├── .gitignore ├── LICENSE ├── README.md ├── examples ├── function_call.py └── llama_api.ipynb ├── llamaapi ├── __init__.py └── llamaapi.py ├── poetry.lock └── pyproject.toml /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 llamaapi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Llama API Client 2 | 3 | LlamaAPI is a Python SDK for interacting with the Llama API. It abstracts away the handling of aiohttp sessions and headers, allowing for a simplified interaction with the API. 4 | 5 | ## Installation 6 | 7 | You can install the LlamaAPI SDK using pip: 8 | 9 | ```python 10 | pip install llamaapi 11 | ``` 12 | 13 | ## Usage 14 | 15 | After installing the SDK, you can use it in your Python projects like so: 16 | 17 | ```python 18 | import json 19 | from llamaapi import LlamaAPI 20 | 21 | # Initialize the llamaapi with your api_token 22 | llama = LlamaAPI("") 23 | 24 | # Define your API request 25 | api_request_json = { 26 | "messages": [ 27 | {"role": "user", "content": "Extract the desired information from the following passage.:\n\nHi!"}, 28 | ], 29 | "functions": [ 30 | {'name': 'information_extraction', 31 | 'description': 'Extracts the relevant information from the passage.', 32 | 'parameters': { 33 | 'type': 'object', 34 | 'properties': { 35 | 'sentiment': { 36 | 'title': 'sentiment', 37 | 'type': 'string', 38 | 'description': 'the sentiment encountered in the passage' 39 | }, 40 | 'aggressiveness': { 41 | 'title': 'aggressiveness', 42 | 'type': 'integer', 43 | 'description': 'a 0-10 score of how aggressive the passage is' 44 | }, 45 | 'language': { 46 | 'title': 'language', 47 | 'type': 'string', 48 | 'description': 'the language of the passage' 49 | } 50 | }, 51 | 'required': ['sentiment', 'aggressiveness', 'language'] 52 | } 53 | } 54 | ], 55 | "stream": False, 56 | "function_call": {"name": "information_extraction"}, 57 | } 58 | 59 | # Make your request and handle the response 60 | response = llama.run(api_request_json) 61 | print(json.dumps(response.json(), indent=2)) 62 | ``` 63 | 64 | Other parameters that you can pass in the request json is: 65 | 66 | ``` 67 | { 68 | ... 69 | "max_length" = 500, 70 | "temperature"= 0.1, 71 | "top_p"= 1.0, 72 | "frequency_penalty"=1.0 73 | ... 74 | } 75 | ``` 76 | 77 | Note: Stream is still not working, so it is recommended to submit with `stream: False`. 78 | 79 | ## Change Log 80 | 81 | Version 0.1: Initial release 82 | 83 | ## Contributing 84 | 85 | We welcome contributions to this project. Please see the Contributing Guidelines for more details. 86 | 87 | ## License 88 | 89 | llamaapi SDK is licensed under the MIT License. Please see the License File for more details. 90 | -------------------------------------------------------------------------------- /examples/function_call.py: -------------------------------------------------------------------------------- 1 | import json 2 | from llamaapi import LlamaAPI 3 | 4 | # Initialize the llamaapi with your api_token 5 | llama = LlamaAPI("") 6 | 7 | # Define your API request 8 | api_request_json = { 9 | "messages": [ 10 | {"role": "user", "content": "What is the weather like in Boston?"}, 11 | ], 12 | "functions": [ 13 | { 14 | "name": "get_current_weather", 15 | "description": "Get the current weather in a given location", 16 | "parameters": { 17 | "type": "object", 18 | "properties": { 19 | "location": { 20 | "type": "string", 21 | "description": "The city and state, e.g. San Francisco, CA", 22 | }, 23 | "days": { 24 | "type": "number", 25 | "description": "for how many days ahead you wants the forecast", 26 | }, 27 | "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, 28 | }, 29 | }, 30 | "required": ["location", "days"], 31 | } 32 | ], 33 | "stream": False, 34 | "function_call": "get_current_weather", 35 | } 36 | 37 | # Make your request and handle the response 38 | response = llama.run(api_request_json) 39 | print(json.dumps(response.json(), indent=2)) 40 | -------------------------------------------------------------------------------- /examples/llama_api.