Spaces:
Runtime error
Runtime error
File size: 1,966 Bytes
13dee23 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
from dotenv import load_dotenv
import os
from langchain.memory import VectorStoreRetrieverMemory
from langchain_community.vectorstores.redis import Redis
from langchain.embeddings import OpenAIEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_core.runnables import ConfigurableField
load_dotenv()
redis_url = os.getenv("REDIS_URL")
openai_key = os.getenv("OPENAI_API_KEY")
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
embedding_fn = OpenAIEmbeddings(openai_api_key=openai_key)
#! Alternatively, can use Hugging Face embeddings if you don't have one
# modelPath = "HuggingFaceH4/zephyr-7b-beta"
# model_kwargs = {'device':'cpu'}
# encode_kwargs = {'normalize_embeddings':False}
# embedding_fn = HuggingFaceEmbeddings(
# model_name = modelPath,
# model_kwargs = model_kwargs,
# encode_kwargs=encode_kwargs
# )
schema = {'text': [{'name': 'content',
'weight': 1,
'no_stem': False,
'withsuffixtrie': False,
'no_index': False,
'sortable': False}],
'vector': [{'name': 'content_vector',
'dims': 1536,
'algorithm': 'FLAT',
'datatype': 'FLOAT32',
'distance_metric': 'COSINE'}]}
def vectorstore_as_memory(username):
try:
new_rds = Redis.from_existing_index(
embedding=embedding_fn,
index_name=username,
redis_url=redis_url,
# schema=rds.schema,
schema=schema,
)
retriever = new_rds.as_retriever(search_type="similarity", search_kwargs={"k": 3})
memory = VectorStoreRetrieverMemory(retriever=retriever)
return memory
except ValueError:
rds = Redis.from_texts(
texts=["Hi there"],
embedding=embedding_fn,
redis_url=redis_url,
index_name=username
)
retriever = rds.as_retriever(search_type="similarity", search_kwargs={"k": 3})
memory = VectorStoreRetrieverMemory(retriever=retriever)
return memory
|