initial commit

venv/Lib/site-packages/langchain_classic/indexes/prompts/__init__.py

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+"""Relevant prompts for constructing indexes."""
+
+from langchain_core._api import warn_deprecated
+
+warn_deprecated(
+    since="0.1.47",
+    message=(
+        "langchain.indexes.prompts will be removed in the future."
+        "If you're relying on these prompts, please open an issue on "
+        "GitHub to explain your use case."
+    ),
+    pending=True,
+)

venv/Lib/site-packages/langchain_classic/indexes/prompts/entity_extraction.py

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+from langchain_core.prompts.prompt import PromptTemplate
+
+_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
+
+The conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.
+
+Return the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.
+Output: Langchain
+END OF EXAMPLE
+
+EXAMPLE
+Conversation history:
+Person #1: how's it going today?
+AI: "It's going great! How about you?"
+Person #1: good! busy working on Langchain. lots to do.
+AI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"
+Last line:
+Person #1: i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Person #2.
+Output: Langchain, Person #2
+END OF EXAMPLE
+
+Conversation history (for reference only):
+{history}
+Last line of conversation (for extraction):
+Human: {input}
+
+Output:"""  # noqa: E501
+ENTITY_EXTRACTION_PROMPT = PromptTemplate(
+    input_variables=["history", "input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE
+)

venv/Lib/site-packages/langchain_classic/indexes/prompts/entity_summarization.py

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+from langchain_core.prompts.prompt import PromptTemplate
+
+_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE = """You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.
+The update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.
+
+If there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.
+
+Full conversation history (for context):
+{history}
+
+Entity to summarize:
+{entity}
+
+Existing summary of {entity}:
+{summary}
+
+Last line of conversation:
+Human: {input}
+Updated summary:"""  # noqa: E501
+
+ENTITY_SUMMARIZATION_PROMPT = PromptTemplate(
+    input_variables=["entity", "summary", "history", "input"],
+    template=_DEFAULT_ENTITY_SUMMARIZATION_TEMPLATE,
+)

venv/Lib/site-packages/langchain_classic/indexes/prompts/knowledge_triplet_extraction.py

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+from langchain_core.prompts.prompt import PromptTemplate
+
+KG_TRIPLE_DELIMITER = "<|>"
+
+_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
+    "You are a networked intelligence helping a human track knowledge triples"
+    " about all relevant people, things, concepts, etc. and integrating"
+    " them with your knowledge stored within your weights"
+    " as well as that stored in a knowledge graph."
+    " Extract all of the knowledge triples from the text."
+    " A knowledge triple is a clause that contains a subject, a predicate,"
+    " and an object. The subject is the entity being described,"
+    " the predicate is the property of the subject that is being"
+    " described, and the object is the value of the property.\n\n"
+    "EXAMPLE\n"
+    "It's a state in the US. It's also the number 1 producer of gold in the US.\n\n"
+    f"Output: (Nevada, is a, state){KG_TRIPLE_DELIMITER}(Nevada, is in, US)"
+    f"{KG_TRIPLE_DELIMITER}(Nevada, is the number 1 producer of, gold)\n"
+    "END OF EXAMPLE\n\n"
+    "EXAMPLE\n"
+    "I'm going to the store.\n\n"
+    "Output: NONE\n"
+    "END OF EXAMPLE\n\n"
+    "EXAMPLE\n"
+    "Oh huh. I know Descartes likes to drive antique scooters and play the mandolin.\n"
+    f"Output: (Descartes, likes to drive, antique scooters){KG_TRIPLE_DELIMITER}(Descartes, plays, mandolin)\n"  # noqa: E501
+    "END OF EXAMPLE\n\n"
+    "EXAMPLE\n"
+    "{text}"
+    "Output:"
+)
+
+KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT = PromptTemplate(
+    input_variables=["text"],
+    template=_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE,
+)