Merge branch 'main' of https://github.com/arc53/DocsGPT

application/api/user/routes.py

@@ -63,12 +63,15 @@ user_logs_collection = db["user_logs"]
 user_tools_collection = db["user_tools"]
 attachments_collection = db["attachments"]
 
-agents_collection.create_index(
-    [("shared", 1)],
-    name="shared_index",
-    background=True,
-)
-users_collection.create_index("user_id", unique=True)
+try:
+    agents_collection.create_index(
+        [("shared_publicly", 1)],
+        name="shared_index",
+        background=True,
+    )
+    users_collection.create_index("user_id", unique=True)
+except Exception as e:
+    print("Error creating indexes:", e)
 
 user = Blueprint("user", __name__)
 user_ns = Namespace("user", description="User related operations", path="/")

application/core/settings.py

@@ -30,6 +30,7 @@ class Settings(BaseSettings):
     }
     UPLOAD_FOLDER: str = "inputs"
     PARSE_PDF_AS_IMAGE: bool = False
+    PARSE_IMAGE_REMOTE: bool = False
     VECTOR_STORE: str = (
         "faiss"  #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
     )
@@ -89,6 +90,8 @@ class Settings(BaseSettings):
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
     
+    # PGVector vectorstore config
+    PGVECTOR_CONNECTION_STRING: Optional[str] = None
     # Milvus vectorstore config
     MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
     MILVUS_URI: Optional[str] = "./milvus_local.db"  # milvus lite version as default

application/parser/file/image_parser.py

@@ -8,6 +8,7 @@ import requests
 from typing import Dict, Union
 
 from application.parser.file.base_parser import BaseParser
+from application.core.settings import settings
 
 
 class ImageParser(BaseParser):
@@ -18,10 +19,13 @@ class ImageParser(BaseParser):
         return {}
 
     def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
-        doc2md_service = "https://llm.arc53.com/doc2md"
-        # alternatively you can use local vision capable LLM
-        with open(file, "rb") as file_loaded:
-            files = {'file': file_loaded}
-            response = requests.post(doc2md_service, files=files)   
-            data = response.json()["markdown"] 
+        if settings.PARSE_IMAGE_REMOTE:
+            doc2md_service = "https://llm.arc53.com/doc2md"
+            # alternatively you can use local vision capable LLM
+            with open(file, "rb") as file_loaded:
+                files = {'file': file_loaded}
+                response = requests.post(doc2md_service, files=files)   
+                data = response.json()["markdown"] 
+        else:
+            data = ""
         return data

