Merge pull request #10 from topoteretes/feature/modulator_auto_eval

added initial version of the code for the modulator auto eval logic
2023-09-12 16:26:48 +02:00 · 2023-09-12 16:26:48 +02:00 · fe9f9e6ba8
commit fe9f9e6ba8
parent 23a0437fbd 6e01e9af79
16 changed files with 2067 additions and 513 deletions
--- a/level_2/api.py
+++ b/level_2/api.py
@ -216,6 +216,35 @@ async def create_context(
    except Exception as e:
        return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)

+
+@app.post("/buffer/provide-feedback", response_model=dict)
+async def provide_feedback(
+    payload: Payload,
+    # files: List[UploadFile] = File(...),
+):
+    try:
+        decoded_payload = payload.payload
+
+        Memory_ = Memory(user_id=decoded_payload["user_id"])
+
+        await Memory_.async_init()
+
+        # memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
+        if decoded_payload["total_score"] is None:
+
+            output = await Memory_._provide_feedback(
+                user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=None, total_score=decoded_payload["total_score"]
+            )
+            return JSONResponse(content={"response": output}, status_code=200)
+        else:
+            output = await Memory_._provide_feedback(
+                user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=decoded_payload["attention_modulators"], total_score=None
+            )
+            return JSONResponse(content={"response": output}, status_code=200)
+
+
+    except Exception as e:
+        return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 def start_api_server(host: str = "0.0.0.0", port: int = 8000):
    """
    Start the API server using uvicorn.
--- a/level_2/auth/auth.py
+++ b/level_2/auth/auth.py
@ -0,0 +1,35 @@
+import os
+
+import requests
+from dotenv import load_dotenv
+from fastapi import Depends, HTTPException
+from starlette.status import HTTP_403_FORBIDDEN
+
+from auth.cognito.JWTBearer import JWKS, JWTBearer, JWTAuthorizationCredentials
+
+load_dotenv()  # Automatically load environment variables from a '.env' file.
+
+# jwks = JWKS.parse_obj(
+#     requests.get(
+#         f"https://cognito-idp.{os.environ.get('eu-west-1:46372257029')}.amazonaws.com/"
+#         f"{os.environ.get('eu-west-1_3VUqKzMgj')}/.well-known/jwks.json"
+#     ).json()
+# )
+# Construct the Cognito User Pool URL using the correct syntax
+region = "eu-west-1"
+user_pool_id = "eu-west-1_viUyNCqKp"
+cognito_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/jwks.json"
+
+# Fetch the JWKS using the updated URL
+jwks = JWKS.parse_obj(requests.get(cognito_url).json())
+
+auth = JWTBearer(jwks)
+
+
+async def get_current_user(
+    credentials: JWTAuthorizationCredentials = Depends(auth)
+) -> str:
+    try:
+        return credentials.claims["username"]
+    except KeyError:
+        HTTPException(status_code=HTTP_403_FORBIDDEN, detail="Username missing")
--- a/level_2/auth/auth_utils.py
+++ b/level_2/auth/auth_utils.py
@ -0,0 +1,62 @@
+
+from cognito.JWTBearer import JWKS, JWTBearer, JWTAuthorizationCredentials
+
+import requests
+
+region = "eu-west-1"
+user_pool_id = "" #needed
+cognito_url = f"https://cognito-idp.{region}.amazonaws.com/{user_pool_id}/.well-known/jwks.json"
+
+# Fetch the JWKS using the updated URL
+jwks = JWKS.parse_obj(requests.get(cognito_url).json())
+print(jwks)
+
+auth = JWTBearer(jwks)
+
+
+import requests
+
+# Set the Cognito authentication endpoint URL
+
+auth = JWTBearer(jwks)
+
+# Set the user credentials
+
+username = "" #needed
+password = "" #needed
+
+# Create the authentication payload
+payload = {
+    "username": username,
+    "password": password
+}
+
+# Set the Cognito authentication endpoint URL
+# Set the Cognito token endpoint URL
+token_endpoint = f"https://your-cognito-domain.auth.{region}.amazoncognito.com/oauth2/token"
+
+# Set the client credentials
+client_id = "" #needed
+client_secret = ""
+
+import boto3
+def authenticate_and_get_token(username: str, password: str,
+                               user_pool_id: str, app_client_id: str) -> None:
+    client = boto3.client('cognito-idp')
+
+    resp = client.admin_initiate_auth(
+        UserPoolId=user_pool_id,
+        ClientId=app_client_id,
+        AuthFlow='ADMIN_NO_SRP_AUTH',
+        AuthParameters={
+            "USERNAME": username,
+            "PASSWORD": password
+        }
+    )
+
+    print("Log in success")
+    print("Access token:", resp['AuthenticationResult']['AccessToken'])
+    print("ID token:", resp['AuthenticationResult']['IdToken'])
+
+
+authenticate_and_get_token(username, password, user_pool_id, client_id)
--- a/level_2/auth/cognito/JWTBearer.py
+++ b/level_2/auth/cognito/JWTBearer.py
@ -0,0 +1,72 @@
+from typing import Dict, Optional, List
+
+from fastapi import HTTPException
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from jose import jwt, jwk, JWTError
+from jose.utils import base64url_decode
+from pydantic import BaseModel
+from starlette.requests import Request
+from starlette.status import HTTP_403_FORBIDDEN
+
+JWK = Dict[str, str]
+
+
+class JWKS(BaseModel):
+    keys: List[JWK]
+
+
+class JWTAuthorizationCredentials(BaseModel):
+    jwt_token: str
+    header: Dict[str, str]
+    claims: Dict[str, str]
+    signature: str
+    message: str
+
+
+class JWTBearer(HTTPBearer):
+    def __init__(self, jwks: JWKS, auto_error: bool = True):
+        super().__init__(auto_error=auto_error)
+
+        self.kid_to_jwk = {jwk["kid"]: jwk for jwk in jwks.keys}
+
+    def verify_jwk_token(self, jwt_credentials: JWTAuthorizationCredentials) -> bool:
+        try:
+            public_key = self.kid_to_jwk[jwt_credentials.header["kid"]]
+        except KeyError:
+            raise HTTPException(
+                status_code=HTTP_403_FORBIDDEN, detail="JWK public key not found"
+            )
+
+        key = jwk.construct(public_key)
+        decoded_signature = base64url_decode(jwt_credentials.signature.encode())
+
+        return key.verify(jwt_credentials.message.encode(), decoded_signature)
+
+    async def __call__(self, request: Request) -> Optional[JWTAuthorizationCredentials]:
+        credentials: HTTPAuthorizationCredentials = await super().__call__(request)
+
+        if credentials:
+            if not credentials.scheme == "Bearer":
+                raise HTTPException(
+                    status_code=HTTP_403_FORBIDDEN, detail="Wrong authentication method"
+                )
+
+            jwt_token = credentials.credentials
+
+            message, signature = jwt_token.rsplit(".", 1)
+
+            try:
+                jwt_credentials = JWTAuthorizationCredentials(
+                    jwt_token=jwt_token,
+                    header=jwt.get_unverified_header(jwt_token),
+                    claims=jwt.get_unverified_claims(jwt_token),
+                    signature=signature,
+                    message=message,
+                )
+            except JWTError:
+                raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail="JWK invalid")
+
+            if not self.verify_jwk_token(jwt_credentials):
+                raise HTTPException(status_code=HTTP_403_FORBIDDEN, detail="JWK invalid")
+
+            return jwt_credentials
--- a/level_2/level_2_pdf_vectorstore__dlt_contracts.py
+++ b/level_2/level_2_pdf_vectorstore__dlt_contracts.py
--- a/level_2/modulators/modulators.py
+++ b/level_2/modulators/modulators.py
@ -0,0 +1,32 @@
+import numpy as np
+
+
+class DifferentiableLayer:
+    def __init__(self, attention_modulators: dict):
+        self.weights = {modulator: 1.0 for modulator in attention_modulators}
+        self.learning_rate = 0.1
+        self.regularization_lambda = 0.01
+        self.weight_decay = 0.99
+
+    async def adjust_weights(self, feedbacks: list[float]):
+        """
+        Adjusts the weights of the attention modulators based on user feedbacks.
+
+        Parameters:
+        - feedbacks: A list of feedback scores (between 0 and 1).
+        """
+        avg_feedback = np.mean(feedbacks)
+        feedback_diff = 1.0 - avg_feedback
+
+        # Adjust weights based on average feedback
+        for modulator in self.weights:
+            self.weights[modulator] += self.learning_rate * (-feedback_diff) - self.regularization_lambda * \
+                                       self.weights[modulator]
+            self.weights[modulator] *= self.weight_decay
+
+        # Decaying the learning rate
+        self.learning_rate *= 0.99
+
+    async def get_weights(self):
+        return self.weights
+
--- a/level_2/poetry.lock
+++ b/level_2/poetry.lock
@ -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 = [
-    {file = "langsmith-0.0.26-py3-none-any.whl", hash = "sha256:61c1d4582104d96edde04e1eea1dae347645b691c44489a5871341a2a1a2a1eb"},
-    {file = "langsmith-0.0.26.tar.gz", hash = "sha256:80a4ef1b663a24a460d25b9986ab2010c5d06b6061c65be473abafc0647d191a"},
+    {file = "langsmith-0.0.28-py3-none-any.whl", hash = "sha256:f398782f41526c74e141e68fa28b9020e0be4bde18a1d4a76b357c8272fb81bd"},
+    {file = "langsmith-0.0.28.tar.gz", hash = "sha256:34c15f9a8908be180001c58048b659ece6320d0bf8ffce4ca496a2428b35646e"},
 ]

 [package.dependencies]
@ -1620,6 +1697,23 @@ files = [
    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]

+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "multidict"
 version = "6.0.4"
@ -1751,6 +1845,24 @@ files = [
    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]

+[[package]]
+name = "networkx"
+version = "3.1"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
+    {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
+developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
+doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
+extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
+test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "nltk"
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+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune]"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (<10.0.0)", "torchvision"]
+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)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (<10.0.0)"]
+
 [[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"
--- a/level_2/pyproject.toml
+++ b/level_2/pyproject.toml
@ -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"



--- a/level_2/tests/pycache/crud_test.cpython-311-pytest-7.4.0.pyc
+++ b/level_2/tests/pycache/crud_test.cpython-311-pytest-7.4.0.pyc
--- a/level_2/tests/pycache/semantic_tests.cpython-311-pytest-7.4.0.pyc
+++ b/level_2/tests/pycache/semantic_tests.cpython-311-pytest-7.4.0.pyc
--- a/level_2/tests/base_test_set.json
+++ b/level_2/tests/base_test_set.json
@ -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."
+        }
+    }
--- a/level_2/tests/crud_test.py
+++ b/level_2/tests/crud_test.py
@ -74,3 +74,5 @@ class TestMemory(unittest.TestCase):

 if __name__ == '__main__':
    unittest.main()
+
+
--- a/level_2/tests/semantic_tests.py
+++ b/level_2/tests/semantic_tests.py
@ -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())
--- a/level_2/utils.py
+++ b/level_2/utils.py
--- a/level_2/vectordb/basevectordb.py
+++ b/level_2/vectordb/basevectordb.py
@ -0,0 +1,131 @@
+# Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
+import logging
+from io import BytesIO
+
+from level_2.vectordb.vectordb import PineconeVectorDB, WeaviateVectorDB
+
+logging.basicConfig(level=logging.INFO)
+import marvin
+import requests
+from dotenv import load_dotenv
+from langchain.document_loaders import PyPDFLoader
+from langchain.retrievers import WeaviateHybridSearchRetriever
+from weaviate.gql.get import HybridFusion
+
+load_dotenv()
+from typing import Optional
+
+import tracemalloc
+
+tracemalloc.start()
+
+import os
+from datetime import datetime
+from langchain.embeddings.openai import OpenAIEmbeddings
+from dotenv import load_dotenv
+from langchain.schema import Document
+import uuid
+import weaviate
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+marvin.settings.openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+LTM_MEMORY_ID_DEFAULT = "00000"
+ST_MEMORY_ID_DEFAULT = "0000"
+BUFFER_ID_DEFAULT = "0000"
+
+
+class VectorDBFactory:
+    def create_vector_db(
+        self,
+        user_id: str,
+        index_name: str,
+        memory_id: str,
+        ltm_memory_id: str = LTM_MEMORY_ID_DEFAULT,
+        st_memory_id: str = ST_MEMORY_ID_DEFAULT,
+        buffer_id: str = BUFFER_ID_DEFAULT,
+        db_type: str = "pinecone",
+        namespace: str = None,
+    ):
+        db_map = {"pinecone": PineconeVectorDB, "weaviate": WeaviateVectorDB}
+
+        if db_type in db_map:
+            return db_map[db_type](
+                user_id,
+                index_name,
+                memory_id,
+                ltm_memory_id,
+                st_memory_id,
+                buffer_id,
+                namespace,
+            )
+
+        raise ValueError(f"Unsupported database type: {db_type}")
+
+
+
+
+
+
+class BaseMemory:
+    def __init__(
+        self,
+        user_id: str,
+        memory_id: Optional[str],
+        index_name: Optional[str],
+        db_type: str,
+        namespace: str,
+    ):
+        self.user_id = user_id
+        self.memory_id = memory_id
+        self.index_name = index_name
+        self.namespace = namespace
+        self.memory_type_id = str(uuid.uuid4())
+        self.db_type = db_type
+        factory = VectorDBFactory()
+        self.vector_db = factory.create_vector_db(
+            self.user_id,
+            self.index_name,
+            self.memory_id,
+            db_type=self.db_type,
+            namespace=self.namespace,
+        )
+
+    def init_client(self, namespace: str):
+
+        return self.vector_db.init_weaviate_client(namespace)
+
+    async def add_memories(
+        self,
+        observation: Optional[str] = None,
+        loader_settings: dict = None,
+        params: Optional[dict] = None,
+        namespace: Optional[str] = None,
+    ):
+
+        return await self.vector_db.add_memories(
+            observation=observation, loader_settings=loader_settings,
+            params=params, namespace=namespace
+        )
+        # Add other db_type conditions if necessary
+
+    async def fetch_memories(
+        self,
+        observation: str,
+        params: Optional[str] = None,
+        namespace: Optional[str] = None,
+        n_of_observations: Optional[int] = 2,
+    ):
+
+        return await self.vector_db.fetch_memories(
+            observation=observation, params=params,
+            namespace=namespace,
+            n_of_observations=n_of_observations
+        )
+
+    async def delete_memories(self, params: Optional[str] = None):
+        return await self.vector_db.delete_memories(params)
+
+    # Additional methods for specific Memory can be added here
--- a/level_2/vectordb/vectordb.py
+++ b/level_2/vectordb/vectordb.py
@ -0,0 +1,355 @@
+
+# Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
+import logging
+from io import BytesIO
+
+
+
+logging.basicConfig(level=logging.INFO)
+import marvin
+import requests
+from dotenv import load_dotenv
+from langchain.document_loaders import PyPDFLoader
+from langchain.retrievers import WeaviateHybridSearchRetriever
+from weaviate.gql.get import HybridFusion
+
+load_dotenv()
+from typing import Optional
+
+import tracemalloc
+
+tracemalloc.start()
+
+import os
+from datetime import datetime
+from langchain.embeddings.openai import OpenAIEmbeddings
+from dotenv import load_dotenv
+from langchain.schema import Document
+import uuid
+import weaviate
+
+load_dotenv()
+
+
+LTM_MEMORY_ID_DEFAULT = "00000"
+ST_MEMORY_ID_DEFAULT = "0000"
+BUFFER_ID_DEFAULT = "0000"
+class VectorDB:
+    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
+
+    def __init__(
+        self,
+        user_id: str,
+        index_name: str,
+        memory_id: str,
+        ltm_memory_id: str = LTM_MEMORY_ID_DEFAULT,
+        st_memory_id: str = ST_MEMORY_ID_DEFAULT,
+        buffer_id: str = BUFFER_ID_DEFAULT,
+        namespace: str = None,
+    ):
+        self.user_id = user_id
+        self.index_name = index_name
+        self.namespace = namespace
+        self.memory_id = memory_id
+        self.ltm_memory_id = ltm_memory_id
+        self.st_memory_id = st_memory_id
+        self.buffer_id = buffer_id
+
+class PineconeVectorDB(VectorDB):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.init_pinecone(self.index_name)
+
+    def init_pinecone(self, index_name):
+        # Pinecone initialization logic
+        pass
+
+
+class WeaviateVectorDB(VectorDB):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.init_weaviate(self.namespace)
+
+    def init_weaviate(self, namespace: str):
+        # Weaviate initialization logic
+        embeddings = OpenAIEmbeddings()
+        auth_config = weaviate.auth.AuthApiKey(
+            api_key=os.environ.get("WEAVIATE_API_KEY")
+        )
+        client = weaviate.Client(
+            url=os.environ.get("WEAVIATE_URL"),
+            auth_client_secret=auth_config,
+            additional_headers={"X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY")},
+        )
+        retriever = WeaviateHybridSearchRetriever(
+            client=client,
+            index_name=namespace,
+            text_key="text",
+            attributes=[],
+            embedding=embeddings,
+            create_schema_if_missing=True,
+        )
+        return retriever  # If this is part of the initialization, call it here.
+
+    def init_weaviate_client(self, namespace: str):
+        # Weaviate client initialization logic
+        auth_config = weaviate.auth.AuthApiKey(
+            api_key=os.environ.get("WEAVIATE_API_KEY")
+        )
+        client = weaviate.Client(
+            url=os.environ.get("WEAVIATE_URL"),
+            auth_client_secret=auth_config,
+            additional_headers={"X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY")},
+        )
+        return client
+
+    def _document_loader(self, observation: str, loader_settings: dict):
+        # Create an in-memory file-like object for the PDF content
+
+        if loader_settings.get("format") == "PDF":
+
+            if loader_settings.get("source") == "url":
+                pdf_response = requests.get(loader_settings["path"])
+                pdf_stream = BytesIO(pdf_response.content)
+                contents = pdf_stream.read()
+                tmp_location = os.path.join("/tmp", "tmp.pdf")
+                with open(tmp_location, "wb") as tmp_file:
+                    tmp_file.write(contents)
+
+                # Process the PDF using PyPDFLoader
+                loader = PyPDFLoader(tmp_location)
+                # adapt this for different chunking strategies
+                pages = loader.load_and_split()
+                return pages
+
+            if loader_settings.get("source") == "file":
+                # Process the PDF using PyPDFLoader
+                # might need adapting for different loaders + OCR
+                # need to test the path
+                loader = PyPDFLoader(loader_settings["path"])
+                pages = loader.load_and_split()
+
+                return pages
+        else:
+            # Process the text by just loading the base text
+            return observation
+
+
+    async def add_memories(
+        self, observation: str, loader_settings: dict = None, params: dict = None ,namespace:str=None
+    ):
+        # Update Weaviate memories here
+        print(self.namespace)
+        if namespace is None:
+            namespace = self.namespace
+        retriever = self.init_weaviate(namespace)
+
+        def _stuct(observation, params):
+            """Utility function to not repeat metadata structure"""
+            # needs smarter solution, like dynamic generation of metadata
+            return [
+            Document(
+                metadata={
+                    # "text": observation,
+                    "user_id": str(self.user_id),
+                    "memory_id": str(self.memory_id),
+                    "ltm_memory_id": str(self.ltm_memory_id),
+                    "st_memory_id": str(self.st_memory_id),
+                    "buffer_id": str(self.buffer_id),
+                    "version": params.get("version", None) or "",
+                    "agreement_id": params.get("agreement_id", None) or "",
+                    "privacy_policy": params.get("privacy_policy", None) or "",
+                    "terms_of_service": params.get("terms_of_service", None) or "",
+                    "format": params.get("format", None) or "",
+                    "schema_version": params.get("schema_version", None) or "",
+                    "checksum": params.get("checksum", None) or "",
+                    "owner": params.get("owner", None) or "",
+                    "license": params.get("license", None) or "",
+                    "validity_start": params.get("validity_start", None) or "",
+                    "validity_end": params.get("validity_end", None) or ""
+                    # **source_metadata,
+                },
+                page_content=observation,
+            )
+        ]
+
+        if loader_settings:
+            # Load the document
+            document = self._document_loader(observation, loader_settings)
+            print("DOC LENGTH", len(document))
+            for doc in document:
+                document_to_load = _stuct(doc.page_content, params)
+                retriever.add_documents(
+                    document_to_load
+                )
+
+        return retriever.add_documents(
+            _stuct(observation, params)
+        )
+
+    async def fetch_memories(
+        self, observation: str, namespace: str, params: dict = None, n_of_observations =int(2)
+    ):
+        """
+        Get documents from weaviate.
+
+            Parameters:
+            - observation (str): User query.
+            - namespace (str): Type of memory we access.
+            - params (dict, optional):
+            - n_of_observations (int, optional): For weaviate, equals to autocut, defaults to 1. Ranges from 1 to 3. Check weaviate docs for more info.
+
+            Returns:
+            Describe the return type and what the function returns.
+
+        Args a json containing:
+            query (str): The query string.
+            path (list): The path for filtering, e.g., ['year'].
+            operator (str): The operator for filtering, e.g., 'Equal'.
+            valueText (str): The value for filtering, e.g., '2017*'.
+
+        Example:
+            get_from_weaviate(query="some query", path=['year'], operator='Equal', valueText='2017*')
+
+        """
+        client = self.init_weaviate_client(self.namespace)
+
+        print(self.namespace)
+        print(str(datetime.now()))
+        print(observation)
+        if namespace is None:
+            namespace = self.namespace
+
+        params_user_id = {
+            "path": ["user_id"],
+            "operator": "Like",
+            "valueText": self.user_id,
+        }
+
+        if params:
+            query_output = (
+                client.query.get(
+                    namespace,
+                    [
+                        # "text",
+                        "user_id",
+                        "memory_id",
+                        "ltm_memory_id",
+                        "st_memory_id",
+                        "buffer_id",
+                        "version",
+                        "agreement_id",
+                        "privacy_policy",
+                        "terms_of_service",
+                        "format",
+                        "schema_version",
+                        "checksum",
+                        "owner",
+                        "license",
+                        "validity_start",
+                        "validity_end",
+                    ],
+                )
+                .with_where(params)
+                .with_near_text({"concepts": [observation]})
+                .with_additional(
+                    ["id", "creationTimeUnix", "lastUpdateTimeUnix", "score",'distance']
+                )
+                .with_where(params_user_id)
+                .with_limit(10)
+                .do()
+            )
+            return query_output
+        else:
+            query_output = (
+                client.query.get(
+                    namespace,
+
+                    [
+                        "text",
+                        "user_id",
+                        "memory_id",
+                        "ltm_memory_id",
+                        "st_memory_id",
+                        "buffer_id",
+                        "version",
+                        "agreement_id",
+                        "privacy_policy",
+                        "terms_of_service",
+                        "format",
+                        "schema_version",
+                        "checksum",
+                        "owner",
+                        "license",
+                        "validity_start",
+                        "validity_end",
+                    ],
+                )
+                .with_additional(
+                    ["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", 'distance']
+                )
+                .with_hybrid(
+                    query=observation,
+                    fusion_type=HybridFusion.RELATIVE_SCORE
+                )
+                .with_autocut(n_of_observations)
+                .with_where(params_user_id)
+                .with_limit(10)
+                .do()
+            )
+            return query_output
+
+    async def delete_memories(self, params: dict = None):
+        client = self.init_weaviate_client(self.namespace)
+        if params:
+            where_filter = {
+                "path": ["id"],
+                "operator": "Equal",
+                "valueText": params.get("id", None),
+            }
+            return client.batch.delete_objects(
+                class_name=self.namespace,
+                # Same `where` filter as in the GraphQL API
+                where=where_filter,
+            )
+        else:
+            # Delete all objects
+            print("HERE IS THE USER ID", self.user_id)
+            return client.batch.delete_objects(
+                class_name=self.namespace,
+                where={
+                    "path": ["user_id"],
+                    "operator": "Equal",
+                    "valueText": self.user_id,
+                },
+            )
+
+    def update_memories(self, observation, namespace: str, params: dict = None):
+        client = self.init_weaviate_client(self.namespace)
+
+        client.data_object.update(
+            data_object={
+                # "text": observation,
+                "user_id": str(self.user_id),
+                "memory_id": str(self.memory_id),
+                "ltm_memory_id": str(self.ltm_memory_id),
+                "st_memory_id": str(self.st_memory_id),
+                "buffer_id": str(self.buffer_id),
+                "version": params.get("version", None) or "",
+                "agreement_id": params.get("agreement_id", None) or "",
+                "privacy_policy": params.get("privacy_policy", None) or "",
+                "terms_of_service": params.get("terms_of_service", None) or "",
+                "format": params.get("format", None) or "",
+                "schema_version": params.get("schema_version", None) or "",
+                "checksum": params.get("checksum", None) or "",
+                "owner": params.get("owner", None) or "",
+                "license": params.get("license", None) or "",
+                "validity_start": params.get("validity_start", None) or "",
+                "validity_end": params.get("validity_end", None) or ""
+                # **source_metadata,
+            },
+            class_name="Test",
+            uuid=params.get("id", None),
+            consistency_level=weaviate.data.replication.ConsistencyLevel.ALL,  # default QUORUM
+        )
+        return