turn '                                          'leads to higher prices and higher '                                          'inflation.'}]],                'llm_output': None,                'run': None}})__call__: string in -> string out‚ÄãThe simplest way to use an LLM is a callable: pass in a string, get a string completion.llm("Tell me a joke")    '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!'generate: batch calls, richer outputs‚Äãgenerate lets you call the model with a list of strings, getting back a more complete response than just the text. This complete response can include things like multiple top responses and other LLM provider-specific information:llm_result = llm.generate(["Tell me a joke", "Tell me a poem"]*15)len(llm_result.generations)    30llm_result.generations[0]    [Generation(text="\n\nQ: Why don't scientists trust atoms?\nA: Because they make up everything!", generation_info={'finish_reason': 'stop', 'logprobs': None})]llm_result.generations[-1]    [Generation(text='\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you!', generation_info={'finish_reason': 'stop', 'logprobs': None})]You can also access provider specific information that is returned. This information is not standardized across providers.llm_result.llm_output    {'token_usage': {'completion_tokens': 900,      'total_tokens': 1020,      'prompt_tokens': 120},     'model_name': 'text-davinci-003'}PreviousLanguage modelsNextAsync APIGet startedSetupLCEL__call__: string in -> string outgenerate: batch calls, richer outputsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Head to Integrations for documentation on built-in integrations with LLM providers.
Head to Integrations for documentation on built-in integrations with LLM providers. ->: turn '                                          'leads to higher prices and higher '                                          'inflation.'}]],                'llm_output': None,                'run': None}})__call__: string in -> string out‚ÄãThe simplest way to use an LLM is a callable: pass in a string, get a string completion.llm("Tell me a joke")    '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!'generate: batch calls, richer outputs‚Äãgenerate lets you call the model with a list of strings, getting back a more complete response than just the text. This complete response can include things like multiple top responses and other LLM provider-specific information:llm_result = llm.generate(["Tell me a joke", "Tell me a poem"]*15)len(llm_result.generations)    30llm_result.generations[0]    [Generation(text="\n\nQ: Why don't scientists trust atoms?\nA: Because they make up everything!", generation_info={'finish_reason': 'stop', 'logprobs': None})]llm_result.generations[-1]    [Generation(text='\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you!', generation_info={'finish_reason': 'stop', 'logprobs': None})]You can also access provider specific information that is returned. This information is not standardized across providers.llm_result.llm_output    {'token_usage': {'completion_tokens': 900,      'total_tokens': 1020,      'prompt_tokens': 120},     'model_name': 'text-davinci-003'}PreviousLanguage modelsNextAsync APIGet startedSetupLCEL__call__: string in -> string outgenerate: batch calls, richer outputsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.