mirror of
https://github.com/hwchase17/langchain.git
synced 2025-10-27 05:41:31 +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 ```
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
import os
|
|
|
|
from langchain.prompts import PromptTemplate
|
|
from langchain_community.embeddings import VertexAIEmbeddings
|
|
from langchain_community.llms import VertexAI
|
|
from langchain_community.vectorstores import MatchingEngine
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
|
|
|
|
# you need to preate the index first, for example, as described here:
|
|
# https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-qa/question_answering_documents_langchain_matching_engine.ipynb
|
|
expected_variables = [
|
|
"project_id",
|
|
"me_region",
|
|
"gcs_bucket",
|
|
"me_index_id",
|
|
"me_endpoint_id",
|
|
]
|
|
variables = []
|
|
for variable_name in expected_variables:
|
|
variable = os.environ.get(variable_name.upper())
|
|
if not variable:
|
|
raise Exception(f"Missing `{variable_name}` environment variable.")
|
|
variables.append(variable)
|
|
|
|
project_id, me_region, gcs_bucket, me_index_id, me_endpoint_id = variables
|
|
|
|
|
|
vectorstore = MatchingEngine.from_components(
|
|
project_id=project_id,
|
|
region=me_region,
|
|
gcs_bucket_name=gcs_bucket,
|
|
embedding=VertexAIEmbeddings(),
|
|
index_id=me_index_id,
|
|
endpoint_id=me_endpoint_id,
|
|
)
|
|
|
|
model = VertexAI()
|
|
|
|
template = (
|
|
"SYSTEM: You are an intelligent assistant helping the users with their questions"
|
|
"on research papers.\n\n"
|
|
"Question: {question}\n\n"
|
|
"Strictly Use ONLY the following pieces of context to answer the question at the "
|
|
"end. Think step-by-step and then answer.\n\n"
|
|
"Do not try to make up an answer:\n"
|
|
"- If the answer to the question cannot be determined from the context alone, "
|
|
'say \n"I cannot determine the answer to that."\n'
|
|
'- If the context is empty, just say "I do not know the answer to that."\n\n'
|
|
"=============\n{context}\n=============\n\n"
|
|
"Question: {question}\nHelpful Answer: "
|
|
)
|
|
|
|
prompt = PromptTemplate.from_template(template)
|
|
|
|
retriever = vectorstore.as_retriever(
|
|
search_type="similarity",
|
|
search_kwargs={
|
|
"k": 10,
|
|
"search_distance": 0.6,
|
|
},
|
|
)
|
|
|
|
chain = (
|
|
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
|
|
| prompt
|
|
| model
|
|
| StrOutputParser()
|
|
)
|
|
|
|
|
|
# Add typing for input
|
|
class Question(BaseModel):
|
|
__root__: str
|
|
|
|
|
|
chain = chain.with_types(input_type=Question)
|