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66c41c0dbf
This PR adds a self-querying template using Qdrant as a vector store. The template uses an artificial dataset and was implemented in a way that simplifies passing different components and choosing LLM and embedding providers. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
17 lines
550 B
Python
17 lines
550 B
Python
from langchain.prompts import PromptTemplate
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llm_context_prompt_template = """
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Answer the user query using provided passages. Each passage has metadata given as
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a nested JSON object you can also use. When answering, cite source name of the passages
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you are answering from below the answer in a unique bullet point list.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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----
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{context}
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----
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Query: {query}
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""" # noqa: E501
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LLM_CONTEXT_PROMPT = PromptTemplate.from_template(llm_context_prompt_template)
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