856 lines
28 KiB
Python
856 lines
28 KiB
Python
import logging
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import os
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from typing import Optional, Union
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import requests
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import hashlib
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from concurrent.futures import ThreadPoolExecutor
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from huggingface_hub import snapshot_download
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from langchain.retrievers import ContextualCompressionRetriever, EnsembleRetriever
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from langchain_community.retrievers import BM25Retriever
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from langchain_core.documents import Document
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from open_webui.config import VECTOR_DB
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from open_webui.retrieval.vector.factory import VECTOR_DB_CLIENT
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from open_webui.models.users import UserModel
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from open_webui.models.files import Files
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from open_webui.retrieval.vector.main import GetResult
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from open_webui.env import (
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SRC_LOG_LEVELS,
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OFFLINE_MODE,
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ENABLE_FORWARD_USER_INFO_HEADERS,
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)
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from open_webui.config import (
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RAG_EMBEDDING_QUERY_PREFIX,
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RAG_EMBEDDING_CONTENT_PREFIX,
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RAG_EMBEDDING_PREFIX_FIELD_NAME,
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)
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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from typing import Any
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.retrievers import BaseRetriever
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class VectorSearchRetriever(BaseRetriever):
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collection_name: Any
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embedding_function: Any
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top_k: int
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def _get_relevant_documents(
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self,
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query: str,
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*,
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run_manager: CallbackManagerForRetrieverRun,
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) -> list[Document]:
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result = VECTOR_DB_CLIENT.search(
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collection_name=self.collection_name,
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vectors=[self.embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)],
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limit=self.top_k,
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)
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ids = result.ids[0]
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metadatas = result.metadatas[0]
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documents = result.documents[0]
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results = []
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for idx in range(len(ids)):
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results.append(
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Document(
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metadata=metadatas[idx],
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page_content=documents[idx],
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)
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)
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return results
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def query_doc(
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collection_name: str, query_embedding: list[float], k: int, user: UserModel = None
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):
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try:
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log.debug(f"query_doc:doc {collection_name}")
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result = VECTOR_DB_CLIENT.search(
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collection_name=collection_name,
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vectors=[query_embedding],
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limit=k,
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)
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if result:
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log.info(f"query_doc:result {result.ids} {result.metadatas}")
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return result
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except Exception as e:
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log.exception(f"Error querying doc {collection_name} with limit {k}: {e}")
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raise e
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def get_doc(collection_name: str, user: UserModel = None):
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try:
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log.debug(f"get_doc:doc {collection_name}")
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result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
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if result:
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log.info(f"query_doc:result {result.ids} {result.metadatas}")
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return result
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except Exception as e:
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log.exception(f"Error getting doc {collection_name}: {e}")
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raise e
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def query_doc_with_hybrid_search(
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collection_name: str,
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collection_result: GetResult,
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query: str,
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embedding_function,
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k: int,
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reranking_function,
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k_reranker: int,
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r: float,
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hybrid_bm25_weight: float,
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) -> dict:
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try:
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log.debug(f"query_doc_with_hybrid_search:doc {collection_name}")
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bm25_retriever = BM25Retriever.from_texts(
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texts=collection_result.documents[0],
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metadatas=collection_result.metadatas[0],
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)
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bm25_retriever.k = k
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vector_search_retriever = VectorSearchRetriever(
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collection_name=collection_name,
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embedding_function=embedding_function,
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top_k=k,
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)
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if hybrid_bm25_weight <= 0:
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ensemble_retriever = EnsembleRetriever(
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retrievers=[vector_search_retriever], weights=[1.]
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)
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elif hybrid_bm25_weight >= 1:
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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever], weights=[1.]
