langchain-taiga This package provides Taiga tools and a toolkit for use with LangChain. It includes: create_entity_tool: Creates user stories, tasks and issues in Taiga. search_entities_tool: Searches for user stories, tasks and issues in Taiga. get_entity_by_ref_tool: Gets a user story, task or issue by reference. update_entity_by_ref_tool: Updates a user story, task or issue by reference. add_comment_by_ref_tool: Adds a comment to a user story, task or issue. add_attachment_by_ref_tool: Adds an attachment to a user story, task or issue. Installation pip install -U langchain-taiga Environment Variable Export your taiga logins: export TAIGA_URL="https:/taiga.xyz.org/" export TAIGA_API_URL="https:/taiga.xyz.org/" export TAIGA_USERNAME="username" export TAIGA_PASSWORD="pw" export OPENAI_API_KEY="OPENAI_API_KEY" If this environment variable is not set, the tools will raise a ValueError when instantiated. Usage Direct Tool Usage from langchain_taiga.tools.taiga_tools import create_ent

langchain-tests This is a testing library for LangChain integrations. It contains the base classes for a standard set of tests. Installation We encourage pinning your version to a specific version in order to avoid breaking your CI when we publish new tests. We recommend upgrading to the latest version periodically to make sure you have the latest tests. Not pinning your version will ensure you always have the latest tests, but it may also break your CI if we introduce tests that your integration doesn't pass. Pip: ```bash pip install -U langchain-tests ``` Poetry: ```bash poetry add langchain-tests ``` Usage To add standard tests to an integration package's e.g. ChatModel, you need to create A unit test class that inherits from ChatModelUnitTests An integration test class that inherits from ChatModelIntegrationTests tests/unit_tests/test_standard.py: """Standard LangChain interface tests""" from typing import Type import pytest from langchain_core.language_models import BaseChat

langchain-tests This is a testing library for LangChain integrations. It contains the base classes for a standard set of tests. Installation We encourage pinning your version to a specific version in order to avoid breaking your CI when we publish new tests. We recommend upgrading to the latest version periodically to make sure you have the latest tests. Not pinning your version will ensure you always have the latest tests, but it may also break your CI if we introduce tests that your integration doesn't pass. Pip: ```bash pip install -U langchain-tests ``` Poetry: ```bash poetry add langchain-tests ``` Usage To add standard tests to an integration package's e.g. ChatModel, you need to create A unit test class that inherits from ChatModelUnitTests An integration test class that inherits from ChatModelIntegrationTests tests/unit_tests/test_standard.py: """Standard LangChain interface tests""" from typing import Type import pytest from langchain_core.language_models import BaseChat

🦜✂️ LangChain Text Splitters Quick Install pip install langchain-text-splitters What is it? LangChain Text Splitters contains utilities for splitting into chunks a wide variety of text documents. For full documentation see the API reference and the Text Splitters module in the main docs. 📕 Releases & Versioning langchain-text-splitters is currently on version 0.0.x. Minor version increases will occur for: Breaking changes for any public interfaces NOT marked beta Patch version increases will occur for: Bug fixes New features Any changes to private interfaces Any changes to beta features 💁 Contributing As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation. For detailed information on how to contribute, see the Contributing Guide.

langchain-together This package contains the LangChain integrations for Together AI through their APIs. Installation and Setup Install the LangChain partner package pip install -U langchain-together Get your Together AI api key from the Together Dashboard and set it as an environment variable (TOGETHER_API_KEY) Chat Completions This package contains the ChatTogether class, which is the recommended way to interface with Together AI chat models. ADD USAGE EXAMPLE HERE. Can we add this in the langchain docs? NEED to add image endpoint + completions endpoint as well Embeddings See a usage example Use togethercomputer/m2-bert-80M-8k-retrieval as the default model for embeddings.

langchain-unstructured This package contains the LangChain integration with Unstructured Installation pip install -U langchain-unstructured And you should configure credentials by setting the following environment variables: export UNSTRUCTURED_API_KEY="your-api-key" Loaders Partition and load files using either the unstructured-client sdk and the Unstructured API or locally using the unstructured library. API: To partition via the Unstructured API pip install unstructured-client and set partition_via_api=True and define api_key. If you are running the unstructured API locally, you can change the API rule by defining url when you initialize the loader. The hosted Unstructured API requires an API key. See the links below to learn more about our API offerings and get an API key. Local: By default the file loader uses the Unstructured partition function and will automatically detect the file type. In addition to document specific partition parameters, Unstructured has a rich set of "chu

langchain-xai This package contains the LangChain integrations for xAI through their APIs. Installation and Setup Install the LangChain partner package pip install -U langchain-xai Get your xAI api key from the xAI Dashboard and set it as an environment variable (XAI_API_KEY) Chat Completions This package contains the ChatXAI class, which is the recommended way to interface with xAI chat models.

OpenInference LangChain Instrumentation Python auto-instrumentation library for LangChain. These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as arize-phoenix. Installation pip install openinference-instrumentation-langchain Quickstart Install packages needed for this demonstration. pip install openinference-instrumentation-langchain langchain arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp Start the Phoenix app in the background as a collector. By default, it listens on http:/localhost:6006. You can visit the app via a browser at the same address. The Phoenix app does not send data over the internet. It only operates locally on your machine. python -m phoenix.server.main serve The following Python code sets up the LangChainInstrumentor to trace langchain and send the traces to Phoenix at the endpoint shown below. from langchain.chains import LLMChain from langchain_core.prompts import PromptTemplate from lang