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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.
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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.
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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.
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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.
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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.
Located in
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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.
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python3-langchain-0.3.26-1.lbn36.noarch
🦜️🔗 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.
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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
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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.
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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
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