LangChain. Fireworks really strive to provide good support for LangChain use cases, so if you run into any issues please let us know. You can reach out to us in our Discord channel Installation To use the langchain-fireworks package, follow these installation steps: pip install langchain-fireworks Basic usage Setting up Sign in to Fireworks AI to obtain an API Key to access the models, and make sure it is set as the FIREWORKS_API_KEY environment variable. Once you've signed in and obtained an API key, follow these steps to set the FIREWORKS_API_KEY environment variable: Linux/macOS: Open your terminal and execute the following command: export FIREWORKS_API_KEY='your_api_key' Note: To make this environment variable persistent across terminal sessions, add the above line to your ~/.bashrc, ~/.bash_profile, or ~/.zshrc file. Windows: For Command Prompt, use: set FIREWORKS_API_KEY=your_api_key Set up your

python3-langchain-elasticsearch-0.3.2-1.lbn36.noarch: langchain-elasticsearch This package contains the LangChain integration with Elasticsearch. Installation pip install -U langchain-elasticsearch Elasticsearch setup Elastic Cloud You need a running Elasticsearch deployment. The easiest way to start one is through Elastic Cloud. You can sign up for a free trial. Create a deployment Get your Cloud ID: In the Elastic Cloud console, click "Manage" next to your deployment Copy the Cloud ID and paste it into the es_cloud_id parameter below Create an API key: In the Elastic Cloud console, click "Open" next to your deployment In the left-hand side menu, go to "Stack Management", then to "API Keys" Click "Create API key" Enter a name for the API key and click "Create" Copy the API key and paste it into the es_api_key parameter below Alternatively, you can run Elasticsearch via Docker as described in the docs. Usage ElasticsearchStore The ElasticsearchStore class exposes Elasticsearch as a vector store. from langchain_elasticsearch impor

python3-langchain-deepseek-0.1.3-1.lbn36.noarch: langchain-deepseek This package contains the LangChain integration with the DeepSeek API Installation pip install -U langchain-deepseek And you should configure credentials by setting the following environment variables: DEEPSEEK_API_KEY Chat Models ChatDeepSeek class exposes chat models from DeepSeek. from langchain_deepseek import ChatDeepSeek llm = ChatDeepSeek(model="deepseek-chat") llm.invoke("Sing a ballad of LangChain.")

python3-langchain-core-0.3.62-1.lbn36.noarch: 🦜🍎️ LangChain Core What is it? LangChain Core contains the base abstractions that power the rest of the LangChain ecosystem. These abstractions are designed to be as modular and simple as possible. Examples of these abstractions include those for language models, document loaders, embedding models, vectorstores, retrievers, and more. The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem. For full documentation see the API reference. 1️⃣ Core Interface: Runnables The concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them a common invocation interface (invoke, batch, stream, etc.) built-in utilities for retries, fallbacks, schemas and runtime configurability easy deployment with LangServe

python3-langchain-community-0.3.24-1.lbn36.noarch: 🦜️🧑‍🤝‍🧑 LangChain Community What is it? LangChain Community contains third-party integrations that implement the base interfaces defined in LangChain Core, making them ready-to-use in any LangChain application.

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-0.3.25-1.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-0.3.25-1.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-0.3.25-1.lbn36.noarch: This is a metapackage bringing in fireworks extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+community-0.3.25-1.lbn36.noarch: This is a metapackage bringing in community extras requires for python3-langchain. It makes sure the dependencies are installed.

python3-langchain+cohere-0.3.25-1.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-0.3.25-1.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-0.3.25-1.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.

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