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a0960b4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import json
import os
import csv
import json
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, HumanMessage
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.tools import tool
from langgraph.graph import StateGraph, MessagesState
INPUT_CSV = "data_clean.csv"
def load_docs(csv_path):
docs = []
with open(csv_path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
content = row["content"]
try:
metadata = json.loads(row.get("metadata", "{}"))
except json.JSONDecodeError:
metadata = {}
docs.append(Document(page_content=content, metadata=metadata))
return docs
docs = load_docs(INPUT_CSV)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vector_store = Chroma.from_documents(
docs,
embeddings,
persist_directory="chroma_db"
)
vector_store.persist()
print("vector store created and saved to 'chroma_db'")
def find_answer(query, k=1) -> str:
"""
Searches for an answer in the vector database based on the user's query.
Returns a string with the final answer or the last text of the document.
:param query: User query
:param k: number of possible answers
:return: User's answer
"""
results = vector_store.similarity_search(query, k=k)
if not results:
return "ΠΡΠ²Π΅Ρ Π½Π΅ Π½Π°ΠΉΠ΄Π΅Π½"
content = results[0].page_content
if "Final answer :" in content:
return content.split("Final answer :", 1)[1].strip()
elif "Answer:" in content:
return content.split("Answer:", 1)[1].strip()
else:
return content.strip().splitlines()[-1]
def build_graph():
def retriever_node(state: MessagesState):
user_query = state["messages"][-1].content
answer_text = find_answer(user_query)
return {"messages": state["messages"] + [AIMessage(content=answer_text)]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever_node)
builder.set_entry_point("retriever")
builder.set_finish_point("retriever")
return builder.compile()
graph = build_graph()
import json
import os
import csv
import json
from langchain_core.documents import Document
from langchain_core.messages import AIMessage, HumanMessage
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.tools import tool
from langgraph.graph import StateGraph, MessagesState
INPUT_CSV = "data_clean.csv"
def load_docs(csv_path):
docs = []
with open(csv_path, newline="", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
content = row["content"]
try:
metadata = json.loads(row.get("metadata", "{}"))
except json.JSONDecodeError:
metadata = {}
docs.append(Document(page_content=content, metadata=metadata))
return docs
docs = load_docs(INPUT_CSV)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vector_store = Chroma.from_documents(
docs,
embeddings,
persist_directory="chroma_db"
)
vector_store.persist()
print("vector store created and stored in 'chroma_db'")
def find_answer(query, k=1) -> str:
"""
Searches for an answer in the vector database based on the user's query.
Returns a string with the final answer or the last text of the document.
:param query: User query
:param k: number of possible answers
:return: User's answer
"""
results = vector_store.similarity_search(query, k=k)
if not results:
return "ΠΡΠ²Π΅Ρ Π½Π΅ Π½Π°ΠΉΠ΄Π΅Π½"
content = results[0].page_content
if "Final answer :" in content:
return content.split("Final answer :", 1)[1].strip()
elif "Answer:" in content:
return content.split("Answer:", 1)[1].strip()
else:
return content.strip().splitlines()[-1]
def build_graph():
def retriever_node(state: MessagesState):
user_query = state["messages"][-1].content
answer_text = find_answer(user_query)
return {"messages": state["messages"] + [AIMessage(content=answer_text)]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever_node)
builder.set_entry_point("retriever")
builder.set_finish_point("retriever")
return builder.compile()
graph = build_graph() |