added initial version of the code for the modulator auto eval logic
This commit is contained in:
Vasilije
parent
27df32e421
commit
8113787cce
12 changed files with 1286 additions and 85 deletions
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@ -216,6 +216,35 @@ async def create_context(
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except Exception as e:
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return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
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@app.post("/buffer/provide-feedback", response_model=dict)
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async def provide_feedback(
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payload: Payload,
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# files: List[UploadFile] = File(...),
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):
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try:
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decoded_payload = payload.payload
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Memory_ = Memory(user_id=decoded_payload["user_id"])
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await Memory_.async_init()
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# memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
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if decoded_payload["total_score"] is None:
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output = await Memory_._provide_feedback(
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user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=None, total_score=decoded_payload["total_score"]
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)
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return JSONResponse(content={"response": output}, status_code=200)
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else:
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output = await Memory_._provide_feedback(
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user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=decoded_payload["attention_modulators"], total_score=None
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)
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return JSONResponse(content={"response": output}, status_code=200)
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except Exception as e:
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return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
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def start_api_server(host: str = "0.0.0.0", port: int = 8000):
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"""
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Start the API server using uvicorn.
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35
level_2/auth/auth.py
Normal file
35
level_2/auth/auth.py
Normal file
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@ -0,0 +1,35 @@
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import os
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import requests
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from dotenv import load_dotenv
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from fastapi import Depends, HTTPException
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from starlette.status import HTTP_403_FORBIDDEN
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from auth.cognito.JWTBearer import JWKS, JWTBearer, JWTAuthorizationCredentials
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load_dotenv() # Automatically load environment variables from a '.env' file.
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# jwks = JWKS.parse_obj(
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# requests.get(
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# f"https://cognito-idp.{os.environ.get('eu-west-1:46372257029')}.amazonaws.com/"
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# f"{os.environ.get('eu-west-1_3VUqKzMgj')}/.well-known/jwks.json"
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# ).json()
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# )
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# Construct the Cognito User Pool URL using the correct syntax
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region = "eu-west-1"
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user_pool_id = "eu-west-1_viUyNCqKp"
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cognito_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/jwks.json"
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# Fetch the JWKS using the updated URL
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jwks = JWKS.parse_obj(requests.get(cognito_url).json())
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auth = JWTBearer(jwks)
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async def get_current_user(
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credentials: JWTAuthorizationCredentials = Depends(auth)
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) -> str:
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try:
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return credentials.claims["username"]
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except KeyError:
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HTTPException(status_code=HTTP_403_FORBIDDEN, detail="Username missing")
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62
level_2/auth/auth_utils.py
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62
level_2/auth/auth_utils.py
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@ -0,0 +1,62 @@
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from cognito.JWTBearer import JWKS, JWTBearer, JWTAuthorizationCredentials
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import requests
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region = "eu-west-1"
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user_pool_id = "" #needed
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cognito_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/jwks.json"
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# Fetch the JWKS using the updated URL
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jwks = JWKS.parse_obj(requests.get(cognito_url).json())
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print(jwks)
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auth = JWTBearer(jwks)
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import requests
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# Set the Cognito authentication endpoint URL
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auth = JWTBearer(jwks)
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# Set the user credentials
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username = "" #needed
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password = "" #needed
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# Create the authentication payload
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payload = {
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"username": username,
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"password": password
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}
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# Set the Cognito authentication endpoint URL
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# Set the Cognito token endpoint URL
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token_endpoint = f"https://your-cognito-domain.auth.{region}.amazoncognito.com/oauth2/token"
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# Set the client credentials
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client_id = "" #needed
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client_secret = ""
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import boto3
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def authenticate_and_get_token(username: str, password: str,
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user_pool_id: str, app_client_id: str) -> None:
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client = boto3.client('cognito-idp')
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resp = client.admin_initiate_auth(
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UserPoolId=user_pool_id,
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ClientId=app_client_id,
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AuthFlow='ADMIN_NO_SRP_AUTH',
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AuthParameters={
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"USERNAME": username,
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"PASSWORD": password
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}
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)
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print("Log in success")
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print("Access token:", resp['AuthenticationResult']['AccessToken'])
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print("ID token:", resp['AuthenticationResult']['IdToken'])
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authenticate_and_get_token(username, password, user_pool_id, client_id)
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72
level_2/auth/cognito/JWTBearer.py
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72
level_2/auth/cognito/JWTBearer.py
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@ -0,0 +1,72 @@
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from typing import Dict, Optional, List
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from fastapi import HTTPException
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from jose import jwt, jwk, JWTError
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from jose.utils import base64url_decode
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from pydantic import BaseModel
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from starlette.requests import Request
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from starlette.status import HTTP_403_FORBIDDEN
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JWK = Dict[str, str]
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class JWKS(BaseModel):
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keys: List[JWK]
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class JWTAuthorizationCredentials(BaseModel):
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jwt_token: str
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header: Dict[str, str]
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claims: Dict[str, str]
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signature: str
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message: str
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class JWTBearer(HTTPBearer):
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def __init__(self, jwks: JWKS, auto_error: bool = True):
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super().__init__(auto_error=auto_error)
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self.kid_to_jwk = {jwk["kid"]: jwk for jwk in jwks.keys}
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def verify_jwk_token(self, jwt_credentials: JWTAuthorizationCredentials) -> bool:
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try:
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public_key = self.kid_to_jwk[jwt_credentials.header["kid"]]
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except KeyError:
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raise HTTPException(
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status_code=HTTP_403_FORBIDDEN, detail="JWK public key not found"
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)
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key = jwk.construct(public_key)
