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SurfSense v3 - Highlight: Local LLM Support
This commit is contained in:
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13 changed files with 692 additions and 1345 deletions
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@ -1,42 +1,40 @@
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from __future__ import annotations
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from langchain.chains import GraphCypherQAChain
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from langchain_community.graphs import Neo4jGraph
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.documents import Document
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.vectorstores import Neo4jVector
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from envs import ACCESS_TOKEN_EXPIRE_MINUTES, ALGORITHM, API_SECRET_KEY, SECRET_KEY
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from prompts import CYPHER_QA_PROMPT, DATE_TODAY, DOC_DESCRIPTION_PROMPT, GRAPH_QUERY_GEN_PROMPT, NOTIFICATION_GENERATION_PROMT, SIMILARITY_SEARCH_PROMPT , CYPHER_GENERATION_PROMPT, DOCUMENT_METADATA_EXTRACTION_PROMT
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from pydmodels import ChatToUpdate, DescriptionResponse, GraphDocs, NewUserChat, NewUserData, Notifications, PrecisionQuery, PrecisionResponse, UserQuery, DocMeta, RetrivedDocList, UserQueryResponse, UserQueryWithChatHistory, VectorSearchQuery
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from langchain_experimental.text_splitter import SemanticChunker
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_ollama import OllamaLLM
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from langchain_openai import ChatOpenAI
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from prompts import CONTEXT_ANSWER_PROMPT, DATE_TODAY, SUBQUERY_DECOMPOSITION_PROMT
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from pydmodels import ChatToUpdate, DescriptionResponse, DocWithContent, DocumentsToDelete, NewUserChat, UserCreate, UserQuery, RetrivedDocList, UserQueryResponse, UserQueryWithChatHistory
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from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
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#Our Imps
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from LLMGraphTransformer import LLMGraphTransformer
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from langchain_openai import ChatOpenAI
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from DataExample import examples
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# import nest_asyncio
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# from langchain_community.chains.graph_qa.gremlin import GremlinQAChain
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# from langchain_community.graphs import GremlinGraph
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# from langchain_community.graphs.graph_document import GraphDocument, Node, Relationship
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# from langchain_core.documents import Document
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# from langchain_openai import AzureChatOpenAI
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#Heirerical Indices class
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from HIndices import HIndices
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from Utils.stringify import stringify
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# Auth Libs
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from fastapi import FastAPI, Depends, HTTPException, Request, status
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from fastapi import FastAPI, Depends, HTTPException, status
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from sqlalchemy.orm import Session
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from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
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from jose import JWTError, jwt
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from datetime import datetime, timedelta
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from passlib.context import CryptContext
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from models import Chat, Notification, User
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from database import SessionLocal, engine
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from pydantic import BaseModel
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from models import Chat, Documents, SearchSpace, User
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from database import SessionLocal
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from fastapi.middleware.cors import CORSMiddleware
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from langchain_openai import AzureChatOpenAI
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import os
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from dotenv import load_dotenv
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load_dotenv()
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IS_LOCAL_SETUP = os.environ.get("IS_LOCAL_SETUP")
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ACCESS_TOKEN_EXPIRE_MINUTES = int(os.environ.get("ACCESS_TOKEN_EXPIRE_MINUTES"))
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ALGORITHM = os.environ.get("ALGORITHM")
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API_SECRET_KEY = os.environ.get("API_SECRET_KEY")
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SECRET_KEY = os.environ.get("SECRET_KEY")
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app = FastAPI()
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# Dependency
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@ -47,251 +45,117 @@ def get_db():
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finally:
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db.close()
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class UserCreate(BaseModel):
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username: str
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password: str
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apisecretkey: str
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# General GraphCypherQAChain
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@app.post("/")
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@app.post("/chat/")
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def get_user_query_response(data: UserQuery, response_model=UserQueryResponse):
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if(data.apisecretkey != API_SECRET_KEY):
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raise HTTPException(status_code=401, detail="Unauthorized")
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graph = Neo4jGraph(url=data.neourl, username=data.neouser, password=data.neopass)
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llm = ChatOpenAI(
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model="gpt-4o-mini",
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temperature=0,
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max_tokens=None,
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timeout=None,
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api_key=data.openaikey
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)
