mirror of
https://github.com/open-webui/open-webui
synced 2024-11-24 21:13:59 +00:00
593 lines
18 KiB
Python
593 lines
18 KiB
Python
import logging
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import os
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import uuid
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from typing import Optional, Union
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import asyncio
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import requests
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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.apps.retrieval.vector.connector import VECTOR_DB_CLIENT
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from open_webui.utils.misc import get_last_user_message
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from open_webui.env import SRC_LOG_LEVELS
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from open_webui.config import DEFAULT_RAG_TEMPLATE
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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)],
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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,
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query_embedding: list[float],
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k: int,
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):
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try:
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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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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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print(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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query: str,
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embedding_function,
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k: int,
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reranking_function,
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r: float,
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) -> dict:
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try:
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result = VECTOR_DB_CLIENT.get(collection_name=collection_name)
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bm25_retriever = BM25Retriever.from_texts(
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texts=result.documents[0],
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metadatas=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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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, vector_search_retriever], weights=[0.5, 0.5]
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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,
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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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result = {
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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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}
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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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raise e
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def merge_and_sort_query_results(
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query_results: list[dict], k: int, reverse: bool = False
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) -> list[dict]:
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# Initialize lists to store combined data
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combined_distances = []
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combined_documents = []
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combined_metadatas = []
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for data in query_results:
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combined_distances.extend(data["distances"][0])
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combined_documents.extend(data["documents"][0])
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combined_metadatas.extend(data["metadatas"][0])
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# Create a list of tuples (distance, document, metadata)
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combined = list(zip(combined_distances, combined_documents, combined_metadatas))
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# Sort the list based on distances
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combined.sort(key=lambda x: x[0], reverse=reverse)
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# We don't have anything :-(
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if not combined:
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sorted_distances = []
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sorted_documents = []
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sorted_metadatas = []
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else:
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# Unzip the sorted list
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sorted_distances, sorted_documents, sorted_metadatas = zip(*combined)
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# Slicing the lists to include only k elements
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sorted_distances = list(sorted_distances)[:k]
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sorted_documents = list(sorted_documents)[:k]
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sorted_metadatas = list(sorted_metadatas)[:k]
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# Create the output dictionary
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result = {
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"distances": [sorted_distances],
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"documents": [sorted_documents],
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"metadatas": [sorted_metadatas],
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}
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return result
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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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for query in queries:
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query_embedding = embedding_function(query)
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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 = 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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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_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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r: float,
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) -> dict:
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results = []
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error = False
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for collection_name in collection_names:
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try:
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for query in queries:
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result = query_doc_with_hybrid_search(
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collection_name=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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r=r,
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)
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results.append(result)
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except Exception as e:
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log.exception(
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"Error when querying the collection with " f"hybrid_search: {e}"
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)
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error = True
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if error:
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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, reverse=True)
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def rag_template(template: str, context: str, query: str):
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if template == "":
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template = DEFAULT_RAG_TEMPLATE
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if "[context]" not in template and "{{CONTEXT}}" not in template:
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log.debug(
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"WARNING: The RAG template does not contain the '[context]' or '{{CONTEXT}}' placeholder."
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)
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if "<context>" in context and "</context>" in context:
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log.debug(
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"WARNING: Potential prompt injection attack: the RAG "
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"context contains '<context>' and '</context>'. This might be "
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"nothing, or the user might be trying to hack something."
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)
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query_placeholders = []
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if "[query]" in context:
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query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
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template = template.replace("[query]", query_placeholder)
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query_placeholders.append(query_placeholder)
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if "{{QUERY}}" in context:
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query_placeholder = "{{QUERY" + str(uuid.uuid4()) + "}}"
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template = template.replace("{{QUERY}}", query_placeholder)
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query_placeholders.append(query_placeholder)
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template = template.replace("[context]", context)
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template = template.replace("{{CONTEXT}}", context)
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template = template.replace("[query]", query)
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template = template.replace("{{QUERY}}", query)
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for query_placeholder in query_placeholders:
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template = template.replace(query_placeholder, query)
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return template
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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: embedding_function.encode(query).tolist()
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elif embedding_engine in ["ollama", "openai"]:
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func = lambda query: 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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url=url,
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key=key,
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)
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def generate_multiple(query, 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(func(query[i : i + embedding_batch_size]))
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return embeddings
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else:
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return func(query)
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return lambda query: generate_multiple(query, func)
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def get_rag_context(
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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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r,
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hybrid_search,
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):
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log.debug(f"files: {files} {queries} {embedding_function} {reranking_function}")
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extracted_collections = []
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relevant_contexts = []
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for file in files:
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if file.get("context") == "full":
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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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else:
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context = None
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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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try:
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context = None
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if file.get("type") == "text":
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context = file["content"]
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else:
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if hybrid_search:
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try:
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context = query_collection_with_hybrid_search(
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collection_names=collection_names,
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queries=queries,
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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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r=r,
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)
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except Exception as e:
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log.debug(
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"Error when using hybrid search, using"
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" non hybrid search as fallback."