application/vectorstore/pgvector.py

@@ -0,0 +1,303 @@
+import logging
+from typing import List, Optional, Any, Dict
+from application.core.settings import settings
+from application.vectorstore.base import BaseVectorStore
+from application.vectorstore.document_class import Document
+
+
+class PGVectorStore(BaseVectorStore):
+    def __init__(
+        self,
+        source_id: str = "",
+        embeddings_key: str = "embeddings",
+        table_name: str = "documents",
+        vector_column: str = "embedding",
+        text_column: str = "text",
+        metadata_column: str = "metadata",
+        connection_string: str = None,
+    ):
+        super().__init__()
+        # Store the source_id for use in add_chunk
+        self._source_id = str(source_id).replace("application/indexes/", "").rstrip("/")
+        self._embeddings_key = embeddings_key
+        self._table_name = table_name
+        self._vector_column = vector_column
+        self._text_column = text_column
+        self._metadata_column = metadata_column
+        self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        
+        # Use provided connection string or fall back to settings
+        self._connection_string = connection_string or getattr(settings, 'PGVECTOR_CONNECTION_STRING', None)
+        
+        if not self._connection_string:
+            raise ValueError(
+                "PostgreSQL connection string is required. "
+                "Set PGVECTOR_CONNECTION_STRING in settings or pass connection_string parameter."
+            )
+
+        try:
+            import psycopg2
+            from psycopg2.extras import Json
+            import pgvector.psycopg2
+        except ImportError:
+            raise ImportError(
+                "Could not import required packages. "
+                "Please install with `pip install psycopg2-binary pgvector`."
+            )
+
+        self._psycopg2 = psycopg2
+        self._Json = Json
+        self._pgvector = pgvector.psycopg2
+        self._connection = None
+        self._ensure_table_exists()
+
+    def _get_connection(self):
+        """Get or create database connection"""
+        if self._connection is None or self._connection.closed:
+            self._connection = self._psycopg2.connect(self._connection_string)
+            # Register pgvector types
+            self._pgvector.register_vector(self._connection)
+        return self._connection
+
+    def _ensure_table_exists(self):
+        """Create table and enable pgvector extension if they don't exist"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            # Enable pgvector extension
+            cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+            
+            # Get embedding dimension
+            embedding_dim = getattr(self._embedding, 'dimension', 1536)  # Default to OpenAI dimension
+            
+            # Create table with vector column
+            create_table_query = f"""
+            CREATE TABLE IF NOT EXISTS {self._table_name} (
+                id SERIAL PRIMARY KEY,
+                {self._text_column} TEXT NOT NULL,
+                {self._vector_column} vector({embedding_dim}),
+                {self._metadata_column} JSONB,
+                source_id TEXT NOT NULL,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+            """
+            cursor.execute(create_table_query)
+            
+            # Create index for vector similarity search
+            index_query = f"""
+            CREATE INDEX IF NOT EXISTS {self._table_name}_{self._vector_column}_idx 
+            ON {self._table_name} USING ivfflat ({self._vector_column} vector_cosine_ops)
+            WITH (lists = 100);
+            """
+            cursor.execute(index_query)
+            
+            # Create index for source_id filtering
+            source_index_query = f"""
+            CREATE INDEX IF NOT EXISTS {self._table_name}_source_id_idx 
+            ON {self._table_name} (source_id);
+            """
+            cursor.execute(source_index_query)
+            
+            conn.commit()
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error creating table: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def search(self, question: str, k: int = 2, *args, **kwargs) -> List[Document]:
+        """Search for similar documents using vector similarity"""
+        query_vector = self._embedding.embed_query(question)
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            # Use cosine distance for similarity search with proper vector formatting
+            search_query = f"""
+            SELECT {self._text_column}, {self._metadata_column}, 
+                   ({self._vector_column} <=> %s::vector) as distance
+            FROM {self._table_name}
+            WHERE source_id = %s
+            ORDER BY {self._vector_column} <=> %s::vector
+            LIMIT %s;
+            """
+            
+            cursor.execute(search_query, (query_vector, self._source_id, query_vector, k))
+            results = cursor.fetchall()
+            
+            
+            documents = []
+            for text, metadata, distance in results:
+                metadata = metadata or {}
+                documents.append(Document(page_content=text, metadata=metadata))
+            
+            return documents
+            
+        except Exception as e:
+            logging.error(f"Error searching documents: {e}", exc_info=True)
+            return []
+        finally:
+            cursor.close()
+
+    def add_texts(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        *args,
+        **kwargs,
+    ) -> List[str]:
+        """Add texts with their embeddings to the vector store"""
+        if not texts:
+            return []
+        
+        embeddings = self._embedding.embed_documents(texts)
+        metadatas = metadatas or [{}] * len(texts)
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            insert_query = f"""
+            INSERT INTO {self._table_name} ({self._text_column}, {self._vector_column}, {self._metadata_column}, source_id)
+            VALUES (%s, %s, %s, %s)
+            RETURNING id;
+            """
+            
+            inserted_ids = []
+            for text, embedding, metadata in zip(texts, embeddings, metadatas):
+                cursor.execute(
+                    insert_query,
+                    (text, embedding, self._Json(metadata), self._source_id)
+                )
+                inserted_id = cursor.fetchone()[0]
+                inserted_ids.append(str(inserted_id))
+            
+            conn.commit()
+            return inserted_ids
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error adding texts: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def delete_index(self, *args, **kwargs):
+        """Delete all documents for this source_id"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            delete_query = f"DELETE FROM {self._table_name} WHERE source_id = %s;"
+            cursor.execute(delete_query, (self._source_id,))
+            conn.commit()
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error deleting index: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def save_local(self, *args, **kwargs):
+        """No-op for PostgreSQL - data is already persisted"""
+        pass
+
+    def get_chunks(self) -> List[Dict[str, Any]]:
+        """Get all chunks for this source_id"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            select_query = f"""
+            SELECT id, {self._text_column}, {self._metadata_column}
+            FROM {self._table_name}
+            WHERE source_id = %s;
+            """
+            cursor.execute(select_query, (self._source_id,))
+            results = cursor.fetchall()
+            
+            chunks = []
+            for doc_id, text, metadata in results:
+                chunks.append({
+                    "doc_id": str(doc_id),
+                    "text": text,
+                    "metadata": metadata or {}
+                })
+            
+            return chunks
+            
+        except Exception as e:
+            logging.error(f"Error getting chunks: {e}")
+            return []
+        finally:
+            cursor.close()
+
+    def add_chunk(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> str:
+        """Add a single chunk to the vector store"""
+        metadata = metadata or {}
+        
+        # Create a copy to avoid modifying the original metadata
+        final_metadata = metadata.copy()
+        
+        # Ensure the source_id is in the metadata so the chunk can be found by filters
+        final_metadata["source_id"] = self._source_id
+        
+        embeddings = self._embedding.embed_documents([text])
+        
+        if not embeddings:
+            raise ValueError("Could not generate embedding for chunk")
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            insert_query = f"""
+            INSERT INTO {self._table_name} ({self._text_column}, {self._vector_column}, {self._metadata_column}, source_id)
+            VALUES (%s, %s, %s, %s)
+            RETURNING id;
+            """
+            
+            cursor.execute(
+                insert_query,
+                (text, embeddings[0], self._Json(final_metadata), self._source_id)
+            )
+            inserted_id = cursor.fetchone()[0]
+            conn.commit()
+            
+            return str(inserted_id)
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error adding chunk: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def delete_chunk(self, chunk_id: str) -> bool:
+        """Delete a specific chunk by its ID"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            delete_query = f"DELETE FROM {self._table_name} WHERE id = %s AND source_id = %s;"
+            cursor.execute(delete_query, (int(chunk_id), self._source_id))
+            deleted_count = cursor.rowcount
+            conn.commit()
+            
+            return deleted_count > 0
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error deleting chunk: {e}")
+            return False
+        finally:
+            cursor.close()
+
+    def __del__(self):
+        """Close database connection when object is destroyed"""
+        if hasattr(self, '_connection') and self._connection and not self._connection.closed:
+            self._connection.close()

application/vectorstore/vector_creator.py

@@ -3,6 +3,7 @@ from application.vectorstore.elasticsearch import ElasticsearchStore
 from application.vectorstore.milvus import MilvusStore
 from application.vectorstore.mongodb import MongoDBVectorStore
 from application.vectorstore.qdrant import QdrantStore
+from application.vectorstore.pgvector import PGVectorStore
 
 
 class VectorCreator:
@@ -12,6 +13,7 @@ class VectorCreator:
         "mongodb": MongoDBVectorStore,
         "qdrant": QdrantStore,
         "milvus": MilvusStore,
+        "pgvector": PGVectorStore
     }
 
     @classmethod