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)
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else:
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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, vector_search_retriever],
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weights=[hybrid_bm25_weight, 1. - hybrid_bm25_weight]
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)
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compressor = RerankCompressor(
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embedding_function=embedding_function,
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top_n=k_reranker,
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reranking_function=reranking_function,
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r_score=r,
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)
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compression_retriever = ContextualCompressionRetriever(
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base_compressor=compressor, base_retriever=ensemble_retriever
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)
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result = compression_retriever.invoke(query)
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distances = [d.metadata.get("score") for d in result]
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documents = [d.page_content for d in result]
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metadatas = [d.metadata for d in result]
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# retrieve only min(k, k_reranker) items, sort and cut by distance if k < k_reranker
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if k < k_reranker:
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sorted_items = sorted(
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zip(distances, metadatas, documents), key=lambda x: x[0], reverse=True
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)
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sorted_items = sorted_items[:k]
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distances, documents, metadatas = map(list, zip(*sorted_items))
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result = {
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"distances": [distances],
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"documents": [documents],
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"metadatas": [metadatas],
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}
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log.info(
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"query_doc_with_hybrid_search:result "
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+ f'{result["metadatas"]} {result["distances"]}'
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)
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return result
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except Exception as e:
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log.exception(f"Error querying doc {collection_name} with hybrid search: {e}")
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raise e
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def merge_get_results(get_results: list[dict]) -> dict:
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# Initialize lists to store combined data
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combined_documents = []
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combined_metadatas = []
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combined_ids = []
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for data in get_results:
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combined_documents.extend(data["documents"][0])
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combined_metadatas.extend(data["metadatas"][0])
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combined_ids.extend(data["ids"][0])
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# Create the output dictionary
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result = {
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"documents": [combined_documents],
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"metadatas": [combined_metadatas],
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"ids": [combined_ids],
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}
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return result
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def merge_and_sort_query_results(query_results: list[dict], k: int) -> dict:
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# Initialize lists to store combined data
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combined = dict() # To store documents with unique document hashes
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for data in query_results:
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distances = data["distances"][0]
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documents = data["documents"][0]
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metadatas = data["metadatas"][0]
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for distance, document, metadata in zip(distances, documents, metadatas):
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if isinstance(document, str):
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doc_hash = hashlib.sha256(
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document.encode()
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).hexdigest() # Compute a hash for uniqueness
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if doc_hash not in combined.keys():
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combined[doc_hash] = (distance, document, metadata)
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continue # if doc is new, no further comparison is needed
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# if doc is alredy in, but new distance is better, update
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if distance > combined[doc_hash][0]:
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combined[doc_hash] = (distance, document, metadata)
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combined = list(combined.values())
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# Sort the list based on distances
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combined.sort(key=lambda x: x[0], reverse=True)
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# Slice to keep only the top k elements
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sorted_distances, sorted_documents, sorted_metadatas = (
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zip(*combined[:k]) if combined else ([], [], [])
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)
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# Create and return the output dictionary
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return {
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"distances": [list(sorted_distances)],
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"documents": [list(sorted_documents)],
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"metadatas": [list(sorted_metadatas)],
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}
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def get_all_items_from_collections(collection_names: list[str]) -> dict:
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results = []
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for collection_name in collection_names:
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if collection_name:
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try:
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result = get_doc(collection_name=collection_name)
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if result is not None:
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results.append(result.model_dump())
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except Exception as e:
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log.exception(f"Error when querying the collection: {e}")
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else:
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pass
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return merge_get_results(results)
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def query_collection(
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collection_names: list[str],
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queries: list[str],
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embedding_function,
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k: int,
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) -> dict:
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results = []
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error = False
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def process_query_collection(collection_name, query_embedding):
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try:
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if collection_name:
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result = query_doc(
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collection_name=collection_name,
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k=k,
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query_embedding=query_embedding,
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)
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if result is not None:
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return result.model_dump(), None
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return None, None
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except Exception as e:
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log.exception(f"Error when querying the collection: {e}")
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return None, e
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# Generate all query embeddings (in one call)
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query_embeddings = embedding_function(queries, prefix=RAG_EMBEDDING_QUERY_PREFIX)
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log.debug(
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f"query_collection: processing {len(queries)} queries across {len(collection_names)} collections"
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)
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with ThreadPoolExecutor() as executor:
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future_results = []
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for query_embedding in query_embeddings:
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for collection_name in collection_names:
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result = executor.submit(
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process_query_collection, collection_name, query_embedding
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)
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future_results.append(result)
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task_results = [future.result() for future in future_results]
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for result, err in task_results:
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if err is not None:
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error = True
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elif result is not None:
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results.append(result)
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if error and not results:
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log.warning("All collection queries failed. No results returned.")
