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**Description:** The issue concerns the unexpected behavior observed using the bind_tools method in LangChain's ChatOllama. When tools are not bound, the llm.stream() method works as expected, returning incremental chunks of content, which is crucial for real-time applications such as conversational agents and live feedback systems. However, when bind_tools([]) is used, the streaming behavior changes, causing the output to be delivered in full chunks rather than incrementally. This change negatively impacts the user experience by breaking the real-time nature of the streaming mechanism. **Issue:** #26971 --------- Co-authored-by: 4meyDam1e <amey.damle@mail.utoronto.ca> Co-authored-by: Chester Curme <chester.curme@gmail.com>
langchain-ollama
This package contains the LangChain integration with Ollama
Installation
pip install -U langchain-ollama
You will also need to run the Ollama server locally. You can download it here.
Chat Models
ChatOllama class exposes chat models from Ollama.
from langchain_ollama import ChatOllama
llm = ChatOllama(model="llama3-groq-tool-use")
llm.invoke("Sing a ballad of LangChain.")
Embeddings
OllamaEmbeddings class exposes embeddings from Ollama.
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(model="llama3")
embeddings.embed_query("What is the meaning of life?")
LLMs
OllamaLLM class exposes LLMs from Ollama.
from langchain_ollama import OllamaLLM
llm = OllamaLLM(model="llama3")
llm.invoke("The meaning of life is")