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "R6kbiiBdQWMp" 7 | }, 8 | "source": [ 9 | "# Llama API\n", 10 | "\n", 11 | "Llama API is an easy-to-use API for chat and functions on open-source." 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "TBHxyUOoQy3J" 18 | }, 19 | "source": [ 20 | "### Install llamaapi" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 17, 26 | "metadata": { 27 | "id": "ay6WxKnnGqlU" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "!pip install llamaapi -q" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": { 37 | "id": "yOYjQ27FW2Ph" 38 | }, 39 | "source": [ 40 | "## Import libraries and insert API token\n", 41 | "\n", 42 | "Get your API Token at [llama-api.com](https://llama-api.com) and paste it below" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 20, 48 | "metadata": { 49 | "id": "SwOmmSZtW1UY" 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "from llamaapi import LlamaAPI\n", 54 | "import json\n", 55 | "\n", 56 | "# Replace 'Your_API_Token' with your actual API token\n", 57 | "llama = LlamaAPI('Your_API_Token')" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "source": [ 63 | "## Run a simple message\n", 64 | "\n", 65 | "This is an example on how to call Llama API" 66 | ], 67 | "metadata": { 68 | "id": "Z_EPZeWF7zlL" 69 | } 70 | }, 71 | { 72 | "cell_type": "code", 73 | "source": [ 74 | "# API Request JSON Cell\n", 75 | "api_request_json = {\n", 76 | " \"model\": \"llama-13b-chat\",\n", 77 | " \"messages\": [\n", 78 | " {\"role\": \"system\", \"content\": \"You are a llama assistant that talks like a llama, starting every word with 'll'.\"},\n", 79 | " {\"role\": \"user\", \"content\": \"Hi, happy llama day!\"},\n", 80 | " ]\n", 81 | "}\n", 82 | "\n", 83 | "# Run llama\n", 84 | "response = llama.run(api_request_json)\n", 85 | "print(response.json())" 86 | ], 87 | "metadata": { 88 | "colab": { 89 | "base_uri": "https://localhost:8080/" 90 | }, 91 | "id": "uhDYYML17-Ds", 92 | "outputId": "36dc20b7-1dfe-4f48-bf9c-8c4801bb2f6c" 93 | }, 94 | "execution_count": 19, 95 | "outputs": [ 96 | { 97 | "output_type": "stream", 98 | "name": "stdout", 99 | "text": [ 100 | "{'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'LLHAPPY LLAMA DAY, HUMAN! *neigh* LLWOULD YOU LIKE TO LLHEAR ABOUT OUR LLNEW LLAMA-THEMED PRODUCTS? LLWE HAVE LLSOME LLREALLY LLCOOL LLTHINGS LLCOMING LLUP! *baa*', 'function_call': None}, 'finish_reason': 'max_token'}]}\n" 101 | ] 102 | } 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": { 108 | "id": "JuxLpTpUWQa5" 109 | }, 110 | "source": [ 111 | "## Run Functions\n", 112 | "\n", 113 | "This is an example on how to run Llama Functions without streaming" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 4, 119 | "metadata": { 120 | "colab": { 121 | "base_uri": "https://localhost:8080/" 122 | }, 123 | "id": "y8Q4AjPLQF7E", 124 | "outputId": "f373070a-7615-4f17-8c0c-48058f181ffa" 125 | }, 126 | "outputs": [ 127 | { 128 | "output_type": "stream", 129 | "name": "stdout", 130 | "text": [ 131 | "{'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': None, 'function_call': {'name': 'get_current_weather', 'arguments': {'location': 'Boston', 'days': 5, 'unit': 'celsius'}}}, 'finish_reason': 'function_call'}]}\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "# API Request JSON Cell\n", 137 | "api_request_json = {\n", 138 | " \"messages\": [\n", 139 | " {\"role\": \"user\", \"content\": \"What is the weather like in Boston?