Similarity search with relevance score langchain reddit. : If you have 5 documents with scores of 0.
Similarity search with relevance score langchain reddit similarity_search_with_score( query, k=100 ) However when changing the Score-Treshold I am still getting back the same documents. 0 is dissimilar, 1 is most similar. Is this a bug in Langchain, pls help. I have a trained Mini LM to conduct embedding product searches like a normal e-commerce website search bar. Parameters. - If for example only one doc has a higher similarity than 0. Jul 13, 2023 · On the other hand, I have read that the vectordb. Facebook AI Similarity Search (FAISS) is a library for efficient similarity search and clustering of dense vectors. 8} ) However the score_threshold doesn't return any documents even for the lowest threshold. 7 why pass more documents? and Vice versa, if those 5 have scores of 0. 1 or 0. In my understanding it should work as follows: - Search the top 3 docs that have a similarity score of 0. Guys, I'm doing a similarity search and using relevance scores because I understand relevance scores return scores between 0 and 1. The relevancy score gives you how ‘’close’’ your query is from the retrieved documents. similarity_search_with_score( query, k=100 ) We would like to show you a description here but the site won’t allow us. as_retriever( search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0. However when I use custom code for chroma or faiss, I get scores between 0 and 1. 9. The data is stored in a chroma database and currently, I'm searching it like this: raw_results = chroma_instance. 2 you might want to pass 5 documents more, hoping those additional might improve your answer. query (str) – Input text. similarity_search_with_score() method in dozens of comparisons. To propagate the scores, we subclass MultiVectorRetriever and override its _get_relevant_documents method. 9, only retrieve one doc. Jun 28, 2024 · similarity_search_with_relevance_scores (query: str, k: int = 4, ** kwargs: Any) → List [Tuple [Document, float]] [source] ¶ Return docs and relevance scores in the range [0, 1]. similarity_search_with_score("Was sind die Grundlagen eines Managementsystems (BCMS)?") But with this approach I am not sure how to set 'k' and the 'score_threshold'. . A usual usage is to set a treshold for the scores of the docs from the initial retrieval to only feed your llm the docs with their relevancy score above this treshold. E. Posted by u/Trick-Asparagus-9260 - 1 vote and no comments However when changing the Score-Treshold I am still getting back the same documents. : If you have 5 documents with scores of 0. k (int) – Number of Documents to return. However when I use Langchain to return these scores, they come back in negatives. It also includes supporting code for evaluation and parameter tuning. Here we will make two changes: We will add similarity scores to the metadata of the corresponding "sub-documents" using the similarity_search_with_score method of the underlying vector store as above; Jun 8, 2024 · To implement a similarity search with a score based on a similarity threshold using LangChain and Chroma, you can use the similarity_search_with_relevance_scores method provided in the VectorStore class. Any hints on how I can return the scores with the get_relevant_docs function? Jun 28, 2024 · similarity_search_with_relevance_scores (query: str, k: int = 4, ** kwargs: Any) → List [Tuple [Document, float]] [source] ¶ Return docs and relevance scores in the range [0, 1]. Defaults to 4. Alternatively I want to return the score with this, but don't know how. **kwargs (Any) – # Only retrieve documents that have a relevance score # Above a certain threshold docsearch. 9 or higher. (ChatGPT tells me that they're all mostly relevant) We would like to show you a description here but the site won’t allow us. This method returns a list of documents along with their relevance scores, which are normalized between 0 and 1. Here we will make two changes: We will add similarity scores to the metadata of the corresponding "sub-documents" using the similarity_search_with_score method of the underlying vector store as above; Depending on the score of the retriev documents, one could pass more or less documents to the LLM. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. g. I can return it with: vectorstore. currently just doing vanilla `similarity` search also, to clarify, for search, should I pick an embedder that ranks well among which of these tasks? Bitext mining, classification, clustering, pair classification, reranking, retrieval, STS, summarization. So there is no difference if I set it to 0. similarity_search_with_relevance_scores() method is more sophisticated and requires more processing to calculate the similarity score, but I got exactly the same results nearly same duration with vectordb. udgcfkjcnxwvqucrrxlnapwcullmgbqtbgnfauamrfljitkhxohv