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)
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if (not hybrid_search) or (context is None):
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context = query_collection(
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collection_names=collection_names,
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queries=queries,
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embedding_function=embedding_function,
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k=k,
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)
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except Exception as e:
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log.exception(e)
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extracted_collections.extend(collection_names)
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if context:
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if "data" in file:
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del file["data"]
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relevant_contexts.append({**context, "file": file})
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contexts = []
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citations = []
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for context in relevant_contexts:
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try:
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if "documents" in context:
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file_names = list(
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set(
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[
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metadata["name"]
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for metadata in context["metadatas"][0]
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if metadata is not None and "name" in metadata
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]
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)
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)
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contexts.append(
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((", ".join(file_names) + ":\n\n") if file_names else "")
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+ "\n\n".join(
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[text for text in context["documents"][0] if text is not None]
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)
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)
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if "metadatas" in context:
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citation = {
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"source": context["file"],
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"document": context["documents"][0],
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"metadata": context["metadatas"][0],
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}
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if "distances" in context and context["distances"]:
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citation["distances"] = context["distances"][0]
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citations.append(citation)
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except Exception as e:
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log.exception(e)
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print("contexts", contexts)
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print("citations", citations)
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return contexts, citations
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def get_model_path(model: str, update_model: bool = False):
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# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
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cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
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local_files_only = not update_model
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snapshot_kwargs = {
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"cache_dir": cache_dir,
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"local_files_only": local_files_only,
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}
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log.debug(f"model: {model}")
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log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
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# Inspiration from upstream sentence_transformers
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if (
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os.path.exists(model)
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or ("\\" in model or model.count("/") > 1)
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and local_files_only
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):
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# If fully qualified path exists, return input, else set repo_id
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return model
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elif "/" not in model:
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# Set valid repo_id for model short-name
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model = "sentence-transformers" + "/" + model
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snapshot_kwargs["repo_id"] = model
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# Attempt to query the huggingface_hub library to determine the local path and/or to update
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try:
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model_repo_path = snapshot_download(**snapshot_kwargs)
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log.debug(f"model_repo_path: {model_repo_path}")
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return model_repo_path
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except Exception as e:
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log.exception(f"Cannot determine model snapshot path: {e}")
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return model
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def generate_openai_batch_embeddings(
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model: str, texts: list[str], url: str = "https://api.openai.com/v1", key: str = ""
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) -> Optional[list[list[float]]]:
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try:
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r = requests.post(
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f"{url}/embeddings",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}",
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},
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json={"input": texts, "model": model},
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)
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r.raise_for_status()
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data = r.json()
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if "data" in data:
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return [elem["embedding"] for elem in data["data"]]
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else:
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raise "Something went wrong :/"
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except Exception as e:
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print(e)
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return None
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|
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def generate_ollama_batch_embeddings(
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model: str, texts: list[str], url: str, key: str
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) -> Optional[list[list[float]]]:
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try:
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r = requests.post(
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f"{url}/api/embed",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}",
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},
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json={"input": texts, "model": model},
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)
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r.raise_for_status()
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data = r.json()
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print(data)
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if "embeddings" in data:
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return data["embeddings"]
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else:
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raise "Something went wrong :/"
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except Exception as e:
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print(e)
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return None
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|
|
|
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def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **kwargs):
|
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url = kwargs.get("url", "")
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key = kwargs.get("key", "")
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if engine == "ollama":
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if isinstance(text, list):
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embeddings = generate_ollama_batch_embeddings(
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**{"model": model, "texts": text, "url": url, "key": key}
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)
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else:
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embeddings = generate_ollama_batch_embeddings(
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**{"model": model, "texts": [text], "url": url, "key": key}
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)
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return embeddings[0] if isinstance(text, str) else embeddings
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elif engine == "openai":
|
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if isinstance(text, list):
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embeddings = generate_openai_batch_embeddings(model, text, url, key)
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else:
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embeddings = generate_openai_batch_embeddings(model, [text], url, key)
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return embeddings[0] if isinstance(text, str) else embeddings
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import operator
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from typing import Optional, Sequence
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|
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from langchain_core.callbacks import Callbacks
|
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from langchain_core.documents import BaseDocumentCompressor, Document
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|
|
|
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class RerankCompressor(BaseDocumentCompressor):
|
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embedding_function: Any
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top_n: int
|
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reranking_function: Any
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r_score: float
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|
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class Config:
|
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extra = "forbid"
|
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arbitrary_types_allowed = True
|
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|
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def compress_documents(
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self,
|
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documents: Sequence[Document],
|
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query: str,
|
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callbacks: Optional[Callbacks] = None,
|
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) -> Sequence[Document]:
|
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reranking = self.reranking_function is not None
|
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|
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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)
|
|
document_embedding = self.embedding_function(
|
|
[doc.page_content for doc in documents]
|
|
)
|
|
scores = util.cos_sim(query_embedding, document_embedding)[0]
|
|
|
|
docs_with_scores = list(zip(documents, scores.tolist()))
|
|
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
|