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# Query Expansion
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# searchchain = GRAPH_QUERY_GEN_PROMPT | llm
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# qry = searchchain.invoke({"question": data.query, "context": examples})
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query = data.query #qry.content
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embeddings = OpenAIEmbeddings(
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model="text-embedding-ada-002",
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api_key=data.openaikey,
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)
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chain = GraphCypherQAChain.from_llm(
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graph=graph,
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cypher_prompt=CYPHER_GENERATION_PROMPT,
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cypher_llm=llm,
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verbose=True,
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validate_cypher=True,
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qa_prompt=CYPHER_QA_PROMPT ,
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qa_llm=llm,
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return_intermediate_steps=True,
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top_k=5,
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)
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vector_index = Neo4jVector.from_existing_graph(
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embeddings,
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graph=graph,
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search_type="hybrid",
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node_label="Document",
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text_node_properties=["text"],
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embedding_node_property="embedding",
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)
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graphdocs = vector_index.similarity_search(data.query,k=15)
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docsDict = {}
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for d in graphdocs:
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if d.metadata['BrowsingSessionId'] not in docsDict:
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newVal = d.metadata.copy()
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newVal['VisitedWebPageContent'] = d.page_content
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docsDict[d.metadata['BrowsingSessionId']] = newVal
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else:
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docsDict[d.metadata['BrowsingSessionId']]['VisitedWebPageContent'] += d.page_content
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docstoreturn = []
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for x in docsDict.values():
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docstoreturn.append(DocMeta(
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BrowsingSessionId=x['BrowsingSessionId'],
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VisitedWebPageURL=x['VisitedWebPageURL'],
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VisitedWebPageVisitDurationInMilliseconds=x['VisitedWebPageVisitDurationInMilliseconds'],
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VisitedWebPageTitle=x['VisitedWebPageTitle'],
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VisitedWebPageReffererURL=x['VisitedWebPageReffererURL'],
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VisitedWebPageDateWithTimeInISOString=x['VisitedWebPageDateWithTimeInISOString'],
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VisitedWebPageContent=x['VisitedWebPageContent']
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))
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try:
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responsegrp = chain.invoke({"query": query})
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if "don't know" in responsegrp["result"]:
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raise Exception("No response from graph")
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payload = jwt.decode(data.token, SECRET_KEY, algorithms=[ALGORITHM])
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username: str = payload.get("sub")
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if username is None:
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raise HTTPException(status_code=403, detail="Token is invalid or expired")
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structured_llm = llm.with_structured_output(VectorSearchQuery)
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doc_extract_chain = DOCUMENT_METADATA_EXTRACTION_PROMT | structured_llm
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query = data.query
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search_space = data.search_space
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newquery = doc_extract_chain.invoke(responsegrp["intermediate_steps"][1]["context"])
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graphdocs = vector_index.similarity_search(newquery.searchquery,k=15)
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docsDict = {}
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for d in graphdocs:
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if d.metadata['BrowsingSessionId'] not in docsDict:
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newVal = d.metadata.copy()
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newVal['VisitedWebPageContent'] = d.page_content
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docsDict[d.metadata['BrowsingSessionId']] = newVal
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else:
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docsDict[d.metadata['BrowsingSessionId']]['VisitedWebPageContent'] += d.page_content
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docstoreturn = []
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for x in docsDict.values():
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docstoreturn.append(DocMeta(
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BrowsingSessionId=x['BrowsingSessionId'],
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VisitedWebPageURL=x['VisitedWebPageURL'],
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VisitedWebPageVisitDurationInMilliseconds=x['VisitedWebPageVisitDurationInMilliseconds'],
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VisitedWebPageTitle=x['VisitedWebPageTitle'],
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VisitedWebPageReffererURL=x['VisitedWebPageReffererURL'],
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VisitedWebPageDateWithTimeInISOString=x['VisitedWebPageDateWithTimeInISOString'],
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VisitedWebPageContent=x['VisitedWebPageContent']
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))
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return UserQueryResponse(relateddocs=docstoreturn,response=responsegrp["result"])
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except:
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# Fallback to Similarity Search RAG
