Langchain Agents | EP01 | SQL Chain vs Agent | Langchain | LLM
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- Опубликовано: 16 окт 2024
- Welcome to this video where we are going to explore the world of Langchain agents, focusing specifically on SQL agents. We'll discover how SQL agents offer a more flexible approach to interacting with SQL databases compared to SQL chains. Throughout this video, we'll not only explain these concepts clearly, but we'll also dive into code examples to see them in action.
Agenda for the Video:
1. Implement a SQL Chain
2. Limitations of SQL Chain and Potential Solutions
3. Introduction to SQL Agent and Benefits of SQL Agent over SQL Chain
Code Notebook and Data : github.com/The...
#langchain #agent #chain #llm #openai #tools #sqldatabase #sqldatabasechain #deeplearning #conversationalai #nlp
Very Informative video
Good going
Quite insightful of a video.
Very detailed video and very informative. I also wanted to can we provide some prompt to agent so that we will get only specific result always
1. One way is to pass a prefix to your agent something like this:
prefix = """
You are an agent designed to interact with a sqlite database.
Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k}
results.
"""
agent_executor = create_sql_agent(llm, db = db, prefix=prefix, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose = True)
2. Another way is to create a "custom agent" where you would be having more freedom to customize the agent as you want. Check out the following video:
ruclips.net/video/OaQ6XrhEvjI/видео.htmlsi=BKYbwjQLZbixjmA4
Very good video , can you make one for SQL 0:57 agent using llama 3
You can check out this video : ruclips.net/video/GlyvykfIPJI/видео.html
Awesome thanks !
help me to connect my data base bro .i am facing some problems
What's the issue?