Similarity search with relevance score langchain github. If so, please let me know how
Jul 21, 2023 · vectordb.
Similarity search with relevance score langchain github This is code which i am using. k (int) – Number of Documents to return. query (str) – Input text. Jul 13, 2023 · I have been working with langchain's chroma vectordb. It has two methods for running similarity search with scores. 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]. @mikquinlan, _similarity_search_with_relevance_scores could certainly be developped to be consistent with the other vector stores. langchain==0. 85, but I got warning No relevant docs were retrieved using the relevance score threshold 0. Defaults to 4. The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package). This could be due to the way the relevance score function is determined in the _select_relevance_score_fn method. Example Code. I am sure that this is a b Aug 3, 2023 · It seems like you're having trouble with the similarity_search_with_score() function in your chat app that uses the faiss document store. it seems that the similarity_search_with_score (supposedly ranked by distance: low to high) and similarity_search_with_relevance_scores((supposedly ranked by relevance: high to low) produce conflicting results when specifying MAX_INNER_PRODUCT as the distance strategy. System Info. The relevance score function normalizes the raw similarity scores, and if it is not appropriately defined, it can result Facebook AI Similarity Search (FAISS) is a library for efficient similarity search and clustering of dense vectors. NodeJS Code:. 9 mac. Mar 25, 2024 · Based on the information provided, it seems like you're using the correct methods in both Python and NodeJS for similarity search with relevance scores. similarity_search_with_score() vectordb. similarity_search(query_document, k=n_results, filter = {}) I have checked through documentation of chroma but didnt get any solution. Here are some suggestions that might help improve the performance of your similarity search: Improve the Embeddings: The quality of the embeddings plays a crucial role in the performance of the similarity The returned score is not in the range of [0,1], rather it is a relatively large negative number such as -172. Who can help? I used the FAISS as the vector store. 597. [ ] Jul 27, 2024 · The similarity_search_with_relevance_scores method in LangChain may return a score of 0. From cosine similarity score to relevance score, we could simple return 1 - consine_similarity (or similar transformation) and ensure it is consistent with what the other vector stores return in their implementations. 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; To obtain scores from a vector store retriever, we wrap the underlying vector store's . 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. The similarity_search_with_relevance_scores method from the VectorStore class is used to retrieve documents along with their relevance scores, which are then added to the document metadata . 75 for a query that you believe should have a higher similarity score due to the way the relevance score function is defined and applied. 0. `def similarity_search(self, query: str, k: int = DEFAULT_K, filter: Optional[Dict[str, str Jul 7, 2024 · Yes, after configuring Chroma, Faiss, and Pinecone to use cosine similarity instead of cosine distance, higher scores indicate higher similarity in both the similarity_search_with_score and similarity_search_by_vector_with_relevance_scores functions . 0 is dissimilar, 1 is most similar. Aug 30, 2023 · The similarity scores returned by the similarity_search_with_score and similarity_search_by_vector_with_relevance_scores methods in the ElasticsearchStore class are indeed not directly interpretable as percentages. I am sure that this is a bug in LangChain rather than my code. From my understanding it should return me at least 3 docs since we do have docs similar higher than 0. The similarity_search_with_relevance_scores method in Python and the similaritySearchWithScore method in NodeJS should ideally provide similar functionality. They are based on the distance metric used (cosine similarity, dot product, or Euclidean distance) and the specific vectors involved. Parameters. I used the GitHub search to find a similar question and didn't find it. I wanted to let you know that we are marking this issue as stale. I am only providing the query. 268 python=3. I searched the LangChain documentation with the integrated search. Smaller the better. **kwargs (Any) – To propagate the scores, we subclass MultiVectorRetriever and override its _get_relevant_documents method. vectordb. 8 with an empty return Checked other resources I added a very descriptive title to this issue. similarity_search_with_score method in a short function that packages scores into the associated document's metadata. Jun 8, 2024 · This code ensures that the similarity scores are included in the metadata of the documents returned by the ContextualCompressionRetriever. similarity_search_with_relevance_scores() According to the documentation, the first one should return a cosine distance in float. This method returns a list of documents along with their relevance scores, which are normalized between 0 and 1. I am using langchain with bedrock under aws. Can you please help me out filer Like what i need to pass in filter section. We add a @chain decorator to the function to create a Runnable that can be used similarly to a typical retriever. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. To continue talking to Dosu, mention @dosu. If so, please let me know how Jul 21, 2023 · vectordb. From what I understand, you opened this issue regarding a missing "kwargs" parameter in the chroma function _similarity_search_with_relevance_scores. Dec 4, 2023 · Based on your description, it seems like the similarity_search_with_score method in the Neo4jVector class of LangChain is returning incoherent relevance scores. I wonder if should set something at the point of building the vectorstore or should have set the score function before using it. Jun 24, 2023 · Hi, @sudolong!I'm Dosu, and I'm helping the LangChain team manage their backlog. uyftfmmpnizblaxcoskszzdlwuzotpgnzpwhkkhrlzefrpx