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decoded_signature = base64url_decode(jwt_credentials.signature.encode())
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return key.verify(jwt_credentials.message.encode(), decoded_signature)
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async def __call__(self, request: Request) -> Optional[JWTAuthorizationCredentials]:
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credentials: HTTPAuthorizationCredentials = await super().__call__(request)
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if credentials:
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if not credentials.scheme == "Bearer":
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raise HTTPException(
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status_code=HTTP_403_FORBIDDEN, detail="Wrong authentication method"
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)
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jwt_token = credentials.credentials
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message, signature = jwt_token.rsplit(".", 1)
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try:
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jwt_credentials = JWTAuthorizationCredentials(
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jwt_token=jwt_token,
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header=jwt.get_unverified_header(jwt_token),
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claims=jwt.get_unverified_claims(jwt_token),
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signature=signature,
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message=message,
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)
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except JWTError:
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raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail="JWK invalid")
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if not self.verify_jwk_token(jwt_credentials):
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raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail="JWK invalid")
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return jwt_credentials
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@ -15,11 +15,12 @@ from langchain.retrievers import WeaviateHybridSearchRetriever
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from langchain.tools import tool
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from marvin import ai_classifier
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from pydantic import parse_obj_as
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from weaviate.gql.get import HybridFusion
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import numpy as np
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load_dotenv()
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from langchain import OpenAI
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from langchain.chat_models import ChatOpenAI
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from typing import Optional
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from typing import Optional, Dict, List, Union
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import tracemalloc
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@ -77,6 +78,35 @@ ST_MEMORY_ID_DEFAULT = "0000"
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BUFFER_ID_DEFAULT = "0000"
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class DifferentiableLayer:
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def __init__(self, attention_modulators: dict):
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self.weights = {modulator: 1.0 for modulator in attention_modulators}
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self.learning_rate = 0.1
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self.regularization_lambda = 0.01
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self.weight_decay = 0.99
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async def adjust_weights(self, feedbacks: list[float]):
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"""
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Adjusts the weights of the attention modulators based on user feedbacks.
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Parameters:
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- feedbacks: A list of feedback scores (between 0 and 1).
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"""
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avg_feedback = np.mean(feedbacks)
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feedback_diff = 1.0 - avg_feedback
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# Adjust weights based on average feedback
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for modulator in self.weights:
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self.weights[modulator] += self.learning_rate * (-feedback_diff) - self.regularization_lambda * \
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self.weights[modulator]
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self.weights[modulator] *= self.weight_decay
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# Decaying the learning rate
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self.learning_rate *= 0.99
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async def get_weights(self):
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return self.weights
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class VectorDBFactory:
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def create_vector_db(
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self,
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@ -352,6 +382,7 @@ class WeaviateVectorDB(VectorDB):
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)
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.with_hybrid(
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query=observation,
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fusion_type=HybridFusion.RELATIVE_SCORE
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)
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.with_autocut(1)
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.with_where(params_user_id)
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@ -452,7 +483,8 @@ class BaseMemory:
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):
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if self.db_type == "weaviate":
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return await self.vector_db.add_memories(
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observation=observation, loader_settings=loader_settings, params=params, namespace=namespace
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observation=observation, loader_settings=loader_settings,
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params=params, namespace=namespace
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)
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# Add other db_type conditions if necessary
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@ -464,7 +496,8 @@ class BaseMemory:
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):
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if self.db_type == "weaviate":
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return await self.vector_db.fetch_memories(
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observation=observation, params=params, namespace=namespace
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observation=observation, params=params,
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namespace=namespace
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)
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async def delete_memories(self, params: Optional[str] = None):
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@ -483,8 +516,7 @@ class SemanticMemory(BaseMemory):
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db_type: str = "weaviate",
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):
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super().__init__(
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user_id, memory_id, index_name, db_type, namespace="SEMANTICMEMORY"
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)
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user_id, memory_id, index_name, db_type, namespace="SEMANTICMEMORY")
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class EpisodicMemory(BaseMemory):
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@ -580,12 +612,66 @@ class EpisodicBuffer(BaseMemory):
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)
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return [str(frequency), result_output["data"]["Get"]["EPISODICMEMORY"][0]]
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async def relevance(self, observation: str, namespace: str) -> list[str]:
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"""Relevance - Score between 0 and 1 on how often was the final information relevant to the user in the past.
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Stored in the episodic memory, mainly to show how well a buffer did the job
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Starts at 0, gets updated based on the user feedback"""
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async def repetition(self, observation: str, namespace: str) -> list[str]:
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"""Repetition - Score between 0 and 1 based on how often and at what intervals a memory has been revisited.
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Accounts for the spacing effect, where memories accessed at increasing intervals are given higher scores.
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"""
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weaviate_client = self.init_client(namespace=namespace)
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return ["0", "memory"]
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result_output = await self.fetch_memories(
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observation=observation, params=None, namespace=namespace
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)
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access_times = result_output["data"]["Get"]["EPISODICMEMORY"][0]["_additional"]["accessTimes"]
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# Calculate repetition score based on access times
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if not access_times or len(access_times) == 1:
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return ["0", result_output["data"]["Get"]["EPISODICMEMORY"][0]]
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# Sort access times
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access_times = sorted(access_times)
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# Calculate intervals between consecutive accesses
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intervals = [access_times[i + 1] - access_times[i] for i in range(len(access_times) - 1)]
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# A simple scoring mechanism: Longer intervals get higher scores, as they indicate spaced repetition
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repetition_score = sum([1.0 / (interval + 1) for interval in intervals]) / len(intervals)
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return [str(repetition_score), result_output["data"]["Get"]["EPISODICMEMORY"][0]]
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async def relevance(self, observation: str, namespace: str) -> list[str]:
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"""
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Fetches the relevance score for a given observation from the episodic memory.