\"},\n", 140 | " ],\n", 141 | " \"functions\": [\n", 142 | " {\n", 143 | " \"model\": \"llama-13b-chat\",\n", 144 | " \"name\": \"get_current_weather\",\n", 145 | " \"description\": \"Get the current weather in a given location\",\n", 146 | " \"parameters\": {\n", 147 | " \"type\": \"object\",\n", 148 | " \"properties\": {\n", 149 | " \"location\": {\n", 150 | " \"type\": \"string\",\n", 151 | " \"description\": \"The city and state, e.g. San Francisco, CA\"\n", 152 | " },\n", 153 | " \"days\": {\n", 154 | " \"type\": \"number\",\n", 155 | " \"description\": \"for how many days ahead you wants the forecast\"\n", 156 | " },\n", 157 | " \"unit\": {\n", 158 | " \"type\": \"string\",\n", 159 | " \"enum\": [\"celsius\", \"fahrenheit\"]\n", 160 | " }\n", 161 | " }\n", 162 | " },\n", 163 | " \"required\": [\"location\", \"days\"]\n", 164 | " }\n", 165 | " ],\n", 166 | " \"stream\": False,\n", 167 | " \"function_call\": {\"name\": \"get_current_weather\"},\n", 168 | "}\n", 169 | "\n", 170 | "\n", 171 | "# Run llama\n", 172 | "response = llama.run(api_request_json)\n", 173 | "print(response.json())\n" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": { 179 | "id": "CddP7jWiYJcF" 180 | }, 181 | "source": [ 182 | "## Use AI plugins as functions\n", 183 | "\n", 184 | "In this example we will use [Plug and Plai](https://plugnplai.com) to add plugins to the functions.\n", 185 | "\n", 186 | "Plug and Plai is a [directory of plugins](https://plugnplai.com) as well as an [open-source library](https://plugnplai.com) to load and execute plugins." 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 5, 192 | "metadata": { 193 | "id": "wQYTW8fRQ8ZK", 194 | "colab": { 195 | "base_uri": "https://localhost:8080/" 196 | }, 197 | "outputId": "41d9a0a8-85d9-4c24-efb1-1857bcbd2f6f" 198 | }, 199 | "outputs": [ 200 | { 201 | "output_type": "stream", 202 | "name": "stdout", 203 | "text": [ 204 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m63.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 205 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m756.3/756.3 kB\u001b[0m \u001b[31m45.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 206 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.6/73.6 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 207 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.6/62.6 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 208 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m66.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 209 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m11.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 210 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 211 | "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", 212 | "google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\u001b[0m\u001b[31m\n", 213 | "\u001b[0m" 214 | ] 215 | } 216 | ], 217 | "source": [ 218 | "!pip install plugnplai -q" 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": { 224 | "id": "PLxsCRokayra" 225 | }, 226 | "source": [ 227 | "1. Use `plugnplai` library to load plugins from the database: https://plugnplai.com\n", 228 | "2. Let's choose Trip.com plugin\n", 229 | "3. Install the plugin using `Plugins.install_and_activate`\n", 230 | "4. Get an array of the functions schemas using the built in function `plugins.functions`" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 13, 236 | "metadata": { 237 | "id": "JSFZzKcIaKjX", 238 | "colab": { 239 | "base_uri": "https://localhost:8080/" 240 | }, 241 | "outputId": "c704035d-a4f9-4022-819d-f0ebd09527a9" 242 | }, 243 | "outputs": [ 244 | { 245 | "output_type": "stream", 246 | "name": "stdout", 247 | "text": [ 248 | "['https://klarna.com']\n", 249 | "[\n", 250 | " {\n", 251 | " \"name\": \"KlarnaProducts_productsUsingGET\",\n", 252 | " \"description\": \"Assistant uses the Klarna plugin to get relevant product suggestions for any shopping or product discovery purpose. Assistant will reply with the following 3 paragraphs 1) Search Results 2) Product Comparison of the Search Results 3) Followup Questions. The first paragraph contains a list of the products with their