mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
9ffca3b92a
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'
```
76 lines
3.4 KiB
Python
76 lines
3.4 KiB
Python
from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import ConfigurableField
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WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text." # noqa: E501
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# Report prompts from https://github.com/assafelovic/gpt-researcher/blob/master/gpt_researcher/master/prompts.py
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RESEARCH_REPORT_TEMPLATE = """Information:
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--------
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{research_summary}
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--------
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Using the above information, answer the following question or topic: "{question}" in a detailed report -- \
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The report should focus on the answer to the question, should be well structured, informative, \
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in depth, with facts and numbers if available and a minimum of 1,200 words.
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You should strive to write the report as long as you can using all relevant and necessary information provided.
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You must write the report with markdown syntax.
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You MUST determine your own concrete and valid opinion based on the given information. Do NOT deter to general and meaningless conclusions.
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Write all used source urls at the end of the report, and make sure to not add duplicated sources, but only one reference for each.
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You must write the report in apa format.
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Please do your best, this is very important to my career.""" # noqa: E501
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RESOURCE_REPORT_TEMPLATE = """Information:
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--------
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{research_summary}
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--------
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Based on the above information, generate a bibliography recommendation report for the following question or topic: "{question}". \
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The report should provide a detailed analysis of each recommended resource, explaining how each source can contribute to finding answers to the research question. \
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Focus on the relevance, reliability, and significance of each source. \
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Ensure that the report is well-structured, informative, in-depth, and follows Markdown syntax. \
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Include relevant facts, figures, and numbers whenever available. \
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The report should have a minimum length of 1,200 words.
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Please do your best, this is very important to my career.""" # noqa: E501
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OUTLINE_REPORT_TEMPLATE = """Information:
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--------
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{research_summary}
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--------
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Using the above information, generate an outline for a research report in Markdown syntax for the following question or topic: "{question}". \
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The outline should provide a well-structured framework for the research report, including the main sections, subsections, and key points to be covered. \
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The research report should be detailed, informative, in-depth, and a minimum of 1,200 words. \
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Use appropriate Markdown syntax to format the outline and ensure readability.
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Please do your best, this is very important to my career.""" # noqa: E501
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model = ChatOpenAI(temperature=0)
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", WRITER_SYSTEM_PROMPT),
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("user", RESEARCH_REPORT_TEMPLATE),
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]
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).configurable_alternatives(
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ConfigurableField("report_type"),
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default_key="research_report",
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resource_report=ChatPromptTemplate.from_messages(
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[
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("system", WRITER_SYSTEM_PROMPT),
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("user", RESOURCE_REPORT_TEMPLATE),
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]
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),
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outline_report=ChatPromptTemplate.from_messages(
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[
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("system", WRITER_SYSTEM_PROMPT),
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("user", OUTLINE_REPORT_TEMPLATE),
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]
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),
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)
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chain = prompt | model | StrOutputParser()
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