mirror of
https://github.com/open-webui/open-webui
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90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
from typing import List
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from config import CHROMA_CLIENT
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def query_doc(collection_name: str, query: str, k: int, embedding_function):
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try:
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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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embedding_function=embedding_function,
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)
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result = collection.query(
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query_texts=[query],
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n_results=k,
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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(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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# 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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# Create a list of tuples (distance, id, metadata, document)
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combined = list(
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zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
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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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# 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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# 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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"embeddings": None,
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"uris": None,
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"data": None,
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}
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return merged_query_results
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def query_collection(
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collection_names: List[str], query: str, k: int, embedding_function
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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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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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embedding_function=embedding_function,
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)
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result = collection.query(
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query_texts=[query],
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n_results=k,
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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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return merge_and_sort_query_results(results, k)
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