fix: resolve import path errors causing module load failures

examples/basic_usage.py

@@ -1,6 +1,5 @@
 """Basic usage example for insta_rag library."""
 
-import os
 from pathlib import Path
 
 from dotenv import load_dotenv
@@ -93,7 +92,7 @@ def main():
         metadata={"project": "insta_rag_demo"},
     )
 
-    print(f"\n✓ Documents processed successfully!")
+    print("\n✓ Documents processed successfully!")
     print(f"  - Documents processed: {response.documents_processed}")
     print(f"  - Total chunks created: {response.total_chunks}")
     print(f"  - Total tokens: {response.processing_stats.total_tokens}")
@@ -103,12 +102,12 @@ def main():
     print(f"  - Total time: {response.processing_stats.total_time_ms:.2f}ms")
 
     if response.errors:
-        print(f"\n⚠ Errors encountered:")
+        print("\n⚠ Errors encountered:")
         for error in response.errors:
             print(f"  - {error}")
 
     # Display chunk information
-    print(f"\nChunk Details:")
+    print("\nChunk Details:")
     for i, chunk in enumerate(response.chunks[:3]):  # Show first 3 chunks
         print(f"\nChunk {i + 1}:")
         print(f"  - ID: {chunk.chunk_id}")

src/insta_rag/chunking/semantic.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-from ..utils.exceptions import ChunkingError
+from insta_rag.utils.exceptions import ChunkingError
 from ..models.chunk import Chunk, ChunkMetadata
 from .base import BaseChunker
 from .utils import (

src/insta_rag/chunking/utils.py

@@ -94,7 +94,7 @@ def split_into_sentences(text: str) -> List[str]:
     """
     # Use regex to split on sentence boundaries
     # This handles common cases but may not be perfect for all texts
-    sentence_endings = re.compile(r'(?<=[.!?])\s+(?=[A-Z])')
+    sentence_endings = re.compile(r"(?<=[.!?])\s+(?=[A-Z])")
     sentences = sentence_endings.split(text)
 
     # Clean up sentences
@@ -113,7 +113,7 @@ def split_into_paragraphs(text: str) -> List[str]:
         List of paragraphs
     """
     # Split on double newlines
-    paragraphs = re.split(r'\n\s*\n', text)
+    paragraphs = re.split(r"\n\s*\n", text)
 
     # Clean up paragraphs
     paragraphs = [p.strip() for p in paragraphs if p.strip()]
@@ -156,7 +156,9 @@ def validate_chunk_quality(chunk: str) -> bool:
     return True
 
 
-def add_overlap_to_chunks(chunks: List[str], overlap_percentage: float = 0.2) -> List[str]:
+def add_overlap_to_chunks(
+    chunks: List[str], overlap_percentage: float = 0.2
+) -> List[str]:
     """Add overlap between consecutive chunks.
 
     Args:

src/insta_rag/core/client.py

@@ -6,14 +6,14 @@
 
 from ..chunking.semantic import SemanticChunker
 from ..embedding.openai import OpenAIEmbedder
-from ..utils.exceptions import ValidationError, VectorDBError
+from insta_rag.utils.exceptions import ValidationError, VectorDBError
 from ..models.document import DocumentInput, SourceType
 from ..models.response import (
     AddDocumentsResponse,
     ProcessingStats,
     UpdateDocumentsResponse,
 )
-from ..utils.pdf_processing import extract_text_from_pdf
+from insta_rag.utils.pdf_processing import extract_text_from_pdf
 from ..vectordb.qdrant import QdrantVectorDB
 from .config import RAGConfig
 
@@ -325,7 +325,10 @@ def update_documents(
             NoDocumentsFoundError: No documents match criteria (for delete/replace)
             VectorDBError: Qdrant operation failures
         """
-        from ..utils.exceptions import CollectionNotFoundError, NoDocumentsFoundError
+        from insta_rag.utils.exceptions import (
+            CollectionNotFoundError,
+            NoDocumentsFoundError,
+        )
 
         start_time = time.time()
         errors = []

src/insta_rag/core/config.py

@@ -4,7 +4,7 @@
 from dataclasses import dataclass, field
 from typing import Any, Dict, Optional
 
-from ..utils.exceptions import ConfigurationError
+from insta_rag.utils.exceptions import ConfigurationError
 