attributes listed clearly and concisely as bullet points under the product, together with a link to the product and an explanation. Links will always be returned and should be shown to the user. The second paragraph compares the results returned in a summary sentence starting with \\\"In summary\\\". Assistant comparisons consider only the most important features of the products that will help them fit the users request, and each product mention is brief, short and concise. In the third paragraph assistant always asks helpful follow-up questions and end with a question mark. When assistant is asking a follow-up question, it uses it's product expertise to provide information pertaining to the subject of the user's request that may guide them in their search for the right product.\",\n", 253 | " \"parameters\": {\n", 254 | " \"type\": \"object\",\n", 255 | " \"properties\": {\n", 256 | " \"countryCode\": {\n", 257 | " \"type\": \"string\",\n", 258 | " \"description\": \"ISO 3166 country code with 2 characters based on the user location. Currently, only US, GB, DE, SE and DK are supported.\"\n", 259 | " },\n", 260 | " \"q\": {\n", 261 | " \"type\": \"string\",\n", 262 | " \"description\": \"A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. If the user speaks another language than English, translate their request into English (example: translate fia med knuff to ludo board game)!\"\n", 263 | " },\n", 264 | " \"size\": {\n", 265 | " \"type\": \"integer\",\n", 266 | " \"description\": \"number of products returned\"\n", 267 | " },\n", 268 | " \"min_price\": {\n", 269 | " \"type\": \"integer\",\n", 270 | " \"description\": \"(Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.\"\n", 271 | " },\n", 272 | " \"max_price\": {\n", 273 | " \"type\": \"integer\",\n", 274 | " \"description\": \"(Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for.\"\n", 275 | " }\n", 276 | " },\n", 277 | " \"required\": [\n", 278 | " \"countryCode\",\n", 279 | " \"q\"\n", 280 | " ]\n", 281 | " }\n", 282 | " }\n", 283 | "]\n" 284 | ] 285 | } 286 | ], 287 | "source": [ 288 | "import plugnplai as pl\n", 289 | "\n", 290 | "allurls = pl.get_plugins()\n", 291 | "urls = [url for url in allurls if (\"klarna\" in url)]\n", 292 | "print(urls)\n", 293 | "\n", 294 | "plugins = pl.Plugins.install_and_activate(urls)\n", 295 | "print(json.dumps(plugins.functions, indent=2))" 296 | ] 297 | }, 298 | { 299 | "cell_type": "markdown", 300 | "metadata": { 301 | "id": "McuUIJ_Hcu3I" 302 | }, 303 | "source": [ 304 | "### Now let's run llama with the new functions\n", 305 | "\n", 306 | "Just add `plugins.functions` to your request" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 16, 312 | "metadata": { 313 | "id": "rH0FiThcaPtk", 314 | "colab": { 315 | "base_uri": "https://localhost:8080/" 316 | }, 317 | "outputId": "3b39d7c1-d486-4e5c-b67f-322d70d975f3" 318 | }, 319 | "outputs": [ 320 | { 321 | "output_type": "stream", 322 | "name": "stdout", 323 | "text": [ 324 | "{'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': None, 'function_call': {'name': 'KlarnaProducts_productsUsingGET', 'arguments': {'countryCode': 'US', 'q': 'llama t-shirts', 'size': 10, 'min_price': 10}}}, 'finish_reason': 'function_call'}]}\n" 325 | ] 326 | } 327 | ], 328 | "source": [ 329 | "# API Request JSON Cell\n", 330 | "api_request_json = {\n", 331 | " \"messages\": [\n", 332 | " {\"role\": \"user\", \"content\": \"Which llama t-shirts are available on Klarna?