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return merge_and_sort_query_results(results, k=k)
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def query_collection_with_hybrid_search(
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collection_names: list[str],
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queries: list[str],
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embedding_function,
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k: int,
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reranking_function,
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k_reranker: int,
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r: float,
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hybrid_bm25_weight: float,
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) -> dict:
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results = []
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error = False
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# Fetch collection data once per collection sequentially
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# Avoid fetching the same data multiple times later
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collection_results = {}
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for collection_name in collection_names:
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try:
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log.debug(
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f"query_collection_with_hybrid_search:VECTOR_DB_CLIENT.get:collection {collection_name}"
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)
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collection_results[collection_name] = VECTOR_DB_CLIENT.get(
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collection_name=collection_name
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)
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except Exception as e:
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log.exception(f"Failed to fetch collection {collection_name}: {e}")
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collection_results[collection_name] = None
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log.info(
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f"Starting hybrid search for {len(queries)} queries in {len(collection_names)} collections..."
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)
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def process_query(collection_name, query):
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try:
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result = query_doc_with_hybrid_search(
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collection_name=collection_name,
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collection_result=collection_results[collection_name],
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query=query,
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embedding_function=embedding_function,
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k=k,
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reranking_function=reranking_function,
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k_reranker=k_reranker,
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r=r,
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hybrid_bm25_weight=hybrid_bm25_weight,
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)
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return result, None
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except Exception as e:
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log.exception(f"Error when querying the collection with hybrid_search: {e}")
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return None, e
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# Prepare tasks for all collections and queries
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# Avoid running any tasks for collections that failed to fetch data (have assigned None)
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tasks = [
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(cn, q)
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for cn in collection_names
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if collection_results[cn] is not None
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for q in queries
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]
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with ThreadPoolExecutor() as executor:
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future_results = [executor.submit(process_query, cn, q) for cn, q in tasks]
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task_results = [future.result() for future in future_results]
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for result, err in task_results:
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if err is not None:
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error = True
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elif result is not None:
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results.append(result)
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if error and not results:
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raise Exception(
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"Hybrid search failed for all collections. Using Non-hybrid search as fallback."
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)
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return merge_and_sort_query_results(results, k=k)
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def get_embedding_function(
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embedding_engine,
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embedding_model,
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embedding_function,
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url,
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key,
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embedding_batch_size,
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):
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if embedding_engine == "":
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return lambda query, prefix=None, user=None: embedding_function.encode(
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query, **({"prompt": prefix} if prefix else {})
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).tolist()
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elif embedding_engine in ["ollama", "openai"]:
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func = lambda query, prefix=None, user=None: generate_embeddings(
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engine=embedding_engine,
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model=embedding_model,
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text=query,
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prefix=prefix,
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url=url,
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key=key,
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user=user,
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)
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def generate_multiple(query, prefix, user, func):
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if isinstance(query, list):
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embeddings = []
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for i in range(0, len(query), embedding_batch_size):
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embeddings.extend(
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func(
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query[i : i + embedding_batch_size],
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prefix=prefix,
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user=user,
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)
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)
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return embeddings
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else:
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return func(query, prefix, user)
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return lambda query, prefix=None, user=None: generate_multiple(
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query, prefix, user, func
