commit
10cc7c5267
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
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from langchain.prompts import load_from_hub
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from langchain.chains import APIChain
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llm = ...
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api_docs = ...
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prompt = load_from_hub('api/api_response/<file-name>')
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chain = APIChain.from_llm_and_api_docs(llm, api_docs, api_response_prompt=prompt)
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```
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|
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
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2. ...
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## Inputs
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||||
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||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
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||||
2. ...
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|
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## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
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from langchain.prompts import load_from_hub
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from langchain.chains import APIChain
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llm = ...
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api_docs = ...
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prompt = load_from_hub('api/api_url/<file-name>')
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chain = APIChain.from_llm_and_api_docs(llm, api_docs, api_url_prompt=prompt)
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```
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|
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
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2. ...
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## Inputs
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||||
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This is a description of the inputs that the prompt expects.
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||||
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1. {{input_var}}: {{Description}}
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||||
2. ...
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|
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## Usage
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|
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Below is a code snippet for how to use the prompt.
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```python
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from langchain.prompts import load_from_hub
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from langchain.chains import ConversationChain
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llm = ...
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prompt = load_from_hub('conversation/basic/<file-name>')
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chain = ConversationChain(llm=llm, prompt=prompt)
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```
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
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{{High level text description of the prompt, including use cases.}}
|
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|
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## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
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2. ...
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## Inputs
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This is a description of the inputs that the prompt expects.
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1. {{input_var}}: {{Description}}
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2. ...
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## Usage
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Below is a code snippet for how to use the prompt.
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```python
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from langchain.prompts import load_from_hub
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from langchain.chains import ConversationChain
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from langchain.chains.conversation.memory import ConversationSummaryMemory
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llm = ...
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prompt = load_from_hub('conversation/summarize/<file-name>')
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memory = ConversationSummaryMemory(llm=llm, prompt=prompt)
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chain = ConversationChain(llm=llm, memory=memory)
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```
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# Hello World
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> A simple prompt as an example
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<!-- Add a template for READMEs that capture the utility of prompts -->
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# Description of {{prompt}}
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{{High level text description of the prompt, including use cases.}}
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|
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## Compatible Chains
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||||
|
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Below is a list of chains we expect this prompt to be compatible with.
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|
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1. {{Chain Name}}: {{Path to chain in module}}
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||||
2. ...
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## Inputs
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||||
|
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This is a description of the inputs that the prompt expects.
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||||
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1. {{input_var}}: {{Description}}
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2. ...
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## Configuration
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- input_variables: []
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- There are no inputs
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- output_parser: null
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- There is no output parsing needed
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- template: 'Say hello world.'
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- Just a simple hello.
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template_format: f-string
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- We use standard f-string formatting here.
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## Usage
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Ex:
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```python3
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Below is a code snippet for how to use the prompt.
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```python
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from langchain.prompts import load_from_hub
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from langchain.llms import OpenAI
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from langchain.prompts.loading import load_prompt
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from langchain.chains import LLMChain
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llm = OpenAI(temperature=0.9)
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# prompt = load_from_hub("hello-world/prompt.yaml")
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output = llm(prompt.format())
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print(output)
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llm = ...
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prompt = load_from_hub('hello-world/<file-name>')
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chain = LLMChain(llm=llm, prompt=prompt)
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```
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A prompt to generate bash commands given natural language objectives.
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<!-- Add a template for READMEs that capture the utility of prompts -->
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Inputs:
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1. question
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# Description of {{prompt}}
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|
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{{High level text description of the prompt, including use cases.}}
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||||
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## Compatible Chains
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1. `chains/llm_bash/LLMBashChain`
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Below is a list of chains we expect this prompt to be compatible with.
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||||
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||||
1. {{Chain Name}}: {{Path to chain in module}}
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||||
2. ...
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## Inputs
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||||
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This is a description of the inputs that the prompt expects.
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||||
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||||
1. {{input_var}}: {{Description}}
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||||
2. ...
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||||
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||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
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||||
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```python
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from langchain.prompts import load_from_hub
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from langchain.chains import LLMBash
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llm = ...