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Parameters:
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- observation: The user's query or observation.
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- namespace: The namespace for the data.
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Returns:
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- The relevance score between 0 and 1.
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"""
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# Fetch the memory content based on the observation
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result_output = await self.fetch_memories(
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observation=observation, params=None, namespace=namespace
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)
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# Extract the relevance score from the memory content
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score = result_output["data"]["Get"]["EPISODICMEMORY"][0]["_additional"]["score"]
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return score
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#each of the requests is numbered, and then the previous requests are retrieved . The request is classified based on past and current content as :
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# 1. Very positive request
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# 2. Positive request
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# 3. Neutral request
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# 4. Negative request
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# 5. Very negative request
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# After this, we update the weights of the request based on the classification of the request.
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# After updating the weights, we update the buffer with the new weights. When new weights are calculated, we start from the updated values
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# Which chunking strategy works best?
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# Adding to the buffer - process the weights, and then use them as filters
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async def saliency(self, observation: str, namespace=None) -> list[str]:
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"""Determines saliency by scoring the set of retrieved documents against each other and trying to determine saliency
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@ -617,6 +703,19 @@ class EpisodicBuffer(BaseMemory):
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# Example Usage
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# attention_modulators = {"freshness": 0.8, "frequency": 0.7, "relevance": 0.9, "saliency": 0.85}
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# diff_layer = DifferentiableLayer(attention_modulators)
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#
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# # Sample batch feedback
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# feedbacks = [0.75, 0.8, 0.9]
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#
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# # Adjust weights based on batch feedback
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# diff_layer.adjust_weights(feedbacks)
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#
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# print(diff_layer.get_weights())
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async def handle_modulator(
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self,
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modulator_name: str,
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@ -679,12 +778,6 @@ class EpisodicBuffer(BaseMemory):
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attention_modulators: dict = None,
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):
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"""Generates the context to be used for the buffer and passed to the agent"""
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try:
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# we delete all memories in the episodic buffer, so we can start fresh
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await self.delete_memories()
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except:
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# in case there are no memories, we pass
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pass
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# we just filter the data here to make sure input is clean
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prompt_filter = ChatPromptTemplate.from_template(
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"""Filter and remove uneccessary information that is not relevant in the query to
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@ -695,40 +788,106 @@ class EpisodicBuffer(BaseMemory):
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# this part is partially done but the idea is to apply different attention modulators
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# to the data to fetch the most relevant information from the vector stores
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context = []
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if attention_modulators:
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from typing import Optional, Dict, List, Union
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class BufferModulators(BaseModel):
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"""Value of buffer modulators"""
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frequency: str = Field(..., description="Frequency score of the document")
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saliency: str = Field(..., description="Saliency score of the document")
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relevance: str = Field(..., description="Relevance score of the document")
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description:str = Field(..., description="Latest buffer modulators")
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direction: str= Field(..., description="Increase or a decrease of the modulator")
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lookup_value_semantic = await self.fetch_memories(
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observation=str(output), namespace="SEMANTICMEMORY"
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)
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context = []
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for memory in lookup_value_semantic["data"]["Get"]["SEMANTICMEMORY"]:
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# extract memory id, and pass it to fetch function as a parameter
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modulators = list(attention_modulators.keys())