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)
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else:
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raise ValueError(f"Unknown embedding engine: {embedding_engine}")
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def get_sources_from_files(
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request,
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files,
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queries,
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embedding_function,
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k,
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reranking_function,
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k_reranker,
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r,
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hybrid_bm25_weight,
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hybrid_search,
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full_context=False,
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):
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log.debug(
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f"files: {files} {queries} {embedding_function} {reranking_function} {full_context}"
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)
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extracted_collections = []
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relevant_contexts = []
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for file in files:
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context = None
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if file.get("docs"):
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# BYPASS_WEB_SEARCH_EMBEDDING_AND_RETRIEVAL
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context = {
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"documents": [[doc.get("content") for doc in file.get("docs")]],
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"metadatas": [[doc.get("metadata") for doc in file.get("docs")]],
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}
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elif file.get("context") == "full":
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# Manual Full Mode Toggle
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context = {
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"documents": [[file.get("file").get("data", {}).get("content")]],
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"metadatas": [[{"file_id": file.get("id"), "name": file.get("name")}]],
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}
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elif (
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file.get("type") != "web_search"
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and request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL
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):
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# BYPASS_EMBEDDING_AND_RETRIEVAL
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if file.get("type") == "collection":
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file_ids = file.get("data", {}).get("file_ids", [])
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documents = []
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metadatas = []
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for file_id in file_ids:
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file_object = Files.get_file_by_id(file_id)
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|
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if file_object:
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documents.append(file_object.data.get("content", ""))
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metadatas.append(
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{
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"file_id": file_id,
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"name": file_object.filename,
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"source": file_object.filename,
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}
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)
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context = {
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"documents": [documents],
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"metadatas": [metadatas],
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}
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elif file.get("id"):
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file_object = Files.get_file_by_id(file.get("id"))
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if file_object:
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context = {
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"documents": [[file_object.data.get("content", "")]],
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"metadatas": [
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[
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{
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"file_id": file.get("id"),
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"name": file_object.filename,
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"source": file_object.filename,
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}
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]
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],
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}
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elif file.get("file").get("data"):
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context = {
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"documents": [[file.get("file").get("data", {}).get("content")]],
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"metadatas": [
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[file.get("file").get("data", {}).get("metadata", {})]
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],
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}
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else:
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collection_names = []
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if file.get("type") == "collection":
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if file.get("legacy"):
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collection_names = file.get("collection_names", [])
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else:
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collection_names.append(file["id"])
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elif file.get("collection_name"):
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collection_names.append(file["collection_name"])
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elif file.get("id"):
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if file.get("legacy"):
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collection_names.append(f"{file['id']}")
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else:
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collection_names.append(f"file-{file['id']}")
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collection_names = set(collection_names).difference(extracted_collections)
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if not collection_names:
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log.debug(f"skipping {file} as it has already been extracted")
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continue
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if full_context:
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try:
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|
context = get_all_items_from_collections(collection_names)
|
|
except Exception as e:
|
|
log.exception(e)
|
|
|
|
else:
|
|
try:
|
|
context = None
|
|
if file.get("type") == "text":
|
|
context = file["content"]
|
|
else:
|
|
if hybrid_search:
|
|
try:
|
|
context = query_collection_with_hybrid_search(
|
|
collection_names=collection_names,
|
|
queries=queries,
|
|
embedding_function=embedding_function,
|
|
k=k,
|
|
reranking_function=reranking_function,
|
|
k_reranker=k_reranker,
|
|
r=r,
|
|
hybrid_bm25_weight=hybrid_bm25_weight,
|
|
)
|
|
except Exception as e:
|
|
log.debug(
|
|
"Error when using hybrid search, using"
|
|
" non hybrid search as fallback."
