forked from open-webui/open-webui
feat: hybrid search
This commit is contained in:
Steven Kreitzer
Steven Kreitzer
parent
f3e5700d49
commit
4e0b32b505
7 changed files with 406 additions and 110 deletions
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@ -1,5 +1,8 @@
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import logging
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import requests
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import operator
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import sentence_transformers
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from typing import List
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@ -8,6 +11,11 @@ from apps.ollama.main import (
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GenerateEmbeddingsForm,
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)
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from langchain.retrievers import (
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BM25Retriever,
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EnsembleRetriever,
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)
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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@ -15,60 +23,96 @@ log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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def query_embeddings_doc(collection_name: str, query: str, query_embeddings, k: int):
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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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k: int,
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embeddings_function,
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reranking_function,
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):
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try:
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# if you use docker use the model from the environment variable
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log.info(f"query_embeddings_doc {query_embeddings}")
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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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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# keyword 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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# semantic search (vector)
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chroma_retriever = ChromaRetriever(
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collection=collection,
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k=k,
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embeddings_function=embeddings_function,
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)
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log.info(f"query_embeddings_doc:result {result}")
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# hybrid search (ensemble)
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ensemble_retriever = EnsembleRetriever(
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retrievers=[bm25_retriever, chroma_retriever],
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weights=[0.6, 0.4]
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)
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documents = ensemble_retriever.invoke(query)
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result = query_results_rank(
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query=query,
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documents=documents,
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k=k,
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reranking_function=reranking_function,
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)
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result = {
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"distances": [[d[1].item() for d in result]],
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"documents": [[d[0].page_content for d in result]],
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"metadatas": [[d[0].metadata for d in result]],
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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 query_results_rank(query: str, documents, k: int, reranking_function):
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scores = reranking_function.predict([(query, doc.page_content) for doc in documents])
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docs_with_scores = list(zip(documents, scores))
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result = sorted(docs_with_scores, key=operator.itemgetter(1), reverse=True)
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return result[: k]
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def merge_and_sort_query_results(query_results, k):
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# Initialize lists to store combined data
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combined_ids = []
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combined_distances = []
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combined_metadatas = []
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combined_documents = []
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combined_metadatas = []
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# Combine data from each dictionary
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for data in query_results:
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combined_ids.extend(data["ids"][0])
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combined_distances.extend(data["distances"][0])
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combined_metadatas.extend(data["metadatas"][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, id, metadata, document)
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# Create a list of tuples (distance, document, metadata)
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combined = list(
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zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
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zip(combined_distances, combined_documents, combined_metadatas)
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)
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# Sort the list based on distances
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combined.sort(key=lambda x: x[0])
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# Unzip the sorted list
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sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
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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_ids = list(sorted_ids)[:k]
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sorted_metadatas = list(sorted_metadatas)[: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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merged_query_results = {
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"ids": [sorted_ids],
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"distances": [sorted_distances],
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"metadatas": [sorted_metadatas],
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"documents": [sorted_documents],
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"metadatas": [sorted_metadatas],
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"embeddings": None,
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"uris": None,
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"data": None,
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@ -78,19 +122,23 @@ def merge_and_sort_query_results(query_results, k):
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def query_embeddings_collection(
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collection_names: List[str], query: str, query_embeddings, k: int
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collection_names: List[str],
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query: str,
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k: int,
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embeddings_function,
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reranking_function,
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):
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results = []
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log.info(f"query_embeddings_collection {query_embeddings}")
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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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query_embeddings=query_embeddings,
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k=k,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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)
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results.append(result)
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except:
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@ -105,6 +153,33 @@ def rag_template(template: str, context: str, query: str):
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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 == "ollama":
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return 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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return 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 rag_messages(
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docs,
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messages,
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@ -113,11 +188,12 @@ def rag_messages(
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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} {openai_key} {openai_url}"
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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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@ -155,38 +231,29 @@ def rag_messages(
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if doc["type"] == "text":
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context = doc["content"]
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else:
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if embedding_engine == "":
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query_embeddings = embedding_function.encode(query).tolist()
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elif embedding_engine == "ollama":
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query_embeddings = 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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query_embeddings = 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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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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if 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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query_embeddings=query_embeddings,
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k=k,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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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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query_embeddings=query_embeddings,
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k=k,
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embeddings_function=embeddings_function,
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reranking_function=reranking_function,
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)
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except Exception as e:
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@ -250,3 +317,41 @@ def generate_openai_embeddings(
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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.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.retrievers import BaseRetriever
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class ChromaRetriever(BaseRetriever):
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collection: Any
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k: int
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embeddings_function: Any
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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.k,
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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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