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for modulator in modulators:
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result = await self.handle_modulator(
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modulator,
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attention_modulators,
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str(output),
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namespace="EPISODICMEMORY",
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parser = PydanticOutputParser(pydantic_object=BufferModulators)
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prompt = PromptTemplate(
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template="""Structure the buffer modulators to be used for the buffer. \n
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{format_instructions} \nOriginal observation is:
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{query}\n """,
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input_variables=["query"],
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partial_variables={"format_instructions": parser.get_format_instructions()},
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)
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# check if modulators exist, initialize the modulators if needed
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if attention_modulators is None:
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try:
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attention_modulators = await self.fetch_memories(observation="Attention modulators",
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namespace="BUFFERMEMORY")
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lookup_value_episodic = await self.fetch_memories(
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observation=str(output), namespace="EPISODICCMEMORY"
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)
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prompt_classify = ChatPromptTemplate.from_template(
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||||
"""You are a classifier. Determine if based on the previous query if the user was satisfied with the output : {query}"""
|
||||
)
|
||||
json_structure = {
|
||||
"name": "classifier",
|
||||
"description": "Classification indicating if it's output is satisfactory",
|
||||
"type": "boolean",
|
||||
"required": True
|
||||
}
|
||||
chain_filter = prompt_classify | self.llm.bind(function_call= {"name": "classifier"}, functions= json_structure)
|
||||
classifier_output = await chain_filter.ainvoke({"query": lookup_value_episodic})
|
||||
arguments_str = classifier_output.additional_kwargs['function_call']['arguments']
|
||||
arguments_dict = json.loads(arguments_str)
|
||||
classfier_value = arguments_dict.get('classifier', None)
|
||||
|
||||
if classfier_value:
|
||||
# adjust the weights of the modulators by adding a positive value
|
||||
prompt_classify = ChatPromptTemplate.from_template(
|
||||
""" We know we need to increase the classifiers for our AI system. The classifiers are {modulators} The query is: {query}. Which of the classifiers should we decrease? Return just the modulator and desired value"""
|
||||
)
|
||||
if result:
|
||||
context.append(result)
|
||||
context.append(memory)
|
||||
else:
|
||||
# defaults to semantic search if we don't want to apply algorithms on the vectordb data
|
||||
lookup_value_episodic = await self.fetch_memories(
|
||||
observation=str(output), namespace="EPISODICMEMORY"
|
||||
)
|
||||
lookup_value_semantic = await self.fetch_memories(
|
||||
observation=str(output), namespace="SEMANTICMEMORY"
|
||||
)
|
||||
lookup_value_buffer = await self.fetch_memories(observation=str(output))
|
||||
chain_modulator = prompt_classify | self.llm
|
||||
classifier_output = await chain_modulator.ainvoke({"query": lookup_value_episodic, "modulators": str(attention_modulators)})
|
||||
diff_layer = DifferentiableLayer(attention_modulators)
|
||||
adjusted_modulator = diff_layer.adjust_weights(classifier_output)
|
||||
_input = prompt.format_prompt(query=adjusted_modulator)
|
||||
document_context_result = self.llm_base(_input.to_string())
|
||||
document_context_result_parsed = parser.parse(document_context_result)
|
||||
await self.add_memories(observation=document_context_result_parsed, namespace="BUFFERMEMORY")
|
||||
else:
|
||||
# adjust the weights of the modulators by adding a negative value
|
||||
prompt_classify = ChatPromptTemplate.from_template(
|
||||
""" We know we need to decrease the classifiers for our AI system. The classifiers are {modulators} The query is: {query}. Which of the classifiers should we decrease? Return just the modulator and desired value"""
|
||||
)
|
||||
chain_modulator_reduction = prompt_classify | self.llm
|
||||
|
||||
classifier_output = await chain_modulator_reduction.ainvoke({"query": lookup_value_episodic, "modulators": str(attention_modulators)})
|
||||
diff_layer = DifferentiableLayer(attention_modulators)
|
||||
adjusted_modulator =diff_layer.adjust_weights(classifier_output)
|
||||
_input = prompt.format_prompt(query=adjusted_modulator)
|
||||
document_context_result = self.llm_base(_input.to_string())
|
||||
document_context_result_parsed = parser.parse(document_context_result)
|
||||
await self.add_memories(observation=document_context_result_parsed, namespace="BUFFERMEMORY")
|
||||
except:
|
||||
# initialize the modulators with default values if they are not provided
|
||||
print("Starting with default modulators")
|
||||
attention_modulators = {
|
||||
"freshness": 0.5,
|
||||
"frequency": 0.5,
|
||||
"relevance": 0.5,
|
||||
"saliency": 0.5,
|
||||
}
|
||||
|
||||
elif attention_modulators:
|
||||
pass
|
||||
|
||||
|
||||
lookup_value_semantic = await self.fetch_memories(
|
||||
observation=str(output), namespace="SEMANTICMEMORY"
|
||||
)
|
||||
context = []
|
||||
for memory in lookup_value_semantic["data"]["Get"]["SEMANTICMEMORY"]:
|
||||
# extract memory id, and pass it to fetch function as a parameter
|
||||
modulators = list(attention_modulators.keys())
|
||||
for modulator in modulators:
|
||||
result = await self.handle_modulator(
|
||||
modulator,
|
||||
attention_modulators,
|
||||
str(output),
|
||||
namespace="EPISODICMEMORY",
|
||||
)
|
||||
if result:
|
||||
context.append(result)
|
||||
context.append(memory)
|
||||
|
||||
context.append(lookup_value_buffer)
|
||||
context.append(lookup_value_semantic)
|
||||
context.append(lookup_value_episodic)
|
||||
|
||||
class BufferModulators(BaseModel):
|
||||
frequency: str = Field(..., description="Frequency score of the document")
|
||||
|
|
@ -939,12 +1098,7 @@ class EpisodicBuffer(BaseMemory):
|
|||
result_tasks.append(task)
|
||||
result_tasks.append(output)
|
||||
|
||||
# print("HERE IS THE RESULT TASKS", str(result_tasks))
|
||||
#
|
||||
# buffer_result = await self.fetch_memories(observation=str(user_input))
|
||||
#
|
||||
# print("HERE IS THE RESULT TASKS", str(buffer_result))
|
||||
|
||||
class EpisodicTask(BaseModel):
|
||||
"""Schema for an individual task."""