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searchchain = SIMILARITY_SEARCH_PROMPT | llm
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response = searchchain.invoke({"question": data.query, "context": docstoreturn})
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return UserQueryResponse(relateddocs=docstoreturn,response=response.content)
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# Precision Search
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@app.post("/precision")
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def get_precision_search_response(data: PrecisionQuery, response_model=PrecisionResponse):
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if(data.apisecretkey != API_SECRET_KEY):
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raise HTTPException(status_code=401, detail="Unauthorized")
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graph = Neo4jGraph(url=data.neourl, username=data.neouser, password=data.neopass)
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GRAPH_QUERY = "MATCH (d:Document) WHERE d.VisitedWebPageDateWithTimeInISOString >= " + "'" + data.daterange[0] + "'" + " AND d.VisitedWebPageDateWithTimeInISOString <= " + "'" + data.daterange[1] + "'"
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if(data.timerange[0] >= data.timerange[1]):
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GRAPH_QUERY += " AND d.VisitedWebPageVisitDurationInMilliseconds >= 0"
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else:
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GRAPH_QUERY += " AND d.VisitedWebPageVisitDurationInMilliseconds >= "+ str(data.timerange[0]) + " AND d.VisitedWebPageVisitDurationInMilliseconds <= " + str(data.timerange[1])
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if(data.webpageurl):
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GRAPH_QUERY += " AND d.VisitedWebPageURL CONTAINS " + "'" + data.webpageurl.lower() + "'"
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if(data.sessionid):
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GRAPH_QUERY += " AND d.BrowsingSessionId = " + "'" + data.sessionid + "'"
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GRAPH_QUERY += " RETURN d;"
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graphdocs = graph.query(GRAPH_QUERY)
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docsDict = {}
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for d in graphdocs:
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if d['d']['VisitedWebPageVisitDurationInMilliseconds'] not in docsDict:
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docsDict[d['d']['VisitedWebPageVisitDurationInMilliseconds']] = d['d']
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if(IS_LOCAL_SETUP == 'true'):
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sub_query_llm = OllamaLLM(model="mistral-nemo",temperature=0)
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qa_llm = OllamaLLM(model="mistral-nemo",temperature=0)
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else:
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docsDict[d['d']['VisitedWebPageVisitDurationInMilliseconds']]['text'] += d['d']['text']
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docs = []
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for x in docsDict.values():
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docs.append(DocMeta(
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BrowsingSessionId=x['BrowsingSessionId'],
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VisitedWebPageURL=x['VisitedWebPageURL'],
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VisitedWebPageVisitDurationInMilliseconds=x['VisitedWebPageVisitDurationInMilliseconds'],
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VisitedWebPageTitle=x['VisitedWebPageTitle'],
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VisitedWebPageReffererURL=x['VisitedWebPageReffererURL'],
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VisitedWebPageDateWithTimeInISOString=x['VisitedWebPageDateWithTimeInISOString'],
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VisitedWebPageContent=x['text']
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))
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return PrecisionResponse(documents=docs)
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sub_query_llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini", api_key=data.openaikey)
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qa_llm = ChatOpenAI(temperature=0.5, model_name="gpt-4o-mini", api_key=data.openaikey)
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# Multi DOC Chat
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@app.post("/chat/docs")
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def doc_chat_with_history(data: UserQueryWithChatHistory, response_model=DescriptionResponse):
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if(data.apisecretkey != API_SECRET_KEY):
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raise HTTPException(status_code=401, detail="Unauthorized")
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llm = ChatOpenAI(
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model="gpt-4o-mini",
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temperature=0,
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max_tokens=None,
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timeout=None,
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api_key=data.openaikey
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)
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chatHistory = []
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for chat in data.chat:
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if(chat.type == 'system'):
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chatHistory.append(SystemMessage(content=DATE_TODAY + """You are an helpful assistant for question-answering tasks.
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Use the following pieces of retrieved context to answer the question.
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If you don't know the answer, just say that you don't know.
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Context:""" + str(chat.content)))
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if(chat.type == 'ai'):
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chatHistory.append(AIMessage(content=chat.content))
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if(chat.type == 'human'):
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chatHistory.append(HumanMessage(content=chat.content))
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chatHistory.append(("human", "{input}"));
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qa_prompt = ChatPromptTemplate.from_messages(chatHistory)
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# Create an LLMChain for sub-query decomposition
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subquery_decomposer_chain = SUBQUERY_DECOMPOSITION_PROMT | sub_query_llm
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descriptionchain = qa_prompt | llm
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def decompose_query(original_query: str):
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"""
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Decompose the original query into simpler sub-queries.
