Spaces:
Running
Running
File size: 2,326 Bytes
b0747e4 |
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 |
import os
import yaml
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_community.graphs import Neo4jGraph
from langchain_core.prompts.prompt import PromptTemplate
from langchain.chains import GraphCypherQAChain
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
def config():
# load_dotenv()
# Set up Neo4J & Gemini API
os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI")
os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME")
os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD")
os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
def load_prompt(filepath):
with open(filepath, "r") as file:
prompt = yaml.safe_load(file)
return prompt
def init_():
config()
knowledge_graph = Neo4jGraph()
llm_chat = ChatGoogleGenerativeAI(
model= "gemini-1.5-flash-latest"
)
# Connect to Neo4J Knowledge Graph
cypher_prompt = load_prompt("hf_space/prompts/cypher_prompt.yaml")
qa_prompt = load_prompt("hf_space/prompts/qa_prompt.yaml")
CYPHER_GENERATION_PROMPT = PromptTemplate(**cypher_prompt)
QA_GENERATION_PROMPT = PromptTemplate(**qa_prompt)
chain = GraphCypherQAChain.from_llm(
llm_chat, graph=knowledge_graph, verbose=True,
cypher_prompt= CYPHER_GENERATION_PROMPT,
qa_prompt= QA_GENERATION_PROMPT
)
return chain
# Init GraphQA Chain
chain = init_()
def get_llm_response(query):
return chain.invoke({"query": query})["result"]
def llm_answer(message, history):
# history_langchain_format = []
#
# for human, ai in history:
# history_langchain_format.append(HumanMessage(content= human))
# history_langchain_format.append(AIMessage(content= ai))
#
# history_langchain_format.append(HumanMessage(content= message["text"]))
try:
response = get_llm_response(message["text"])
except Exception:
response = "Exception"
except Error:
response = "Error"
return response
# if __name__ == "__main__":
# message = "Have any company recruiting jobs about Machine Learning and coresponding job titles?"
# history = [("What's your name?", "My name is Gemini")]
# resp = llm_answer(message, history)
# print(resp)
|