|
|
)
|
|
|
|
if (not hybrid_search) or (context is None):
|
|
context = query_collection(
|
|
collection_names=collection_names,
|
|
queries=queries,
|
|
embedding_function=embedding_function,
|
|
k=k,
|
|
)
|
|
except Exception as e:
|
|
log.exception(e)
|
|
|
|
extracted_collections.extend(collection_names)
|
|
|
|
if context:
|
|
if "data" in file:
|
|
del file["data"]
|
|
|
|
relevant_contexts.append({**context, "file": file})
|
|
|
|
sources = []
|
|
for context in relevant_contexts:
|
|
try:
|
|
if "documents" in context:
|
|
if "metadatas" in context:
|
|
source = {
|
|
"source": context["file"],
|
|
"document": context["documents"][0],
|
|
"metadata": context["metadatas"][0],
|
|
}
|
|
if "distances" in context and context["distances"]:
|
|
source["distances"] = context["distances"][0]
|
|
|
|
sources.append(source)
|
|
except Exception as e:
|
|
log.exception(e)
|
|
|
|
return sources
|
|
|
|
|
|
def get_model_path(model: str, update_model: bool = False):
|
|
# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
|
|
cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
|
|
|
|
local_files_only = not update_model
|
|
|
|
if OFFLINE_MODE:
|
|
local_files_only = True
|
|
|
|
snapshot_kwargs = {
|
|
"cache_dir": cache_dir,
|
|
"local_files_only": local_files_only,
|
|
}
|
|
|
|
log.debug(f"model: {model}")
|
|
log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
|
|
|
|
# Inspiration from upstream sentence_transformers
|
|
if (
|
|
os.path.exists(model)
|
|
or ("\\" in model or model.count("/") > 1)
|
|
and local_files_only
|
|
):
|
|
# If fully qualified path exists, return input, else set repo_id
|
|
return model
|
|
elif "/" not in model:
|
|
# Set valid repo_id for model short-name
|
|
model = "sentence-transformers" + "/" + model
|
|
|
|
snapshot_kwargs["repo_id"] = model
|
|
|
|
# Attempt to query the huggingface_hub library to determine the local path and/or to update
|
|
try:
|
|
model_repo_path = snapshot_download(**snapshot_kwargs)
|
|
log.debug(f"model_repo_path: {model_repo_path}")
|
|
return model_repo_path
|
|
except Exception as e:
|
|
log.exception(f"Cannot determine model snapshot path: {e}")
|
|
return model
|
|
|
|
|
|
def generate_openai_batch_embeddings(
|
|
model: str,
|
|
texts: list[str],
|
|
url: str = "https://api.openai.com/v1",
|
|
key: str = "",
|
|
prefix: str = None,
|
|
user: UserModel = None,
|
|
) -> Optional[list[list[float]]]:
|
|
try:
|
|
log.debug(
|
|
f"generate_openai_batch_embeddings:model {model} batch size: {len(texts)}"
|
|
)
|
|
json_data = {"input": texts, "model": model}
|
|
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
|
|
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
|
|
|
|
r = requests.post(
|
|
f"{url}/embeddings",
|
|
headers={
|
|
"Content-Type": "application/json",
|
|
"Authorization": f"Bearer {key}",
|
|
**(
|
|
{
|
|
"X-OpenWebUI-User-Name": user.name,
|
|
"X-OpenWebUI-User-Id": user.id,
|
|
"X-OpenWebUI-User-Email": user.email,
|
|
"X-OpenWebUI-User-Role": user.role,
|
|
}
|
|
if ENABLE_FORWARD_USER_INFO_HEADERS and user
|
|
else {}
|
|
),
|
|
},
|
|
json=json_data,
|
|
)
|
|
r.raise_for_status()
|
|
data = r.json()
|
|
if "data" in data:
|
|
return [elem["embedding"] for elem in data["data"]]
|
|
else:
|
|
raise "Something went wrong :/"
|
|
except Exception as e:
|
|
log.exception(f"Error generating openai batch embeddings: {e}")
|
|
return None
|
|
|
|
|
|
def generate_ollama_batch_embeddings(
|
|
model: str,
|
|
texts: list[str],
|
|
url: str,
|
|
key: str = "",
|
|
prefix: str = None,
|
|
user: UserModel = None,
|
|
) -> Optional[list[list[float]]]:
|
|
try:
|
|
log.debug(
|
|
f"generate_ollama_batch_embeddings:model {model} batch size: {len(texts)}"
|
|
)
|
|
json_data = {"input": texts, "model": model}
|
|
