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import os
import torch
from qdrant_client import QdrantClient, models
from sentence_transformers import SentenceTransformer, CrossEncoder
from pymongo import MongoClient
from bson import ObjectId
from typing import List, Dict
import google.generativeai as genai
from groq import Groq
def build_content(doc: dict, entity_type: str) -> str:
"""Convert MongoDB document into natural text for embeddings."""
parts = [f"{entity_type} ID: {doc.get('id', str(doc.get('_id', '')))}"]
for k, v in doc.items():
if k in ["_id"]: # skip ObjectId
continue
if isinstance(v, list):
parts.append(f"{k}: {', '.join(map(str, v))}")
elif isinstance(v, dict):
nested = "; ".join([f"{nk}: {nv}" for nk, nv in v.items() if nv])
parts.append(f"{k}: {nested}")
else:
if v:
parts.append(f"{k}: {v}")
return "\n".join(parts)
class ErrorBot:
"""Chatbot using RAG (Qdrant + Gemini API)."""
def __init__(self, embedding_model_name: str, llm_model_name: str, google_api_key: str):
print("🚀 Initializing ErrorBot...")
# --- Embedding model
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {self.device}")
self.embedding_model = SentenceTransformer(embedding_model_name, device=self.device)
self.embedding_dim = self.embedding_model.get_sentence_embedding_dimension()
# --- Qdrant client
print("Connecting to Qdrant...")
self.qdrant = QdrantClient(
url=os.getenv("QDRANT_URL"),
api_key=os.getenv("QDRANT_API_KEY"),
)
self.collection_name = "technical_errors"
self._setup_collection()
# --- Gemini LLM
genai.configure(api_key=google_api_key)
self.llm_model_name = llm_model_name
self.llm = genai.GenerativeModel(llm_model_name)
# --- Cross encoder reranker
print("Loading cross-encoder reranker...")
self.reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
print("✅ ErrorBot ready.")
def _setup_collection(self):
if not self.qdrant.collection_exists(self.collection_name):
self.qdrant.create_collection(
collection_name=self.collection_name,
vectors_config=models.VectorParams(
size=self.embedding_dim,
distance=models.Distance.COSINE,
),
)
def ingest_from_mongodb(self, mongo_uri: str, db_name: str, batch_size: int = 32):
client = MongoClient(mongo_uri)
db = client[db_name]
collections = {
"ProblemReport": db["problemReports"],
"FaultAnalysis": db["faultanalysis"],
"Correction": db["corrections"],
}
docs = []
for entity_type, coll in collections.items():
for doc in coll.find():
if "_id" in doc and isinstance(doc["_id"], ObjectId):
doc["_id"] = str(doc["_id"])
docs.append({"entity_type": entity_type, "data": doc})
contents = [build_content(d["data"], d["entity_type"]) for d in docs]
all_embeddings = []
for i in range(0, len(contents), batch_size):
batch_contents = contents[i:i + batch_size]
embeddings = self.embedding_model.encode(batch_contents, show_progress_bar=True).tolist()
all_embeddings.extend(embeddings)
self.qdrant.upsert(
collection_name=self.collection_name,
points=[
models.PointStruct(
id=i,
vector=emb,
payload={
"id": d["data"].get("id", str(d["data"].get("_id", i))),
"entity_type": d["entity_type"],
"raw": d["data"],
"content": c,
},
)
for i, (d, emb, c) in enumerate(zip(docs, all_embeddings, contents))
],
wait=True,
)
print(f"✅ Ingested {len(docs)} documents into '{self.collection_name}'")
def retrieve(self, query: str, top_k: int = 5, score_threshold: float = 0.3, rerank: bool = True):
query_embedding = self.embedding_model.encode(query).tolist()
hits = self.qdrant.query_points(
collection_name=self.collection_name,
query=query_embedding,
limit=top_k * 3 if rerank else top_k,
with_payload=True,
score_threshold=score_threshold,
).points
candidates = [
{
"id": hit.payload.get("id"),
"entity_type": hit.payload.get("entity_type", ""),
"content": hit.payload.get("content", ""),
"score": hit.score,
}
for hit in hits
]
if rerank and candidates:
pairs = [(query, c["content"]) for c in candidates]
scores = self.reranker.predict(pairs)
for i, score in enumerate(scores):
candidates[i]["rerank_score"] = float(score)
candidates = sorted(candidates, key=lambda x: x["rerank_score"], reverse=True)
return candidates[:top_k]
def generate_answer(self, query: str, context: List[Dict], history: list = None):
context_str = "\n---\n".join(
[f"{c['entity_type']} (Score: {c['score']:.2f}):\n{c['content']}" for c in context]
)
convo_str = ""
if history:
for msg in history:
role = "User" if msg["role"] == "user" else "Assistant"
convo_str += f"{role}: {msg['content']}\n"
convo_str += f"User: {query}\nAssistant:"
prompt = f"""
You are a technical assistant. You have access to Problem Reports (PR), Fault Analyses (FA), and Corrections (CR).
Use the provided context and conversation history to answer the question clearly and concisely.
If context is not relevant, say you do not have enough information.
### Context
{context_str}
### Conversation
{convo_str}
"""
response = self.llm.generate_content(prompt)
return response.text.strip()
def ask(self, query: str, history: list = None):
print(f"\n❓ Query: {query}")
retrieved_context = self.retrieve(query)
if not retrieved_context:
print("💬 No relevant context found.")
return "I could not find any relevant information."
print(f"✅ Retrieved {len(retrieved_context)} documents.")
for i, doc in enumerate(retrieved_context):
print(f" - Context {i+1} ({doc['entity_type']}, ID: {doc['id']}, Score: {doc['score']:.2f})")
answer = self.generate_answer(query, retrieved_context, history)
print(f"\n🤖 Answer: {answer}")
return answer
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