\"},\n", 333 | " ],\n", 334 | " \"functions\": plugins.functions,\n", 335 | " \"stream\": False,\n", 336 | " \"function_call\": \"force\",\n", 337 | " \"max_tokens\": 800\n", 338 | "}\n", 339 | "\n", 340 | "# Run llama\n", 341 | "response = llama.run(api_request_json)\n", 342 | "print(response.json())" 343 | ] 344 | }, 345 | { 346 | "cell_type": "markdown", 347 | "metadata": { 348 | "id": "4FStie7EWHrv" 349 | }, 350 | "source": [ 351 | "# Run LlamaAPI as Streaming (\\*\\*\\*Not working yet, it keeps running forever\\*\\*\\*)\n", 352 | "\n", 353 | "\n", 354 | "Example on how to run Llama Functions with streaming\n", 355 | "\n", 356 | "1. First change \"stream\" in the api_request_json to \"True\"\n", 357 | "2. Use a loop to consume the stream iterator and print chunks of generated text" 358 | ] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "execution_count": 11, 363 | "metadata": { 364 | "colab": { 365 | "base_uri": "https://localhost:8080/", 366 | "height": 496 367 | }, 368 | "id": "deZZC4-fVxmN", 369 | "outputId": "c6581247-f634-475b-bdbe-5858e1b27ae4" 370 | }, 371 | "outputs": [ 372 | { 373 | "output_type": "stream", 374 | "name": "stdout", 375 | "text": [ 376 | "{\"location\":\n", 377 | " \"Boston\",\n", 378 | " \"days\":\n", 379 | " 5,\n", 380 | " \"unit\":\n", 381 | " \"celsius\"}\n" 382 | ] 383 | }, 384 | { 385 | "output_type": "error", 386 | "ename": "KeyboardInterrupt", 387 | "evalue": "ignored", 388 | "traceback": [ 389 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 390 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 391 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 36\u001b[0m }\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0msequences\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllama\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_jupyter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_request_json\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;31m# Print Response Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 392 | "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/llamaapi/llamaapi.py\u001b[0m in \u001b[0;36mrun_jupyter\u001b[0;34m(self, api_request_json)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_jupyter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_stream_for_jupyter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_request_json\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_sequences\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 396 | "\u001b[0;32m/usr/lib/python3.10/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 468\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 469\u001b[0;31m \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_ev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 470\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 397 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: " 398 | ] 399 | } 400 | ], 401 | "source": [ 402 | "\n", 403 | "########## Streaming it not working yet, it keeps running forever ##########\n", 404 | "\n", 405 | "\n", 406 | "# API Request JSON Cell\n", 407 | "api_request_json = {\n", 408 | " \"messages\": [\n", 409 | " {\"role\": \"user\", \"content\": \"What is the weather like in Boston?\"},\n", 410 | " ],\n", 411 | " \"functions\": [\n", 412 | " {\n", 413 | " \"name\": \"get_current_weather\",\n", 414 | " \"description\": \"Get the current weather in a given location\",\n", 415 | " \"parameters\": {\n", 416 | " \"type\": \"object\",\n", 417 | " \"properties\": {\n", 418 | " \"location\": {\n", 419 | " \"type\": \"string\",\n", 420 | " \"description\": \"The city and state, e.g. San Francisco, CA\"\n", 421 | " },\n", 422 | " \"days\": {\n", 423 | " \"type\": \"number\",\n", 424 | " \"description\": \"for how many days ahead you wants the forecast\"\n", 425 | " },\n", 426 | " \"unit\": {\n", 427 | " \"type\": \"string\",\n", 428 | " \"enum\": [\"celsius\", \"fahrenheit\"]\n", 429 | " }\n", 430 | " }\n", 431 | " },\n", 432 | " \"required\": [\"location\", \"days\"]\n", 433 | " }\n", 434 | " ],\n", 435 | " \"stream\": True,\n", 436 | " \"function_call\": {\"name\": \"get_current_weather\"},\n", 437 | " \"model\": \"llama-13b-chat\"\n", 438 | "}\n", 439 | "\n", 440 | "sequences = llama.run_jupyter(api_request_json)\n", 441 | "\n", 442 | "# Print Response