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prompt = load_from_hub('llm_bash/<file-name>')
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chain = LLMBash(llm=llm, prompt=prompt)
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```
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|
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
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||||
2. ...
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||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
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||||
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||||
```python
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||||
from langchain.prompts import load_from_hub
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from langchain.chains import LLMMathChain
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llm = ...
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||||
prompt = load_from_hub('llm_math/<file-name>')
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chain = LLMMathChain(llm=llm, prompt=prompt)
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```
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|
@ -1,4 +1,36 @@
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Answer a math question in python.
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<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
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||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
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||||
from langchain.prompts import load_from_hub
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||||
from langchain.chains import PALChain
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||||
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||||
llm = ...
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||||
stop = ...
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||||
get_answer_expr = ...
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||||
prompt = load_from_hub('pal/<file-name>')
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||||
chain = PALChain(llm=llm, prompt=prompt, stop=stop, get_answer_expr=get_answer_expr)
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||||
```
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||||
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||||
Inputs:
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||||
1. question
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||||
This prompt is a basic implementation of a question answering prompt for a map-reduce chain.
|
||||
Specifically, it is the "map" prompt that gets applied to all input documents.
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||||
It takes in a single variable for the document (`context`) and then a variable for the question (`question`)
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('qa/map_reduce/question/<file-name>')
|
||||
chain = load_qa_chain(llm, chain_type="map_reduce", question_prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,3 +1,34 @@
|
||||
This prompt is a basic implementation of a question answering prompt for a map-reduce chain.
|
||||
Specifically, it is the "reduce" prompt that gets applied documents after they have initially been asked for an answer.
|
||||
It takes in a single variable for the initial responses (`summaries`) and then a variable for the question (`question`)
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('qa/map_reduce/reduce/<file-name>')
|
||||
chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,6 +1,34 @@
|
||||
This prompt refines an existing answer to a question given context, with additional instructions to update the sources used.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('qa/refine/<file-name>')
|
||||
chain = load_qa_chain(llm, chain_type="refine", refine_prompt=prompt)
|
||||
```
|
||||
|
||||
The inputs are:
|
||||
1. question
|
||||
2. existing_answer
|
||||
3. context_str
|
@ -1,6 +1,34 @@
|
||||
This is a question answering prompt for a "stuff" chain that cites sources.
|
||||
It takes variables for all the documents (`context`) and then a variable for the question (`question`)
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
1. `chains/qa_with_sources`
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.question_answering import load_qa_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('qa/stuff/<file-name>')
|
||||
chain = load_qa_chain(llm, chain_type="stuff", prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,6 +1,35 @@
|
||||
This prompt takes a natural language question, converts the question into a SQL query, executes the query and gets the result, and finally returns the final answer to the user.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
1. `chains/sql_database.py`
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains import SQLDatabaseChain
|
||||
|
||||
llm = ...
|
||||
database = ...
|
||||
prompt = load_from_hub('sql_query/language_to_sql_output/<file-name>')
|
||||
chain = SQLDatabaseChain(llm=llm, database=database, prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,6 +1,35 @@
|
||||
This prompt takes a question and a list of potential tables, and determines which tables in a database would be most relevant.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
1. `chains/sql_database.py`
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains import SQLDatabaseSequentialChain
|
||||
|
||||
llm = ...
|
||||
database = ...
|
||||
prompt = load_from_hub('sql_query/relevant_tables/<file-name>')
|
||||
chain = SQLDatabaseSequentialChain.from_llm(llm, database, decider_prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,9 +0,0 @@
|
||||
# Summarize
|
||||
This is a type of chain to distill large amounts of information into a small amount.
|
||||
|
||||
There are three types of summarize chains:
|
||||
1. Stuff: This is a simple chain to stuff all the text from each document into one propmt. This is limited by token window so this approach only works for smaller amounts of data.
|
||||
2. Map Reduce: This maps a summarize prompt onto each data chunk and then combines all the outputs to finally reduce using a summarization prompt.
