You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
127 lines
3.2 KiB
Python
127 lines
3.2 KiB
Python
r"""°°°
|
|
# Getting Started
|
|
°°°"""
|
|
# |%%--%%| <gOPXQazOh0|yMwLn4kyVM>
|
|
|
|
from langchain.llms import OpenAI
|
|
|
|
# n: how many completions to generate for each prompt
|
|
llm = OpenAI(model_name='text-ada-001', n=2, best_of=2, temperature=1)
|
|
llm("tell me a joke !")
|
|
|
|
|
|
#|%%--%%| <yMwLn4kyVM|5F2CisYISi>
|
|
r"""°°°
|
|
you can call it with a list of inputs, getting back a more complete response than just the text. This complete response includes things like multiple top responses, as well as LLM provider specific information.
|
|
°°°"""
|
|
#|%%--%%| <5F2CisYISi|4hSGpG9eHG>
|
|
|
|
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 15)
|
|
|
|
#|%%--%%| <4hSGpG9eHG|kZMW1cS1Qk>
|
|
|
|
len(llm_result.generations)
|
|
|
|
#llm_result.to_dict() # see result details
|
|
|
|
print(llm_result.generations[0])
|
|
print("\n\n")
|
|
print(llm_result.generations[-1])
|
|
llm_result.llm_output
|
|
|
|
|
|
#|%%--%%| <kZMW1cS1Qk|1Kg9Ct1muS>
|
|
r"""°°°
|
|
## estimate number of tokens in prompt
|
|
°°°"""
|
|
#|%%--%%| <1Kg9Ct1muS|qz7lnBufdW>
|
|
|
|
llm.get_num_tokens("what is a joke")
|
|
|
|
#|%%--%%| <qz7lnBufdW|Q9fgYuBKEK>
|
|
r"""°°°
|
|
# Key Concepts
|
|
|
|
- Core method exposed by llms is `generate`: takes list of str returns LLMResult
|
|
- Can also be called directly with single string as input and returns a stirng
|
|
- Main result is `LLMResult`, input list of strings -> list of LLMResult
|
|
Each result is a list of generations (since you can request `n` generations per input str)
|
|
- `llm_output` contains provider specific ouptput
|
|
°°°"""
|
|
#|%%--%%| <Q9fgYuBKEK|3k0hrhqP7F>
|
|
r"""°°°
|
|
## LLM Serialization
|
|
|
|
Wrinting and reading llms to disk
|
|
°°°"""
|
|
#|%%--%%| <3k0hrhqP7F|9V7QPvLLcT>
|
|
|
|
from langchain.llms.loading import load_llm
|
|
|
|
llm.save("llm.json")
|
|
|
|
#|%%--%%| <9V7QPvLLcT|rcdMSwUd3W>
|
|
|
|
llm = load_llm("llm.json")
|
|
|
|
#|%%--%%| <rcdMSwUd3W|JfkBS05EUP>
|
|
r"""°°°
|
|
## Token Usage Tracking
|
|
|
|
|
|
°°°"""
|
|
#|%%--%%| <JfkBS05EUP|FUnKToXsu6>
|
|
|
|
from langchain.callbacks import get_openai_callback
|
|
|
|
#|%%--%%| <FUnKToXsu6|9AoUdMfzg7>
|
|
|
|
llm = OpenAI(model_name="text-davinci-002", n=2, best_of=2)
|
|
|
|
#|%%--%%| <9AoUdMfzg7|aiTKa2iDUx>
|
|
|
|
with get_openai_callback() as cb:
|
|
result = llm("tell me a joke")
|
|
print(cb.total_tokens)
|
|
|
|
#|%%--%%| <aiTKa2iDUx|6G5rwEeItx>
|
|
r"""°°°
|
|
Anything inside the context manager will get tracked.
|
|
|
|
Example tracking multiple calls
|
|
°°°"""
|
|
#|%%--%%| <6G5rwEeItx|uDJtapBCby>
|
|
|
|
with get_openai_callback() as cb:
|
|
result = llm("Tell me a joke")
|
|
result2 = llm("Tell me a funny joke")
|
|
print(cb.total_tokens)
|
|
|
|
#|%%--%%| <uDJtapBCby|sLuvD8dXnk>
|
|
r"""°°°
|
|
If a chain or agent with multiple steps in it is used, it will track all those steps.
|
|
°°°"""
|
|
#|%%--%%| <sLuvD8dXnk|uUQOyO8XIw>
|
|
|
|
from langchain.agents import load_tools
|
|
from langchain.agents import initialize_agent
|
|
from langchain.llms import OpenAI
|
|
|
|
llm = OpenAI(temperature=0)
|
|
tools = load_tools(["searx-search", "llm-math"], llm=llm)
|
|
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
|
|
|
|
#|%%--%%| <uUQOyO8XIw|nv0tixnGfg>
|
|
|
|
with get_openai_callback() as cb:
|
|
res = agent.run("What is the temperature in Paris Berlin and Granada ? \
|
|
Print every city's temperature in Celcius and Fahrenheit. Think step by step")
|
|
|
|
#|%%--%%| <nv0tixnGfg|vpDGqKnagk>
|
|
|
|
print(cb.total_tokens)
|
|
|
|
# |%%--%%| <vpDGqKnagk|xUoMvs6XZ0>
|
|
|
|
|