Spaces:
Sleeping
Sleeping
File size: 5,099 Bytes
8ce97f0 583f6dd 8ce97f0 6f57d05 8ce97f0 3c1150c 38cf703 8ce97f0 38cf703 6f57d05 8ce97f0 6f57d05 8ce97f0 6f57d05 8ce97f0 6f57d05 8ce97f0 6f57d05 8ce97f0 6f57d05 8ce97f0 38cf703 8ce97f0 38cf703 6f57d05 8ce97f0 6f57d05 8ce97f0 38cf703 6874dac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
from .prompts import tool_return_prompt , extract_user_reference_prompt , query_response_prompt,captioning_prompt
from langchain_core.messages import SystemMessage, HumanMessage, FunctionMessage
from src.genai.utils.models_loader import llm_gpt
from src.genai.utils.base_endpoint import base_url
from .state import State
from .tools import InfluencerRetrievalTool
from .schemas import ToolResponseFormatter , UserReferenceResponseFormatter
import os
import requests
from groq import Groq
retriever=InfluencerRetrievalTool()
class ImageCaptionNode:
def __init__(self, api_key=os.environ.get('GROQ_API_KEY')):
self.client = Groq(api_key=api_key)
def run(self,state:State):
if len(state['image_base64'])>0:
print('Captioning image')
chat_completion = self.client.chat.completions.create(
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": captioning_prompt(state['messages'])},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpg;base64,{state['image_base64[-1]']}",
},
},
],
}
],
model="meta-llama/llama-4-scout-17b-16e-instruct",
max_completion_tokens=50,
temperature = 1
)
response=chat_completion.choices[0].message.content
return {'image_caption': response}
else:
print('No image provided')
return {'image_caption':None}
class ToolReturnNode:
"""Node for determining which tools to use based on user messages."""
def __init__(self, llm=llm_gpt):
self.llm = llm
def run(self, state:State):
if len(state["messages"]) > 23:
state["messages"] = state["messages"][-18:]
template = [SystemMessage(content=tool_return_prompt)] + state["messages"]
response = self.llm.with_structured_output(ToolResponseFormatter, method='function_calling').invoke(template)
print('The response is:', response)
return {"messages": [{'role': 'assistant', 'content': f"Tool invoked: {response.tools}"}],
"tools":response.tools}
class QueryResponseNode:
def __init__(self):
self.llm = llm_gpt
def run(self,state:State):
print('Entered to query response')
if len(state['tools'])<1:
print('Going for retrieval')
retrieved_data=retriever.retrieve_for_orchestration(state['messages'])
print('The data is retrieved.')
template = [SystemMessage(content=query_response_prompt),
FunctionMessage(name='inf-data-retrieval',content=retrieved_data)] + state["messages"]
response = self.llm.invoke(template)
print('Query Response:', response)
return {"messages": [{'role': 'assistant', 'content': response.content}],
"query_response":response.content}
else:
return{
"query_response": f'''Okay i will perform {" ".join(state['tools'])} for you.'''
}
class ExtractUserReferenceNode:
"""Node for extracting video idea and story from user's messages."""
def __init__(self, llm=llm_gpt):
self.llm = llm
def run(self, state):
latest_human_message = next(
(msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
None
)
print('Latest human message:', latest_human_message)
template = [SystemMessage(content=extract_user_reference_prompt),
HumanMessage(content=latest_human_message.content)]
response = self.llm.with_structured_output(UserReferenceResponseFormatter, method='function_calling').invoke(template)
print('The extracted reference:', response)
return{
'video_idea': response.video_idea,
'video_story': response.video_story
}
class InvokeToolNode:
def __init__(self):
self.base_url = base_url
self.headers = {
"Authorization": "Bearer YOUR_API_KEY", # replace with your API key if needed
"Content-Type": "application/json"
}
def run(self,state:State):
latest_human_message = next(
(msg for msg in reversed(state['messages']) if isinstance(msg, HumanMessage)),
None
)
data_to_return=[]
for tool in state['tools']:
if 'analytics' in tool:
url = f'''{self.base_url}{tool}'''
response = requests.get(url, params=latest_human_message.content,headers=self.headers)
return {
'analytics_response':response.json()
}
|