mirror of https://github.com/hwchase17/langchain
add llamaapi (#8140)
parent
f0eb5db670
commit
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@ -0,0 +1,134 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "90a1faf2",
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"metadata": {},
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"source": [
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"# Llama API\n",
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"\n",
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"This notebook shows how to use LangChain with [LlamaAPI](https://llama-api.com/) - a hosted version of Llama2 that adds in support for function calling."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5b652cf",
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"metadata": {},
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"source": [
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"!pip install -U llamaapi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "bfd385fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llamaapi import LlamaAPI\n",
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"\n",
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"# Replace 'Your_API_Token' with your actual API token\n",
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"llama = LlamaAPI('Your_API_Token')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "632eb3e5",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.12) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain_experimental.llms import ChatLlamaAPI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "6f850e82",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = ChatLlamaAPI(client=llama)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "975c2bf4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import create_tagging_chain\n",
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"\n",
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"schema = {\n",
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" \"properties\": {\n",
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" \"sentiment\": {\"type\": \"string\", 'description': 'the sentiment encountered in the passage'},\n",
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" \"aggressiveness\": {\"type\": \"integer\", 'description': 'a 0-10 score of how aggressive the passage is'},\n",
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" \"language\": {\"type\": \"string\", 'description': 'the language of the passage'},\n",
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" }\n",
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"}\n",
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"\n",
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"chain = create_tagging_chain(schema, model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ef9638c3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'sentiment': 'aggressive', 'aggressiveness': 8}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.run(\"give me your money\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "238b4f62",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1,6 +1,7 @@
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"""Experimental LLM wrappers."""
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from langchain_experimental.llms.jsonformer_decoder import JsonFormer
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from langchain_experimental.llms.llamaapi import ChatLlamaAPI
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from langchain_experimental.llms.rellm_decoder import RELLM
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__all__ = ["RELLM", "JsonFormer"]
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__all__ = ["RELLM", "JsonFormer", "ChatLlamaAPI"]
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@ -0,0 +1,136 @@
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import json
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import logging
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from typing import (
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Any,
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Dict,
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List,
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Mapping,
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Optional,
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Tuple,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import BaseChatModel
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from langchain.schema import (
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ChatGeneration,
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ChatResult,
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)
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from langchain.schema.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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FunctionMessage,
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HumanMessage,
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SystemMessage,
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)
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logger = logging.getLogger(__name__)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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_dict["function_call"]["arguments"] = json.dumps(
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_dict["function_call"]["arguments"]
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)
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class ChatLlamaAPI(BaseChatModel):
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client: Any #: :meta private:
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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message_dicts, params = self._create_message_dicts(messages, stop)
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_params = {"messages": message_dicts}
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final_params = {**params, **kwargs, **_params}
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response = self.client.run(final_params).json()
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return self._create_chat_result(response)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = dict(self._client_params)
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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generations = []
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.get("finish_reason")),
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)
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generations.append(gen)
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return ChatResult(generations=generations)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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raise NotImplementedError
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@property
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def _client_params(self) -> Mapping[str, Any]:
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"""Get the parameters used for the client."""
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return {}
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "llama-api"
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