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"""
Multi-Source Retrieval Agent
Intelligently decides which sources to query based on query type
"""
import os
from dotenv import load_dotenv
from groq import Groq
from hybrid_search import HybridSearch
from sentence_transformers import SentenceTransformer
load_dotenv()
class RetrievalAgent:
def __init__(self, chromadb_collection, groq_api_key=None):
"""Initialize Retrieval Agent"""
print("🔍 Initializing Multi-Source Retrieval Agent...\n")
self.groq_client = Groq(api_key=groq_api_key)
self.model_name = "llama-3.3-70b-versatile"
self.collection = chromadb_collection
self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
# Initialize retrieval sources
all_docs = self._get_all_documents()
self.hybrid_search = HybridSearch(all_docs)
self.classification_prompt = """Analyze this query and classify it:
QUERY: "{query}"
Determine:
1. Query Type: factual, conceptual, procedural, comparative
2. Information Need: general knowledge, specific details, step-by-step guide, comparison
3. Search Strategy: broad (many results), narrow (specific results), mixed
Respond in this format ONLY:
TYPE: [type]
NEED: [need]
STRATEGY: [strategy]"""
print("✅ Retrieval Agent ready!\n")
def _get_all_documents(self):
"""Get all documents from ChromaDB collection"""
try:
results = self.collection.get()
docs = []
for i, doc in enumerate(results['documents']):
docs.append(doc)
return docs
except:
return []
def classify_query(self, query):
"""Use LLM to classify query for optimal retrieval strategy"""
print(f"📊 Classifying query: '{query}'")
try:
response = self.groq_client.chat.completions.create(
messages=[
{
"role": "user",
"content": self.classification_prompt.format(query=query)
}
],
model=self.model_name,
temperature=0.3,
max_tokens=100
)
classification = response.choices[0].message.content.strip()
print(f"✅ Classification:\n{classification}\n")
return classification
except Exception as e:
print(f"❌ Classification error: {e}\n")
return "TYPE: mixed\nNEED: general\nSTRATEGY: mixed"
def vector_search(self, query, top_k=5):
"""Search using vector embeddings (semantic similarity)"""
print(f" 📌 Performing vector search...")
try:
query_embedding = self.embedding_model.encode([query])[0]
results = self.collection.query(
query_embeddings=[query_embedding.tolist()],
n_results=top_k
)
vector_results = []
if results and results['documents']:
for i, doc in enumerate(results['documents'][0]):
vector_results.append({
'index': i,
'content': doc,
'source': results['metadatas'][0][i]['source_file'],
'score': 1 - results['distances'][0][i],
'method': 'vector_search'
})
print(f" ✓ Found {len(vector_results)} results via vector search")
return vector_results
except Exception as e:
print(f" ✗ Vector search error: {e}")
return []
def bm25_search(self, query, top_k=5):
"""Search using BM25 (keyword matching)"""
print(f" 📌 Performing BM25 search...")
try:
bm25_results = self.hybrid_search.bm25_search(query, top_k)
# Get all documents to find sources
all_results = self.collection.get()
doc_to_source = {}
if all_results and all_results['metadatas']:
for i, metadata in enumerate(all_results['metadatas']):
if i < len(all_results['documents']):
doc_text = all_results['documents'][i][:50] # First 50 chars as key
doc_to_source[doc_text] = metadata.get('source_file', 'unknown')
formatted_results = []
for result in bm25_results:
# Normalize BM25 score (typically 0-100, divide by 100)
normalized_score = min(result['score'] / 100.0, 1.0)
# Find source
doc_preview = result['content'][:50]
source = 'unknown'
for key, val in doc_to_source.items():
if key in result['content']:
source = val
break
formatted_results.append({
'index': result['index'],
'content': result['content'],
'source': source,
'score': normalized_score,
'method': 'bm25_search'
})
print(f" ✓ Found {len(formatted_results)} results via BM25")
return formatted_results
except Exception as e:
print(f" ✗ BM25 search error: {e}")
return []
def retrieve(self, query, top_k=5):
"""
Main retrieval method: intelligently combines multiple sources
"""
print(f"\n🔍 RETRIEVING FOR QUERY: '{query}'")
print("-" * 70)
# Step 1: Classify query
classification = self.classify_query(query)
# Step 2: Decide which sources to use
use_vector = True # Always use vector
use_bm25 = True # Always use BM25
all_results = []
print(f"🔎 Searching sources:")
# Step 3: Search vector database
if use_vector:
vector_results = self.vector_search(query, top_k)
all_results.extend(vector_results)
# Step 4: Search BM25
if use_bm25:
bm25_results = self.bm25_search(query, top_k)
all_results.extend(bm25_results)
# Step 5: Deduplicate and rank
seen = set()
unique_results = []
for result in all_results:
content_hash = hash(result['content'][:100])
if content_hash not in seen:
seen.add(content_hash)
unique_results.append(result)
# Sort by score (descending)
unique_results.sort(key=lambda x: x['score'], reverse=True)
final_results = unique_results[:top_k]
print(f"\n✅ Retrieved {len(final_results)} unique documents")
print("-" * 70 + "\n")
return final_results
# Test the agent
if __name__ == "__main__":
import chromadb
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")
# Connect to ChromaDB
client = chromadb.PersistentClient(path="data/vectordb")
collection = client.get_collection(name="documents")
# Initialize agent
agent = RetrievalAgent(collection, groq_api_key=api_key)
# Test queries
test_queries = [
"How do I create a FastAPI endpoint?",
"What is the leave policy?",
"Remote work guidelines"
]
print("=" * 70)
print("🔍 MULTI-SOURCE RETRIEVAL AGENT TEST")
print("=" * 70)
for query in test_queries:
results = agent.retrieve(query, top_k=3)
print(f"Results for '{query}':")
for i, result in enumerate(results, 1):
print(f" {i}. [{result['method']}] Score: {result['score']:.2f}")
print()
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