chat.py

import requests
from langchain_openai import ChatOpenAI
import json
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
from langchain.chains import ConversationChain
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.tools import tool
from langchain.pydantic_v1 import BaseModel, Field
import urllib.parse


def langchain_functions_parser(thisjson):
    try:
        function_call_data = thisjson['additional_kwargs']['function_call']
        thisjson['additional_kwargs']['tool_calls'] = []
        thisjson['additional_kwargs']['tool_calls'].append({'id':'No ID','function':function_call_data,'type':'function'})
        try:
            thisjson['additional_kwargs'].pop('function_call')
        except Exception as e:
            # print('error '+ str(e))
            pass
        return thisjson
    
    except Exception as e:
        # print('Error'+str(e))
        return thisjson
    

from typing import List, Dict, Any, Optional
from langchain.tools import BaseTool
from pydantic import BaseModel, Field

def convert_openai_tools_to_langchain(tools: List[Dict]) -> List[BaseTool]:
    """
    Converts a list of OpenAI tool dictionaries to a list of LangChain BaseTool objects.
    """
    langchain_tools = []
    for tool in tools:
        name = tool["name"]
        description = tool["description"]
        parameters = tool["parameters"]

        # Create a Pydantic model for the tool's arguments
        class ToolArgsSchema(BaseModel):
            # Dynamically create fields based on the parameters
            for param_name, param_info in parameters["properties"].items():
                param_type = str if param_info.get("type") == "string" else param_info.get("type")
                field_definition = f"{param_name}: {param_type.__name__} = Field(..., description='{param_info.get('description')}')"
                exec(field_definition)

        # Create a subclass of BaseTool with the _run method implemented
        class CustomTool(BaseTool):
            def __init__(self, name: str, description: str, args_schema: BaseModel):
                super().__init__(name=name, description=description, args_schema=args_schema)  # Pass name and description to super()
                # self.name = name  # No longer needed
                # self.description = description  # No longer needed
                # self.args_schema = args_schema  # No longer needed

            def _run(self, query: str) -> str:  # Replace with appropriate argument and return types
                # Implement your tool logic here
                print(f"Running tool {self.name} with query: {query}")
                # Example: Replace with your actual web search logic
                return f"Web search results for: {query}"

        # Create an instance of the custom tool
        langchain_tool = CustomTool(name=name, description=description, args_schema=ToolArgsSchema)
        langchain_tools.append(langchain_tool)

    return langchain_tools
class SearchInput(BaseModel):
    query: str = Field(description="a search query  follo")

from langchain_cohere import ChatCohere

def langchainConversation(conversation):
    prompts = []
    for message in conversation:
        prompts.append((message['role'],urllib.parse.quote((message['context']))))
    chat_template = ChatPromptTemplate.from_messages(prompts)
    toreturn = []
     
    for z in chat_template.format_messages():
        z.content = urllib.parse.unquote(z.content)
        toreturn.append(z)
    return(toreturn)
def segmind_input_parser(input):
    toreturn = []
    for thisdict in input:
        if(thisdict['role'] == 'ai'):
            toreturn.append({'role':'assistant','content':thisdict['context']})
        else:
            toreturn.append({'role':thisdict['role'],'content':thisdict['context']})
    return toreturn
def workers_input_parser(input):
    toreturn = []
    for thisdict in input:
        toreturn.append({'role':thisdict['role'],'content':thisdict['context']})
    return toreturn
def segmind_output_parser(input):
    return {"content": input['choices'][0]['message']['content'], "additional_kwargs": {}, "response_metadata": {}, "type": "ai", "name": None, "id": input['id'], "example": False, "tool_calls": [], "invalid_tool_calls": [], "usage_metadata": {"input_tokens": input['usage']['prompt_tokens'], "output_tokens": input['usage']['completion_tokens'], "total_tokens": input['usage']['total_tokens']}}
def converse(conversation,provider,model,key,other:dict={}):
    if(provider=='groq'):
        chat = ChatGroq(temperature=0, groq_api_key=key, model_name=model)
    elif(provider=='gemini'):
        chat = ChatGoogleGenerativeAI(model=model,google_api_key=key)
    elif(provider=='cohere'):
        chat = ChatCohere(model=model,cohere_api_key=key)
    elif(provider=='lepton'):
        url = f'https://{model}.lepton.run/api/v1/'
        print(url)
        chat = ChatOpenAI(openai_api_base = url,openai_api_key=key)
    elif(provider == 'cloudflare'):
        try:
            account_id = key.split('~')[0]
            api_token = key.split('~')[1]
        except:
            raise Exception('Invalid Accound Id or api token')
        API_BASE_URL = f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/"
        headers = {"Authorization": f"Bearer {api_token}"}


        def run(model, inputs):
            input = { "messages": inputs }
            response = requests.post(f"{API_BASE_URL}{model}", headers=headers, json=input)
            return response.json()
        inputs = workers_input_parser(conversation)
        output = run(model, inputs)
        print(output)
        return json.dumps({'content':output['result']['response']},indent=4)
    elif(provider == 'openrouter'):
        chat = ChatOpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=key,
            model=model
        )
    elif(provider == 'segmind'):
        url = f"https://api.segmind.com/v1/{model}"

        # Request payload
        data = {
            "messages": segmind_input_parser(conversation)
        }

        response = requests.post(url, json=data, headers={'x-api-key': key})
        output = json.loads(response.text)
        print(json.dumps(output,indent=4))
        return json.dumps(segmind_output_parser(output),indent=4)
    else:
        return json.dumps({'content':'unspported Provider'})
    try:
        tools = other['tools']
    except:
        tools = []

    if(provider not in ['cohere'] ):
        return json.dumps(langchain_functions_parser(json.loads(chat.invoke(langchainConversation(conversation),functions=tools).json())),indent=4)
    else:
        
        chat = chat.bind_tools(convert_openai_tools_to_langchain(tools))
        return json.dumps(langchain_functions_parser(json.loads(chat.invoke(langchainConversation(conversation)).json())),indent=4)