 
 @dataclass

src/insta_rag/core/retrieval_method.py

@@ -14,7 +14,7 @@
 
 import time
 from collections import defaultdict
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 from ..models.response import (
     RetrievalResponse,
@@ -138,7 +138,7 @@ def retrieve(
         ...     collection_name="knowledge_base",
         ...     filters={"user_id": "user_123"},
         ...     top_k=10,
-        ...     enable_reranking=True
+        ...     enable_reranking=True,
         ... )
         >>> for chunk in response.chunks:
         ...     print(f"Score: {chunk.relevance_score:.4f}")
@@ -230,7 +230,10 @@ def retrieve(
             chunk_dict = {}
             for chunk in all_chunks:
                 chunk_id = chunk.chunk_id
-                if chunk_id not in chunk_dict or chunk.score > chunk_dict[chunk_id].score:
+                if (
+                    chunk_id not in chunk_dict
+                    or chunk.score > chunk_dict[chunk_id].score
+                ):
                     chunk_dict[chunk_id] = chunk
             unique_chunks = list(chunk_dict.values())
         else:

src/insta_rag/embedding/openai.py

@@ -2,7 +2,7 @@
 
 from typing import List, Optional
 
-from ..utils.exceptions import EmbeddingError
+from insta_rag.utils.exceptions import EmbeddingError
 from .base import BaseEmbedder
 
 

src/insta_rag/models/document.py

@@ -90,7 +90,9 @@ def from_binary(
     def get_source_path(self) -> Optional[Path]:
         """Get source as Path if it's a file, None otherwise."""
         if self.source_type == SourceType.FILE:
-            return Path(self.source) if not isinstance(self.source, Path) else self.source
+            return (
+                Path(self.source) if not isinstance(self.source, Path) else self.source
+            )
         return None
 
     def get_source_text(self) -> Optional[str]:

src/insta_rag/models/response.py

@@ -93,7 +93,9 @@ class UpdateDocumentsResponse:
     chunks_added: int = 0
     chunks_updated: int = 0
     updated_document_ids: List[str] = field(default_factory=list)
-    chunks: List[Chunk] = field(default_factory=list)  # NEW: For external storage (e.g., MongoDB)
+    chunks: List[Chunk] = field(
+        default_factory=list
+    )  # NEW: For external storage (e.g., MongoDB)
     errors: List[str] = field(default_factory=list)
 
     def to_dict(self) -> Dict[str, Any]:

src/insta_rag/retrieval/keyword_search.py

@@ -78,14 +78,18 @@ def _build_corpus(self):
                     mongodb_id = point.payload.get("mongodb_id")
                     if mongodb_id and self.rag_client.mongodb:
                         try:
-                            mongo_doc = self.rag_client.mongodb.get_chunk_content_by_mongo_id(
-                                str(mongodb_id)
+                            mongo_doc = (
+                                self.rag_client.mongodb.get_chunk_content_by_mongo_id(
+                                    str(mongodb_id)
+                                )
                             )
                             if mongo_doc:
                                 content = mongo_doc.get("content", "")
                                 mongodb_fetch_count += 1
                         except Exception as e:
-                            print(f"   Warning: Failed to fetch content from MongoDB for chunk {point.payload.get('chunk_id')}: {e}")
+                            print(
+                                f"   Warning: Failed to fetch content from MongoDB for chunk {point.payload.get('chunk_id')}: {e}"
+                            )
                             skipped_count += 1
                             continue
 
@@ -112,7 +116,9 @@ def _build_corpus(self):
                 )
 
             if mongodb_fetch_count > 0:
-                print(f"   Fetched content for {mongodb_fetch_count} chunks from MongoDB")
+                print(
+                    f"   Fetched content for {mongodb_fetch_count} chunks from MongoDB"
+                )
             if skipped_count > 0:
                 print(f"   Skipped {skipped_count} chunks without content")
 
@@ -122,7 +128,7 @@ def _build_corpus(self):
                 print(f"   ✓ BM25 corpus built: {len(self.corpus)} documents indexed")
             else:
                 self.bm25 = None
-                print(f"   ⚠️ BM25 corpus is empty - no documents indexed")
+                print("   ⚠️ BM25 corpus is empty - no documents indexed")
 
         except ImportError:
             print(
@@ -171,11 +177,7 @@ def search(
                     if filters:
                         match = True
                         for key, value in filters.items():
-                            if (
-                                value is not None
-                                and value != ""
-                                and value != {}
-                            ):
+                            if value is not None and value != "" and value != {}:
                                 if chunk_data["metadata"].get(key) != value:
                                     match = False
                                     break

src/insta_rag/retrieval/query_generator.py

@@ -3,7 +3,7 @@
 import json
 from typing import Dict
 
-from ..utils.exceptions import QueryGenerationError
+from insta_rag.utils.exceptions import QueryGenerationError
 
 
 class HyDEQueryGenerator:

src/insta_rag/retrieval/reranker.py

@@ -1,6 +1,5 @@
 """Reranking implementations for improving retrieval results."""
 
-import time
 import json
 from typing import Any, Dict, List, Tuple
 import requests
@@ -30,7 +29,7 @@ def __init__(
         api_key: str,
         api_url: str = "http://118.67.212.45:8000/rerank",
         normalize: bool = False,
-        timeout: int = 30
+        timeout: int = 30,
     ):
         """Initialize BGE reranker.
 
