2023-10-27 02:44:30 +00:00
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import json
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2023-10-26 01:47:42 +00:00
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from typing import List, Optional
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2023-10-27 02:44:30 +00:00
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2023-10-26 01:47:42 +00:00
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from langchain.utils.openai_functions import convert_pydantic_to_openai_function
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2024-01-02 20:32:16 +00:00
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from langchain_community.chat_models import ChatOpenAI
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2024-01-03 21:28:05 +00:00
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from langchain_core.prompts import ChatPromptTemplate
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docs[patch], templates[patch]: Import from core (#14575)
Update imports to use core for the low-hanging fruit changes. Ran
following
```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'
```
2023-12-12 00:49:10 +00:00
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from langchain_core.pydantic_v1 import BaseModel
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2023-10-26 01:47:42 +00:00
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template = """A article will be passed to you. Extract from it all papers that are mentioned by this article.
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Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
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2023-10-27 02:44:30 +00:00
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Do not make up or guess ANY extra information. Only extract what exactly is in the text.""" # noqa: E501
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prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")])
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2023-10-26 01:47:42 +00:00
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# Function output schema
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class Paper(BaseModel):
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"""Information about papers mentioned."""
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title: str
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author: Optional[str]
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class Info(BaseModel):
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"""Information to extract"""
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papers: List[Paper]
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2023-10-27 02:44:30 +00:00
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2023-10-26 01:47:42 +00:00
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# Function definition
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model = ChatOpenAI()
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function = [convert_pydantic_to_openai_function(Info)]
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2023-10-27 02:44:30 +00:00
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chain = (
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prompt
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| model.bind(functions=function, function_call={"name": "Info"})
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| (
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lambda x: json.loads(x.additional_kwargs["function_call"]["arguments"])[
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"papers"
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]
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)
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)
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# chain = prompt | model.bind(
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# functions=function, function_call={"name": "Info"}
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# ) | JsonKeyOutputFunctionsParser(key_name="papers")
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