U hN(@sddlmZddlZddlmZddlmZmZmZm Z m Z ddl Z ddl m Z ddlmZddlmZddlmZGd d d eZdS) ) annotationsN)StringIO)AnyDictIteratorListOptional)CallbackManagerForLLMRun)LLM)GenerationChunk)get_pydantic_field_namesc@seZdZUdZdZded<dZded<dZd ed <d Zd ed <dZ ded<dZ d ed<dZ ded<dZ ded<d Z d ed<dZd ed<dZded<dZded<dZded<dZd ed <d!Zd ed"<d#Zded$<d#Zded%<dZd ed&<d'Zded(<d)Zded*<Gd+d,d,Zedd-d.d/Zed0d-d1d2Zed3d-d4d5ZdEd6d7d3d8d9d:ZdFdd6d;d7dd<d=d>ZdGdd6d;d7d?d<d@dAZ dddBdCdDZ!dS)H LlamafileaOLlamafile lets you distribute and run large language models with a single file. To get started, see: https://github.com/Mozilla-Ocho/llamafile To use this class, you will need to first: 1. Download a llamafile. 2. Make the downloaded file executable: `chmod +x path/to/model.llamafile` 3. Start the llamafile in server mode: `./path/to/model.llamafile --server --nobrowser` Example: .. code-block:: python from langchain_community.llms import Llamafile llm = Llamafile() llm.invoke("Tell me a joke.") zhttp://localhost:8080strbase_urlNz Optional[int]request_timeoutFbool streamingintseedg?float temperature(top_kgffffff?top_pg?min_p n_predictrn_keepg?tfs_z typical_pg?repeat_penalty@ repeat_last_nT penalize_nlgpresence_penaltyfrequency_penaltymirostatg@ mirostat_taug? mirostat_etac@seZdZdZdS)zLlamafile.ConfigZforbidN)__name__ __module__ __qualname__extrar-r-F/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/llms/llamafile.pyConfigssr/)returncCsdS)NZ llamafiler-)selfr-r-r. _llm_typevszLlamafile._llm_typez List[str]c s6ddddddddd d d g fd d t|jD}|S)NrcacheZcallback_manager callbacksmetadatanamerrtagsverboseZcustom_get_token_idscsg|]}|kr|qSr-r-).0kZ ignore_keysr-r. sz/Llamafile._param_fieldnames..)r __class__)r1attrsr-r;r._param_fieldnameszs  zLlamafile._param_fieldnameszDict[str, Any]cCs"i}|jD]}t||||<q |S)N)r?getattr)r1params fieldnamer-r-r._default_paramss zLlamafile._default_paramszOptional[List[str]]r)stopkwargsr0cKsV|j}|D]\}}||kr|||<q|dk rDt|dkrD||d<|jrRd|d<|S)NrrDTstream)rCitemslenr)r1rDrErAr:vr-r-r._get_parameterss zLlamafile._get_parametersz"Optional[CallbackManagerForLLMRun])promptrD run_managerrEr0c Ks|jrNt8}|j|f||d|D]}||jq&|}W5QRX|S|jfd|i|}d|i|} z&tj|j dddi| d|j d} Wn.tj j k rtj d |j d YnX| d | _| d }|Sd S)aRequest prompt completion from the llamafile server and return the output. Args: prompt: The prompt to use for generation. stop: A list of strings to stop generation when encountered. run_manager: **kwargs: Any additional options to pass as part of the generation request. Returns: The string generated by the model. )rDrLrDrK /completion Content-Typeapplication/jsonFurlheadersjsonrFtimeoutTCould not connect to Llamafile server. Please make sure that a server is running at .zutf-8contentN)rr_streamwritetextgetvaluerJrequestspostrr exceptionsConnectionErrorraise_for_statusencodingrS) r1rKrDrLrEZbuffchunkrZrApayloadresponser-r-r._callsB      zLlamafile._callzIterator[GenerationChunk]c ks|jfd|i|}d|kr$d|d<d|i|}z&tj|jdddi|d|jd}Wn.tjjk rtjd |jd YnXd |_|jdd D]0}| |} t | d } |r|j | j d| VqdS)a"Yields results objects as they are generated in real time. It also calls the callback manager's on_llm_new_token event with similar parameters to the OpenAI LLM class method of the same name. Args: prompt: The prompts to pass into the model. stop: Optional list of stop words to use when generating. run_manager: **kwargs: Any additional options to pass as part of the generation request. Returns: A generator representing the stream of tokens being generated. Yields: Dictionary-like objects each containing a token Example: .. code-block:: python from langchain_community.llms import Llamafile llm = Llamafile( temperature = 0.0 ) for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", stop=["'"," "]): result = chunk["choices"][0] print(result["text"], end='', flush=True) rDrFTrKrMrNrOrPrUrVutf8)decode_unicode)rZ)tokenN) rJr\r]rrr^r_ra iter_lines_get_chunk_contentr Zon_llm_new_tokenrZ) r1rKrDrLrErArcrdZ raw_chunkrWrbr-r-r.rXs2&      zLlamafile._stream)rbr0cCs.|dr&|d}t|}|dS|SdS)zWhen streaming is turned on, llamafile server returns lines like: 'data: {"content":" They","multimodal":true,"slot_id":0,"stop":false}' Here, we convert this to a dict and return the value of the 'content' field zdata:zdata: rWN) startswithlstriprSloads)r1rbcleaneddatar-r-r.rj/s   zLlamafile._get_chunk_content)N)NN)NN)"r)r*r+__doc__r__annotations__rrrrrrrrrrrr r"r#r$r%r&r'r(r/propertyr2r?rCrJrerXrjr-r-r-r.r sJ                     ?Fr ) __future__rrSiortypingrrrrrr\Z langchain_core.callbacks.managerr Z#langchain_core.language_models.llmsr Zlangchain_core.outputsr Zlangchain_core.utilsr r r-r-r-r.s