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| # import basics | |
| import os | |
| from dotenv import load_dotenv | |
| # import pinecone | |
| from pinecone import Pinecone, ServerlessSpec | |
| # import langchain | |
| from langchain_pinecone import PineconeVectorStore | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings | |
| from langchain_core.documents import Document | |
| load_dotenv() | |
| # initialize pinecone database | |
| pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY")) | |
| # set the pinecone index | |
| index_name = "sample-index" | |
| index = pc.Index(index_name) | |
| # initialize embeddings model + vector store | |
| embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001") | |
| vector_store = PineconeVectorStore(index=index, embedding=embeddings) | |
| # retrieval | |
| ''' | |
| ###### add docs to db ############################## | |
| results = vector_store.similarity_search_with_score( | |
| "what did you have for breakfast?", | |
| #k=2, | |
| filter={"source": "tweet"}, | |
| ) | |
| print("RESULTS:") | |
| for res in results: | |
| print(f"* {res[0].page_content} [{res[0].metadata}] -- {res[1]}") | |
| ''' | |
| retriever = vector_store.as_retriever( | |
| search_type="similarity_score_threshold", | |
| search_kwargs={"k": 5, "score_threshold": 0.6}, | |
| ) | |
| results = retriever.invoke("what did you have for breakfast?") | |
| print("RESULTS:") | |
| for res in results: | |
| print(f"* {res.page_content} [{res.metadata}]") | |
| #''' | |