file is here with the model version in place of latest~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0You can easily access models in a few ways:1/ if the app is running:All of your local models are automatically served on localhost:11434Select your model when setting llm = Ollama(..., model="<model family>:<version>")If you set llm = Ollama(..., model="<model family") withoout a version it will simply look for latest2/ if building from source or just running the binary: Then you must run ollama serveAll of your local models are automatically served on localhost:11434Then, select as shown aboveUsage‚ÄãYou can see a full list of supported parameters on the API reference page.from langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  llm = Ollama(model="llama2",              callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.llm("Tell me about the history of AI")Ollama supports embeddings via OllamaEmbeddings:from langchain.embeddings import OllamaEmbeddingsoembed = OllamaEmbeddings(base_url="http://localhost:11434", model="llama2")oembed.embed_query("Llamas are social animals and live with others as a herd.")RAG‚ÄãWe can use Olama with RAG, just as shown here.Let's use the 13b model:ollama pull llama2:13bLet's also use local embeddings from OllamaEmbeddings and Chroma.pip install chromadb# Load web pagefrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()# Split into chunks from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)all_splits = text_splitter.split_documents(data)# Embed and storefrom langchain.vectorstores import
Ollama allows you to run open-source large language models, such as Llama 2, locally.
Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: file is here with the model version in place of latest~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0You can easily access models in a few ways:1/ if the app is running:All of your local models are automatically served on localhost:11434Select your model when setting llm = Ollama(..., model="<model family>:<version>")If you set llm = Ollama(..., model="<model family") withoout a version it will simply look for latest2/ if building from source or just running the binary: Then you must run ollama serveAll of your local models are automatically served on localhost:11434Then, select as shown aboveUsage‚ÄãYou can see a full list of supported parameters on the API reference page.from langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  llm = Ollama(model="llama2",              callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.llm("Tell me about the history of AI")Ollama supports embeddings via OllamaEmbeddings:from langchain.embeddings import OllamaEmbeddingsoembed = OllamaEmbeddings(base_url="http://localhost:11434", model="llama2")oembed.embed_query("Llamas are social animals and live with others as a herd.")RAG‚ÄãWe can use Olama with RAG, just as shown here.Let's use the 13b model:ollama pull llama2:13bLet's also use local embeddings from OllamaEmbeddings and Chroma.pip install chromadb# Load web pagefrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")data = loader.load()# Split into chunks from langchain.text_splitter import RecursiveCharacterTextSplittertext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)all_splits = text_splitter.split_documents(data)# Embed and storefrom langchain.vectorstores import