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Args:
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original_query (str): The original complex query
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Returns:
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List[str]: A list of simpler sub-queries
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"""
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if(IS_LOCAL_SETUP == 'true'):
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response = subquery_decomposer_chain.invoke(original_query)
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else:
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response = subquery_decomposer_chain.invoke(original_query).content
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sub_queries = [q.strip() for q in response.split('\n') if q.strip() and not q.strip().startswith('Sub-queries:')]
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return sub_queries
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response = descriptionchain.invoke({"input": data.query})
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return DescriptionResponse(response=response.content)
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# Create Heirarical Indecices
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if(IS_LOCAL_SETUP == 'true'):
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index = HIndices(username=username)
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else:
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index = HIndices(username=username,api_key=data.openaikey)
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# For Those Who Want HyDe Questions
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# sub_queries = decompose_query(query)
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sub_queries = []
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sub_queries.append(query)
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# DOC DESCRIPTION
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@app.post("/kb/doc")
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def get_doc_description(data: UserQuery, response_model=DescriptionResponse):
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if(data.apisecretkey != API_SECRET_KEY):
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raise HTTPException(status_code=401, detail="Unauthorized")
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document = data.query
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llm = ChatOpenAI(
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model="gpt-4o-mini",
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temperature=0,
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max_tokens=None,
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timeout=None,
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api_key=data.openaikey
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)
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descriptionchain = DOC_DESCRIPTION_PROMPT | llm
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response = descriptionchain.invoke({"document": document})
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return DescriptionResponse(response=response.content)
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duplicate_related_summary_docs = []
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context_to_answer = ""
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for sub_query in sub_queries:
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localreturn = index.local_search(query=sub_query, search_space=search_space)
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globalreturn, related_summary_docs = index.global_search(query=sub_query, search_space=search_space)
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context_to_answer += localreturn + "\n\n" + globalreturn
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# SAVE DOCS TO GRAPH DB
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@app.post("/kb/")
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def populate_graph(apires: RetrivedDocList, db: Session = Depends(get_db)):
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duplicate_related_summary_docs.extend(related_summary_docs)
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combined_docs_seen_metadata = set()
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combined_docs_unique_documents = []
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for doc in duplicate_related_summary_docs:
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# Convert metadata to a tuple of its items (this allows it to be added to a set)
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doc.metadata['relevance_score'] = 0.0
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metadata_tuple = tuple(sorted(doc.metadata.items()))
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if metadata_tuple not in combined_docs_seen_metadata:
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combined_docs_seen_metadata.add(metadata_tuple)
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combined_docs_unique_documents.append(doc)
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returnDocs = []
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for doc in combined_docs_unique_documents:
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entry = DocWithContent(
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BrowsingSessionId=doc.metadata['BrowsingSessionId'],
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VisitedWebPageURL=doc.metadata['VisitedWebPageURL'],
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VisitedWebPageContent=doc.page_content,
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VisitedWebPageTitle=doc.metadata['VisitedWebPageTitle'],
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VisitedWebPageDateWithTimeInISOString=doc.metadata['VisitedWebPageDateWithTimeInISOString'],
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VisitedWebPageReffererURL=doc.metadata['VisitedWebPageReffererURL'],
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VisitedWebPageVisitDurationInMilliseconds=doc.metadata['VisitedWebPageVisitDurationInMilliseconds'],
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)
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returnDocs.append(entry)
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ans_chain = CONTEXT_ANSWER_PROMPT | qa_llm
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finalans = ans_chain.invoke({"query": query, "context": context_to_answer})
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if(IS_LOCAL_SETUP == 'true'):
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return UserQueryResponse(response=finalans, relateddocs=returnDocs)
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else:
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return UserQueryResponse(response=finalans.content, relateddocs=returnDocs)
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except JWTError:
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raise HTTPException(status_code=403, detail="Token is invalid or expired")
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# SAVE DOCS
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@app.post("/save/")
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def save_data(apires: RetrivedDocList, db: Session = Depends(get_db)):
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try:
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payload = jwt.decode(apires.token, SECRET_KEY, algorithms=[ALGORITHM])
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@ -299,58 +163,49 @@ def populate_graph(apires: RetrivedDocList, db: Session = Depends(get_db)):
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if username is None:
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raise HTTPException(status_code=403, detail="Token is invalid or expired")
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print("STARTED")
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# print(apires)
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graph = Neo4jGraph(url=apires.neourl, username=apires.neouser, password=apires.neopass)
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llm = ChatOpenAI(
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model="gpt-4o-mini",
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temperature=0,
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max_tokens=None,
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timeout=None,
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api_key=apires.openaikey
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)
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embeddings = OpenAIEmbeddings(
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model="text-embedding-ada-002",
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api_key=apires.openaikey,
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)
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llm_transformer = LLMGraphTransformer(llm=llm)
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|
||||
DocumentPgEntry = []
|
||||
raw_documents = []
|
||||
|
||||
searchspace = db.query(SearchSpace).filter(SearchSpace.search_space == apires.search_space).first()
|
||||
|
||||
for doc in apires.documents:
|
||||
raw_documents.append(Document(page_content=doc.pageContent, metadata=doc.metadata))
|
||||
|
||||
text_splitter = SemanticChunker(embeddings=embeddings)
|
||||
|
||||
documents = text_splitter.split_documents(raw_documents)
|
||||
graph_documents = llm_transformer.convert_to_graph_documents(documents)
|
||||
|
||||
|
||||
graph.add_graph_documents(
|
||||
graph_documents,
|
||||
baseEntityLabel=True,
|
||||
include_source=True
|
||||
)
|
||||
|
||||
|
||||
structured_llm = llm.with_structured_output(Notifications)
|
||||
notifs_extraction_chain = NOTIFICATION_GENERATION_PROMT | structured_llm
|
||||
|
||||
notifications = notifs_extraction_chain.invoke({"documents": raw_documents})
|
||||
|
||||
notifsdb = []
|
||||
|
||||
for text in notifications.notifications:
|
||||
notifsdb.append(Notification(text=text))
|
||||
content = f"USER BROWSING SESSION EVENT: \n"
|
||||
content += f"=======================================METADATA==================================== \n"
|
||||
content += f"User Browsing Session ID : {doc.metadata.BrowsingSessionId} \n"
|
||||
content += f"User Visited website with url : {doc.metadata.VisitedWebPageURL} \n"
|
||||
content += f"This visited website url had title : {doc.metadata.VisitedWebPageTitle} \n"
|
||||
content += f"User Visited this website from reffering url : {doc.metadata.VisitedWebPageReffererURL} \n"
|
||||
content += f"User Visited this website url at this Date and Time : {doc.metadata.VisitedWebPageDateWithTimeInISOString} \n"
|
||||
content += f"User Visited this website for : {str(doc.metadata.VisitedWebPageVisitDurationInMilliseconds)} milliseconds. \n"
|
||||
content += f"===================================================================================== \n"
|
||||
content += f"Webpage Content of the visited webpage url in markdown format : \n\n {doc.pageContent} \n\n"
|
||||
content += f"===================================================================================== \n"
|
||||
raw_documents.append(Document(page_content=content,metadata=doc.metadata.__dict__))
|
||||
|
||||
pgdocmeta = stringify(doc.metadata.__dict__)
|
||||
|
||||
if(searchspace):
|
||||
DocumentPgEntry.append(Documents(file_type='WEBPAGE',title=doc.metadata.VisitedWebPageTitle,search_space=searchspace, document_metadata=pgdocmeta, page_content=content))
|
||||
else:
|
||||
DocumentPgEntry.append(Documents(file_type='WEBPAGE',title=doc.metadata.VisitedWebPageTitle,search_space=SearchSpace(search_space=apires.search_space.upper()), document_metadata=pgdocmeta, page_content=content))
|
||||
|
||||
user = db.query(User).filter(User.username == username).first()
|
||||
user.notifications.extend(notifsdb)
|
||||
|
||||
db.commit()
|
||||
#Save docs in PG
|
||||
user = db.query(User).filter(User.username == username).first()
|
||||
user.documents.extend(DocumentPgEntry)
|
||||
|
||||
db.commit()
|
||||
|
||||
|
||||
# Create Heirarical Indecices
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username,api_key=apires.openaikey)
|
||||
|
||||
#Save Indices in vector Stores
|
||||
index.encode_docs_hierarchical(documents=raw_documents, files_type='WEBPAGE',search_space=apires.search_space.upper(), db=db)
|
||||
|
||||
print("FINISHED")
|
||||
|
||||
|
|
@ -360,21 +215,83 @@ def populate_graph(apires: RetrivedDocList, db: Session = Depends(get_db)):
|
|||
|
||||
except JWTError:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
|
||||
#Fuction to populate db ( Comment out when running on server )
|
||||
@app.post("/add/")
|
||||
def add_user(user: UserCreate, db: Session = Depends(get_db)):
|
||||
db_user = User(username=user.username, hashed_password=user.password, graph_config="", llm_config="")
|
||||
db.add(db_user)
|
||||
db.commit()
|
||||
return "Success"
|
||||
|
||||
# Multi DOC Chat
|
||||
@app.post("/chat/docs")
|
||||
def doc_chat_with_history(data: UserQueryWithChatHistory, response_model=DescriptionResponse):
|
||||
try:
|
||||
payload = jwt.decode(data.token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
username: str = payload.get("sub")
|
||||
if username is None:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
llm = OllamaLLM(model="mistral-nemo",temperature=0)
|
||||
else:
|
||||
llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini", api_key=data.openaikey)
|
||||
|
||||
chatHistory = []
|
||||
|
||||
for chat in data.chat:
|
||||
if(chat.type == 'system'):
|
||||
chatHistory.append(SystemMessage(content=DATE_TODAY + """You are an helpful assistant for question-answering tasks.