|
||||
|
||||
|
|
@ -972,18 +1126,19 @@ class EpisodicBuffer(BaseMemory):
|
|||
user_query: str = Field(
|
||||
..., description="The order at which the task needs to be performed"
|
||||
)
|
||||
|
||||
attention_modulators: str = Field(..., description="List of attention modulators")
|
||||
parser = PydanticOutputParser(pydantic_object=EpisodicList)
|
||||
date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template="Format the result.\n{format_instructions}\nOriginal query is: {query}\n Steps are: {steps}, buffer is: {buffer}",
|
||||
input_variables=["query", "steps", "buffer"],
|
||||
template="Format the result.\n{format_instructions}\nOriginal query is: {query}\n Steps are: {steps}, buffer is: {buffer}, date is:{date}, attention modulators are: {attention_modulators} \n",
|
||||
input_variables=["query", "steps", "buffer", "date", "attention_modulators"],
|
||||
partial_variables={"format_instructions": parser.get_format_instructions()},
|
||||
)
|
||||
|
||||
_input = prompt.format_prompt(
|
||||
query=user_input, steps=str(tasks_list)
|
||||
, buffer=str(result_tasks)
|
||||
, buffer=str(result_tasks), date= date, attention_modulators=attention_modulators
|
||||
)
|
||||
|
||||
# return "a few things to do like load episodic memory in a structured format"
|
||||
|
|
@ -992,8 +1147,7 @@ class EpisodicBuffer(BaseMemory):
|
|||
lookup_value = await self.add_memories(
|
||||
observation=str(result_parsing.json()), params=params, namespace='EPISODICMEMORY'
|
||||
)
|
||||
# print("THE RESULT OF THIS QUERY IS ", result_parsing.json())
|
||||
await self.delete_memories()
|
||||
# await self.delete_memories()
|
||||
return result_parsing.json()
|
||||
|
||||
|
||||
|
|
@ -1188,6 +1342,9 @@ class Memory:
|
|||
async def _available_operations(self):
|
||||
return await self.long_term_memory.episodic_buffer.available_operations()
|
||||
|
||||
async def _provide_feedback(self, score:str =None, params: dict = None, attention_modulators: dict = None):
|
||||
return await self.short_term_memory.episodic_buffer.provide_feedback(score=score, params=params, attention_modulators=attention_modulators)
|
||||
|
||||
|
||||
async def main():
|
||||
|
||||
|
|
@ -1212,8 +1369,8 @@ async def main():
|
|||
"source": "url",
|
||||
"path": "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf"
|
||||
}
|
||||
load_jack_london = await memory._add_semantic_memory(observation = "bla", loader_settings=loader_settings, params=params)
|
||||
print(load_jack_london)
|
||||
# load_jack_london = await memory._add_semantic_memory(observation = "bla", loader_settings=loader_settings, params=params)
|
||||
# print(load_jack_london)
|
||||
|
||||
modulator = {"relevance": 0.0, "saliency": 0.0, "frequency": 0.0}
|
||||
# #
|
||||
|
|
|
|||
734
level_2/poetry.lock
generated
734
level_2/poetry.lock
generated
|
|
@ -261,17 +261,17 @@ numpy = {version = ">=1.19.0", markers = "python_version >= \"3.9\""}
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.28.32"
|
||||
version = "1.28.37"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
|
||||
{file = "boto3-1.28.32-py3-none-any.whl", hash = "sha256:ed787f250ce2562c7744395bdf32b5a7bc9184126ef50a75e97bcb66043dccf3"},
|
||||
{file = "boto3-1.28.32.tar.gz", hash = "sha256:b505faa126db84e226f6f8d242a798fae30a725f0cac8a76c6aca9ace4e8eb28"},
|
||||
{file = "boto3-1.28.37-py3-none-any.whl", hash = "sha256:709cf438ad3ea48d426e4659538fe1148fc2719469b52179d07a11c5d26abac6"},
|
||||
{file = "boto3-1.28.37.tar.gz", hash = "sha256:4aec1b54ba6cd352abba2cdd7cdc76e631a4d3ce79c55c0719f85f9c9842e4a2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.31.32,<1.32.0"
|
||||
botocore = ">=1.31.37,<1.32.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.6.0,<0.7.0"
|
||||
|
||||
|
|
@ -280,13 +280,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
|||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.31.32"
|
||||
version = "1.31.37"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">= 3.7"
|
||||
files = [
|
||||
{file = "botocore-1.31.32-py3-none-any.whl", hash = "sha256:8992ac186988c4b4cc168e8e479e9472da1442b193c1bf7c9dcd1877ec62d23c"},
|
||||
{file = "botocore-1.31.32.tar.gz", hash = "sha256:7a07d8dc8cc47bf23af39409ada81f388eb78233e1bb2cde0c415756da753664"},
|
||||
{file = "botocore-1.31.37-py3-none-any.whl", hash = "sha256:72e10759be3dff39c5eeb29f85c11a227c369c946d044f2caf62c352d6a6fc06"},
|
||||
{file = "botocore-1.31.37.tar.gz", hash = "sha256:5c92c8bc3c6b49950c95501b30f0ac551fd4952359b53a6fba243094028157de"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -662,6 +662,32 @@ ai = ["openai (>=0.27.6,<0.28.0)"]
|
|||
docx = ["docx2txt (>=0.8,<0.9)"]
|
||||
pdf = ["pypdf (>=3.3.0,<4.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "deepeval"
|
||||
version = "0.10.12"
|
||||
description = "Deep eval provides evaluation platform to accelerate development of LLMs and Agents"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "deepeval-0.10.12-py3-none-any.whl", hash = "sha256:239eb720e8a205afab1ae2425e483177bd76cde658bdac98658a6559bdba4f3f"},
|
||||
{file = "deepeval-0.10.12.tar.gz", hash = "sha256:80968d57a9da6c4fce6247d31ebf7fea228c76393e0d985804be68b722090732"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
protobuf = "<=3.20.5"
|
||||
pytest = "*"
|
||||
requests = "*"
|
||||
rich = "*"
|
||||
sentence-transformers = "*"
|
||||
tabulate = "*"
|
||||
tqdm = "*"
|
||||
transformers = "*"
|
||||
typer = "*"
|
||||
|
||||
[package.extras]
|
||||
bias = ["Dbias", "tensorflow"]
|
||||
toxic = ["detoxify"]
|
||||
|
||||
[[package]]
|
||||
name = "deprecated"
|
||||
version = "1.2.14"
|
||||
|
|
@ -681,13 +707,13 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
|
|||
|
||||
[[package]]
|
||||
name = "dlt"
|
||||
version = "0.3.12"
|
||||
version = "0.3.14"
|
||||
description = "DLT is an open-source python-native scalable data loading framework that does not require any devops efforts to run."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "dlt-0.3.12-py3-none-any.whl", hash = "sha256:f9695a7fb98f5802e34f3f27d64e6ec1c0ccede48f26a1fdcd2db2c9280de9ac"},
|
||||
{file = "dlt-0.3.12.tar.gz", hash = "sha256:1fb4a947b2215627c1ee5725f2af80c6c7fcab60e7c3a57d49ab84b495444117"},
|
||||
{file = "dlt-0.3.14-py3-none-any.whl", hash = "sha256:b7672e153065796d0e7b0bc7eacfc48feff32a28e091eeca30f5a7180e42da2c"},
|
||||
{file = "dlt-0.3.14.tar.gz", hash = "sha256:b398ee07a1b87a6ac93130fc8e143d77e99a30d1bf957468d0252f23f563c01e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -732,6 +758,7 @@ gs = ["gcsfs (>=2022.4.0)"]
