from langchain.indexes import VectorstoreIndexCreator from langchain_community.docstore.document import Document from langchain_community.utilities import ApifyWrapper from langchain_community.vectorstores import FAISS from langchain_cohere import CohereEmbeddings from langchain_cohere import CohereRagRetriever,...
System Info Langchain 0.0.171, Python 3.9.0, OS Ubuntu 20.04.6 LTS Hi @hwchase17 @agola11 Using dolly-v2-7b model with Langchain, I am running into this issue my question is how to chain the input properly so that chunk from the first ch...
from langchain.document_loaders import PyPDFLoader from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import FAISS from dotenv import load_dotenv import openai import os #load environment variables load_dotenv() OPENAI_API_KEY = os.getenv("...
To use theEnsembleRetriever, you need to initialize it with a list of retrievers and their corresponding weights. The retrievers can be instances of different retrieval algorithms, such asBM25RetrieverandFAISSRetriever. The weights determine the importance of each retriever in the ensemble. TheEnsemble...
Then, you can proceed with creating the FAISS instance and the retriever as you've done in your code. This solution is based on a similar issue found in the LangChain repository: AttributeError: Document object has not attribute replace. The user in that issue encountered a similar problem ...
langchain==0.2.14 langchain_community==0.2.12 langchain_core==0.2.35 langchain_openai==0.1.22 python-dotenv==1.0.1 streamlit==1.37.1 faiss-cpu pypdf and install them: $ pip install -r requirements.txt Also, create a .gitignore file to hide files from git indexing: # .gitignore ...
langchain==0.2.14 langchain_community==0.2.12 langchain_core==0.2.35 langchain_openai==0.1.22 python-dotenv==1.0.1 streamlit==1.37.1 faiss-cpu pypdf and install them: $ pip install -r requirements.txt Also, create a .gitignore file to hide files from git indexing: # .gi...
environment variables are set, it means we are running locallycontext.log('No Azure OpenAI endpoint set, using Ollama models and local DB');constembeddings=newOllamaEmbeddings({model:ollamaEmbeddingsModel});conststore=awaitFaissStore.fromDocuments(documents,embeddings,{});awaitstore.sa...
We used the specific method provided by FAISS, similarity_search_with_score, when searching for relevant documents. This is because it can also provide the metadata and similarity score of the retrieved source file. The returned distance score is L2 distance. Th...
A Proof of Concept / Quick Start for Retrieval Augmented Generation using Langchain, Python, FAISS and Streamlit. ragpoc.streamlit.app/ Topics python openai faiss streamlit langchain Resources Readme License Apache-2.0 license Activity Stars 0 stars Watchers 1 watching Forks 0 forks ...