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52c281c 0e99494 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import os
from dotenv import load_dotenv
from google import genai
from google.genai import types
from pinecone import Pinecone
load_dotenv()
# Initialize Pinecone
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index = pc.Index("portfolio-chat")
# Initialize Google GenAI Client
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
# Constants
EMBEDDING_MODEL = "gemini-embedding-001"
LLM_MODEL = "gemini-2.5-flash-lite"
EMBEDDING_DIMENSION = 768
def get_embedding(text: str) -> list[float]:
"""Generate embedding for a given text using Gemini embedding model."""
try:
response = client.models.embed_content(
model=EMBEDDING_MODEL,
contents=text,
config=types.EmbedContentConfig(
output_dimensionality=EMBEDDING_DIMENSION
)
)
return response.embeddings[0].values
except Exception as e:
print(f"Error generating embedding: {e}")
return []
def get_rag_response(query: str) -> str:
"""
RAG pipeline: embed query, retrieve context from Pinecone, generate response.
"""
try:
# Step 1: Embed the query
query_embedding = get_embedding(query)
if not query_embedding:
return "I'm having a little trouble accessing my brain right now. Please try again!"
# Step 2: Query Pinecone for top 5 matches
results = index.query(
vector=query_embedding,
top_k=10,
include_metadata=True
)
# Step 3: Extract context from matches
context_chunks = []
for match in results.matches:
if match.metadata and "text" in match.metadata:
context_chunks.append(match.metadata["text"])
# Handle case where no context is found
if not context_chunks:
return "I couldn't find any specific details about that in Mrigank's portfolio, but feel free to ask about his patents, DASES, or other projects!"
# Join chunks to create the context text
context_text = "\n\n---\n\n".join(context_chunks)
# Step 4: Construct the system prompt
system_prompt = f"""You are the Advanced AI Assistant for **Mrigank Singh**, a Full Stack AI Developer and Innovator.
Your goal is to impress recruiters and engineers by accurately showcasing Mrigank's technical depth, innovation, and leadership.
### CORE INSTRUCTIONS:
1. **Identity:** You are NOT Mrigank. You are his digital assistant. Refer to him as "Mrigank" or "he".
2. **Tone:** Professional, confident, and technically precise. Sound like a Software Engineer, not a marketing brochure.
3. **Formatting:** Use **Markdown** to make answers readable.
- Use **bold** for key technologies or metrics.
- Use `bullet points` for lists (skills, projects).
- Do not output large walls of text; break it up.
4. **Source of Truth:** Answer ONLY based on the "CONTEXT" provided below. Do not make up facts.
- If the answer isn't in the context, say: "I don't have that specific detail, but I can tell you about his patents, his projects or more about him."
### CRITICAL BEHAVIORS:
- **Recruiters:** If asked about hiring, availability, or contact info, explicitly provide his **Email** and **LinkedIn** from the context.
- **Patents:** If asked about innovation, ALWAYS mention his 3 filed patents (Terms & Conditions AI, LexiBot, MealMatch).
- **Group Projects:** Credit **Konal Puri and Aviral Khanna** for DASES/UPES Career Platform. Specify Mrigank's role (Mobile App/Frontend).
- **Technical Depth:** Mention specific algorithms (e.g., "Knapsack Pruning", "Isolation Forests", "Regex Chunking") to show engineering depth.
### CONTEXT FROM KNOWLEDGE BASE:
{context_text}
"""
# Step 5: Generate response using Gemini
response = client.models.generate_content(
model=LLM_MODEL,
contents=[
types.Content(
role="user",
parts=[
types.Part.from_text(text=system_prompt + "\n\nUser Question: " + query)
]
)
],
config=types.GenerateContentConfig(
temperature=0.7,
max_output_tokens=1024
)
)
return response.text
except Exception as e:
print(f"Error in RAG pipeline: {e}")
return "I'm encountering a temporary issue connecting to the knowledge base. Please try again in a moment." |