|
|||
motherduck = ["duckdb (>=0.6.1,<0.9.0)", "pyarrow (>=8.0.0)"]
|
||||
parquet = ["pyarrow (>=8.0.0)"]
|
||||
postgres = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
|
||||
pydantic = ["pydantic (>=1.10,<2.0)"]
|
||||
redshift = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
|
||||
s3 = ["boto3 (>=1.25)", "s3fs (>=2022.4.0)"]
|
||||
snowflake = ["snowflake-connector-python[pandas] (>=2.9.0)"]
|
||||
|
|
@ -881,6 +908,24 @@ files = [
|
|||
[package.extras]
|
||||
devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
|
||||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.12.3"
|
||||
description = "A platform independent file lock."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
|
||||
{file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "frozenlist"
|
||||
version = "1.4.0"
|
||||
|
|
@ -1232,6 +1277,38 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
|
|||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (==1.*)"]
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.16.4"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "huggingface_hub-0.16.4-py3-none-any.whl", hash = "sha256:0d3df29932f334fead024afc7cb4cc5149d955238b8b5e42dcf9740d6995a349"},
|
||||
{file = "huggingface_hub-0.16.4.tar.gz", hash = "sha256:608c7d4f3d368b326d1747f91523dbd1f692871e8e2e7a4750314a2dd8b63e14"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
fsspec = "*"
|
||||
packaging = ">=20.9"
|
||||
pyyaml = ">=5.1"
|
||||
requests = "*"
|
||||
tqdm = ">=4.42.1"
|
||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[package.extras]
|
||||
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
|
||||
cli = ["InquirerPy (==0.3.4)"]
|
||||
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"]
|
||||
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
|
||||
inference = ["aiohttp", "pydantic"]
|
||||
quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
|
||||
torch = ["torch"]
|
||||
typing = ["pydantic", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
|
||||
|
||||
[[package]]
|
||||
name = "humanize"
|
||||
version = "4.8.0"
|
||||
|
|
@ -1414,13 +1491,13 @@ data = ["language-data (>=1.1,<2.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.0.26"
|
||||
version = "0.0.28"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1620,6 +1697,23 @@ files = [
|
|||
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|
||||
|
||||
[[package]]
|
||||
name = "mpmath"
|
||||
version = "1.3.0"
|
||||
description = "Python library for arbitrary-precision floating-point arithmetic"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
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|
||||
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|
||||
|
||||
[package.extras]
|
||||
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|
||||
docs = ["sphinx"]
|
||||
gmpy = ["gmpy2 (>=2.1.0a4)"]
|
||||
tests = ["pytest (>=4.6)"]
|
||||
|
||||
[[package]]
|
||||
name = "multidict"
|
||||
version = "6.0.4"
|
||||
|
|
@ -1751,6 +1845,24 @@ files = [
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|
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|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
[package.extras]
|
||||
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|
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "nltk"
|
||||
version = "3.8.1"
|
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|
|
@ -2053,6 +2165,75 @@ files = [
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|||
[package.dependencies]
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ptyprocess = ">=0.5"
|
||||
|
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[[package]]
|
||||
name = "pillow"
|
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version = "10.0.0"
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description = "Python Imaging Library (Fork)"
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optional = false
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[package.extras]
|
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|
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tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "pinecone-client"
|
||||
version = "2.2.2"
|
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|
|
@ -2100,13 +2281,13 @@ pyee = "9.0.4"
|
|||
|
||||
[[package]]
|
||||
name = "pluggy"
|
||||
version = "1.2.0"
|
||||
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|
||||
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|
||||
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python-versions = ">=3.7"
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[package.extras]
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|
@ -2165,6 +2346,37 @@ files = [
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|
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[[package]]
|
||||
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|
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python-versions = ">=3.7"
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||||
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||||
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|
||||
{file = "sentencepiece-0.1.99-cp39-cp39-win_amd64.whl", hash = "sha256:350e5c74d739973f1c9643edb80f7cc904dc948578bcb1d43c6f2b173e5d18dd"},
|
||||
{file = "sentencepiece-0.1.99.tar.gz", hash = "sha256:189c48f5cb2949288f97ccdb97f0473098d9c3dcf5a3d99d4eabe719ec27297f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "68.1.2"
|
||||
|
|
@ -3190,6 +3637,34 @@ anyio = ">=3.4.0,<5"
|
|||
[package.extras]
|
||||
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.12"
|
||||
description = "Computer algebra system (CAS) in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
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||||
{file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
mpmath = ">=0.19"
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.9.0"
|
||||
description = "Pretty-print tabular data"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
{file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"},
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{file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"},
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||||
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|
||||
|
||||
[package.extras]
|
||||
widechars = ["wcwidth"]
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.2.3"
|
||||
|
|
@ -3290,6 +3765,17 @@ mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
