mirror of
https://github.com/hwchase17/langchain
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9eb7e6e27f
Still retain: - Comparison Examples - Data + QA walkthrough - QA (but really minimize it)
162 lines
3.8 KiB
Plaintext
162 lines
3.8 KiB
Plaintext
```python
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from langchain import PromptTemplate, OpenAI, LLMChain
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prompt_template = "What is a good name for a company that makes {product}?"
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llm = OpenAI(temperature=0)
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llm_chain = LLMChain(
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llm=llm,
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prompt=PromptTemplate.from_template(prompt_template)
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)
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llm_chain("colorful socks")
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```
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<CodeOutputBlock lang="python">
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```
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{'product': 'colorful socks', 'text': '\n\nSocktastic!'}
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```
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</CodeOutputBlock>
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## Additional ways of running LLM Chain
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Aside from `__call__` and `run` methods shared by all `Chain` object, `LLMChain` offers a few more ways of calling the chain logic:
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- `apply` allows you run the chain against a list of inputs:
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```python
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input_list = [
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{"product": "socks"},
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{"product": "computer"},
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{"product": "shoes"}
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]
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llm_chain.apply(input_list)
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```
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<CodeOutputBlock lang="python">
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```
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[{'text': '\n\nSocktastic!'},
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{'text': '\n\nTechCore Solutions.'},
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{'text': '\n\nFootwear Factory.'}]
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```
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</CodeOutputBlock>
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- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason.
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```python
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llm_chain.generate(input_list)
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```
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<CodeOutputBlock lang="python">
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```
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LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})
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```
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</CodeOutputBlock>
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- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict.
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```python
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# Single input example
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llm_chain.predict(product="colorful socks")
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```
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<CodeOutputBlock lang="python">
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```
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'\n\nSocktastic!'
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```
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</CodeOutputBlock>
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```python
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# Multiple inputs example
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template = """Tell me a {adjective} joke about {subject}."""
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prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"])
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llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))
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llm_chain.predict(adjective="sad", subject="ducks")
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```
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<CodeOutputBlock lang="python">
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```
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'\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'
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```
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</CodeOutputBlock>
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## Parsing the outputs
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By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`.
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With `predict`:
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```python
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from langchain.output_parsers import CommaSeparatedListOutputParser
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output_parser = CommaSeparatedListOutputParser()
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template = """List all the colors in a rainbow"""
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prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
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llm_chain = LLMChain(prompt=prompt, llm=llm)
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llm_chain.predict()
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```
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<CodeOutputBlock lang="python">
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```
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'\n\nRed, orange, yellow, green, blue, indigo, violet'
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```
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</CodeOutputBlock>
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With `predict_and_parse`:
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```python
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llm_chain.predict_and_parse()
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```
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<CodeOutputBlock lang="python">
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```
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['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']
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```
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</CodeOutputBlock>
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## Initialize from string
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You can also construct an LLMChain from a string template directly.
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```python
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template = """Tell me a {adjective} joke about {subject}."""
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llm_chain = LLMChain.from_string(llm=llm, template=template)
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```
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```python
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llm_chain.predict(adjective="sad", subject="ducks")
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```
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<CodeOutputBlock lang="python">
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```
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'\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'
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```
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</CodeOutputBlock>
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