Spaces:
Runtime error
Runtime error
File size: 3,412 Bytes
c6cd0dd cf966ea c6cd0dd cf966ea c6cd0dd 8ddaa0b c6cd0dd cf966ea c6cd0dd cf966ea c6cd0dd |
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 |
import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_core.messages import SystemMessage, HumanMessage
from langchain.tools.retriever import create_retriever_tool
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from tools import multiply,add,subtract,divide,modulus,wiki_search,duckduckgo_search,arvix_search
load_dotenv()
with open("system_prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
# System message
sys_msg = SystemMessage(content=system_prompt)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/static-similarity-mrl-multilingual-v1", model_kwargs={'device': 'cpu'})
qdrant = QdrantClient(
url=os.environ.get("QDRANT_URL"),
api_key=os.environ.get("QDRANT_SERVICE_KEY")
)
vector_store = QdrantVectorStore(
client=qdrant,
embeddings=embeddings,
collection_name="documents",
)
create_retriever_tool = create_retriever_tool(
retriever=vector_store.as_retriever(),
name="Question Search",
description="A tool to retrieve similar questions from a vector store.",
)
tools = [
multiply,
add,
subtract,
divide,
modulus,
wiki_search,
duckduckgo_search,
arvix_search,
]
def build_graph(provider: str = "groq"):
"""Build the graph"""
# Load environment variables from .env file
model=""
if provider == "google":
# Google Gemini
model = os.environ.get("GEMINI_MODEL")
llm = ChatGoogleGenerativeAI(model=model, temperature=0)
elif provider == "groq":
# Groq https://console.groq.com/docs/models
model = os.environ.get("GROQ_MODEL")
llm = ChatGroq(model=model, temperature=0)
elif provider == "huggingface":
model = os.environ.get("HUGGINGFACEHUB_URL")
llm = ChatHuggingFace(
llm=HuggingFaceEndpoint(
url=model,
temperature=0,
),
)
else:
raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
# Bind tools to LLM
llm_with_tools = llm.bind_tools(tools)
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": [llm_with_tools.invoke(state["messages"])]}
def retriever(state: MessagesState):
"""Retriever node"""
print(state["messages"][0])
similar_question = vector_store.similarity_search(state["messages"][0].page_content)
example_msg = HumanMessage(
content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].content}",
)
return {"messages": [sys_msg] + state["messages"] + [example_msg]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "retriever")
builder.add_edge("retriever", "assistant")
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools", "assistant")
return builder.compile() |