|
|||
tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
|
||||
torch = ["torch (>=1.6.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "threadpoolctl"
|
||||
version = "3.2.0"
|
||||
description = "threadpoolctl"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
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||||
files = [
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||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.4.0"
|
||||
|
|
@ -3335,6 +3821,60 @@ requests = ">=2.26.0"
|
|||
[package.extras]
|
||||
blobfile = ["blobfile (>=2)"]
|
||||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.13.3"
|
||||
description = "Fast and Customizable Tokenizers"
|
||||
optional = false
|
||||
python-versions = "*"
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||||
files = [
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||||
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||||
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||||
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|
||||
{file = "tokenizers-0.13.3.tar.gz", hash = "sha256:2e546dbb68b623008a5442353137fbb0123d311a6d7ba52f2667c8862a75af2e"},
|
||||
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|
||||
|
||||
[package.extras]
|
||||
dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
|
||||
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
|
||||
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
|
|
@ -3357,6 +3897,83 @@ files = [
|
|||
{file = "tomlkit-0.12.1.tar.gz", hash = "sha256:38e1ff8edb991273ec9f6181244a6a391ac30e9f5098e7535640ea6be97a7c86"},
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||||
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|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
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|
||||
torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "trio"
|
||||
version = "0.22.2"
|
||||
|
|
@ -3546,13 +4232,13 @@ colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python
|
|||
|
||||
[[package]]
|
||||
name = "weaviate-client"
|
||||
version = "3.23.0"
|
||||
version = "3.23.2"
|
||||
description = "A python native Weaviate client"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "weaviate-client-3.23.0.tar.gz", hash = "sha256:3ffd7f1460c9e32755d84d4f5fc63dfc0bd990dbe2c3dc20d5c68119d467680e"},
|
||||
{file = "weaviate_client-3.23.0-py3-none-any.whl", hash = "sha256:3d3bb75c1d96b2b71e213c5eb885ae3e3f42e4304955383c467d100187d9ff8e"},
|
||||
{file = "weaviate-client-3.23.2.tar.gz", hash = "sha256:1c8c94df032dd2fa5a4ea615fc69ccb983ffad5cc02974f78c793839e61ac150"},
|
||||
{file = "weaviate_client-3.23.2-py3-none-any.whl", hash = "sha256:88ffc38cca07806d64726cc74bc194c7da50b222aa4e2cd129f4c1f5e53e9b61"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3780,4 +4466,4 @@ multidict = ">=4.0"
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "5629225437c5aec01f9f862d46d6d1e68abde4c42a0c1ad709df875883171991"
|
||||
content-hash = "761b58204631452d77e13bbc2d61034704e8e109619db4addd26ec159b9bb176"
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ weaviate-client = "^3.22.1"
|
|||
python-multipart = "^0.0.6"
|
||||
deep-translator = "^1.11.4"
|
||||
humanize = "^4.8.0"
|
||||
deepeval = "^0.10.12"
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
BIN
level_2/tests/__pycache__/crud_test.cpython-311-pytest-7.4.0.pyc
Normal file
BIN
level_2/tests/__pycache__/crud_test.cpython-311-pytest-7.4.0.pyc
Normal file
Binary file not shown.
Binary file not shown.
38
level_2/tests/base_test_set.json
Normal file
38
level_2/tests/base_test_set.json
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
{
|
||||
"q1": {
|
||||
"question": "What does Buck learn from being harnessed and made to work with François?",
|
||||
"answer": "Buck learns several important lessons from being harnessed and made to work with François. First, he learns the lesson of obedience and the consequences of disobedience. He quickly realizes that François demands instant obedience and uses his whip to enforce it, and that he must listen and follow commands promptly to avoid the whip [16]. Second, Buck learns the value of hard work and the satisfaction it brings. Despite the new and strange nature of the work, Buck learns quickly and makes remarkable progress, earning respect from François [16][19]. Furthermore, Buck learns important skills related to pulling a sled, such as stopping at command ('ho'), going ahead ('mush'), swinging wide on bends, and staying clear of the wheeler when the sled goes downhill. He also learns the dynamics of working in a team and how to respond to the lead dog's instructions [16][19]."
|
||||
},
|
||||
"q2": {
|
||||
"question": "How many chapters does the document have",
|
||||
"answer": "The document has a total of 7 chapters. The chapter titles are: Into the Primitive [Chapter 1012], The Law of Club and Fang [Chapter 1013], The Dominant Primordial Beast [Chapter 1014], Who Has Won to Mastership [Chapter 1015], The Toil of Trace and Trail [Chapter 1016], For the Love of a Man [Chapter 1017], The Sounding of the Call [Chapter 1018]."
|
||||
},
|
||||
"q3": {
|
||||
"question": "Who kidnapped Buck?",
|
||||
"answer": "Buck was kidnapped by one of the gardener's helpers named Manuel, who sold him to strangers for a profit."
|
||||
},
|
||||
"q4": {
|
||||
"question": "What is the name of the gardener's helper who kidnapped Buck?",
|
||||
"answer": "The name of the gardener's helper who kidnapped Buck is Manuel."
|
||||
},
|
||||
"q5": {
|
||||
"question": "Where was Buck taken after being kidnapped?",
|
||||
"answer": "After being kidnapped, Buck was taken by Manuel through the orchard to a little flag station known as College Park [7a]. Eventually, Buck was thrown into a baggage car of a train, where he remained unconscious until he woke up and watched the man in the red sweater [8] [11a]. The specific location Buck was taken to after being kidnapped is not explicitly mentioned in the given texts."
|
||||
},
|
||||
"q6": {
|
||||
"question": "What is the law of club and fang?",
|
||||
"answer": "The law of club and fang refers to the harsh and primal rules of survival in the wild, where physical strength and aggression determine dominance and power. The club represents the power wielded by humans over animals. Buck realizes that in order to survive in this new environment, he must adapt and submit to this law. The law of club and fang signifies the brutal and primitive nature of life in the wild."
