From 1eed3a0378dbf163122be27990b95a111c2054b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Moura?= Date: Mon, 5 Feb 2024 23:13:10 -0800 Subject: [PATCH] updating docs and readme --- README.md | 4 ++-- docs/how-to/LLM-Connections.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index cc94a3350..c337a2368 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,7 @@ from crewai import Agent, Task, Crew, Process os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" # You can choose to use a local model through Ollama for example. See ./docs/how-to/llm-connections.md for more information. -# from langchain.llms import Ollama +# from langchain_community.llms import Ollama # ollama_llm = Ollama(model="openhermes") # Install duckduckgo-search for this example: @@ -87,7 +87,7 @@ researcher = Agent( # # Examples: # - # from langchain.llms import Ollama + # from langchain_community.llms import Ollama # llm=ollama_llm # was defined above in the file # # from langchain_openai import ChatOpenAI diff --git a/docs/how-to/LLM-Connections.md b/docs/how-to/LLM-Connections.md index 1556d227b..502fd0990 100644 --- a/docs/how-to/LLM-Connections.md +++ b/docs/how-to/LLM-Connections.md @@ -20,7 +20,7 @@ Ollama is preferred for local LLM integration, offering customization and privac Instantiate Ollama and pass it to your agents within CrewAI, enhancing them with the local model's capabilities. ```python -from langchain.llms import Ollama +from langchain_community.llms import Ollama # Assuming you have Ollama installed and downloaded the openhermes model ollama_openhermes = Ollama(model="openhermes")