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
|
|
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
|
|
|
|
r = requests.post(
|
|
f"{url}/api/embed",
|
|
headers={
|
|
"Content-Type": "application/json",
|
|
"Authorization": f"Bearer {key}",
|
|
**(
|
|
{
|
|
"X-OpenWebUI-User-Name": user.name,
|
|
"X-OpenWebUI-User-Id": user.id,
|
|
"X-OpenWebUI-User-Email": user.email,
|
|
"X-OpenWebUI-User-Role": user.role,
|
|
}
|
|
if ENABLE_FORWARD_USER_INFO_HEADERS
|
|
else {}
|
|
),
|
|
},
|
|
json=json_data,
|
|
)
|
|
r.raise_for_status()
|
|
data = r.json()
|
|
|
|
if "embeddings" in data:
|
|
return data["embeddings"]
|
|
else:
|
|
raise "Something went wrong :/"
|
|
except Exception as e:
|
|
log.exception(f"Error generating ollama batch embeddings: {e}")
|
|
return None
|
|
|
|
|
|
def generate_embeddings(
|
|
engine: str,
|
|
model: str,
|
|
text: Union[str, list[str]],
|
|
prefix: Union[str, None] = None,
|
|
**kwargs,
|
|
):
|
|
url = kwargs.get("url", "")
|
|
key = kwargs.get("key", "")
|
|
user = kwargs.get("user")
|
|
|
|
if prefix is not None and RAG_EMBEDDING_PREFIX_FIELD_NAME is None:
|
|
if isinstance(text, list):
|
|
text = [f"{prefix}{text_element}" for text_element in text]
|
|
else:
|
|
text = f"{prefix}{text}"
|
|
|
|
if engine == "ollama":
|
|
if isinstance(text, list):
|
|
embeddings = generate_ollama_batch_embeddings(
|
|
**{
|
|
"model": model,
|
|
"texts": text,
|
|
"url": url,
|
|
"key": key,
|
|
"prefix": prefix,
|
|
"user": user,
|
|
}
|
|
)
|
|
else:
|
|
embeddings = generate_ollama_batch_embeddings(
|
|
**{
|
|
"model": model,
|
|
"texts": [text],
|
|
"url": url,
|
|
"key": key,
|
|
"prefix": prefix,
|
|
"user": user,
|
|
}
|
|
)
|
|
return embeddings[0] if isinstance(text, str) else embeddings
|
|
elif engine == "openai":
|
|
if isinstance(text, list):
|
|
embeddings = generate_openai_batch_embeddings(
|
|
model, text, url, key, prefix, user
|
|
)
|
|
else:
|
|
embeddings = generate_openai_batch_embeddings(
|
|
model, [text], url, key, prefix, user
|
|
)
|
|
return embeddings[0] if isinstance(text, str) else embeddings
|
|
|
|
|
|
import operator
|
|
from typing import Optional, Sequence
|
|
|
|
from langchain_core.callbacks import Callbacks
|
|
from langchain_core.documents import BaseDocumentCompressor, Document
|
|
|
|
|
|
class RerankCompressor(BaseDocumentCompressor):
|
|
embedding_function: Any
|
|
top_n: int
|
|
reranking_function: Any
|
|
r_score: float
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
arbitrary_types_allowed = True
|
|
|
|
def compress_documents(
|
|
self,
|
|
documents: Sequence[Document],
|
|
query: str,
|
|
callbacks: Optional[Callbacks] = None,
|
|
) -> Sequence[Document]:
|
|
reranking = self.reranking_function is not None
|
|
|
|
if reranking:
|
|
scores = self.reranking_function.predict(
|
|
[(query, doc.page_content) for doc in documents]
|
|
)
|
|
else:
|
|
from sentence_transformers import util
|
|
|
|
query_embedding = self.embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)
|
|
document_embedding = self.embedding_function(
|
|
[doc.page_content for doc in documents], RAG_EMBEDDING_CONTENT_PREFIX
|
|
)
|
|
scores = util.cos_sim(query_embedding, document_embedding)[0]
|
|
|
|
docs_with_scores = list(
|
|
zip(documents, scores.tolist() if not isinstance(scores, list) else scores)
|
|
)
|
|
if self.r_score:
|
|
docs_with_scores = [
|
|
(d, s) for d, s in docs_with_scores if s >= self.r_score
|
|
]
|
|
|
|
result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
|
|
final_results = []
|
|
for doc, doc_score in result[: self.top_n]:
|
|
metadata = doc.metadata
|
|
metadata["score"] = doc_score
|
|
doc = Document(
|
|
page_content=doc.page_content,
|
|
metadata=metadata,
|
|
)
|
|
final_results.append(doc)
|
|
return final_results
|