Merge pull request #49 from arjbingly/main

Merge main into tests

llm_quantize/quantize.py

@@ -1,6 +1,6 @@
+import os
 import subprocess
 import sys
-import os
 
 
 def execute_commands(model_dir_path, quantization=None):
@@ -13,7 +13,7 @@ def execute_commands(model_dir_path, quantization=None):
         if quantization:
             model_file = f"llama.cpp/models/{model_dir_path}/ggml-model-f16.gguf"
             quantized_model_file = f"llama.cpp/models/{model_dir_path.split('/')[-1]}/ggml-model-{quantization}.gguf"
-            subprocess.run(["llama.cpp/llm_quantize", model_file, quantized_model_file, quantization], check=True)
+            subprocess.run(["llama.cpp/quantize", model_file, quantized_model_file, quantization], check=True)
 
     else:
         print("llama.cpp doesn't exist, check readme how to clone.")

projects/Basic-RAG/BasicRAG_stuff.py

@@ -1,6 +1,10 @@
-from grag.grag.rag import BasicRAG
+from grag.components.multivec_retriever import Retriever
+from grag.components.vectordb.deeplake_client import DeepLakeClient
+from grag.rag.basic_rag import BasicRAG
 
-rag = BasicRAG(doc_chain="stuff")
+client = DeepLakeClient(collection_name="test")
+retriever = Retriever(vectordb=client)
+rag = BasicRAG(doc_chain="stuff", retriever=retriever)
 
 if __name__ == "__main__":
     while True:

projects/Retriver-GUI/retriever_app.py

@@ -46,7 +46,7 @@ def render_search_results(self):
                     st.write(result.metadata)
 
     def check_connection(self):
-        response = self.app.retriever.client.test_connection()
+        response = self.app.retriever.vectordb.test_connection()
         if response:
             return True
         else:
@@ -55,14 +55,14 @@ def check_connection(self):
     def render_stats(self):
         st.write(f'''
         **Chroma Client Details:** \n
-            Host Address    : {self.app.retriever.client.host}:{self.app.retriever.client.port} \n
-            Collection Name : {self.app.retriever.client.collection_name} \n
-            Embeddings Type : {self.app.retriever.client.embedding_type} \n
-            Embeddings Model: {self.app.retriever.client.embedding_model} \n
-            Number of docs  : {self.app.retriever.client.collection.count()} \n
+            Host Address    : {self.app.retriever.vectordb.host}:{self.app.retriever.vectordb.port} \n
+            Collection Name : {self.app.retriever.vectordb.collection_name} \n
+            Embeddings Type : {self.app.retriever.vectordb.embedding_type} \n
+            Embeddings Model: {self.app.retriever.vectordb.embedding_model} \n
+            Number of docs  : {self.app.retriever.vectordb.collection.count()} \n
         ''')
         if st.button('Check Connection'):
-            response = self.app.retriever.client.test_connection()
+            response = self.app.retriever.vectordb.test_connection()
             if response:
                 st.write(':green[Connection Active]')
             else:

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "huggingface_hub>=0.20.2",
     "pydantic>=2.5.0",
     "rouge-score>=0.1.2",
+    "deeplake>=3.8.27"
 ]
 
 [project.urls]

src/config.ini

@@ -14,6 +14,12 @@ n_gpu_layers_cpp : -1
 std_out : True
 base_dir : ${root:root_path}/models
 
+[deeplake]
+collection_name : arxiv
+embedding_type : instructor-embedding
+embedding_model : hkunlp/instructor-xl
+store_path : ${data:data_path}/vectordb
+
 [chroma]
 host : localhost
 port : 8000
@@ -59,4 +65,7 @@ table_as_html : True
 data_path : ${root:root_path}/data
 
 [root]
-root_path : /home/ubuntu/CapStone/Capstone_5
+root_path : /home/ubuntu/volume_2k/Capstone_5
+
+[quantize]
+llama_cpp_path : ${root:root_path}

src/grag/components/multivec_retriever.py

@@ -6,11 +6,12 @@
 
 import asyncio
 import uuid
-from typing import List
+from typing import Any, Dict, List, Optional
 
-from grag.components.chroma_client import ChromaClient
 from grag.components.text_splitter import TextSplitter
 from grag.components.utils import get_config
+from grag.components.vectordb.base import VectorDB
+from grag.components.vectordb.deeplake_client import DeepLakeClient
 from langchain.retrievers.multi_vector import MultiVectorRetriever
 from langchain.storage import LocalFileStore
 from langchain_core.documents import Document
@@ -28,7 +29,7 @@ class Retriever:
     Attributes:
         store_path: Path to the local file store
         id_key: A key prefix for identifying documents
-        client: ChromaClient class instance from components.chroma_client
+        vectordb: ChromaClient class instance from components.client
         store: langchain.storage.LocalFileStore object, stores the key value pairs of document id and parent file
         retriever: langchain.retrievers.multi_vector.MultiVectorRetriever class instance, langchain's multi-vector retriever
         splitter: TextSplitter class instance from components.text_splitter
@@ -39,10 +40,12 @@ class Retriever:
 
     def __init__(
         self,
+        vectordb: Optional[VectorDB] = None,
         store_path: str = multivec_retriever_conf["store_path"],
         id_key: str = multivec_retriever_conf["id_key"],
         namespace: str = multivec_retriever_conf["namespace"],
         top_k=1,
+        client_kwargs: Optional[Dict[str, Any]] = None,
     ):
         """Initialize the Retriever.
 
