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8a682b5 | 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 | """
Semantic search tool implementation.
"""
import os
from typing import Optional, List, Dict, Any
from langchain_core.tools import tool
from pydantic import BaseModel, Field
import pandas as pd
import logging
logger = logging.getLogger(__name__)
# Try to import sentence transformers with fallback
try:
from sentence_transformers import SentenceTransformer
import numpy as np
SENTENCE_TRANSFORMERS_AVAILABLE = True
except ImportError:
SENTENCE_TRANSFORMERS_AVAILABLE = False
logger.warning("sentence_transformers not available - semantic search disabled")
class SemanticSearchInput(BaseModel):
"""Input schema for semantic search tool."""
query: str = Field(description="Search query")
filename: str = Field(description="Path to the knowledge base file")
top_k: int = Field(default=3, description="Number of results to return")
@tool
def semantic_search_tool(query: str, filename: str, top_k: int = 3) -> str:
"""
Perform semantic search on a knowledge base.
Args:
query (str): Search query
filename (str): Path to the knowledge base file
top_k (int): Number of results to return
Returns:
str: Search results or error message
"""
if not SENTENCE_TRANSFORMERS_AVAILABLE:
return "Error: sentence_transformers not available. Please install with: pip install sentence-transformers"
try:
if not os.path.exists(filename):
return f"Error: Knowledge base file not found: {filename}"
# Load knowledge base
df = pd.read_csv(filename)
# Initialize sentence transformer
model = SentenceTransformer('all-MiniLM-L6-v2')
# Encode query
query_embedding = model.encode(query)
# Encode documents
document_embeddings = model.encode(df['text'].tolist())
# Calculate similarities
similarities = np.dot(document_embeddings, query_embedding) / (
np.linalg.norm(document_embeddings, axis=1) * np.linalg.norm(query_embedding)
)
# Get top k results
top_indices = np.argsort(similarities)[-top_k:][::-1]
# Format results
results = []
for idx in top_indices:
results.append({
'text': df.iloc[idx]['text'],
'similarity': float(similarities[idx])
})
return str(results)
except Exception as e:
return f"Error performing semantic search: {str(e)}" |