|
||||
3. Refine: This iteratively passes in each chunk of data and update a continously update an evolving summary to be more accurate based on the new chunk of data given.
|
||||
|
||||
## Usage
|
@ -1,2 +0,0 @@
|
||||
# Map Reduce
|
||||
This maps a summarize prompt onto each data chunk and then combines all the outputs to finally reduce using a summarization prompt.
|
@ -0,0 +1,34 @@
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.summarize import load_summarize_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('summarize/map_reduce/map/<file-name>')
|
||||
chain = load_summarize_chain(llm, chain_type="map_reduce", map_prompt=prompt)
|
||||
```
|
||||
|
@ -1,2 +1,34 @@
|
||||
# Refine
|
||||
This iteratively passes in each chunk of data and update a continously update an evolving summary to be more accurate based on the new chunk of data given.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.summarize import load_summarize_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('summarize/refine/<file-name>')
|
||||
chain = load_summarize_chain(llm, chain_type="refine", refine_prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,13 +0,0 @@
|
||||
# Prompt config for a chain?
|
||||
|
||||
input_variables: ['text']
|
||||
output_parser: null
|
||||
# what primitives do we allow?
|
||||
template: [
|
||||
map:
|
||||
input: 'text'
|
||||
prompt: 'prompt.yaml',
|
||||
reduce:
|
||||
prompt: 'promptSummarize.yaml'
|
||||
]
|
||||
template_format: f-string
|
@ -1,10 +0,0 @@
|
||||
input_variables: [text]
|
||||
output_parser: null
|
||||
template: 'Write a concise summary of the following:
|
||||
|
||||
|
||||
{text}
|
||||
|
||||
|
||||
CONCISE SUMMARY:'
|
||||
template_format: f-string
|
@ -1,2 +1,34 @@
|
||||
# Stuff
|
||||
This is a simple chain to stuff all the text from each document into one propmt. This is limited by token window so this approach only works for smaller amounts of data.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.chains.summarize import load_summarize_chain
|
||||
|
||||
llm = ...
|
||||
prompt = load_from_hub('summarize/stuff/<file-name>')
|
||||
chain = load_summarize_chain(llm, chain_type="stuff", prompt=prompt)
|
||||
```
|
||||
|
||||
|
@ -1,19 +1,35 @@
|
||||
This prompt is a simple prompt that answers a question given some context. If the answer isn't provided in the context, then the prompt suggests the LLM to return `I don't know`.
|
||||
<!-- Add a template for READMEs that capture the utility of prompts -->
|
||||
|
||||
# Description of {{prompt}}
|
||||
|
||||
{{High level text description of the prompt, including use cases.}}
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
Below is a list of chains we expect this prompt to be compatible with.
|
||||
|
||||
1. {{Chain Name}}: {{Path to chain in module}}
|
||||
2. ...
|
||||
|
||||
## Inputs
|
||||
|
||||
This is a description of the inputs that the prompt expects.
|
||||
|
||||
1. {{input_var}}: {{Description}}
|
||||
2. ...
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
```
|
||||
Below is a code snippet for how to use the prompt.
|
||||
|
||||
```python
|
||||
from langchain.prompts import load_from_hub
|
||||
from langchain.llms import OpenAI
|
||||
from langchain.prompts.loading import load_prompt
|
||||
from langchain.chains import VectorDBQA
|
||||
|
||||
llm = OpenAI(temperature=0.9)
|
||||
prompt = load_from_hub("vector_db_qa/prompt.py")
|
||||
output = llm(prompt.format(context="...", question="..."))
|
||||
print(output)
|
||||
llm = ...
|
||||
vectorstore = ...
|
||||
prompt = load_from_hub('vector_db_qa/<file-name>')
|
||||
chain = VectorDBQA.from_llm(llm, prompt=prompt, vectorstore=vectorstore)
|
||||
```
|
||||
|
||||
## Compatible Chains
|
||||
|
||||
1. `chains/vector_db_qa`
|
Loading…
Reference in New Issue