openai import OpenAIEmbeddings from langchain.embeddings import OllamaEmbeddings from langchain.chat_models import ChatOpenAI, ChatOllama from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory from langchain.chains import RetrievalQAWithSourcesChain...
If you are using the Ollama class in the LangChain framework, you can use the _stream method to stream the response. Here is an example: from langchain.llms import Ollama from langchain.callbacks.manager import CallbackManagerForLLMRun ollama = Ollama(model="llama2") prompt = "Tell me...
importpickledocs=Docs(llm="langchain",client=ChatAnthropic())model_str=pickle.dumps(docs)docs=pickle.loads(model_str)# but you have to set the client after loadingdocs.set_client(ChatAnthropic()) You can use llama.cpp to be the LLM. Note that you should be using relatively large models,...
from langchain.llms import Ollama from langchain.document_loaders import WebBaseLoader from langchain_community.llms import Ollama from langchain_community.document_loaders import WebBaseLoader from langchain.chains.summarize import load_summarize_chain loader = WebBaseLoader("https://ollama.com/blo...
Langchain + Docker + Neo4j + Ollama. Contribute to Samk13/ollama-langchain-genai-stack development by creating an account on GitHub.