forked from Archives/langchain
change to baselanguagemodel (#1496)
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@ -3,7 +3,6 @@ from typing import Any, Dict, List, Optional
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from pydantic import BaseModel
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from langchain.chains.llm import LLMChain
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from langchain.llms.base import BaseLLM
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.memory.prompt import (
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ENTITY_EXTRACTION_PROMPT,
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@ -11,7 +10,7 @@ from langchain.memory.prompt import (
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)
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from langchain.memory.utils import get_buffer_string, get_prompt_input_key
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import BaseMessage
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from langchain.schema import BaseLanguageModel, BaseMessage
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class ConversationEntityMemory(BaseChatMemory, BaseModel):
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@ -19,7 +18,7 @@ class ConversationEntityMemory(BaseChatMemory, BaseModel):
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human_prefix: str = "Human"
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ai_prefix: str = "AI"
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llm: BaseLLM
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llm: BaseLanguageModel
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entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
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entity_summarization_prompt: BasePromptTemplate = ENTITY_SUMMARIZATION_PROMPT
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store: Dict[str, Optional[str]] = {}
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@ -5,7 +5,6 @@ from pydantic import BaseModel, Field
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from langchain.chains.llm import LLMChain
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from langchain.graphs import NetworkxEntityGraph
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from langchain.graphs.networkx_graph import KnowledgeTriple, get_entities, parse_triples
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from langchain.llms.base import BaseLLM
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.memory.prompt import (
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ENTITY_EXTRACTION_PROMPT,
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@ -13,7 +12,7 @@ from langchain.memory.prompt import (
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)
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from langchain.memory.utils import get_buffer_string, get_prompt_input_key
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import SystemMessage
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from langchain.schema import BaseLanguageModel, SystemMessage
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class ConversationKGMemory(BaseChatMemory, BaseModel):
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@ -29,7 +28,7 @@ class ConversationKGMemory(BaseChatMemory, BaseModel):
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kg: NetworkxEntityGraph = Field(default_factory=NetworkxEntityGraph)
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knowledge_extraction_prompt: BasePromptTemplate = KNOWLEDGE_TRIPLE_EXTRACTION_PROMPT
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entity_extraction_prompt: BasePromptTemplate = ENTITY_EXTRACTION_PROMPT
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llm: BaseLLM
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llm: BaseLanguageModel
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"""Number of previous utterances to include in the context."""
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memory_key: str = "history" #: :meta private:
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@ -3,18 +3,17 @@ from typing import Any, Dict, List
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from pydantic import BaseModel, root_validator
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from langchain.chains.llm import LLMChain
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from langchain.llms.base import BaseLLM
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.memory.prompt import SUMMARY_PROMPT
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from langchain.memory.utils import get_buffer_string
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import BaseMessage, SystemMessage
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from langchain.schema import BaseLanguageModel, BaseMessage, SystemMessage
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class SummarizerMixin(BaseModel):
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human_prefix: str = "Human"
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ai_prefix: str = "AI"
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llm: BaseLLM
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llm: BaseLanguageModel
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prompt: BasePromptTemplate = SUMMARY_PROMPT
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def predict_new_summary(
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