|
import chainlit as cl |
|
from langchain.embeddings.openai import OpenAIEmbeddings |
|
from langchain.document_loaders.csv_loader import CSVLoader |
|
from langchain.embeddings import CacheBackedEmbeddings |
|
from langchain.text_splitter import RecursiveCharacterTextSplitter |
|
from langchain.vectorstores import FAISS |
|
from langchain.chains import RetrievalQA |
|
from langchain.chat_models import ChatOpenAI |
|
from langchain.storage import LocalFileStore |
|
from langchain.prompts.chat import ( |
|
ChatPromptTemplate, |
|
SystemMessagePromptTemplate, |
|
HumanMessagePromptTemplate, |
|
) |
|
import chainlit as cl |
|
|
|
import build_langchain_vector_store |
|
|
|
build_langchain_vector_store |
|
|
|
from langchain.vectorstores import Chroma |
|
from langchain.embeddings import OpenAIEmbeddings |
|
import openai |
|
import os |
|
|
|
openai.api_key = os.getenv("OPENAI_API_KEY") |
|
openai.api_base = 'https://api.openai.com/v1' |
|
|
|
embedding_model_name = "text-embedding-ada-002" |
|
embedding_model = OpenAIEmbeddings(model=embedding_model_name) |
|
read_vector_store = Chroma( |
|
persist_directory="langchain-chroma-pulze-docs", embedding_function=embedding_model |
|
) |
|
query_results = read_vector_store.similarity_search("How do I use Pulze?") |
|
print(query_results[0].page_content) |