mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
fa5d49f2c1
ran ```bash g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests/*/test_imports.py gco master libs/langchain/tests/unit_tests/**/test_public_api.py ```
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
import os
|
|
|
|
import cassio
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain_community.chat_models import ChatOpenAI
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores import Cassandra
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.runnables import RunnablePassthrough
|
|
|
|
from .populate_vector_store import populate
|
|
|
|
use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0"))
|
|
if use_cassandra:
|
|
from .cassandra_cluster_init import get_cassandra_connection
|
|
|
|
session, keyspace = get_cassandra_connection()
|
|
cassio.init(
|
|
session=session,
|
|
keyspace=keyspace,
|
|
)
|
|
else:
|
|
cassio.init(
|
|
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
|
|
database_id=os.environ["ASTRA_DB_ID"],
|
|
keyspace=os.environ.get("ASTRA_DB_KEYSPACE"),
|
|
)
|
|
|
|
|
|
# inits
|
|
llm = ChatOpenAI()
|
|
embeddings = OpenAIEmbeddings()
|
|
vector_store = Cassandra(
|
|
session=None,
|
|
keyspace=None,
|
|
embedding=embeddings,
|
|
table_name="langserve_rag_demo",
|
|
)
|
|
retriever = vector_store.as_retriever(search_kwargs={"k": 3})
|
|
|
|
# For demo reasons, let's ensure there are rows on the vector store.
|
|
# Please remove this and/or adapt to your use case!
|
|
inserted_lines = populate(vector_store)
|
|
if inserted_lines:
|
|
print(f"Done ({inserted_lines} lines inserted).")
|
|
|
|
entomology_template = """
|
|
You are an expert entomologist, tasked with answering enthusiast biologists' questions.
|
|
You must answer based only on the provided context, do not make up any fact.
|
|
Your answers must be concise and to the point, but strive to provide scientific details
|
|
(such as family, order, Latin names, and so on when appropriate).
|
|
You MUST refuse to answer questions on other topics than entomology,
|
|
as well as questions whose answer is not found in the provided context.
|
|
|
|
CONTEXT:
|
|
{context}
|
|
|
|
QUESTION: {question}
|
|
|
|
YOUR ANSWER:"""
|
|
|
|
entomology_prompt = ChatPromptTemplate.from_template(entomology_template)
|
|
|
|
chain = (
|
|
{"context": retriever, "question": RunnablePassthrough()}
|
|
| entomology_prompt
|
|
| llm
|
|
| StrOutputParser()
|
|
)
|