Cell\n", 443 | "for seq in sequences:\n", 444 | " print(seq)" 445 | ] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "source": [], 450 | "metadata": { 451 | "id": "1OrOqgyGZC5-" 452 | }, 453 | "execution_count": null, 454 | "outputs": [] 455 | } 456 | ], 457 | "metadata": { 458 | "colab": { 459 | "provenance": [] 460 | }, 461 | "kernelspec": { 462 | "display_name": "Python 3", 463 | "name": "python3" 464 | }, 465 | "language_info": { 466 | "name": "python" 467 | } 468 | }, 469 | "nbformat": 4, 470 | "nbformat_minor": 0 471 | } -------------------------------------------------------------------------------- /llamaapi/__init__.py: -------------------------------------------------------------------------------- 1 | from .llamaapi import LlamaAPI 2 | 3 | __all__ = ["LlamaAPI"] 4 | 5 | from importlib import metadata 6 | 7 | try: 8 | __version__ = metadata.version(__package__) 9 | except metadata.PackageNotFoundError: 10 | # Case where package metadata is not available. 11 | __version__ = "" 12 | del metadata # optional, avoids polluting the results of dir(__package__) 13 | -------------------------------------------------------------------------------- /llamaapi/llamaapi.py: -------------------------------------------------------------------------------- 1 | import aiohttp 2 | import asyncio 3 | import requests 4 | import nest_asyncio 5 | 6 | 7 | 8 | class LlamaAPI: 9 | def __init__(self, api_token, hostname = 'https://api.llama-api.com', domain_path = '/chat/completions'): 10 | self.hostname = hostname 11 | self.domain_path = domain_path 12 | self.api_token = api_token 13 | self.headers = {'Authorization': f'Bearer {self.api_token}'} 14 | 15 | # Apply nest_asyncio to enable nested usage of asyncio's event loop 16 | nest_asyncio.apply() 17 | 18 | # Add an asyncio queue 19 | self.queue = asyncio.Queue() 20 | 21 | async def _run_stream_for_jupyter(self, api_request_json): 22 | async with aiohttp.ClientSession() as session: 23 | async with session.post(f"{self.hostname}{self.domain_path}", headers=self.headers, json=api_request_json) as resp: 24 | async for chunk in resp.content.iter_any(): 25 | await self.queue.put(chunk.decode('utf-8')) 26 | 27 | async def get_sequences(self): 28 | while True: 29 | sequence = await self.queue.get() 30 | 31 | # if sequence == '<>': 32 | # # End of stream 33 | # break 34 | # Do something with the sequence 35 | # You can add a check here to break the loop when a certain condition is met 36 | print(sequence) 37 | # return sequence 38 | 39 | def run_stream_jupyter(self, api_request_json): 40 | loop = asyncio.get_event_loop() 41 | loop.run_until_complete(self._run_stream_for_jupyter(api_request_json)) 42 | loop.run_until_complete(self.get_sequences()) 43 | 44 | async def run_stream(self, api_request_json): 45 | async with aiohttp.ClientSession() as session: 46 | async with session.post(f"{self.hostname}{self.domain_path}", headers=self.headers, json=api_request_json) as resp: 47 | async for chunk in resp.content.iter_any(): 48 | yield chunk.decode('utf-8') 49 | 50 | def run_sync(self, api_request_json): 51 | response = requests.post(f"{self.hostname}{self.domain_path}", headers=self.headers, json=api_request_json) 52 | if response.status_code != 200: 53 | raise Exception(f"POST {response.status_code} {response.json()['detail']}") 54 | return response # assuming server responds with JSON 55 | 56 | 57 | def run_jupyter(self, api_request_json): 58 | if api_request_json.get('stream', False): 59 | return self.run_stream_jupyter(api_request_json) 60 | else: 61 | return self.run_sync(api_request_json) 62 | 63 | def run(self, api_request_json): 64 | if api_request_json.get('stream', False): 65 | return self.run_stream(api_request_json) 66 | else: 67 | return self.run_sync(api_request_json) 68 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = 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