creating an agent

mixtral_agent.py

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+# LangChain supports many other chat models. Here, we're using Ollama
+from langchain_community.chat_models import ChatOllama
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.tools.retriever import create_retriever_tool
+from langchain_community.utilities import SerpAPIWrapper
+from langchain.retrievers import ArxivRetriever
+from langchain_core.tools import Tool
+from langchain import hub
+from langchain.agents import AgentExecutor, load_tools
+from langchain.agents.format_scratchpad import format_log_to_str
+from langchain.agents.output_parsers import (
+    ReActJsonSingleInputOutputParser,
+)
+from langchain.tools.render import render_text_description
+
+# supports many more optional parameters. Hover on your `ChatOllama(...)`
+# class to view the latest available supported parameters
+llm = ChatOllama(
+    model="mistral",
+    base_url="https://0013-35-201-206-176.ngrok-free.app"
+    )
+prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}")
+
+# using LangChain Expressive Language chain syntax
+# learn more about the LCEL on
+# https://python.langchain.com/docs/expression_language/why
+chain = prompt | llm | StrOutputParser()
+
+# for brevity, response is printed in terminal
+# You can use LangServe to deploy your application for
+# production
+print(chain.invoke({"topic": "Space travel"}))
+
+retriever = ArxivRetriever(load_max_docs=2)
+
+tools = [
+    create_retriever_tool(
+    retriever,
+    "search arxiv's database for",
+    "Use this to recomend the user a paper to read Unless stated please choose the most recent models",
+    # "Searches and returns excerpts from the 2022 State of the Union.",
+    ),
+
+    Tool(
+        name="SerpAPI",
+        description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
+        func=SerpAPIWrapper().run,
+    )
+
+]
+
+
+
+prompt = hub.pull("hwchase17/react-json")
+prompt = prompt.partial(
+    tools=render_text_description(tools),
+    tool_names=", ".join([t.name for t in tools]),
+)
+
+chat_model = llm
+# define the agent
+chat_model_with_stop = chat_model.bind(stop=["\nObservation"])
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
+    }
+    | prompt
+    | chat_model_with_stop
+    | ReActJsonSingleInputOutputParser()
+)
+
+# instantiate AgentExecutor
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True,handle_parsing_errors=True)
+
+# agent_executor.invoke(
+#     {
+#         "input": "Who is the current holder of the speed skating world record on 500 meters? What is her current age raised to the 0.43 power?"
+#     }
+# )
+
+# agent_executor.invoke(
+#     {
+#         "input": "what are large language models and why are they so expensive to run?"
+#     }
+# )
+
+agent_executor.invoke(
+    {
+        "input": "How to generate videos from images using state of the art macchine learning models"
+    }
+)
+
+