Our Subscription Manager utilises X509 certificates to define the product(s) a system is built upon/subscribed to. This is the subscription definition for Python3-Langchain

🦜🔗 Using Composio With LangChain Integrate Composio with LangChain agents to allow them to interact seamlessly with external apps, enhancing their functionality and reach. Goal Star a repository on GitHub using natural language commands through a LangChain Agent. Installation and Setup Ensure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities. pip install composio-langchain composio-cli add github composio-cli show-apps Usage Steps 1. Import Base Packages Prepare your environment by initializing necessary imports from LangChain and setting up your agent. from langchain.agents import create_openai_functions_agent, AgentExecutor from langchain import hub from langchain_openai import ChatOpenAI llm = ChatOpenAI() prom

🦜️🔗 LangChain Building applications with LLMs through composability 🤔 What is this? Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This is a metapackage bringing in azure-ai extras requires for python3-langchain. It makes sure the dependencies are installed.

This is a metapackage bringing in cohere extras requires for python3-langchain. It makes sure the dependencies are installed.

This is a metapackage bringing in openai extras requires for python3-langchain. It makes sure the dependencies are installed.

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

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.

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.

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