mirror of
https://github.com/hwchase17/langchain
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73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
from langchain.callbacks import streaming_stdout
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from langchain_chroma import Chroma
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain_community.llms import HuggingFaceEndpoint
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from langchain_core.vectorstores import VectorStoreRetriever
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# Make this look better in the docs.
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class Question(BaseModel):
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__root__: str
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# Init Embeddings
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embedder = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
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knowledge_base = Chroma(
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persist_directory="/tmp/xeon_rag_db",
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embedding_function=embedder,
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collection_name="xeon-rag",
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)
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query = "What was Nike's revenue in 2023?"
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docs = knowledge_base.similarity_search(query)
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print(docs[0].page_content)
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retriever = VectorStoreRetriever(
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vectorstore=knowledge_base, search_type="mmr", search_kwargs={"k": 1, "fetch_k": 5}
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)
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# Define our prompt
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template = """
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Use the following pieces of context from retrieved
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dataset to answer the question. Do not make up an answer if there is no
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context provided to help answer it.
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Context:
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---------
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{context}
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---------
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Question: {question}
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---------
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Answer:
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"""
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prompt = ChatPromptTemplate.from_template(template)
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ENDPOINT_URL = "http://localhost:8080"
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callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()]
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model = HuggingFaceEndpoint(
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endpoint_url=ENDPOINT_URL,
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max_new_tokens=512,
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top_k=10,
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top_p=0.95,
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typical_p=0.95,
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temperature=0.01,
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repetition_penalty=1.03,
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streaming=True,
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)
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# RAG Chain
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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).with_types(input_type=Question)
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