removed tools that did not work too well

ollama_fucntion_sample.py

@@ -1,76 +0,0 @@
-# LangChain supports many other chat models. Here, we're using Ollama
-
-
-# https://python.langchain.com/docs/integrations/chat/ollama_functions
-# https://python.langchain.com/docs/integrations/chat/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
-import os
-
-import dotenv
-
-dotenv.load_dotenv()
-
- 
-OLLMA_BASE_URL = os.getenv("OLLMA_BASE_URL")
-
-
-# supports many more optional parameters. Hover on your `ChatOllama(...)`
-# class to view the latest available supported parameters
-llm = ChatOllama(
-    model="mistral:instruct",
-    base_url= OLLMA_BASE_URL
-    )
-
-from langchain_experimental.llms.ollama_functions import OllamaFunctions
-
-# model = OllamaFunctions(model="mistral")
-model = OllamaFunctions(
-    model="mistral:instruct",
-    base_url= OLLMA_BASE_URL
-    )
-
-
-model = model.bind(
-    functions=[
-        {
-            "name": "get_current_weather",
-            "description": "Get the current weather in a given location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {
-                        "type": "string",
-                        "description": "The city and state, " "e.g. San Francisco, CA",
-                    },
-                    "unit": {
-                        "type": "string",
-                        "enum": ["celsius", "fahrenheit"],
-                    },
-                },
-                "required": ["location"],
-            },
-        }
-    ],
-    function_call={"name": "get_current_weather"},
-)
-
-from langchain.schema import HumanMessage
-
-output = model.invoke("what is the weather in Boston?")
-
-x=0

ollama_tools.py

@@ -1,44 +0,0 @@
-from langchain.chains import create_extraction_chain
-
-# Schema
-schema = {
-    "properties": {
-        "name": {"type": "string"},
-        "height": {"type": "integer"},
-        "hair_color": {"type": "string"},
-    },
-    "required": ["name", "height"],
-}
-
-# Input
-input = """Alex is 5 feet tall. Claudia is 1 feet taller than Alex and jumps higher than him. Claudia is a brunette and Alex is blonde."""
-
-
-
-from langchain_experimental.llms.ollama_functions import OllamaFunctions
-
-
-import os
-
-import dotenv
-
-dotenv.load_dotenv()
-
- 
-OLLMA_BASE_URL = os.getenv("OLLMA_BASE_URL")
-
-
-# supports many more optional parameters. Hover on your `ChatOllama(...)`
-# class to view the latest available supported parameters
-model = llm = OllamaFunctions(
-    model="mistral:instruct",
-    base_url= OLLMA_BASE_URL
-    )
-
-# model = OllamaFunctions(model="mistral")
-
-# Run chain
-# llm = OllamaFunctions(model="mistral:instruct", temperature=0)
-chain = create_extraction_chain(schema, llm)
-output = chain.run(input)
-x = 0