|
||||
},
|
||||
"q7": {
|
||||
"question": "What is the mother of Buck?",
|
||||
"answer": "The mother of Buck, the dog in the story 'The Call of the Wild' by Jack London, is a Scottish shepherd dog named Shep."
|
||||
},
|
||||
"q8": {
|
||||
"question": "How did Buck feel after being kidnapped?",
|
||||
"answer": "After being kidnapped, Buck felt anger and resentment towards his captors [7]. He was initially cooperative but grew angry as he was mistreated and felt violated and vilely treated during his transportation [8]."
|
||||
},
|
||||
"q9": {
|
||||
"question": "Was Buck beaten in captivity?",
|
||||
"answer": "Yes, Buck was beaten while in captivity. In document snippet [11], Buck rushed at the man who had been tormenting him, and the man delivered a blow that rendered Buck senseless."
|
||||
}
|
||||
}
|
||||
|
|
@ -74,3 +74,5 @@ class TestMemory(unittest.TestCase):
|
|||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
|
||||
|
|
|
|||
119
level_2/tests/semantic_tests.py
Normal file
119
level_2/tests/semantic_tests.py
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
import os
|
||||
import openai
|
||||
from deepeval.metrics.factual_consistency import assert_factual_consistency
|
||||
import dotenv
|
||||
dotenv.load_dotenv()
|
||||
from level_2.level_2_pdf_vectorstore__dlt_contracts import Memory
|
||||
openai.api_key = os.getenv("OPENAI_API_KEY", "")
|
||||
from deepeval.metrics.overall_score import assert_overall_score
|
||||
import json
|
||||
from deepeval.metrics.overall_score import OverallScoreMetric
|
||||
|
||||
# Write a sample ChatGPT function
|
||||
|
||||
|
||||
async def main():
|
||||
async def generate_context(query: str='bla', context:str=None):
|
||||
memory = Memory(user_id="TestUser")
|
||||
|
||||
await memory.async_init()
|
||||
|
||||
|
||||
memory_loaded = await memory._fetch_semantic_memory(observation=query, params=None)
|
||||
|
||||
if memory_loaded:
|
||||
return memory_loaded["data"]["Get"]["SEMANTICMEMORY"][0]["text"]
|
||||
else:
|
||||
params = {
|
||||
"version": "1.0",
|
||||
"agreement_id": "AG123456",
|
||||
"privacy_policy": "https://example.com/privacy",
|
||||
"terms_of_service": "https://example.com/terms",
|
||||
"format": "json",
|
||||
"schema_version": "1.1",
|
||||
"checksum": "a1b2c3d4e5f6",
|
||||
"owner": "John Doe",
|
||||
"license": "MIT",
|
||||
"validity_start": "2023-08-01",
|
||||
"validity_end": "2024-07-31",
|
||||
}
|
||||
loader_settings = {
|
||||
"format": "PDF",
|
||||
"source": "url",
|
||||
"path": "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf"
|
||||
}
|
||||
load_jack_london = await memory._add_semantic_memory(observation = query, loader_settings=loader_settings, params=params)
|
||||
memory_loaded = await memory._fetch_semantic_memory(observation=query, params=None)
|
||||
return memory_loaded["data"]["Get"]["SEMANTICMEMORY"][0]["text"]
|
||||
|
||||
# return load_jack_london
|
||||
#
|
||||
# modulator = {"relevance": 0.0, "saliency": 0.0, "frequency": 0.0}
|
||||
# # #
|
||||
# run_main_buffer = await memory._create_buffer_context(
|
||||
# user_input="I want to know how does Buck adapt to life in the wild and then have that info translated to german ",
|
||||
# params=params,
|
||||
# attention_modulators=modulator,
|
||||
# )
|
||||
|
||||
async def generate_chatgpt_output(query:str, context:str=None):
|
||||
if context is None:
|
||||
context = await generate_context(query=query)
|
||||
# print(context)
|
||||
else:
|
||||
pass
|
||||
response = openai.ChatCompletion.create(
|
||||
model="gpt-3.5-turbo",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "assistant", "content": f"{context}"},
|
||||
{"role": "user", "content": query}
|
||||
]
|
||||
)
|
||||
llm_output = response.choices[0].message.content
|
||||
# print(llm_output)
|
||||
return llm_output
|
||||
|
||||
with open('base_test_set.json', 'r') as f:
|
||||
data = json.load(f)
|
||||
#
|
||||
async def test_overall_score(query:str, output:str=None, expected_output:str=None, context:str=None, context_type:str=None):
|
||||
if context_type == "gpt_search":
|
||||
context = ""
|
||||
elif context_type == "base_memory_context":
|
||||
context = await generate_context(query=query)
|
||||
output = context
|
||||
elif context_type == "hybrid_search":
|
||||
context = await generate_context(query=query)
|
||||
output = await generate_chatgpt_output(query)
|
||||
elif context_type == "memory_search":
|
||||
pass
|
||||
|
||||
metric = OverallScoreMetric()
|
||||
score = metric.measure(
|
||||
query=query,
|
||||
output=output,
|
||||
expected_output=expected_output,
|
||||
context=context
|
||||
)
|
||||
print('here is the score', score)
|
||||
|
||||
return score
|
||||
|
||||
# await generate_chatgpt_output(query=" When was call of the wild written?")
|
||||
scores = {}
|
||||
for key, item in data.items():
|
||||
question = item['question']
|
||||
expected_ans = item['answer']
|
||||
values = await test_overall_score(query=question, expected_output=expected_ans, context_type="hybrid_search")
|
||||
scores[key] = values
|
||||
|
||||
print(scores)
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
Loading…
Add table
Reference in a new issue