@@ -46,10 +45,7 @@ def __init__(
         self.timeout = timeout
 
     def rerank(
-        self,
-        query: str,
-        chunks: List[Tuple[str, Dict[str, Any]]],
-        top_k: int
+        self, query: str, chunks: List[Tuple[str, Dict[str, Any]]], top_k: int
     ) -> List[Tuple[int, float]]:
         """Rerank chunks based on relevance to query using BGE reranker.
 
@@ -75,22 +71,19 @@ def rerank(
             "query": query,
             "documents": documents,
             "top_k": min(top_k, len(documents)),  # Don't request more than available
-            "normalize": self.normalize
+            "normalize": self.normalize,
         }
 
         headers = {
             "accept": "application/json",
             "X-API-Key": self.api_key,
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
         }
 
         try:
             # Make API request
             response = requests.post(
-                self.api_url,
-                json=request_data,
-                headers=headers,
-                timeout=self.timeout
+                self.api_url, json=request_data, headers=headers, timeout=self.timeout
             )
             response.raise_for_status()
 
@@ -130,10 +123,7 @@ def __init__(self, api_key: str, model: str = "rerank-english-v3.0"):
         self.model = model
 
     def rerank(
-        self,
-        query: str,
-        chunks: List[Tuple[str, Dict[str, Any]]],
-        top_k: int
+        self, query: str, chunks: List[Tuple[str, Dict[str, Any]]], top_k: int
     ) -> List[Tuple[int, float]]:
         """Rerank chunks using Cohere API.
 
@@ -167,7 +157,7 @@ def __init__(
         api_key: str,
         base_url: str,
         model: str = "gpt-oss-120b",
-        timeout: int = 60
+        timeout: int = 60,
     ):
         """Initialize LLM reranker.
 
@@ -181,17 +171,10 @@ def __init__(
         self.base_url = base_url
         self.model = model
         self.timeout = timeout
-        self.client = OpenAI(
-            base_url=base_url,
-            api_key=api_key,
-            timeout=timeout
-        )
+        self.client = OpenAI(base_url=base_url, api_key=api_key, timeout=timeout)
 
     def rerank(
-        self,
-        query: str,
-        chunks: List[Tuple[str, Dict[str, Any]]],
-        top_k: int
+        self, query: str, chunks: List[Tuple[str, Dict[str, Any]]], top_k: int
     ) -> List[Tuple[int, float]]:
         """Rerank chunks based on relevance to query using LLM.
 
@@ -253,31 +236,28 @@ def rerank(
                 messages=[
                     {
                         "role": "system",
-                        "content": "You are a relevance scoring system that returns only valid JSON arrays. Never include explanations, only return the JSON array."
+                        "content": "You are a relevance scoring system that returns only valid JSON arrays. Never include explanations, only return the JSON array.",
                     },
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
+                    {"role": "user", "content": prompt},
                 ],
                 temperature=0.0,
-                max_tokens=2000
+                max_tokens=2000,
             )
 
             # Extract the response content
             response_text = response.choices[0].message.content.strip()
 
             # Print the OSS model response for logging
-            print(f"\n{'='*80}")
-            print(f"OSS MODEL RESPONSE (gpt-oss-120b):")
-            print(f"{'='*80}")
+            print(f"\n{'=' * 80}")
+            print("OSS MODEL RESPONSE (gpt-oss-120b):")
+            print(f"{'=' * 80}")
             print(response_text)
-            print(f"{'='*80}\n")
+            print(f"{'=' * 80}\n")
 
             # Try to find JSON array in the response
             # Sometimes LLM might add extra text, so we look for the JSON array
-            start_idx = response_text.find('[')
-            end_idx = response_text.rfind(']') + 1
+            start_idx = response_text.find("[")
+            end_idx = response_text.rfind("]") + 1
 
             if start_idx == -1 or end_idx == 0:
                 raise ValueError("No JSON array found in LLM response")
@@ -312,6 +292,8 @@ def rerank(
             return reranked_results
 
         except json.JSONDecodeError as e:
-            raise Exception(f"LLM reranker failed to parse JSON response: {str(e)}. Response: {response_text[:200]}")
+            raise Exception(
+                f"LLM reranker failed to parse JSON response: {str(e)}. Response: {response_text[:200]}"
+            )
         except Exception as e:
-            raise Exception(f"LLM reranker API request failed: {str(e)}")
+            raise Exception(f"LLM reranker API request failed: {str(e)}")

src/insta_rag/vectordb/qdrant.py

@@ -2,8 +2,7 @@
 
 import uuid
 from typing import Any, Dict, List, Optional
-
-from ..utils.exceptions import CollectionNotFoundError, VectorDBError
+from insta_rag.utils.exceptions import CollectionNotFoundError, VectorDBError
 from .base import BaseVectorDB, VectorSearchResult