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.If you are using a LLaMA chat model (e.g., ollama pull llama2:7b-chat) then you can use the ChatOllama interface.This includes special tokens for system message and user input.from langchain.chat_models import ChatOllamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  chat_model = ChatOllama(model="llama2:7b-chat",                         callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.from langchain.schema import HumanMessagemessages = [    HumanMessage(content="Tell me about the history of AI")]chat_model(messages)     Artificial intelligence (AI) has a rich and varied history that spans several decades. Hinweis: The following is a brief overview of the major milestones in the history of AI, but it is by no means exhaustive.        1. Early Beginnings (1950s-1960s): The term "Artificial Intelligence" was coined in 1956 by computer scientist John McCarthy. However, the concept of creating machines that can think and learn like humans dates back to ancient times. In the 1950s and 1960s, researchers began exploring the possibilities of AI using simple algorithms and machine learning techniques.    2. Rule-Based Systems (1970s-1980s): In the 1970s and 1980s, AI research focused on developing rule-based systems, which use predefined rules to reason and make decisions. This led to the development of expert systems, which were designed to mimic the decision-making abilities of human experts in specific domains.    3. Machine Learning (1980s-1990s): The 1980s saw a shift towards machine learning, which enables machines to learn from data without being
Ollama allows you to run open-source large language models, such as LLaMA2, locally.
Ollama allows you to run open-source large language models, such as LLaMA2, locally. ->: 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.If you are using a LLaMA chat model (e.g., ollama pull llama2:7b-chat) then you can use the ChatOllama interface.This includes special tokens for system message and user input.from langchain.chat_models import ChatOllamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  chat_model = ChatOllama(model="llama2:7b-chat",                         callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))With StreamingStdOutCallbackHandler, you will see tokens streamed.from langchain.schema import HumanMessagemessages = [    HumanMessage(content="Tell me about the history of AI")]chat_model(messages)     Artificial intelligence (AI) has a rich and varied history that spans several decades. Hinweis: The following is a brief overview of the major milestones in the history of AI, but it is by no means exhaustive.        1. Early Beginnings (1950s-1960s): The term "Artificial Intelligence" was coined in 1956 by computer scientist John McCarthy. However, the concept of creating machines that can think and learn like humans dates back to ancient times. In the 1950s and 1960s, researchers began exploring the possibilities of AI using simple algorithms and machine learning techniques.    2. Rule-Based Systems (1970s-1980s): In the 1970s and 1980s, AI research focused on developing rule-based systems, which use predefined rules to reason and make decisions. This led to the development of expert systems, which were designed to mimic the decision-making abilities of human experts in specific domains.    3. Machine Learning (1980s-1990s): The 1980s saw a shift towards machine learning, which enables machines to learn from data without being