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Dockerized Langchain / PY example (#175)
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examples/langchain/PY.Dockerfile
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5
examples/langchain/PY.Dockerfile
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FROM python:3.10-bullseye
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COPY ./langchainpy-localai-example /app
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WORKDIR /app
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RUN pip install --no-cache-dir -r requirements.txt
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ENTRYPOINT [ "python", "./simple_demo.py" ];
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@ -1,10 +1,6 @@
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# langchain
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Example of using langchain in TypeScript, with the standard OpenAI llm module, and LocalAI.
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Example for python langchain to follow at a later date
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Set up to make it easy to modify the `index.mts` file to look like any langchain example file.
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Example of using langchain, with the standard OpenAI llm module, and LocalAI. Has docker compose profiles for both the Typescript and Python versions.
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**Please Note** - This is a tech demo example at this time. ggml-gpt4all-j has pretty terrible results for most langchain applications with the settings used in this example.
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@ -22,8 +18,11 @@ cd LocalAI/examples/langchain
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# Download gpt4all-j to models/
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wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
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# start with docker-compose
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docker-compose up --build
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# start with docker-compose for typescript!
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docker-compose --profile ts up --build
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# or start with docker-compose for python!
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docker-compose --profile py up --build
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```
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## Copyright
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@ -15,11 +15,29 @@ services:
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- ./models:/models:cached
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command: ["/usr/bin/local-ai" ]
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langchainjs:
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js:
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build:
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context: .
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dockerfile: JS.Dockerfile
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profiles:
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- js
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- ts
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depends_on:
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- "api"
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environment:
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- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
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- 'OPENAI_API_HOST=http://api:8080/v1'
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- 'OPENAI_API_BASE=http://api:8080/v1'
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- 'MODEL_NAME=gpt-3.5-turbo' #gpt-3.5-turbo' # ggml-gpt4all-j' # ggml-koala-13B-4bit-128g'
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py:
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build:
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context: .
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dockerfile: PY.Dockerfile
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profiles:
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- py
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depends_on:
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- "api"
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environment:
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- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
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- 'OPENAI_API_BASE=http://api:8080/v1'
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- 'MODEL_NAME=gpt-3.5-turbo' #gpt-3.5-turbo' # ggml-gpt4all-j' # ggml-koala-13B-4bit-128g'
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@ -4,7 +4,7 @@ import { Document } from "langchain/document";
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import { initializeAgentExecutorWithOptions } from "langchain/agents";
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import {Calculator} from "langchain/tools/calculator";
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const pathToLocalAi = process.env['OPENAI_API_HOST'] || 'http://api:8080/v1';
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const pathToLocalAi = process.env['OPENAI_API_BASE'] || 'http://api:8080/v1';
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const fakeApiKey = process.env['OPENAI_API_KEY'] || '-';
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const modelName = process.env['MODEL_NAME'] || 'gpt-3.5-turbo';
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examples/langchain/langchainpy-localai-example/.vscode/launch.json
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examples/langchain/langchainpy-localai-example/.vscode/launch.json
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{
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python: Current File",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"console": "integratedTerminal",
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"redirectOutput": true,
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"justMyCode": false
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},
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{
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"name": "Python: Attach to Port 5678",
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"type": "python",
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"request": "attach",
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"connect": {
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"host": "localhost",
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"port": 5678
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},
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"justMyCode": false
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}
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]
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}
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examples/langchain/langchainpy-localai-example/.vscode/settings.json
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examples/langchain/langchainpy-localai-example/.vscode/settings.json
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{
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"python.defaultInterpreterPath": "${workspaceFolder}/.venv/Scripts/python"
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}
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examples/langchain/langchainpy-localai-example/full_demo.py
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examples/langchain/langchainpy-localai-example/full_demo.py
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import os
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from langchain.chat_models import ChatOpenAI
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from langchain import PromptTemplate, LLMChain
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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AIMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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from langchain.schema import (
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AIMessage,
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HumanMessage,
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SystemMessage
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)
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print('Langchain + LocalAI PYTHON Tests')
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base_path = os.environ.get('OPENAI_API_BASE', 'http://api:8080/v1')
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key = os.environ.get('OPENAI_API_KEY', '-')
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model_name = os.environ.get('MODEL_NAME', 'gpt-3.5-turbo')
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chat = ChatOpenAI(temperature=0, openai_api_base=base_path, openai_api_key=key, model_name=model_name, max_tokens=100)
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print("Created ChatOpenAI for ", chat.model_name)
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template = "You are a helpful assistant that translates {input_language} to {output_language}."
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system_message_prompt = SystemMessagePromptTemplate.from_template(template)
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human_template = "{text}"
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human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
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chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
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print("ABOUT to execute")
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# get a chat completion from the formatted messages
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chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
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print(".");
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aiohttp==3.8.4
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aiosignal==1.3.1
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async-timeout==4.0.2
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attrs==23.1.0
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certifi==2022.12.7
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charset-normalizer==3.1.0
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colorama==0.4.6
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dataclasses-json==0.5.7
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debugpy==1.6.7
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frozenlist==1.3.3
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greenlet==2.0.2
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idna==3.4
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langchain==0.0.157
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marshmallow==3.19.0
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marshmallow-enum==1.5.1
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multidict==6.0.4
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mypy-extensions==1.0.0
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numexpr==2.8.4
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numpy==1.24.3
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openai==0.27.6
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openapi-schema-pydantic==1.2.4
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packaging==23.1
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pydantic==1.10.7
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PyYAML==6.0
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requests==2.29.0
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SQLAlchemy==2.0.12
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tenacity==8.2.2
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tqdm==4.65.0
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typing-inspect==0.8.0
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typing_extensions==4.5.0
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urllib3==1.26.15
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yarl==1.9.2
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from langchain.llms import OpenAI
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llm = OpenAI(temperature=0.9,model_name="gpt-3.5-turbo")
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text = "What would be a good company name for a company that makes colorful socks?"
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print(llm(text))
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roles:
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user: " "
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system: " "
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backend: "gptj"
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template:
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completion: completion
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chat: completion # gpt4all
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