mirror of
https://github.com/open-webui/open-webui
synced 2024-11-16 21:42:58 +00:00
493 lines
14 KiB
Python
493 lines
14 KiB
Python
import os
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import logging
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import requests
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from typing import List
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from apps.ollama.main import (
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generate_ollama_embeddings,
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GenerateEmbeddingsForm,
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)
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from huggingface_hub import snapshot_download
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from langchain_core.documents import Document
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from langchain_community.retrievers import BM25Retriever
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from langchain.retrievers import (
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ContextualCompressionRetriever,
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EnsembleRetriever,
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)
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from typing import Optional
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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def query_embeddings_doc(
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collection_name: str,
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query: str,
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embeddings_function,
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reranking_function,
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k: int,
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r: int,
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hybrid_search: bool,
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):
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try:
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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if hybrid_search:
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documents = collection.get() # get all documents
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bm25_retriever = BM25Retriever.from_texts(
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texts=documents.get("documents"),
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metadatas=documents.get("metadatas"),
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)
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bm25_retriever.k = k
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chroma_retriever = ChromaRetriever(
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collection=collection,
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embeddings_function=embeddings_function,
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top_n=k,
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)
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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5]
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)
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compressor = RerankCompressor(
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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r_score=r,
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top_n=k,
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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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else:
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query_embeddings = embeddings_function(query)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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)
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log.info(f"query_embeddings_doc:result {result}")
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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(query_results, k, reverse=False):
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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_embeddings_collection(
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collection_names: List[str],
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query: str,
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k: int,
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r: float,
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embeddings_function,
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reranking_function,
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hybrid_search: bool,
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):
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results = []
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for collection_name in collection_names:
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try:
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result = query_embeddings_doc(
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collection_name=collection_name,
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query=query,
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k=k,
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r=r,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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hybrid_search=hybrid_search,
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)
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results.append(result)
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except:
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pass
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reverse = hybrid and reranking_function is not None
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return merge_and_sort_query_results(results, k=k, reverse=reverse)
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def rag_template(template: str, context: str, query: str):
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template = template.replace("[context]", context)
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template = template.replace("[query]", query)
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return template
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def query_embeddings_function(
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embedding_engine,
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embedding_model,
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embedding_function,
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openai_key,
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openai_url,
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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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if embedding_engine == "ollama":
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func = lambda query: generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{
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"model": embedding_model,
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"prompt": query,
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}
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)
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)
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elif embedding_engine == "openai":
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func = lambda query: generate_openai_embeddings(
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model=embedding_model,
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text=query,
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key=openai_key,
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url=openai_url,
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)
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def generate_multiple(query, f):
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if isinstance(query, list):
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return [f(q) for q in query]
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else:
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return f(query)
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return lambda query: generate_multiple(query, func)
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def rag_messages(
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docs,
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messages,
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template,
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k,
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r,
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hybrid_search,
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embedding_engine,
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embedding_model,
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embedding_function,
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reranking_function,
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openai_key,
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openai_url,
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):
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log.debug(
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f"docs: {docs} {messages} {embedding_engine} {embedding_model} {embedding_function} {reranking_function} {openai_key} {openai_url}"
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)
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last_user_message_idx = None
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for i in range(len(messages) - 1, -1, -1):
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if messages[i]["role"] == "user":
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last_user_message_idx = i
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break
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user_message = messages[last_user_message_idx]
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if isinstance(user_message["content"], list):
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# Handle list content input
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content_type = "list"
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query = ""
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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query = content_item["text"]
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break
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elif isinstance(user_message["content"], str):
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# Handle text content input
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content_type = "text"
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query = user_message["content"]
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else:
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# Fallback in case the input does not match expected types
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content_type = None
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query = ""
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embeddings_function = query_embeddings_function(
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embedding_engine,
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embedding_model,
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embedding_function,
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openai_key,
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openai_url,
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)
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extracted_collections = []
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relevant_contexts = []
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for doc in docs:
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context = None
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collection = doc.get("collection_name")
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if collection:
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collection = [collection]
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else:
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collection = doc.get("collection_names", [])
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collection = set(collection).difference(extracted_collections)
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if not collection:
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log.debug(f"skipping {doc} as it has already been extracted")
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continue
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try:
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if doc["type"] == "text":
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context = doc["content"]
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elif doc["type"] == "collection":
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context = query_embeddings_collection(
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collection_names=doc["collection_names"],
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query=query,
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k=k,
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r=r,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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hybrid_search=hybrid_search,
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)
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else:
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context = query_embeddings_doc(
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collection_name=doc["collection_name"],
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query=query,
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k=k,
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r=r,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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hybrid_search=hybrid_search,
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)
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except Exception as e:
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log.exception(e)
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context = None
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if context:
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relevant_contexts.append(context)
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extracted_collections.extend(collection)
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context_string = ""
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for context in relevant_contexts:
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items = context["documents"][0]
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context_string += "\n\n".join(items)
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context_string = context_string.strip()
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ra_content = rag_template(
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template=template,
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context=context_string,
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query=query,
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)
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log.debug(f"ra_content: {ra_content}")
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if content_type == "list":
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new_content = []
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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# Update the text item's content with ra_content
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new_content.append({"type": "text", "text": ra_content})
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else:
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# Keep other types of content as they are
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new_content.append(content_item)
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new_user_message = {**user_message, "content": new_content}
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else:
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new_user_message = {
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**user_message,
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"content": ra_content,
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}
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messages[last_user_message_idx] = new_user_message
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return messages
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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_embeddings(
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model: str, text: str, key: str, url: str = "https://api.openai.com/v1"
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):
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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": text, "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 data["data"][0]["embedding"]
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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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from typing import Any
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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class ChromaRetriever(BaseRetriever):
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collection: Any
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embeddings_function: Any
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top_n: 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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query_embeddings = self.embeddings_function(query)
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results = self.collection.query(
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query_embeddings=[query_embeddings],
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n_results=self.top_n,
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)
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ids = results["ids"][0]
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metadatas = results["metadatas"][0]
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documents = results["documents"][0]
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return [
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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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for idx in range(len(ids))
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]
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import operator
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from typing import Optional, Sequence
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from langchain_core.documents import BaseDocumentCompressor, Document
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from langchain_core.callbacks import Callbacks
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from langchain_core.pydantic_v1 import Extra
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from sentence_transformers import util
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class RerankCompressor(BaseDocumentCompressor):
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embeddings_function: Any
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reranking_function: Any
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r_score: float
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top_n: int
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class Config:
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extra = Extra.forbid
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arbitrary_types_allowed = True
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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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if self.reranking_function:
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scores = self.reranking_function.predict(
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[(query, doc.page_content) for doc in documents]
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)
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else:
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query_embedding = self.embeddings_function(query)
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document_embedding = self.embeddings_function(
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[doc.page_content for doc in documents]
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)
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scores = util.cos_sim(query_embedding, document_embedding)[0]
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docs_with_scores = list(zip(documents, scores.tolist()))
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if self.r_score:
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docs_with_scores = [
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(d, s) for d, s in docs_with_scores if s >= self.r_score
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]
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reverse = self.reranking_function is not None
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result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=reverse)
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final_results = []
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for doc, doc_score in result[: self.top_n]:
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metadata = doc.metadata
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metadata["score"] = doc_score
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doc = Document(
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page_content=doc.page_content,
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metadata=metadata,
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)
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final_results.append(doc)
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return final_results
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