509d5c66ca
Signed-off-by: chensuyue <suyue.chen@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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#
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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_community.vectorstores import Redis
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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 rag_redis.config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL, TGI_LLM_ENDPOINT
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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=EMBED_MODEL)
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# Setup semantic cache for LLM
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from langchain.cache import RedisSemanticCache
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from langchain.globals import set_llm_cache
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set_llm_cache(RedisSemanticCache(embedding=embedder, redis_url=REDIS_URL))
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# Connect to pre-loaded vectorstore
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# run the ingest.py script to populate this
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vectorstore = Redis.from_existing_index(
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embedding=embedder, index_name=INDEX_NAME, schema=INDEX_SCHEMA, redis_url=REDIS_URL
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)
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# TODO allow user to change parameters
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retriever = vectorstore.as_retriever(search_type="mmr")
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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. Include the 'source' and 'start_index'
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from the metadata included in the context you used to answer the question
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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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# RAG Chain
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model = HuggingFaceEndpoint(
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endpoint_url=TGI_LLM_ENDPOINT,
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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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truncate=1024,
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)
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) | prompt | model | StrOutputParser()
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).with_types(input_type=Question)
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