@@ -55,10 +58,16 @@ def __init__(
         self.store_path = store_path
         self.id_key = id_key
         self.namespace = uuid.UUID(namespace)
-        self.client = ChromaClient()
+        if vectordb is None:
+            if client_kwargs is not None:
+                self.vectordb = DeepLakeClient(**client_kwargs)
+            else:
+                self.vectordb = DeepLakeClient()
+        else:
+            self.vectordb = vectordb
         self.store = LocalFileStore(self.store_path)
         self.retriever = MultiVectorRetriever(
-            vectorstore=self.client.langchain_chroma,
+            vectorstore=self.vectordb.langchain_client,
             byte_store=self.store,
             id_key=self.id_key,
         )
@@ -125,7 +134,7 @@ def add_docs(self, docs: List[Document]):
         """
         chunks = self.split_docs(docs)
         doc_ids = self.gen_doc_ids(docs)
-        self.client.add_docs(chunks)
+        self.vectordb.add_docs(chunks)
         self.retriever.docstore.mset(list(zip(doc_ids, docs)))
 
     async def aadd_docs(self, docs: List[Document]):
@@ -140,11 +149,11 @@ async def aadd_docs(self, docs: List[Document]):
         """
         chunks = self.split_docs(docs)
         doc_ids = self.gen_doc_ids(docs)
-        await asyncio.run(self.client.aadd_docs(chunks))
+        await asyncio.run(self.vectordb.aadd_docs(chunks))
         self.retriever.docstore.mset(list(zip(doc_ids)))
 
     def get_chunk(self, query: str, with_score=False, top_k=None):
-        """Returns the most (cosine) similar chunks from the vector database.
+        """Returns the most similar chunks from the vector database.
 
         Args:
             query: A query string
@@ -155,14 +164,8 @@ def get_chunk(self, query: str, with_score=False, top_k=None):
             list of Documents
 
         """
-        if with_score:
-            return self.client.langchain_chroma.similarity_search_with_relevance_scores(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
-        else:
-            return self.client.langchain_chroma.similarity_search(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
+        _top_k = top_k if top_k else self.retriever.search_kwargs["k"]
+        return self.vectordb.get_chunk(query=query, top_k=_top_k, with_score=with_score)
 
     async def aget_chunk(self, query: str, with_score=False, top_k=None):
         """Returns the most (cosine) similar chunks from the vector database, asynchronously.
@@ -176,14 +179,10 @@ async def aget_chunk(self, query: str, with_score=False, top_k=None):
             list of Documents
 
         """
-        if with_score:
-            return await self.client.langchain_chroma.asimilarity_search_with_relevance_scores(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
-        else:
-            return await self.client.langchain_chroma.asimilarity_search(
-                query=query, **{"k": top_k} if top_k else self.retriever.search_kwargs
-            )
+        _top_k = top_k if top_k else self.retriever.search_kwargs["k"]
+        return await self.vectordb.aget_chunk(
+            query=query, top_k=_top_k, with_score=with_score
+        )
 
     def get_doc(self, query: str):
         """Returns the parent document of the most (cosine) similar chunk from the vector database.

src/grag/components/vectordb/base.py

@@ -0,0 +1,85 @@
+"""Abstract base class for vector database clients.
+
+This module provides:
+- VectorDB
+"""
+
+from abc import ABC, abstractmethod
+from typing import List, Tuple, Union
+
+from langchain_community.vectorstores.utils import filter_complex_metadata
+from langchain_core.documents import Document
+
+
+class VectorDB(ABC):
+    """Abstract base class for vector database clients."""
+
+    @abstractmethod
+    def __len__(self) -> int:
+        """Number of chunks in the vector database."""
+        ...
+
+    @abstractmethod
+    def delete(self) -> None:
+        """Delete all chunks in the vector database."""
+
+    @abstractmethod
+    def add_docs(self, docs: List[Document], verbose: bool = True) -> None:
+        """Adds documents to the vector database.
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        ...
+
+    @abstractmethod
+    async def aadd_docs(self, docs: List[Document], verbose: bool = True) -> None:
+        """Adds documents to the vector database (asynchronous).
+
+        Args:
+            docs: List of Documents
+            verbose: Show progress bar
+
+        Returns:
+            None
+        """
+        ...
+
+    @abstractmethod
+    def get_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the vector database.
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+        """
+        ...
+
+    @abstractmethod
+    async def aget_chunk(
+        self, query: str, with_score: bool = False, top_k: int = None
+    ) -> Union[List[Document], List[Tuple[Document, float]]]:
+        """Returns the most similar chunks from the vector database (asynchronous).
+
+        Args:
+            query: A query string
+            with_score: Outputs scores of returned chunks
+            top_k: Number of top similar chunks to return, if None defaults to self.top_k
+
+        Returns:
+            list of Documents
+        """
+        ...
+
+    def _filter_metadata(self, docs: List[Document]) -> List[Document]:
+        return filter_complex_metadata(docs, allowed_types=self.allowed_metadata_types)