|
||||
Use the following pieces of retrieved context to answer the question.
|
||||
If you don't know the answer, just say that you don't know.
|
||||
Context:""" + str(chat.content)))
|
||||
|
||||
if(chat.type == 'ai'):
|
||||
chatHistory.append(AIMessage(content=chat.content))
|
||||
|
||||
if(chat.type == 'human'):
|
||||
chatHistory.append(HumanMessage(content=chat.content))
|
||||
|
||||
chatHistory.append(("human", "{input}"));
|
||||
|
||||
|
||||
qa_prompt = ChatPromptTemplate.from_messages(chatHistory)
|
||||
|
||||
descriptionchain = qa_prompt | llm
|
||||
|
||||
response = descriptionchain.invoke({"input": data.query})
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
return DescriptionResponse(response=response)
|
||||
else:
|
||||
return DescriptionResponse(response=response.content)
|
||||
|
||||
except JWTError:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
|
||||
# Multi DOC Chat
|
||||
|
||||
@app.post("/delete/docs")
|
||||
def delete_all_related_data(data: DocumentsToDelete, db: Session = Depends(get_db)):
|
||||
try:
|
||||
payload = jwt.decode(data.token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
username: str = payload.get("sub")
|
||||
if username is None:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username,api_key=data.openaikey)
|
||||
|
||||
message = index.delete_vector_stores(summary_ids_to_delete=data.ids_to_delete,db=db )
|
||||
|
||||
return {
|
||||
"message": message
|
||||
}
|
||||
|
||||
except JWTError:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
|
||||
#AUTH CODE
|
||||
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
|
||||
|
||||
# Recommended for Local Setups
|
||||
# Manual Origins
|
||||
# origins = [
|
||||
# "http://localhost:3000", # Adjust the port if your frontend runs on a different one
|
||||
# "https://yourfrontenddomain.com",
|
||||
|
|
@ -395,7 +312,7 @@ def get_user_by_username(db: Session, username: str):
|
|||
|
||||
def create_user(db: Session, user: UserCreate):
|
||||
hashed_password = pwd_context.hash(user.password)
|
||||
db_user = User(username=user.username, hashed_password=hashed_password, graph_config="", llm_config="")
|
||||
db_user = User(username=user.username, hashed_password=hashed_password)
|
||||
db.add(db_user)
|
||||
db.commit()
|
||||
return "complete"
|
||||
|
|
@ -462,9 +379,6 @@ async def verify_user_token(token: str):
|
|||
verify_token(token=token)
|
||||
return {"message": "Token is valid"}
|
||||
|
||||
|
||||
|
||||
|
||||
@app.post("/user/chat/save")
|
||||
def populate_user_chat(chat: NewUserChat, db: Session = Depends(get_db)):
|
||||
try:
|
||||
|
|
@ -533,34 +447,32 @@ async def get_user_with_token(token: str, db: Session = Depends(get_db)):
|
|||
"userid": user.id,
|
||||
"username": user.username,
|
||||
"chats": user.chats,
|
||||
"notifications": user.notifications
|
||||
"documents": user.documents
|
||||
}
|
||||
except JWTError:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
|
||||
@app.get("/user/notification/delete/{token}/{notificationid}")
|
||||
async def delete_chat_of_user(token: str, notificationid: str, db: Session = Depends(get_db)):
|
||||
@app.get("/searchspaces/{token}")
|
||||
async def get_user_with_token(token: str, db: Session = Depends(get_db)):
|
||||
try:
|
||||
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
username: str = payload.get("sub")
|
||||
if username is None:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
notificationindb = db.query(Notification).filter(Notification.id == notificationid).first()
|
||||
db.delete(notificationindb)
|
||||
db.commit()
|
||||
search_spaces = db.query(SearchSpace).all()
|
||||
return {
|
||||
"message": "Notification Deleted"
|
||||
"search_spaces": search_spaces
|
||||
}
|
||||
except JWTError:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="127.0.0.1", port=8000)
|
||||
uvicorn.run(app, host="127.0.0.1", port=8000)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue