diff --git a/.gitignore b/.gitignore
index f35f8a89f..00c0c8742 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,5 +172,32 @@ google-cloud-cli-linux-x86_64.tar.gz
.vennv
newenv
files
+
+# start of ian-added gitignore
+#terraform GITIGNORE
+
+# Local .terraform directories
+**/.terraform/*
+
+
+*.tfvars
+*.tfvars.json
+
+# .tfstate files
+*.tfstate
+*.tfstate.*
+
+# Crash log files
+crash.log
+crash.*.log
+
+# Ignore transient lock info files created by terraform apply
+.terraform.tfstate.lock.info
+
+# Ignore CLI configuration files
+.terraformrc
+terraform.rc
+# end of ian-added gitignore
+
startupbackend.sh
-startupfrontend.sh
\ No newline at end of file
+startupfrontend.sh
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 4488129d9..0e7b1656b 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -1,26 +1,65 @@
-FROM python:3.10-slim
+FROM python:3.10-slim-bullseye
WORKDIR /code
ENV PORT 8000
EXPOSE 8000
-# Install dependencies and clean up in one layer
+# ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION python
+# Clean up apt cache and install keyring packages
+RUN rm -rf /var/lib/apt/lists/* && \
+ apt-get clean && \
+ apt-get -o Acquire::AllowInsecureRepositories=true update && \
+ apt-get install -y --no-install-recommends --no-install-suggests \
+ debian-keyring \
+ debian-archive-keyring \
+ gnupg \
+ ca-certificates && \
+ apt-get clean && \
+ rm -rf /var/lib/apt/lists/*
+
+# Install system dependencies
+# this was added before pinned python:3.10-slim-bullseye, shouldn't need it anymore
+# RUN apt-get update && apt-get install -y --no-install-recommends \
+# libgl1 \
+# && rm -rf /var/lib/apt/lists/*
+
+# Now update and install dependencies including build tools
RUN apt-get update && \
- apt-get install -y --no-install-recommends \
+ apt-get install -y --no-install-recommends --no-install-suggests \
libmagic1 \
libgl1-mesa-glx \
libreoffice \
cmake \
poppler-utils \
- tesseract-ocr && \
+ tesseract-ocr \
+ build-essential \
+ python3-dev \
+ gcc \
+ autoconf \
+ automake \
+ libtool \
+ pkg-config && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
+
+# Manually fetch the latest Debian signing key before updating apt repositories
+RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 648ACFD622F3D138
+
# Set LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH
+
+ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION='python'
+
# Copy requirements file and install Python dependencies
COPY requirements.txt constraints.txt /code/
# --no-cache-dir --upgrade
+RUN pip --version
RUN pip install --upgrade pip
-RUN pip install -r requirements.txt -c constraints.txt
+RUN pip --version
+RUN pip install -r requirements.txt -c constraints.txt
+RUN python -c "import torch; print(f'PyTorch version: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}'); print(f'CUDA device count: {torch.cuda.device_count()}')"
+# RUN pip install -r requirements.txt
# Copy application code
COPY . /code
+
# Set command
-CMD ["gunicorn", "score:app", "--workers", "8","--threads", "8", "--worker-class", "uvicorn.workers.UvicornWorker", "--bind", "0.0.0.0:8000", "--timeout", "300"]
+# CMD ["gunicorn", "score:app", "--workers", "8","--threads", "8", "--worker-class", "uvicorn.workers.UvicornWorker", "--bind", "0.0.0.0:8000", "--timeout", "300"] # original line
+CMD ["gunicorn", "score:app", "--workers", "2","--threads", "2", "--worker-class", "uvicorn.workers.UvicornWorker", "--bind", "0.0.0.0:8000", "--timeout", "300"]
diff --git a/backend/Pipfile b/backend/Pipfile
new file mode 100644
index 000000000..e7f3e8d68
--- /dev/null
+++ b/backend/Pipfile
@@ -0,0 +1,77 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+accelerate = "==1.6.0"
+asyncio = "==3.4.3"
+boto3 = "==1.37.29"
+botocore = "==1.37.29"
+certifi = "==2025.1.31"
+fastapi = "==0.115.11"
+fastapi-health = "==0.4.0"
+google-api-core = "==2.24.2"
+google-auth = "==2.38.0"
+google-auth-oauthlib = "==1.2.1"
+google-cloud-core = "==2.4.3"
+json-repair = "==0.39.1"
+pip-install = "==1.3.5"
+langchain = "==0.3.23"
+langchain-aws = "==0.2.18"
+langchain-anthropic = "==0.3.9"
+langchain-fireworks = "==0.2.9"
+langchain-community = "==0.3.19"
+langchain-core = "==0.3.51"
+langchain-experimental = "==0.3.4"
+langchain-google-vertexai = "==2.0.19"
+langchain-groq = "==0.2.5"
+langchain-openai = "==0.3.12"
+langchain-text-splitters = "==0.3.8"
+langchain-huggingface = "==0.1.2"
+langdetect = "==1.0.9"
+langsmith = "==0.3.26"
+langserve = "==0.3.1"
+neo4j-rust-ext = "*"
+nltk = "==3.9.1"
+openai = "==1.71.0"
+opencv-python = "==4.11.0.86"
+psutil = "==7.0.0"
+pydantic = "==2.10.6"
+python-dotenv = "==1.0.1"
+python-magic = "==0.4.27"
+pypdf2 = "==3.0.1"
+pymupdf = "==1.25.5"
+starlette = "==0.46.1"
+sse-starlette = "==2.2.1"
+starlette-session = "==0.4.3"
+tqdm = "==4.67.1"
+unstructured-client = "==0.32.3"
+unstructured-inference = "==0.8.10"
+urllib3 = "==2.3.0"
+uvicorn = "==0.34.0"
+gunicorn = "==23.0.0"
+wikipedia = "==1.4.0"
+wrapt = "==1.17.2"
+yarl = "==1.18.3"
+youtube-transcript-api = "==1.0.3"
+zipp = "==3.21.0"
+sentence-transformers = "==4.0.2"
+google-cloud-logging = "==3.11.4"
+pypandoc = "==1.15"
+graphdatascience = "==1.14"
+secweb = "==1.18.1"
+ragas = "==0.2.14"
+rouge-score = "==0.1.2"
+langchain-neo4j = "==0.4.0"
+pypandoc-binary = "==1.15"
+chardet = "==5.2.0"
+contextgem = "==0.8.1"
+# we need to backpropagate this unstructured with doctypes to requirements.txt, right now it has unstructured[all-docs]
+unstructured = {extras = ["pptx", "md", "docx"], version = "*"}
+
+[dev-packages]
+
+[requires]
+python_version = "3.10"
+python_full_version = "3.10.15"
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
new file mode 100644
index 000000000..2a018d152
--- /dev/null
+++ b/backend/Pipfile.lock
@@ -0,0 +1,4851 @@
+{
+ "_meta": {
+ "hash": {
+ "sha256": "20809e5d9af171f465777eec6ae0d679e667cc6563d1b1dd8513a9373a99a15b"
+ },
+ "pipfile-spec": 6,
+ "requires": {
+ "python_full_version": "3.10.15",
+ "python_version": "3.10"
+ },
+ "sources": [
+ {
+ "name": "pypi",
+ "url": "https://pypi.org/simple",
+ "verify_ssl": true
+ }
+ ]
+ },
+ "default": {
+ "absl-py": {
+ "hashes": [
+ "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9",
+ "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==2.3.1"
+ },
+ "accelerate": {
+ "hashes": [
+ "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa",
+ "sha256:28c1ef1846e690944f98b68dc7b8bb6c51d032d45e85dcbb3adb0c8b99dffb32"
+ ],
+ "index": "pypi",
+ "markers": "python_full_version >= '3.9.0'",
+ "version": "==1.6.0"
+ },
+ "aiofiles": {
+ "hashes": [
+ "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c",
+ "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==24.1.0"
+ },
+ "aiohappyeyeballs": {
+ "hashes": [
+ "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558",
+ "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"
+ ],
+ "markers": "python_version >= '3.9'",
+ "version": "==2.6.1"
+ },
+ "aiohttp": {
+ "hashes": [
+ "sha256:010cc9bbd06db80fe234d9003f67e97a10fe003bfbedb40da7d71c1008eda0fe",
+ "sha256:049ec0360f939cd164ecbfd2873eaa432613d5e77d6b04535e3d1fbae5a9e645",
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+ "sha256:fe086edf38b2222328cdf89af0dde2439ee173b8ad7cb659b4e4c6f385b2be3d"
+ ],
+ "markers": "python_version >= '3.9'",
+ "version": "==3.12.15"
+ },
+ "aiolimiter": {
+ "hashes": [
+ "sha256:d3f249e9059a20badcb56b61601a83556133655c11d1eb3dd3e04ff069e5f3c7",
+ "sha256:e02a37ea1a855d9e832252a105420ad4d15011505512a1a1d814647451b5cca9"
+ ],
+ "markers": "python_version >= '3.8' and python_version < '4.0'",
+ "version": "==1.2.1"
+ },
+ "aiosignal": {
+ "hashes": [
+ "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e",
+ "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"
+ ],
+ "markers": "python_version >= '3.9'",
+ "version": "==1.4.0"
+ },
+ "annotated-types": {
+ "hashes": [
+ "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
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+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==0.7.0"
+ },
+ "anthropic": {
+ "hashes": [
+ "sha256:adcf2af98aa2b11e3b7c71afb2e0cb0613f679ad4a18ef58c38f17784b3df72e",
+ "sha256:d45389229db9e443ea1a877f8d63309947f134991473cf8e88efee322840d084"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==0.62.0"
+ },
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+ "sha256:9206649ec587e6b02bd124fb7799b86cddec350f6f6c14bc82a2b70183e708ba",
+ "sha256:983b6efd649723474f29ed42e1467f90a35a74793437d0bc64a5bf482bedfa0a",
+ "sha256:98da17ce9cbf3bfe4617e836d561e433f871129e3a7ac16d6ef4c680f13a839c",
+ "sha256:9c236e635582742fee16603042553d276cca506e824fa2e6489db04039521e90",
+ "sha256:9da6bc32faac9a293ddfdcb9108d4b20416219461e4ec64dfea8383cac186690",
+ "sha256:a05e6d6218461eb1b4771d973728f0133b2a4613a6779995df557f70794fd60f",
+ "sha256:a0817825b900fcd43ac5d05b8b3079937073d2b1ff9cf89427590718b70dd840",
+ "sha256:a4ae99c57668ca1e78597d8b06d5af837f377f340f4cce993b551b2d7731778d",
+ "sha256:a8c86881813a78a6f4508ef9daf9d4995b8ac2d147dcb1a450448941398091c9",
+ "sha256:a8fffdbd9d1408006baaf02f1068d7dd1f016c6bcb7538682622c556e7b68e35",
+ "sha256:a9b07268d0c3ca5c170a385a0ab9fb7fdd9f5fd866be004c4ea39e44edce47dd",
+ "sha256:ab19a2d91963ed9e42b4e8d77cd847ae8381576585bad79dbd0a8837a9f6620a",
+ "sha256:ac184f87ff521f4840e6ea0b10c0ec90c6b1dcd0bad2f1e4a9a1b4fa177982ea",
+ "sha256:b0e166f698c5a3e914947388c162be2583e0c638a4703fc6a543e23a88dea3c1",
+ "sha256:b2170c7e0367dde86a2647ed5b6f57394ea7f53545746104c6b09fc1f4223573",
+ "sha256:b2d8c62d08e7255f68f7a740bae85b3c9b8e5466baa9cbf7f57f1cde0ac6bc09",
+ "sha256:b4567955a6bc1b20e9c31612e615af6b53733491aeaa19a6b3b37f3b65477094",
+ "sha256:b69bb4f51daf461b15e7b3db033160937d3ff88303a7bc808c67bbc1eaf98c78",
+ "sha256:b8c0bd73aeac689beacd4e7667d48c299f61b959475cdbb91e7d3d88d27c56b9",
+ "sha256:be9b5b8659dff1f913039c2feee1aca499cfbc19e98fa12bc85e037c17ec6ca5",
+ "sha256:bf0a05b6059c0528477fba9054d09179beb63744355cab9f38059548fedd46a9",
+ "sha256:c16842b846a8d2a145223f520b7e18b57c8f476924bda92aeee3a88d11cfc391",
+ "sha256:c363b53e257246a954ebc7c488304b5592b9c53fbe74d03bc1c64dda153fb847",
+ "sha256:c7c517d74bea1a6afd39aa612fa025e6b8011982a0897768a2f7c8ab4ebb78a2",
+ "sha256:d20fd853fbb5807c8e84c136c278827b6167ded66c72ec6f9a14b863d809211c",
+ "sha256:d2240ddc86b74966c34554c49d00eaafa8200a18d3a5b6ffbf7da63b11d74ee2",
+ "sha256:d477ed829077cd945b01fc3115edd132c47e6540ddcd96ca169facff28173057",
+ "sha256:d50d31bfedd53a928fed6707b15a8dbeef011bb6366297cc435accc888b27c20",
+ "sha256:dc1d33abb8a0d754ea4763bad944fd965d3d95b5baef6b121c0c9013eaf1907d",
+ "sha256:dc5d1a49d3f8262be192589a4b72f0d03b72dcf46c51ad5852a4fdc67be7b9e4",
+ "sha256:e2d1a054f8f0a191004675755448d12be47fa9bebbcffa3cdf01db19f2d30a54",
+ "sha256:e7792606d606c8df5277c32ccb58f29b9b8603bf83b48639b7aedf6df4fe8171",
+ "sha256:ed1708dbf4d2e3a1c5c69110ba2b4eb6678262028afd6c6fbcc5a8dac9cda68e",
+ "sha256:f2d4380bf5f62daabd7b751ea2339c1a21d1c9463f1feb7fc2bdcea2c29c3160",
+ "sha256:f3513916e8c645d0610815c257cbfd3242adfd5c4cfa78be514e5a3ebb42a41b",
+ "sha256:f8346bfa098532bc1fb6c7ef06783e969d87a99dd1d2a5a18a892c1d7a643c58",
+ "sha256:f83fa6cae3fff8e98691248c9320356971b59678a17f20656a9e59cd32cee6d8",
+ "sha256:fa6ce8b52c5987b3e34d5674b0ab529a4602b632ebab0a93b07bfb4dfc8f8a33",
+ "sha256:fb2b1ecfef1e67897d336de3a0e3f52478182d6a47eda86cbd42504c5cbd009a",
+ "sha256:fc9ca1c9718cb3b06634c7c8dec57d24e9438b2aa9a0f02b8bb36bf478538880",
+ "sha256:fd30d9c67d13d891f2360b2a120186729c111238ac63b43dbd37a5a40670b8ca",
+ "sha256:fd7699e8fd9969f455ef2926221e0233f81a2542921471382e77a9e2f2b57f4b",
+ "sha256:fe3b385d996ee0822fd46528d9f0443b880d4d05528fd26a9119a54ec3f91c69"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==0.23.0"
+ }
+ },
+ "develop": {}
+}
diff --git a/backend/constraints.txt b/backend/constraints.txt
index 2c785f6de..0e4dbc4a4 100644
--- a/backend/constraints.txt
+++ b/backend/constraints.txt
@@ -1,4 +1,8 @@
-f https://download.pytorch.org/whl/torch_stable.html
-torch==2.3.1+cpu
-torchvision==0.18.1+cpu
-torchaudio==2.3.1+cpu
\ No newline at end of file
+# torch==2.3.1+cpu
+# torchvision==0.18.1+cpu
+# torchaudio==2.3.1+cpu
+torch==2.3.1
+torchvision==0.18.1
+torchaudio==2.3.1
+
diff --git a/backend/requirements.txt b/backend/requirements.txt
index 7ced40181..fefd3fc6f 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -1,3 +1,4 @@
+#pycryptodome==3.22.0 #added by ian to pin version to prevent bug (maybe move to constraints.txt) commented out hoping the upstream config fixes these errors.
accelerate==1.6.0
asyncio==3.4.3
boto3==1.37.29
@@ -40,7 +41,7 @@ starlette==0.46.1
sse-starlette==2.2.1
starlette-session==0.4.3
tqdm==4.67.1
-unstructured[all-docs]
+unstructured[all-docs] #in the pipfile we have unstructured[pdf,md,docx,pptx], need to reconcile these two
unstructured==0.17.2
unstructured-client==0.32.3
unstructured-inference==0.8.10
@@ -59,6 +60,11 @@ graphdatascience==1.14
Secweb==1.18.1
ragas==0.2.14
rouge_score==0.1.2
+
+ # ian added this langchain-neo4j==0.1.1 to prevent a bug, commented out bc upstream added a newer version
+ #langchain-neo4j==0.1.1 # added by ian to prevent bug (maybe move to constraints.txt)
+#pdfminer.six==20240706 # added by ian to prevent bud (maybe move to constraints.txt) commented out hoping the upstream config fixes these errors.
langchain-neo4j==0.4.0
pypandoc-binary==1.15
chardet==5.2.0
+contextgem==0.8.1
\ No newline at end of file
diff --git a/backend/score.py b/backend/score.py
index 11209a0b4..2da12487a 100644
--- a/backend/score.py
+++ b/backend/score.py
@@ -4,6 +4,7 @@
from src.main import *
from src.QA_integration import *
from src.shared.common_fn import *
+from src.shared.constants import CHAT_SEARCH_KWARG_SCORE_THRESHOLD
from src.shared.llm_graph_builder_exception import LLMGraphBuilderException
import uvicorn
import asyncio
@@ -19,6 +20,8 @@
from src.communities import create_communities
from src.neighbours import get_neighbour_nodes
import json
+from typing import Dict, Any
+from starlette.middleware.sessions import SessionMiddleware
from typing import List, Optional
from google.oauth2.credentials import Credentials
import os
@@ -32,15 +35,63 @@
from src.ragas_eval import *
from starlette.types import ASGIApp, Receive, Scope, Send
from langchain_neo4j import Neo4jGraph
+from pydantic import BaseModel
+
+# this load_dotenv added by ian for non-docker environments
from starlette.middleware.sessions import SessionMiddleware
from starlette.requests import Request
from dotenv import load_dotenv
load_dotenv(override=True)
+# # Get the absolute path to the .env file
+# env_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '.env')
+# logging.info(f"Loading .env file from: {env_path}")
+
+# # Debug: Print contents of .env file
+# try:
+# with open(env_path, 'r') as f:
+# env_contents = f.read()
+# logging.info(f"\nContents of main .env file:\n{env_contents}")
+# except Exception as e:
+# logging.error(f"Error reading .env file: {e}")
+
+
+class Message(BaseModel):
+ role: str
+ content: str
+
+class MessageData(BaseModel):
+ messages: List[Message] # Wrap the messages array in an object
+ question: str
+ filter_properties: Optional[Dict[str, Any]] = None
+ requireGrounding: bool = True
+ document_names: Optional[List[str]] = []
+ uri: Optional[str] = None
+ userName: Optional[str] = None
+ password: Optional[str] = None
+ database: Optional[str] = None
+ model: Optional[str] = None
+ chunk_similarity_threshold: Optional[float] = None
+ session_id: Optional[str] = None
+ # tools: List[str]
+
logger = CustomLogger()
CHUNK_DIR = os.path.join(os.path.dirname(__file__), "chunks")
MERGED_DIR = os.path.join(os.path.dirname(__file__), "merged_files")
+# def printEnvKey(key):
+# if key in os.environ:
+# value = os.environ.get(key)
+# logging.info(f"{key} exists and has value: {value}")
+# else:
+# logging.info(f"{key} does NOT exist in environment variables")
+
+# def printAllEnvVars():
+# logging.info("All environment variables:")
+# for key, value in os.environ.items():
+# logging.info(f"{key} = {value}")
+
+# printAllEnvVars()
def sanitize_filename(filename):
"""
Sanitize the user-provided filename to prevent directory traversal and remove unsafe characters.
@@ -413,10 +464,113 @@ async def post_processing(uri=Form(None), userName=Form(None), password=Form(Non
finally:
gc.collect()
+
+@app.post("/magic-trek-chatbot")
+async def magic_trek_chat_bot(
+ messageData: MessageData,
+ #uri=Form(),
+ # model=Form(None),
+ # userName=Form(),
+ # password=Form(),
+ # database=Form(),
+ # question=Form(None),
+ # document_names=Form(None)
+ # session_id=str,
+ # mode=Form(None)
+ ):
+ logging.info(messageData)
+ messages = messageData.messages
+ question = messageData.question
+ filter_properties = messageData.filter_properties
+ model = messageData.model if hasattr(messageData, 'model') else "openai_gpt_4o_mini"
+ requireGrounding = messageData.requireGrounding if hasattr(messageData, 'requireGrounding') else True
+ document_names = messageData.document_names if hasattr(messageData, 'document_names') else []
+ document_names = json.dumps(document_names)
+ chunk_similarity_threshold = messageData.chunk_similarity_threshold if hasattr(messageData, 'chunk_similarity_threshold') and messageData.chunk_similarity_threshold is not None else CHAT_SEARCH_KWARG_SCORE_THRESHOLD
+ session_id = messageData.session_id if hasattr(messageData, 'session_id') and messageData.session_id is not None else "7f7fb2ac-d849-4569-9647-17d5f3a1615e"
+ # Get connection parameters from API params, fallback to environment variables
+ uri = messageData.uri if messageData.uri else os.getenv('MAGIC_TREK_NEO4J_URI')
+ userName = messageData.userName if messageData.userName else os.getenv('MAGIC_TREK_NEO4J_USERNAME')
+ password = messageData.password if messageData.password else os.getenv('MAGIC_TREK_NEO4J_PASSWORD')
+ database = messageData.database if messageData.database else os.getenv('MAGIC_TREK_NEO4J_DATABASE')
+
+ print(f'question = {question}')
+ print(messages)
+ print(len(messages))
+ print(f"document_names = {document_names}")
+ print(f"filter_properties = {filter_properties}")
+ logging.info(f"requireGrounding = {requireGrounding}")
+ logging.info(f"IAN-TEST called at {datetime.now()}")
+ qa_rag_start_time = time.time()
+ logging.info(f"model = {model}")
+
+ # model = "openai_gpt_3.5" # changed from this model by ian friday mar 21, 2025
+ # model = "openai_gpt_4o_mini"
+ # model = "gpt-4-turbo-preview"
+ mode = os.getenv('MAGIC_TREK_RAG_MODE') ## changed by ian from graph_vector_fulltext to graph_vector april 1, 2025
+ # uri = "neo4j+s://bf80b146.databases.neo4j.io"
+
+ print(f"MAGIC_TREK_NEO4J_URI: {uri}")
+ print(f"MAGIC_TREK_RAG_MODE: {mode}")
+ print(f"CHAT_SEARCH_KWARG_SCORE_THRESHOLD: {CHAT_SEARCH_KWARG_SCORE_THRESHOLD}")
+
+ mode = "graph_vector"
+ # document_names= '["MM Disease State Overview_FGuide_March2025_scrubbed.txt"]'
+ ## end of added by ian
+ try:
+ if mode == "graph":
+ graph = Neo4jGraph( url=uri,username=userName,password=password,database=database,sanitize = True, refresh_schema=True)
+ print("created neo4j graph object")
+ else:
+ graph = create_graph_database_connection(uri, userName, password, database)
+
+ graph_DB_dataAccess = graphDBdataAccess(graph)
+ write_access = graph_DB_dataAccess.check_account_access(database=database)
+ result = await asyncio.to_thread(
+ MAGIC_TREK_QA_RAG,
+ graph=graph,
+ model=model,
+ messages=messages,
+ question=question,
+ document_names=document_names,
+ session_id=session_id,
+ mode=mode,
+ write_access=write_access,
+ filter_properties=filter_properties,
+ requireGrounding=requireGrounding,
+ chunk_similarity_threshold=chunk_similarity_threshold
+ )
+
+ total_call_time = time.time() - qa_rag_start_time
+ logging.info(f"Total Response time is {total_call_time:.2f} seconds")
+ result["info"]["response_time"] = round(total_call_time, 2)
+
+ json_obj = {'api_name':'chat_bot','db_url':uri, 'userName':userName, 'database':database, 'question':question,'document_names':document_names,
+ 'session_id':session_id, 'mode':mode, 'logging_time': formatted_time(datetime.now(timezone.utc)), 'elapsed_api_time':f'{total_call_time:.2f}'}
+ logger.log_struct(json_obj, "INFO")
+
+ logging.info("printing tool calls in magic_trek_chat_bot")
+ logging.info(result["tool_calls"])
+ response = create_api_response('Success',data=result)
+ log_start = time.time()
+ logging.info("response")
+ logging.info(response)
+ log_time = time.time() - log_start
+ logging.info(f"Logging response took {log_time:.2f} seconds")
+ return response
+ except Exception as e:
+ job_status = "Failed"
+ message="Unable to get chat response"
+ error_message = str(e)
+ logging.exception(f'Exception in chat bot:{error_message}')
+ return create_api_response(job_status, message=message, error=error_message,data=mode)
+ finally:
+ gc.collect()
@app.post("/chat_bot")
async def chat_bot(uri=Form(None),model=Form(None),userName=Form(None), password=Form(None), database=Form(None),question=Form(None), document_names=Form(None),session_id=Form(None),mode=Form(None),email=Form(None)):
logging.info(f"QA_RAG called at {datetime.now()}")
+ logging.info(f"document_names = {document_names}")
qa_rag_start_time = time.time()
try:
if mode == "graph":
@@ -446,6 +600,70 @@ async def chat_bot(uri=Form(None),model=Form(None),userName=Form(None), password
finally:
gc.collect()
+@app.post("/chat_bot_grounding")
+async def chat_bot_grounding(
+ uri=Form(None),
+ model=Form(None),
+ userName=Form(None),
+ password=Form(None),
+ database=Form(None),
+ question=Form(None),
+ document_names=Form(None),
+ session_id=Form(None),
+ mode=Form(None),
+ email=Form(None),
+ requireGrounding: bool = Form(True),
+ filterProperties: str = Form(None),
+ chunk_similarity_threshold: float = Form(None)
+):
+ logging.info(f"QA_RAG (grounding) called at {datetime.now()}")
+ logging.info(f"document_names = {document_names}")
+ logging.info(f"filterProperties = {filterProperties}")
+ logging.info(f"model = {model}")
+ qa_rag_start_time = time.time()
+
+ # Parse filterProperties JSON if provided
+ filter_properties = None
+ if filterProperties:
+ try:
+ filter_properties = json.loads(filterProperties)
+ except json.JSONDecodeError as e:
+ logging.warning(f"Invalid JSON in filterProperties: {e}")
+ filter_properties = None
+
+ # Use chunk_similarity_threshold parameter if provided, otherwise fallback to default constant
+ effective_chunk_similarity_threshold = chunk_similarity_threshold if chunk_similarity_threshold is not None else CHAT_SEARCH_KWARG_SCORE_THRESHOLD
+ logging.info(f"effective_chunk_similarity_threshold = {effective_chunk_similarity_threshold}")
+ try:
+ if mode == "graph":
+ graph = Neo4jGraph( url=uri,username=userName,password=password,database=database,sanitize = True, refresh_schema=True)
+ else:
+ graph = create_graph_database_connection(uri, userName, password, database)
+
+ graph_DB_dataAccess = graphDBdataAccess(graph)
+ write_access = graph_DB_dataAccess.check_account_access(database=database)
+ # Select the system template based on requireGrounding (to be used inside QA_RAG or before calling it):
+ # system_template = CHAT_SYSTEM_TEMPLATE if requireGrounding else CHAT_SYSTEM_TEMPLATE_UNGROUNDED
+ result = await asyncio.to_thread(QA_RAG_GROUNDING,graph=graph,model=model,question=question,document_names=document_names,session_id=session_id,mode=mode,write_access=write_access,filter_properties=filter_properties,requireGrounding=requireGrounding,chunk_similarity_threshold=effective_chunk_similarity_threshold)
+
+ total_call_time = time.time() - qa_rag_start_time
+ logging.info(f"Total Response time is {total_call_time:.2f} seconds")
+ result["info"]["response_time"] = round(total_call_time, 2)
+
+ json_obj = {'api_name':'chat_bot_grounding','db_url':uri, 'userName':userName, 'database':database, 'question':question,'document_names':document_names,
+ 'session_id':session_id, 'mode':mode, 'requireGrounding': requireGrounding, 'filterProperties': filterProperties, 'logging_time': formatted_time(datetime.now(timezone.utc)), 'elapsed_api_time':f'{total_call_time:.2f}','email':email}
+ logger.log_struct(json_obj, "INFO")
+
+ return create_api_response('Success',data=result)
+ except Exception as e:
+ job_status = "Failed"
+ message="Unable to get chat response"
+ error_message = str(e)
+ logging.exception(f'Exception in chat bot grounding:{error_message}')
+ return create_api_response(job_status, message=message, error=error_message,data=mode)
+ finally:
+ gc.collect()
+
@app.post("/chunk_entities")
async def chunk_entities(uri=Form(None),userName=Form(None), password=Form(None), database=Form(None), nodedetails=Form(None),entities=Form(),mode=Form(),email=Form(None)):
try:
diff --git a/backend/src/QA_integration.py b/backend/src/QA_integration.py
index 1d3afb8e8..421a32931 100644
--- a/backend/src/QA_integration.py
+++ b/backend/src/QA_integration.py
@@ -8,6 +8,10 @@
from typing import Any
from dotenv import load_dotenv
+# Set tokenizer parallelism to false to avoid warnings and deadlock
+# added by ian because of hugging face threading tokenizer deadlock warning
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
from langchain_neo4j import Neo4jVector
from langchain_neo4j import Neo4jChatMessageHistory
from langchain_neo4j import GraphCypherQAChain
@@ -18,7 +22,7 @@
from langchain_community.document_transformers import EmbeddingsRedundantFilter
from langchain.retrievers.document_compressors import EmbeddingsFilter, DocumentCompressorPipeline
from langchain_text_splitters import TokenTextSplitter
-from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.callbacks import StdOutCallbackHandler, BaseCallbackHandler
@@ -32,7 +36,7 @@
from langchain_community.chat_models import ChatOllama
# Local imports
-from src.llm import get_llm
+from src.llm import get_llm, create_chat_completion_sync
from src.shared.common_fn import load_embedding_model
from src.shared.constants import *
load_dotenv()
@@ -40,6 +44,18 @@
EMBEDDING_MODEL = os.getenv('EMBEDDING_MODEL')
EMBEDDING_FUNCTION , _ = load_embedding_model(EMBEDDING_MODEL)
+# old tool extraction prompt
+# "Only invoke tools based off when the user makes a selection in their messages.
+# Only reference older messages to understand what the user's selection is."
+
+TOOL_EXTRACTION_PROMPT = """
+You are an intelligent assistant responsible for analyzing user inputs and invoking functions when the user makes a selection.
+The conversation you are analyzing is between an ai assistant helping a user to make choices about a lesson they are creating.
+- Call a function only when the user makes an active selection that matches one of the tools.
+- Avoid invoking functions for tools that have already been called.
+- If multiple functions need to be called based on the user's input, invoke all relevant functions simultaneously.
+- Only the user's most recent input should trigger a function, but reference past context when needed to understand their intent.
+"""
class SessionChatHistory:
history_dict = {}
@@ -63,6 +79,7 @@ def on_llm_end(
) -> None:
logging.info("question transformed")
self.transformed_question = response.generations[0][0].text.strip()
+ logging.info(self.transformed_question)
def get_history_by_session_id(session_id):
try:
@@ -225,13 +242,13 @@ def format_documents(documents, model,chat_mode_settings):
return "\n\n".join(formatted_docs), sources,entities,global_communities
-def process_documents(docs, question, messages, llm, model,chat_mode_settings):
+def process_documents(docs, question, messages, llm, model,chat_mode_settings, requireGrounding=True):
start_time = time.time()
try:
formatted_docs, sources, entitydetails, communities = format_documents(docs, model,chat_mode_settings)
-
- rag_chain = get_rag_chain(llm=llm)
+ logging.info(f"process_documents requireGrounding = {requireGrounding}")
+ rag_chain = get_rag_chain(llm=llm, system_template=CHAT_SYSTEM_TEMPLATE if requireGrounding else CHAT_SYSTEM_TEMPLATE_UNGROUNDED)
ai_response = rag_chain.invoke({
"messages": messages[:-1],
@@ -273,9 +290,16 @@ def retrieve_documents(doc_retriever, messages):
start_time = time.time()
try:
+ logging.info(f"=== DOCUMENT RETRIEVAL - MESSAGES BEING USED ===")
+ for i, msg in enumerate(messages):
+ logging.info(f"Retrieval Message {i}: Role={msg.type}, Content={msg.content}")
+ logging.info(msg.content)
+ logging.info(f"=== END RETRIEVAL MESSAGES (Total: {len(messages)} messages) ===")
+
handler = CustomCallback()
docs = doc_retriever.invoke({"messages": messages},{"callbacks":[handler]})
transformed_question = handler.transformed_question
+ logging.info(f"transformed question {transformed_question}")
if transformed_question:
logging.info(f"Transformed question : {transformed_question}")
doc_retrieval_time = time.time() - start_time
@@ -300,6 +324,8 @@ def create_document_retriever_chain(llm, retriever):
MessagesPlaceholder(variable_name="messages")
]
)
+ logging.info("query_transform_prompt created")
+ logging.info(query_transform_prompt)
output_parser = StrOutputParser()
@@ -375,42 +401,110 @@ def initialize_neo4j_vector(graph, chat_mode_settings):
raise
return neo_db
-def create_retriever(neo_db, document_names, chat_mode_settings,search_k, score_threshold,ef_ratio):
+def create_retriever(neo_db, document_names, chat_mode_settings,search_k, score_threshold,ef_ratio, filter_properties=None):
if document_names and chat_mode_settings["document_filter"]:
+ # Combine filter_properties with fileName filter
+ base_filter = {'fileName': {'$in': document_names}}
+ if filter_properties:
+ base_filter.update(filter_properties)
+
retriever = neo_db.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
'top_k': search_k,
'effective_search_ratio': ef_ratio,
'score_threshold': score_threshold,
- 'filter': {'fileName': {'$in': document_names}}
- }
+ 'filter': base_filter
+ },
)
logging.info(f"Successfully created retriever with search_k={search_k}, score_threshold={score_threshold} for documents {document_names}")
else:
+ # note from ian changed the way this is written slightly to match how the upstream team is doing it, passing filter_properties or None to search_kwargs literal.
+
+ # search_kwargs = {
+ # 'k': search_k,
+ # 'score_threshold': score_threshold,
+ # "filter": {
+ # "topics": { "$like": "topic_ENDOC"}
+ # }
+ # }
+ search_kwargs = {
+ 'k': search_k,
+ 'score_threshold': score_threshold,
+ # "filter": filter_properties
+ }
+ if filter_properties is not None:
+ search_kwargs['filter'] = filter_properties
+
retriever = neo_db.as_retriever(
search_type="similarity_score_threshold",
- search_kwargs={'top_k': search_k,'effective_search_ratio': ef_ratio, 'score_threshold': score_threshold}
+ # search_kwargs={'top_k': search_k,'effective_search_ratio': ef_ratio, 'score_threshold': score_threshold, "filter_properties": filter_properties if filter_properties is not None else None }
+ search_kwargs=search_kwargs
)
- logging.info(f"Successfully created retriever with search_k={search_k}, score_threshold={score_threshold}")
+ print(f"search_kwargs = {search_kwargs}")
+ logging.info(f"Successfully created retriever with search_k={search_k}, score_threshold={score_threshold}, filter={filter_properties}")
return retriever
-def get_neo4j_retriever(graph, document_names,chat_mode_settings, score_threshold=CHAT_SEARCH_KWARG_SCORE_THRESHOLD):
+def get_neo4j_retriever(graph, document_names, chat_mode_settings, score_threshold=CHAT_SEARCH_KWARG_SCORE_THRESHOLD, filter_properties=None):
try:
-
neo_db = initialize_neo4j_vector(graph, chat_mode_settings)
# document_names= list(map(str.strip, json.loads(document_names)))
search_k = chat_mode_settings["top_k"]
ef_ratio = int(os.getenv("EFFECTIVE_SEARCH_RATIO", "2")) if os.getenv("EFFECTIVE_SEARCH_RATIO", "2").isdigit() else 2
- retriever = create_retriever(neo_db, document_names,chat_mode_settings, search_k, score_threshold,ef_ratio)
+
+ # Use the parameter instead of hardcoded value
+ retriever = create_retriever(neo_db, document_names,chat_mode_settings, search_k, score_threshold,ef_ratio, filter_properties)
return retriever
except Exception as e:
index_name = chat_mode_settings.get("index_name")
logging.error(f"Error retrieving Neo4jVector index {index_name} or creating retriever: {e}")
raise Exception(f"An error occurred while retrieving the Neo4jVector index or creating the retriever. Please drop and create a new vector index '{index_name}': {e}") from e
+def extract_tool_calls_direct(model, messages):
+ """
+ Synchronous version of extract_tool_calls that uses OpenAI chat completion API directly.
+ Leaves original extract_tool_calls function unchanged.
+ """
+ if not isinstance(messages, list):
+ return []
+ logging.info("extract_tool_calls_direct called with messages below")
+ logging.info(messages)
+
+ # extract tool calling
+ messages_copy = list(messages)
+
+ # remove the system message and insert our tool extraction prompt
+ messages.pop(0)
+ messages.insert(0, SystemMessage(TOOL_EXTRACTION_PROMPT))
+
+ # Convert messages to the format expected by create_chat_completion
+ formatted_messages = [
+ {
+ "role": "system" if isinstance(msg, SystemMessage) else
+ "assistant" if isinstance(msg, AIMessage) else
+ "user",
+ "content": msg.content
+ }
+ for msg in messages
+ ]
-def setup_chat(model, graph, document_names, chat_mode_settings):
+ logging.info("calling tool extracting with direct OpenAI API")
+ start_time = time.time()
+ response = create_chat_completion_sync(
+ messages=formatted_messages,
+ model=model,
+ add_tools=True
+ )
+ tool_extraction_time = time.time() - start_time
+ logging.info(f"Tool extraction took {tool_extraction_time:.2f} seconds")
+
+ logging.info(response)
+ # Extract tool calls from additional_kwargs, handle missing key gracefully
+ tools = response.additional_kwargs.get("tool_calls", [])
+ tools = list(map(remap_tool_names, tools))
+ return tools
+
+def setup_chat(model, graph, document_names, chat_mode_settings, filter_properties=None, chunk_similarity_threshold=None):
start_time = time.time()
try:
if model == "diffbot":
@@ -419,7 +513,9 @@ def setup_chat(model, graph, document_names, chat_mode_settings):
llm, model_name = get_llm(model=model)
logging.info(f"Model called in chat: {model} (version: {model_name})")
- retriever = get_neo4j_retriever(graph=graph, chat_mode_settings=chat_mode_settings, document_names=document_names)
+ # Use chunk_similarity_threshold as score_threshold if provided, otherwise use default
+ effective_score_threshold = chunk_similarity_threshold if chunk_similarity_threshold is not None else CHAT_SEARCH_KWARG_SCORE_THRESHOLD
+ retriever = get_neo4j_retriever(graph=graph, chat_mode_settings=chat_mode_settings, document_names=document_names, filter_properties=filter_properties, score_threshold=effective_score_threshold)
doc_retriever = create_document_retriever_chain(llm, retriever)
chat_setup_time = time.time() - start_time
@@ -431,15 +527,72 @@ def setup_chat(model, graph, document_names, chat_mode_settings):
return llm, doc_retriever, model_name
-def process_chat_response(messages, history, question, model, graph, document_names, chat_mode_settings):
+def remap_tool_names(tool):
+ tool_name_map = {
+ "EducationLevelInput": "onSelectEducationLevel",
+ "LessonTopicInput": "onSelectLessonTopic",
+ "SelectSubjectInput": "onSelectSubject",
+ "OnGenerateKeywordsInput": "onGenerateKeywords"
+ }
+ current_name = tool["function"]["name"]
+ if current_name in tool_name_map:
+ tool["function"]["name"] = tool_name_map[current_name]
+ return tool
+
+def extract_tool_calls(model, messages):
+ if not isinstance(messages, list):
+ return []
+ logging.info("extract_tool_calls called with messages below")
+ logging.info(messages)
+ # extract tool calling
+ messages_copy = list(messages)
+
+ # remove the system
+ messages.pop(0)
+ messages.insert(0, SystemMessage(TOOL_EXTRACTION_PROMPT))
+
+ llm_with_tools,model_name = get_llm(model, add_tools=True)
+ logging.info("calling tool extracting llm")
+
+ response = llm_with_tools.invoke(messages)
+ logging.info(response)
+ # Extract tool calls from additional_kwargs, handle missing key gracefully
+ tools = response.additional_kwargs.get("tool_calls", [])
+ tools = list(map(remap_tool_names, tools))
+ return tools
+
+
+def process_chat_response(messages, history, question, model, graph, document_names, chat_mode_settings, extract_tools=False, filter_properties=None, requireGrounding=True, chunk_similarity_threshold=None):
try:
- llm, doc_retriever, model_version = setup_chat(model, graph, document_names, chat_mode_settings)
-
- docs,transformed_question = retrieve_documents(doc_retriever, messages)
+ llm, doc_retriever, model_version = setup_chat(model, graph, document_names, chat_mode_settings, filter_properties, chunk_similarity_threshold)
+
+ # Shared variable to store tool calls, initialized as empty list
+ tool_calls = []
+ tool_calls_lock = threading.Lock()
+
+ def extract_tools_thread():
+ nonlocal tool_calls
+ if extract_tools:
+ extracted_tools = extract_tool_calls_direct(model, messages)
+ with tool_calls_lock:
+ tool_calls = extracted_tools
+ logging.info("returned tool calls")
+ logging.info(tool_calls)
+
+ # Start tool extraction thread if needed
+ tool_thread = None
+ if extract_tools:
+ tool_thread = threading.Thread(target=extract_tools_thread)
+ tool_thread.start()
+
+ # Run document retrieval and processing in parallel with tool extraction
+ docs, transformed_question = retrieve_documents(doc_retriever, messages)
if docs:
- content, result, total_tokens,formatted_docs = process_documents(docs, question, messages, llm, model, chat_mode_settings)
+ logging.info("documents found, process_documents about to be called")
+ content, result, total_tokens, formatted_docs = process_documents(docs, question, messages, llm, model, chat_mode_settings, requireGrounding)
else:
+ logging.info("No document clause running")
content = "I couldn't find any relevant documents to answer your question."
result = {"sources": list(), "nodedetails": list(), "entities": list()}
total_tokens = 0
@@ -448,16 +601,20 @@ def process_chat_response(messages, history, question, model, graph, document_na
ai_response = AIMessage(content=content)
messages.append(ai_response)
- summarization_thread = threading.Thread(target=summarize_and_log, args=(history, messages, llm))
- summarization_thread.start()
- logging.info("Summarization thread started.")
- # summarize_and_log(history, messages, llm)
- metric_details = {"question":question,"contexts":formatted_docs,"answer":content}
+ if history:
+ summarization_thread = threading.Thread(target=summarize_and_log, args=(history, messages, llm))
+ summarization_thread.start()
+ logging.info("Summarization thread started.")
+
+ # Wait for tool extraction to complete if it was started
+ if tool_thread:
+ tool_thread.join()
+
+ metric_details = {"question": question, "contexts": formatted_docs, "answer": content}
return {
"session_id": "",
"message": content,
"info": {
- # "metrics" : metrics,
"sources": result["sources"],
"model": model_version,
"nodedetails": result["nodedetails"],
@@ -467,7 +624,7 @@ def process_chat_response(messages, history, question, model, graph, document_na
"entities": result["entities"],
"metric_details": metric_details,
},
-
+ "tool_calls": tool_calls,
"user": "chatbot"
}
@@ -531,7 +688,7 @@ def create_graph_chain(model, graph):
logging.info(f"Graph QA Chain using LLM model: {model}")
cypher_llm,model_name = get_llm(model)
- qa_llm,model_name = get_llm(model)
+ qa_llm,model_name = get_llm(model, True)
graph_chain = GraphCypherQAChain.from_llm(
cypher_llm=cypher_llm,
qa_llm=qa_llm,
@@ -572,20 +729,23 @@ def get_graph_response(graph_chain, question):
except Exception as e:
logging.error(f"An error occurred while getting the graph response : {e}")
-def process_graph_response(model, graph, question, messages, history):
+def process_graph_response(model, graph, question, messages, history=None):
try:
graph_chain, qa_llm, model_version = create_graph_chain(model, graph)
-
+ extract_tool_calls(model, question)
graph_response = get_graph_response(graph_chain, question)
-
+ logging.info("graph_response")
+ logging.info(graph_response)
ai_response_content = graph_response.get("response", "Something went wrong")
+ logging.info(ai_response_content)
ai_response = AIMessage(content=ai_response_content)
messages.append(ai_response)
# summarize_and_log(history, messages, qa_llm)
- summarization_thread = threading.Thread(target=summarize_and_log, args=(history, messages, qa_llm))
- summarization_thread.start()
- logging.info("Summarization thread started.")
+ if(history):
+ summarization_thread = threading.Thread(target=summarize_and_log, args=(history, messages, qa_llm))
+ summarization_thread.start()
+ logging.info("Summarization thread started.")
metric_details = {"question":question,"contexts":graph_response.get("context", ""),"answer":ai_response_content}
result = {
"session_id": "",
@@ -640,6 +800,7 @@ def create_neo4j_chat_message_history(graph, session_id, write_access=True):
raise
def get_chat_mode_settings(mode,settings_map=CHAT_MODE_CONFIG_MAP):
+ logging.info(f"get_chat_mode_settings called with mode: {mode}")
default_settings = settings_map[CHAT_DEFAULT_MODE]
try:
chat_mode_settings = settings_map.get(mode, default_settings)
@@ -652,21 +813,163 @@ def get_chat_mode_settings(mode,settings_map=CHAT_MODE_CONFIG_MAP):
raise
return chat_mode_settings
+
+# Function to convert JSON message list to Langchain message objects
+def convert_messages_to_langchain(messages):
+ langchain_messages = []
+
+ for msg in messages:
+ role = msg.role
+ content = msg.content
+
+ if role == 'system':
+ langchain_messages.append(SystemMessage(content=content))
+ elif role == 'assistant':
+ langchain_messages.append(AIMessage(content=content))
+ elif role == 'user': # assuming the user role maps to HumanMessage
+ langchain_messages.append(HumanMessage(content=content))
+ else:
+ raise ValueError(f"Unknown role: {role}")
+ return langchain_messages
+
+def MAGIC_TREK_QA_RAG(graph,model, messages, question, document_names, session_id, mode, write_access=True, filter_properties=None, requireGrounding=True, chunk_similarity_threshold=None):
+ logging.info(f"Chat Mode: {mode}")
+ logging.info(f"document_names = {document_names}")
+ logging.info(f"document_names type: {type(document_names)}")
+ # document_names = "[]"
+ logging.info(f"session_id = {session_id}")
+ logging.info(f"question = {question}")
+ logging.info(f"filter_properties = {filter_properties}")
+ logging.info(f"chunk_similarity_threshold = {chunk_similarity_threshold}")
+ # receive the message history from our frontend
+ messages = convert_messages_to_langchain(messages)
+ logging.info("translated message history:")
+ logging.info(messages)
+
+ # NOTE: for now we are going to take our message history from the one maintained on magic trek
+ # frontend
+ # history = create_neo4j_chat_message_history(graph, session_id, write_access)
+ # messages = history.messages
+
+ # print("message history")
+ # print(messages)
+
+ user_question = HumanMessage(content=question)
+ messages.append(user_question)
+
+ logging.info("messages")
+ logging.info(messages)
+
+ if mode == CHAT_GRAPH_MODE:
+ logging.info("process_graph_response called")
+ result = process_graph_response(model, graph, question, messages, history=None)
+ else:
+ chat_mode_settings = get_chat_mode_settings(mode=mode)
+ document_names= list(map(str.strip, json.loads(document_names)))
+ if document_names and not chat_mode_settings["document_filter"]:
+ result = {
+ "session_id": "",
+ "message": "Please deselect all documents in the table before using this chat mode",
+ "info": {
+ "sources": [],
+ "model": "",
+ "nodedetails": [],
+ "total_tokens": 0,
+ "response_time": 0,
+ "mode": chat_mode_settings["mode"],
+ "entities": [],
+ "metric_details": [],
+ },
+ "user": "chatbot"
+ }
+ else:
+ logging.info("process_chat_response called")
+ result = process_chat_response(
+ messages=messages,
+ history=None, # TODO: once we set up history pass it again instead of None
+ question=question,
+ model=model,
+ graph=graph,
+ document_names=document_names,
+ chat_mode_settings=chat_mode_settings,
+ extract_tools=True,
+ filter_properties=filter_properties,
+ requireGrounding=requireGrounding,
+ chunk_similarity_threshold=chunk_similarity_threshold
+ )
+
+ # result["session_id"] = session_id
+ # return False
+ return result
+
def QA_RAG(graph,model, question, document_names, session_id, mode, write_access=True):
logging.info(f"Chat Mode: {mode}")
history = create_neo4j_chat_message_history(graph, session_id, write_access)
+ print(history)
+ messages = history.messages
+
+ print("message history")
+ print(messages)
+
+ user_question = HumanMessage(content=question)
+ messages.append(user_question)
+
+ tool_calls = extract_tool_calls(model, user_question)
+ logging.info(tool_calls)
+
+ if mode == CHAT_GRAPH_MODE:
+ result = process_graph_response(model, graph, question, messages, history)
+ else:
+ chat_mode_settings = get_chat_mode_settings(mode=mode)
+ document_names= list(map(str.strip, json.loads(document_names)))
+ logging.info(f"chat_mode_settings['document_filter'] = {chat_mode_settings['document_filter']}")
+ if document_names and not chat_mode_settings["document_filter"]:
+ result = {
+ "session_id": "",
+ "message": "Please deselect all documents in the table before using this chat mode",
+ "info": {
+ "sources": [],
+ "model": "",
+ "nodedetails": [],
+ "total_tokens": 0,
+ "response_time": 0,
+ "mode": chat_mode_settings["mode"],
+ "entities": [],
+ "metric_details": [],
+ },
+ "user": "chatbot"
+ }
+ else:
+ result = process_chat_response(messages,history, question, model, graph, document_names,chat_mode_settings, extract_tools=False, filter_properties={})
+
+ result["session_id"] = session_id
+
+ return result
+
+def QA_RAG_GROUNDING(graph,model, question, document_names, session_id, mode, write_access=True, filter_properties=None, requireGrounding=True, chunk_similarity_threshold=None):
+ logging.info(f"Chat Mode: {mode}")
+
+ history = create_neo4j_chat_message_history(graph, session_id, write_access)
+ print(history)
messages = history.messages
+ print("message history")
+ print(messages)
+
user_question = HumanMessage(content=question)
messages.append(user_question)
+ tool_calls = extract_tool_calls(model, user_question)
+ logging.info(tool_calls)
+
if mode == CHAT_GRAPH_MODE:
result = process_graph_response(model, graph, question, messages, history)
else:
chat_mode_settings = get_chat_mode_settings(mode=mode)
document_names= list(map(str.strip, json.loads(document_names)))
+ logging.info(f"chat_mode_settings['document_filter'] = {chat_mode_settings['document_filter']}")
if document_names and not chat_mode_settings["document_filter"]:
result = {
"session_id": "",
@@ -684,7 +987,7 @@ def QA_RAG(graph,model, question, document_names, session_id, mode, write_access
"user": "chatbot"
}
else:
- result = process_chat_response(messages,history, question, model, graph, document_names,chat_mode_settings)
+ result = process_chat_response(messages,history, question, model, graph, document_names,chat_mode_settings, extract_tools=False, filter_properties=filter_properties, requireGrounding=requireGrounding, chunk_similarity_threshold=chunk_similarity_threshold)
result["session_id"] = session_id
diff --git a/backend/src/chunkid_entities.py b/backend/src/chunkid_entities.py
index b9bccbf84..a00dcfc68 100644
--- a/backend/src/chunkid_entities.py
+++ b/backend/src/chunkid_entities.py
@@ -98,7 +98,7 @@ def process_chunkids(driver, chunk_ids, entities):
logging.info(f"Query process completed successfully for chunk ids: {chunk_ids}")
return result
except Exception as e:
- logging.error(f"chunkid_entities module: Error processing chunk ids: {chunk_ids}. Error: {e}")
+ logging.error(f"_entities module: Error processing chunk ids: {chunk_ids}. Error: {e}")
raise
def remove_duplicate_nodes(nodes,property="element_id"):
diff --git a/backend/src/document_metadata_extractor/__init__.py b/backend/src/document_metadata_extractor/__init__.py
new file mode 100644
index 000000000..90af1e0c8
--- /dev/null
+++ b/backend/src/document_metadata_extractor/__init__.py
@@ -0,0 +1,16 @@
+from .metadata_processor import MetadataProcessor, DocumentMetadata, MetadataExtractor
+from .domain_extractor import DomainExtractor
+
+def create_metadata_processor() -> MetadataProcessor:
+ """Create and configure a metadata processor with default extractors"""
+ processor = MetadataProcessor()
+ processor.add_extractor(DomainExtractor())
+ return processor
+
+__all__ = [
+ 'MetadataProcessor',
+ 'DocumentMetadata',
+ 'MetadataExtractor',
+ 'DomainExtractor',
+ 'create_metadata_processor'
+]
\ No newline at end of file
diff --git a/backend/src/document_metadata_extractor/domain_extractor.py b/backend/src/document_metadata_extractor/domain_extractor.py
new file mode 100644
index 000000000..e7e893a23
--- /dev/null
+++ b/backend/src/document_metadata_extractor/domain_extractor.py
@@ -0,0 +1,54 @@
+from contextgem import Document as ContextGemDocument, DocumentLLM, LabelConcept
+from langchain.schema import Document
+from .metadata_processor import MetadataExtractor, DocumentMetadata
+from typing import List
+
+DOMAIN_LABELS = [
+ "Multiple Myeloma",
+ "Emotional Intelligence",
+ "Business Management",
+ "Instruction Manual",
+ "Sales Training",
+ "Leadership Coaching",
+]
+
+class DomainExtractor(MetadataExtractor):
+ """Classifies document into knowledge domains"""
+
+ def __init__(self):
+ super().__init__()
+ self.num_pages = 20
+
+ def extract(self, pages: List[Document], file_name: str) -> DocumentMetadata:
+ raw_text = f"filename: {file_name}" + "\n".join([page.page_content for page in pages[:self.num_pages]])
+ print("classification raw text: ", raw_text)
+ contextgem_doc = ContextGemDocument(raw_text=raw_text)
+
+ # Define a LabelConcept for contract type classification
+ document_type_concept = LabelConcept(
+ name="Document Domain Label",
+ description="Classify the knowledge domain of the document",
+ labels=DOMAIN_LABELS,
+ classification_type="multi_class", # only one label can be selected (mutually exclusive labels)
+ singular_occurrence=True, # expect only one classification result
+ )
+
+ contextgem_doc.add_concepts([document_type_concept])
+
+ model = "ollama/phi4:14b"
+ llm = DocumentLLM(
+ model=model,
+ # api_key=os.getenv("CONTEXTGEM_OPENAI_API_KEY"),
+ )
+
+ concept_label = llm.extract_concepts_from_document(contextgem_doc)[0]
+
+ if concept_label.extracted_items:
+ # Get the classified document type
+ classified_type = concept_label.extracted_items[0].value
+ print(f"Document: {file_name}, classified as: {classified_type}") # Output: ['NDA']
+ return DocumentMetadata(domain=classified_type)
+ else:
+ print(f"no label assigned to document {file_name}")
+ return DocumentMetadata(domain=None)
+
diff --git a/backend/src/document_metadata_extractor/metadata_processor.py b/backend/src/document_metadata_extractor/metadata_processor.py
new file mode 100644
index 000000000..938bed2a8
--- /dev/null
+++ b/backend/src/document_metadata_extractor/metadata_processor.py
@@ -0,0 +1,66 @@
+from typing import Dict, Any, List, Optional
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+import logging
+from datetime import datetime
+
+@dataclass
+class DocumentMetadata:
+ """Container for document-level metadata"""
+ title: Optional[str] = None
+ author: Optional[str] = None
+ date: Optional[datetime] = None
+ language: Optional[str] = None
+ domain: Optional[str] = None
+ keywords: List[str] = None
+ summary: Optional[str] = None
+ custom_metadata: Dict[str, Any] = None
+
+ def __post_init__(self):
+ if self.keywords is None:
+ self.keywords = []
+ if self.custom_metadata is None:
+ self.custom_metadata = {}
+
+class MetadataExtractor(ABC):
+ """Abstract base class for metadata extractors"""
+
+ @abstractmethod
+ def extract(self, pages: str, file_name: str) -> DocumentMetadata:
+ """Extract metadata from document content"""
+ pass
+
+class MetadataProcessor:
+ """Orchestrates metadata extraction using multiple extractors"""
+
+ def __init__(self):
+ self.extractors: List[MetadataExtractor] = []
+
+ def add_extractor(self, extractor: MetadataExtractor):
+ """Add a metadata extractor to the pipeline"""
+ self.extractors.append(extractor)
+
+ def process_document(self, document_content: str, file_name: str) -> DocumentMetadata:
+ """Process document through all extractors and combine results"""
+ metadata = DocumentMetadata()
+
+ for extractor in self.extractors:
+ try:
+ extracted = extractor.extract(document_content, file_name)
+ # Merge extracted metadata
+ for field in metadata.__dataclass_fields__:
+ value = getattr(extracted, field)
+ if value is not None:
+ setattr(metadata, field, value)
+ except Exception as e:
+ logging.error(f"Error in {extractor.__class__.__name__}: {str(e)}")
+
+ return metadata
+
+# Factory function to create a configured metadata processor
+def create_metadata_processor() -> MetadataProcessor:
+ """Create and configure a metadata processor with default extractors"""
+ processor = MetadataProcessor()
+ # processor.add_extractor(TitleExtractor())
+ # processor.add_extractor(DomainClassifier())
+ return processor
\ No newline at end of file
diff --git a/backend/src/llm.py b/backend/src/llm.py
index 854e5926f..838a2d1fc 100644
--- a/backend/src/llm.py
+++ b/backend/src/llm.py
@@ -13,22 +13,375 @@
from langchain_community.chat_models import ChatOllama
import boto3
import google.auth
+# added by ian
+from langchain_core.messages import AIMessage
+from typing import Dict, Optional
+from openai import OpenAI
+#end of added by ian
from src.shared.constants import ADDITIONAL_INSTRUCTIONS
from src.shared.llm_graph_builder_exception import LLMGraphBuilderException
import re
from typing import List
-def get_llm(model: str):
+# Added by ian
+from pydantic import BaseModel, Field
+from enum import Enum
+
+
+# Step 1: Define the Pydantic model for the function parameters
+class EducationLevelInput(BaseModel):
+ course: str = Field(
+ ...,
+ description="Call this tool when the user selects an education level. Provide the education level from the enum, for example grade9.",
+ enum=["grade9", "grade10", "grade11", "grade12"],
+ )
+
+
+# class LessonTopicInput(BaseModel):
+# lessonTopic: str = Field(..., description="Call this tool when the user selects an lesson topic. Provide a topic name for a lesson, e.g. History of Astronomy.")
+
+
+class LessonTopicInput(BaseModel):
+ lessonTopic: str = Field(
+ ...,
+ description=(
+ "Call this tool ONLY when the user explicitly selects a lesson topic, "
+ "such as saying 'I want to select lesson topic X' or 'I choose X'. "
+ "DO NOT call this tool when the user is asking for keywords or selecting keywords or subtopics."
+ "DO NOT call this tool when the user is asking for topic suggestions, "
+ "such as 'Can you suggest some topics?' or 'I want to teach a lesson related to X, "
+ "please suggest some related lesson topics.' "
+ "Provide the specific topic name the user has selected, e.g., 'History of Astronomy'."
+ ),
+ )
+
+
+# Define the enum of subjects
+class SubjectEnum(str, Enum):
+ Anatomy = "Anatomy"
+ Astronomy = "Astronomy"
+ Chemistry = "Chemistry"
+ US_History = "U.S. History"
+ Zoology = "Zoology"
+ History_of_Science = "History of Science"
+ Theater = "Theater"
+ Artificial_Intelligence = "Artificial Intelligence"
+ Mythology = "Mythology"
+ SEL = "Social and Emotional Learning"
+
+
+# Define the input model using Pydantic
+class SelectSubjectInput(BaseModel):
+ subject: SubjectEnum = Field(
+ ...,
+ description="Call this when the user selects a subject in their message. Get a subject from the enum, for example Chemistry.",
+ )
+
+
+# end of added by ian
+
+
+class OnGenerateKeywordsInput(BaseModel):
+ keywords: str = Field(
+ ...,
+ description=(
+ "ONLY call this tool when ALL of these conditions are met:\n"
+ "1. The MOST RECENT message was from the human/user (not from the assistant)\n"
+ "2. The user EXPLICITLY APPROVES previously suggested keywords\n"
+ "3. The user says something like 'yes, those keywords look good' or "
+ "4. The user is not making any changes to the keywords"
+ "'I approve these keywords' or 'let's use these keywords'\n\n"
+ "DO NOT call this tool when:\n"
+ "1. You (the assistant) are suggesting initial keywords to the user\n"
+ "2. The user asks for keyword suggestions\n"
+ "3. The user hasn't explicitly approved the keywords\n"
+ "4. The most recent message was from you (the assistant)\n\n"
+ "Example valid triggers (must be the most recent message):\n"
+ "- User: 'Yes, those keywords look good'\n"
+ "- User: 'I approve these keywords'\n"
+ "- User: 'Let's use these keywords'\n\n"
+ "The keywords should be provided as a comma-separated string of 3 terms that were "
+ "previously suggested and approved by the user."
+ ),
+ )
+
+ class Config:
+ schema_extra = {"example": {"keywords": "education, technology, AI"}}
+
+
+# OpenAI-compatible tool definitions
+OPENAI_TOOLS = [
+ # learning objective tool
+ {
+ "type": "function",
+ "function": {
+ "name": "onSelectTeacherIntent",
+ "description": (
+ "Call this tool when the user sets an intention for the content of the lesson. "
+ "Extract ONLY the content/subject matter intention, excluding any pedagogy style or lesson format preferences.\n\n"
+ "Examples:\n"
+ "- If user says: 'I want to teach a lesson on the scientific process. I want to use behaviourism pedagogy style'\n"
+ " → Return: 'I want to teach a lesson on the scientific process'\n"
+ "- If user says: 'I want to teach a lesson on safety precautions in the science lab using a constructivist pedagogy style'\n"
+ " → Return: 'I want to teach a lesson on safety precautions in the science lab'\n\n"
+ "Note: Only include the teaching content intention - omit any mentions of pedagogy style, teaching methods, or lesson format."
+ ),
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "teacherIntent": {
+ "type": "string",
+ "description": "The teacher's content intention for what the lesson should cover, excluding pedagogy style or format",
+ }
+ },
+ "required": ["teacherIntent"],
+ },
+ },
+ },
+ # {
+ # "type": "function",
+ # "function": {
+ # "name": "onSelectLearningObjective",
+ # "description": "Call this tool when the user selects a learning objective for the lesson.",
+ # "parameters": {
+ # "type": "object",
+ # "properties": {
+ # "learningObjective": {
+ # "type": "string",
+ # "description": "The learning objective for the lesson"
+ # }
+ # },
+ # "required": ["learningObjective"]
+ # }
+ # }
+ # },
+ # pedagogySelection tool
+ {
+ "type": "function",
+ "function": {
+ "name": "onSelectPedagogy",
+ "description": (
+ "Call this tool when the user specifies HOW they want to teach the lesson - their preferred teaching style, "
+ "format, or approach. This should capture the METHOD of teaching, not the lesson content itself.\n\n"
+ "Examples:\n"
+ "- If user says: 'I want to teach this through hands-on experiments' → Return: 'hands-on experiments'\n"
+ "- If user says: 'I want to use group discussions and peer learning' → Return: 'group discussions and peer learning'\n"
+ "- If user says: 'I want to teach about chemical reactions using demonstrations' → Return: 'demonstrations'\n\n"
+ "Note: Only include the teaching style/format - omit the actual lesson content or subject matter."
+ ),
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "pedagogyApproach": {
+ "type": "string",
+ "description": "The teaching style, format, or themed approach the teacher wants to use for delivering the lesson",
+ }
+ },
+ "required": ["pedagogyApproach"],
+ },
+ },
+ },
+ # {
+ # "type": "function",
+ # "function": {
+ # "name": "onSelectEducationLevel",
+ # "description": "Call this tool when the user selects an education level. Only provide the education levels from the enum, for example grade9.",
+ # "parameters": {
+ # "type": "object",
+ # "properties": {
+ # "course": {
+ # "type": "string",
+ # "enum": ["grade9", "grade10", "grade11", "grade12"]
+ # }
+ # },
+ # "required": ["course"]
+ # }
+ # }
+ # },
+ {
+ "type": "function",
+ "function": {
+ "name": "onSelectLessonTopic",
+ "description": (
+ "Call this tool ONLY when the user explicitly selects a lesson topic or a lesson summary. "
+ "if the user selects a lesson summary from a list, provide just the lesson title as the lesson topic. "
+ "such as saying 'I want to select lesson topic X', 'I want to select lesson summary X', or 'I choose X'. "
+ "DO NOT call this tool when the user is asking for keywords or selecting keywords or subtopics. "
+ "DO NOT call this tool when the user is asking for topic suggestions, "
+ "such as 'Can you suggest some topics?' or 'I want to teach a lesson related to X, "
+ "please suggest some related lesson topics.' "
+ "Provide the specific lesson topic name the user has selected, e.g., 'History of Astronomy'."
+ ),
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "lessonTopic": {
+ "type": "string",
+ "description": "The specific lesson topic selected by the user",
+ }
+ },
+ "required": ["lessonTopic"],
+ },
+ },
+ },
+ {
+ "type": "function",
+ "function": {
+ "name": "onSelectLessonSummary",
+ "description": (
+ "ONLY call this tool when ALL of these conditions are met:\n"
+ "1. The MOST RECENT message was from the human/user (not from the assistant)\n"
+ "2. The user EXPLICITLY SELECTS a specific lesson summary from a presented list\n"
+ "3. The user says something like 'I choose lesson summary X' or 'I want to select lesson summary X'\n\n"
+ "DO NOT call this tool when:\n"
+ "1. You (the assistant) are generating or suggesting lesson summaries\n"
+ "2. The user asks for lesson summary suggestions\n"
+ "3. The user hasn't explicitly selected a specific lesson summary\n"
+ "4. The most recent message was from you (the assistant)\n\n"
+ "Example valid triggers (must be the most recent message):\n"
+ "- User: 'I choose lesson summary 2'\n"
+ "- User: 'Let's go with the Chemical Reactions Lab summary'\n"
+ "- User: 'I want to select the second lesson summary'\n\n"
+ "When called, extract the lesson title from the selected summary for the lessonTopic parameter, "
+ "and include the complete lesson summary text in the lessonSummary parameter.\n\n"
+ "Example lesson summary format:\n"
+ '"2. **Lesson Summary 2: "Chemical Reactions Lab"**\n'
+ "- Key Concepts: Chemical Reactions, Variables, Data Analysis\n"
+ "- Key Vocabulary: Reactants, Products, Catalyst\n"
+ '- In this lab-based lesson, students design and conduct experiments..."'
+ ),
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "lessonSummary": {
+ "type": "string",
+ "description": "The complete lesson summary text that was selected by the user",
+ },
+ "lessonTopic": {
+ "type": "string",
+ "description": "The lesson title extracted from the selected summary",
+ },
+ },
+ "required": ["lessonTopic", "lessonSummary"],
+ },
+ },
+ },
+ # {
+ # "type": "function",
+ # "function": {
+ # "name": "onSelectSubject",
+ # "description": "Call this when the user selects a subject in their message. Get a subject from the enum, for example Chemistry.",
+ # "parameters": {
+ # "type": "object",
+ # "properties": {
+ # "subject": {
+ # "type": "string",
+ # "enum": [
+ # "Anatomy",
+ # "Astronomy",
+ # "Chemistry",
+ # "U.S. History",
+ # "Zoology",
+ # "History of Science",
+ # "Theater",
+ # "Artificial Intelligence",
+ # "Mythology",
+ # "Social and Emotional Learning"
+ # ]
+ # }
+ # },
+ # "required": ["subject"]
+ # }
+ # }
+ # },
+ # this onConfirmKeywords below was working well at NOT calling on generation, commented out for more experimentation
+ # {
+ # "type": "function",
+ # "function": {
+ # "name": "onConfirmKeywords",
+ # "description": (
+ # "STRICT REQUIREMENTS:\n"
+ # "1. LAST MESSAGE MUST BE role='user'\n"
+ # "2. NEVER call if last message is role='system' or role='assistant'\n"
+ # "3. ONLY call when user FULLY ACCEPTS ALL previous keywords WITHOUT ANY CHANGES\n\n"
+ # "Valid last messages (role='user' only):\n"
+ # "✓ 'Yes, those keywords look good'\n"
+ # "✓ 'I approve these keywords'\n"
+ # "✓ 'Let's use these keywords'\n\n"
+ # "DO NOT CALL if last message contains:\n"
+ # "✗ Any requests to modify keywords\n"
+ # "✗ Any suggestions for new keywords\n"
+ # "✗ Any requests to add/remove keywords\n"
+ # "✗ Any questions about the keywords"
+ # ),
+ # "parameters": {
+ # "type": "object",
+ # "properties": {
+ # "keywords": {
+ # "type": "string",
+ # "description": "Comma-separated string of the user-approved keywords"
+ # }
+ # },
+ # "required": ["keywords"]
+ # }
+ # }
+ # },
+ {
+ "type": "function",
+ "function": {
+ "name": "onUserAcceptsKeywords",
+ "description": (
+ "STRICT REQUIREMENTS:\n"
+ "1. LAST MESSAGE MUST BE role='user'\n"
+ "2. NEVER call if last message is role='system' or role='assistant'\n"
+ "3. ONLY call when the user has CLEARLY and UNAMBIGUOUSLY accepted ALL keywords WITHOUT ANY CHANGES\n\n"
+ "Valid last messages to trigger this tool (role='user' only):\n"
+ "✓ 'Yes, those keywords look good'\n"
+ "✓ 'I approve these keywords'\n"
+ "✓ 'Let's use these keywords'\n\n"
+ "DO NOT CALL if last message contains ANY of the following:\n"
+ "- ✗ Requests to modify, remove, or add keywords\n"
+ "- ✗ Suggestions for new keywords\n"
+ "- ✗ Questions, uncertainty, or dissatisfaction with the keywords\n"
+ "- ✗ Words like 'replace,' 'remove,' 'change,' 'adjust,' 'modify,' or 'not satisfied'\n"
+ "- ✗ Phrases such as:\n"
+ " - 'These keywords are not ready yet'\n"
+ " - 'Please remove the last one'\n"
+ " - 'Can you adjust this list?'\n"
+ " - 'I'm not sure about these keywords'\n"
+ " - 'I'm not satisfied with these keywords'\n"
+ "4. WHEN IN DOUBT, DO NOT CALL.\n"
+ ),
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "keywords": {
+ "type": "string",
+ "description": "Comma-separated string of the user-approved keywords",
+ }
+ },
+ "required": ["keywords"],
+ },
+ },
+ },
+]
+
+
+def get_llm(model: str, add_tools=False):
"""Retrieve the specified language model based on the model name."""
+
+ logging.info(f"get_llm called with add_tools {add_tools}")
model = model.lower().strip()
env_key = f"LLM_MODEL_CONFIG_{model}"
env_value = os.environ.get(env_key)
if not env_value:
- err = f"Environment variable '{env_key}' is not defined as per format or missing"
+ err = (
+ f"Environment variable '{env_key}' is not defined as per format or missing"
+ )
logging.error(err)
raise Exception(err)
-
+
logging.info("Model: {}".format(env_key))
try:
if "gemini" in model:
@@ -36,7 +389,7 @@ def get_llm(model: str):
credentials, project_id = google.auth.default()
llm = ChatVertexAI(
model_name=model_name,
- #convert_system_message_to_human=True,
+ # convert_system_message_to_human=True,
credentials=credentials,
project=project_id,
temperature=0,
@@ -50,7 +403,7 @@ def get_llm(model: str):
)
elif "openai" in model:
model_name, api_key = env_value.split(",")
- if "o3-mini" in model:
+ if "o3-mini" in model or "gpt-5" in model_name:
llm= ChatOpenAI(
api_key=api_key,
model=model_name)
@@ -59,7 +412,18 @@ def get_llm(model: str):
api_key=api_key,
model=model_name,
temperature=0,
+ )
+ if add_tools == True:
+ logging.info("binding tool")
+ llm = llm.bind_tools(
+ [
+ EducationLevelInput,
+ LessonTopicInput,
+ SelectSubjectInput,
+ OnGenerateKeywordsInput,
+ ]
)
+ logging.info("bound the tool")
elif "azure" in model:
model_name, api_endpoint, api_key, api_version = env_value.split(",")
@@ -88,7 +452,9 @@ def get_llm(model: str):
llm = ChatGroq(api_key=api_key, model_name=model_name, temperature=0)
elif "bedrock" in model:
- model_name, aws_access_key, aws_secret_key, region_name = env_value.split(",")
+ model_name, aws_access_key, aws_secret_key, region_name = env_value.split(
+ ","
+ )
bedrock_client = boto3.client(
service_name="bedrock-runtime",
region_name=region_name,
@@ -105,14 +471,14 @@ def get_llm(model: str):
llm = ChatOllama(base_url=base_url, model=model_name)
elif "diffbot" in model:
- #model_name = "diffbot"
+ # model_name = "diffbot"
model_name, api_key = env_value.split(",")
llm = DiffbotGraphTransformer(
diffbot_api_key=api_key,
extract_types=["entities", "facts"],
)
-
- else:
+
+ else:
model_name, api_endpoint, api_key = env_value.split(",")
llm = ChatOpenAI(
api_key=api_key,
@@ -124,7 +490,7 @@ def get_llm(model: str):
err = f"Error while creating LLM '{model}': {str(e)}"
logging.error(err)
raise Exception(err)
-
+
logging.info(f"Model created - Model Version: {model}")
return llm, model_name
@@ -163,16 +529,17 @@ def get_combined_chunks(chunkId_chunkDoc_list, chunks_to_combine):
)
return combined_chunk_document_list
+
def get_chunk_id_as_doc_metadata(chunkId_chunkDoc_list):
combined_chunk_document_list = [
- Document(
- page_content=document["chunk_doc"].page_content,
- metadata={"chunk_id": [document["chunk_id"]]},
- )
- for document in chunkId_chunkDoc_list
- ]
+ Document(
+ page_content=document["chunk_doc"].page_content,
+ metadata={"chunk_id": [document["chunk_id"]]},
+ )
+ for document in chunkId_chunkDoc_list
+ ]
return combined_chunk_document_list
-
+
async def get_graph_document_list(
llm, combined_chunk_document_list, allowedNodes, allowedRelationship, additional_instructions=None
@@ -202,11 +569,15 @@ async def get_graph_document_list(
ignore_tool_usage=ignore_tool_usage,
additional_instructions=ADDITIONAL_INSTRUCTIONS+ (additional_instructions if additional_instructions else "")
)
-
- if isinstance(llm,DiffbotGraphTransformer):
- graph_document_list = llm_transformer.convert_to_graph_documents(combined_chunk_document_list)
+
+ if isinstance(llm, DiffbotGraphTransformer):
+ graph_document_list = llm_transformer.convert_to_graph_documents(
+ combined_chunk_document_list
+ )
else:
- graph_document_list = await llm_transformer.aconvert_to_graph_documents(combined_chunk_document_list)
+ graph_document_list = await llm_transformer.aconvert_to_graph_documents(
+ combined_chunk_document_list
+ )
return graph_document_list
async def get_graph_from_llm(model, chunkId_chunkDoc_list, allowedNodes, allowedRelationship, chunks_to_combine, additional_instructions=None):
@@ -236,7 +607,6 @@ async def get_graph_from_llm(model, chunkId_chunkDoc_list, allowedNodes, allowed
logging.info(f"Allowed relationships: {allowed_relationships}")
else:
logging.info("No allowed relationships provided")
-
graph_document_list = await get_graph_document_list(
llm,
combined_chunk_document_list,
@@ -271,3 +641,88 @@ def sanitize_additional_instruction(instruction: str) -> str:
# Step 4: Normalize spaces
instruction = re.sub(r'\s+', ' ', instruction).strip()
return instruction
+
+def create_chat_completion_sync(
+ messages: List[Dict[str, str]],
+ model: str = "openai_gpt_4o_mini",
+ add_tools: bool = False,
+ temperature: float = 0,
+) -> AIMessage:
+ """
+ Synchronous version of create_chat_completion using OpenAI's synchronous client.
+ Returns an AIMessage object matching Langchain's ChatOpenAI output format.
+ """
+ try:
+ # Get model configuration
+ env_key = f"LLM_MODEL_CONFIG_{model.lower().strip()}"
+ env_value = os.environ.get(env_key)
+
+ if not env_value:
+ err = f"Environment variable '{env_key}' is not defined"
+ logging.error(err)
+ raise Exception(err)
+
+ # Parse model config
+ if "openai" in model.lower():
+ model_name, api_key = env_value.split(",")
+ else:
+ model_name, api_endpoint, api_key = env_value.split(",")
+
+ # Initialize client
+ client = OpenAI(
+ api_key=api_key,
+ base_url=api_endpoint if "openai" not in model.lower() else None,
+ )
+
+ # Prepare request parameters
+ completion_params = {
+ "model": model_name,
+ "messages": messages,
+ # "temperature": temperature, # commented out so only add if not gpt-5
+ }
+
+ if not "gpt-5" in model_name:
+ completion_params["temperature"] = temperature
+
+ # Add tools if requested
+ if add_tools:
+ logging.info("Adding tool_choice = auto")
+ completion_params["tools"] = OPENAI_TOOLS
+ completion_params["tool_choice"] = "auto"
+
+ # Make the API call
+ response = client.chat.completions.create(**completion_params)
+
+ # Extract content and tool calls
+ content = (
+ response.choices[0].message.content or ""
+ ) # Default to empty string if None
+ additional_kwargs = {}
+
+ # Handle tool calls if present
+ if (
+ hasattr(response.choices[0].message, "tool_calls")
+ and response.choices[0].message.tool_calls
+ ):
+ additional_kwargs["tool_calls"] = [
+ {
+ "id": tool_call.id,
+ "type": "function",
+ "function": {
+ "name": tool_call.function.name,
+ "arguments": tool_call.function.arguments,
+ },
+ }
+ for tool_call in response.choices[0].message.tool_calls
+ ]
+
+ # Return in Langchain's AIMessage format
+ return AIMessage(
+ content=content, # Will never be None now
+ additional_kwargs=additional_kwargs,
+ )
+
+ except Exception as e:
+ err = f"Error in create_chat_completion_sync: {str(e)}"
+ logging.error(err)
+ raise Exception(err)
\ No newline at end of file
diff --git a/backend/src/main.py b/backend/src/main.py
index 4bdb6ba51..5b8469c73 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -32,6 +32,7 @@
import urllib.parse
import json
from src.shared.llm_graph_builder_exception import LLMGraphBuilderException
+from src.document_metadata_extractor import create_metadata_processor, DocumentMetadata
warnings.filterwarnings("ignore")
load_dotenv()
@@ -322,6 +323,16 @@ async def processing_source(uri, userName, password, database, model, file_name,
uri_latency["create_connection"] = f'{elapsed_create_connection:.2f}'
graphDb_data_Access = graphDBdataAccess(graph)
create_chunk_vector_index(graph)
+
+ # Process document metadata
+ # if pages and len(pages) > 0:
+ # metadata_processor = create_metadata_processor()
+ # # document_content = " ".join([page.page_content for page in pages])
+ # metadata = metadata_processor.process_document(pages, file_name)
+ # logging.info(f"Extracted metadata for {file_name}: {metadata}")
+ # else:
+ # metadata = DocumentMetadata()
+
start_get_chunkId_chunkDoc_list = time.time()
total_chunks, chunkId_chunkDoc_list = get_chunkId_chunkDoc_list(graph, file_name, pages, token_chunk_size, chunk_overlap, retry_condition)
end_get_chunkId_chunkDoc_list = time.time()
@@ -330,6 +341,25 @@ async def processing_source(uri, userName, password, database, model, file_name,
uri_latency["create_list_chunk_and_document"] = f'{elapsed_get_chunkId_chunkDoc_list:.2f}'
uri_latency["total_chunks"] = total_chunks
+ # Add hardcoded label to Document node
+ # uncomment this bloc when ingesting - Ian
+ # start_add_hardcoded_label = time.time()
+ # add_hardcoded_label_to_document(graph, file_name, "persona_FIRST_LINE_MANAGER")
+ # end_add_hardcoded_label = time.time()
+ # elapsed_add_hardcoded_label = end_add_hardcoded_label - start_add_hardcoded_label
+ # logging.info(f'Time taken to add hardcoded label to document: {elapsed_add_hardcoded_label:.2f} seconds')
+ # uri_latency["add_hardcoded_label"] = f'{elapsed_add_hardcoded_label:.2f}'
+
+ # this was autogenerated by cursor - we may want to write the data to the document differenttly
+ # Update document node with metadata
+ # if metadata.domain:
+ # query = """
+ # MATCH (d:Document {fileName: $file_name})
+ # SET d.domain = $domain
+ # """
+ # graph.query(query, {"file_name": file_name, "domain": metadata.domain})
+ # end of cursor generated code
+
start_status_document_node = time.time()
result = graphDb_data_Access.get_current_status_document_node(file_name)
end_status_document_node = time.time()
@@ -765,4 +795,26 @@ def failed_file_process(uri,file_name, merged_file_path):
delete_file_from_gcs(BUCKET_UPLOAD,folder_name,file_name)
else:
logging.info(f'Deleted File Path: {merged_file_path} and Deleted File Name : {file_name}')
- delete_uploaded_local_file(merged_file_path,file_name)
\ No newline at end of file
+ delete_uploaded_local_file(merged_file_path,file_name)
+
+def add_hardcoded_label_to_document(graph, file_name, label):
+ """
+ Add a hardcoded label to the Document node for the given file.
+
+ Args:
+ graph: Neo4jGraph connection object
+ file_name: Name of the file to add label to
+ label: The label to add to the Document node
+ """
+ try:
+ # Add a hardcoded label to the Document node
+ # Using string formatting for the label since Cypher doesn't support parameterized labels
+ query = f"""
+ MATCH (d:Document {{fileName: $file_name}})
+ SET d:Document:{label}
+ """
+ graph.query(query, {"file_name": file_name})
+ logging.info(f"Successfully added hardcoded label '{label}' to Document node for file: {file_name}")
+ except Exception as e:
+ logging.error(f"Error adding hardcoded label '{label}' to Document node for file {file_name}: {e}")
+ # Don't raise the exception to avoid stopping the processing pipeline
\ No newline at end of file
diff --git a/backend/src/shared/constants.py b/backend/src/shared/constants.py
index 30cedfdfd..820fa0e76 100644
--- a/backend/src/shared/constants.py
+++ b/backend/src/shared/constants.py
@@ -260,6 +260,7 @@
}
### CHAT TEMPLATES
+## THIS IS ORIGINAL
CHAT_SYSTEM_TEMPLATE = """
You are an AI-powered question-answering agent. Your task is to provide accurate and comprehensive responses to user queries based on the given context, chat history, and available resources.
@@ -302,6 +303,46 @@
Note: This system does not generate answers based solely on internal knowledge. It answers from the information provided in the user's current and previous inputs, and from the context.
"""
+## this one below is MODIFIED by ian to take out the line saying don't use your knowledge base
+CHAT_SYSTEM_TEMPLATE_UNGROUNDED = """
+You are an AI-powered question-answering agent. Your task is to provide accurate and comprehensive responses to user queries based on the given context, chat history, and available resources.
+
+### Response Guidelines:
+1. **Direct Answers**: Provide clear and thorough answers to the user's queries without headers unless requested. Avoid speculative responses.
+2. **Utilize History and Context**: Leverage relevant information from previous interactions, the current user input, and the context provided below.
+3. **No Greetings in Follow-ups**: Start with a greeting in initial interactions. Avoid greetings in subsequent responses unless there's a significant break or the chat restarts.
+4. **Admit Unknowns**: Clearly state if an answer is unknown. Avoid making unsupported statements.
+5. **Avoid Hallucination**: Only provide information based on the context provided. Do not invent information.
+6. **Response Length**: Keep responses concise and relevant. Aim for clarity and completeness within 4-5 sentences unless more detail is requested.
+7. **Tone and Style**: Maintain a professional and informative tone. Be friendly and approachable.
+8. **Error Handling**: If a query is ambiguous or unclear, ask for clarification rather than providing a potentially incorrect answer.
+
+
+
+### Context:
+
+{context}
+
+
+### Example Responses:
+User: Hi
+AI Response: 'Hello there! How can I assist you today?'
+
+User: "What is Langchain?"
+AI Response: "Langchain is a framework that enables the development of applications powered by large language models, such as chatbots. It simplifies the integration of language models into various applications by providing useful tools and components."
+
+User: "Can you explain how to use memory management in Langchain?"
+AI Response: "Langchain's memory management involves utilizing built-in mechanisms to manage conversational context effectively. It ensures that the conversation remains coherent and relevant by maintaining the history of interactions and using it to inform responses."
+
+User: "I need help with PyCaret's classification model."
+AI Response: "PyCaret simplifies the process of building and deploying machine learning models. For classification tasks, you can use PyCaret's setup function to prepare your data. After setup, you can compare multiple models to find the best one, and then fine-tune it for better performance."
+
+User: "What can you tell me about the latest realtime trends in AI?"
+AI Response: "I don't have that information right now. Is there something else I can help with?"
+
+"""
+
+
QUESTION_TRANSFORM_TEMPLATE = "Given the below conversation, generate a search query to look up in order to get information relevant to the conversation. Only respond with the query, nothing else."
## CHAT QUERIES
diff --git a/deploy.sh b/deploy.sh
new file mode 100755
index 000000000..014f5c899
--- /dev/null
+++ b/deploy.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Variables - Replace with your actual values
+
+
+# uncomment htis for 'dev' (labs llm-graphbuilder)
+# AWS_REGION="us-east-1"
+# AWS_ENV="dev"
+# AWS_ACCOUNT_ID="288761770129" # 288 is graphbuilder-dev
+
+# uncomment this for 'metrix-demo-dev' (metrix-demo-dev)
+AWS_REGION="us-east-2"
+AWS_ENV="metrix-demo-dev"
+AWS_ACCOUNT_ID="860839672899"
+
+ECR_REPOSITORY="graphbuilder-docker-repo-$AWS_ENV"
+ENV_FILE="./${AWS_ENV}.env"
+echo "Using ENV_FILE $ENV_FILE"
+TAG="latest" # You can change this if you need a specific tag
+
+folder_name=$(basename "$PWD")
+
+echo "Using AWS_PROFILE $AWS_PROFILE"
+echo "Using AWS_ENV $AWS_ENV"
+echo "Using ECR_REPOSITORY $ECR_REPOSITORY"
+
+# 1. Load environment variables from .env file
+echo "Loading environment variables from $ENV_FILE..."
+set -a # automatically export all variables
+source $ENV_FILE
+set +a # turn off automatic export
+
+# 2. Build Docker images using Docker Compose
+echo "Building Docker images..."
+docker-compose build --no-cache --progress=plain
+# docker-compose build
+
+# 2. Authenticate Docker to AWS ECR
+echo "Authenticating to AWS ECR..."
+aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com
+
+# 3. Tag Docker images for ECR
+echo "Tagging Docker images..."
+for service in $(docker-compose config --services); do
+ # Get the image name (service name) from Docker Compose
+ IMAGE_NAME="${folder_name}_${service}:${TAG}"
+ ECR_IMAGE="$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPOSITORY/$service:$TAG"
+
+ echo "Tagging image $IMAGE_NAME as $ECR_IMAGE..."
+ docker tag $IMAGE_NAME $ECR_IMAGE
+done
+
+echo "Docker images build successfully!"
+
+# 4. Push Docker images to AWS ECR
+echo "Pushing Docker images to ECR..."
+for service in $(docker-compose config --services); do
+ ECR_IMAGE="$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPOSITORY/$service:$TAG"
+
+ echo "Pushing $ECR_IMAGE..."
+ docker push $ECR_IMAGE
+done
+
+echo "Docker images pushed to ECR successfully!"
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
index 4b166f490..0702dd12e 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -11,7 +11,7 @@ services:
- NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
- NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
- NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
- - OPENAI_API_KEY=${OPENAI_API_KEY-}
+ # - OPENAI_API_KEY=${OPENAI_API_KEY-}
- DIFFBOT_API_KEY=${DIFFBOT_API_KEY-}
- EMBEDDING_MODEL=${EMBEDDING_MODEL-all-MiniLM-L6-v2}
- LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-}
@@ -34,6 +34,10 @@ services:
# - LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet=${LLM_MODEL_CONFIG_bedrock_claude_3_5_sonnet-}
# - LLM_MODEL_CONFIG_fireworks_qwen_72b=${LLM_MODEL_CONFIG_fireworks_qwen_72b-}
- LLM_MODEL_CONFIG_ollama_llama3=${LLM_MODEL_CONFIG_ollama_llama3-}
+
+ # this env_file line added by ian
+ env_file:
+ - ./backend/.env
container_name: backend
extra_hosts:
- host.docker.internal:host-gateway
@@ -50,6 +54,7 @@ services:
dockerfile: Dockerfile
args:
- VITE_BACKEND_API_URL=${VITE_BACKEND_API_URL-http://localhost:8000}
+ # - VITE_BACKEND_API_URL=http://3.216.126.151:8000
- VITE_REACT_APP_SOURCES=${VITE_REACT_APP_SOURCES-local,wiki,s3}
- VITE_GOOGLE_CLIENT_ID=${VITE_GOOGLE_CLIENT_ID-}
- VITE_BLOOM_URL=${VITE_BLOOM_URL-https://workspace-preview.neo4j.io/workspace/explore?connectURL={CONNECT_URL}&search=Show+me+a+graph&featureGenAISuggestions=true&featureGenAISuggestionsInternal=true}
@@ -72,7 +77,8 @@ services:
- ./frontend:/app
- /app/node_modules
env_file:
- - ./frontend/.env
+ # - ./frontend/dev.env
+ - ./frontend/metrix-demo-dev.env
container_name: frontend
ports:
- "8080:8080"
diff --git a/frontend/public/favicons/favicon-16x16.png b/frontend/public/favicons/favicon-16x16.png
index 5c1ca662d..6e3a9a90c 100644
Binary files a/frontend/public/favicons/favicon-16x16.png and b/frontend/public/favicons/favicon-16x16.png differ
diff --git a/frontend/public/favicons/favicon-194x194.png b/frontend/public/favicons/favicon-194x194.png
deleted file mode 100644
index 378c74cd3..000000000
Binary files a/frontend/public/favicons/favicon-194x194.png and /dev/null differ
diff --git a/frontend/public/favicons/favicon-32x32.png b/frontend/public/favicons/favicon-32x32.png
index 55bab3058..67968e9da 100644
Binary files a/frontend/public/favicons/favicon-32x32.png and b/frontend/public/favicons/favicon-32x32.png differ
diff --git a/frontend/public/favicons/favicon.ico b/frontend/public/favicons/favicon.ico
index e6d2fdef4..679c89688 100644
Binary files a/frontend/public/favicons/favicon.ico and b/frontend/public/favicons/favicon.ico differ
diff --git a/frontend/src/logo-color.svg b/frontend/src/logo-color.svg
index 0d9f1b264..07f02f254 100644
--- a/frontend/src/logo-color.svg
+++ b/frontend/src/logo-color.svg
@@ -1,9 +1,20 @@
-
+
+
\ No newline at end of file
diff --git a/frontend/src/logo.svg b/frontend/src/logo.svg
index d0c86c5f5..d0eeaa350 100644
--- a/frontend/src/logo.svg
+++ b/frontend/src/logo.svg
@@ -1,32 +1,27 @@
-
-
-