progress on a python langchain example. Requires upstream changes from langchain and the usage fix

examples/langchain/PY.Dockerfile

@@ -0,0 +1,5 @@
+FROM python:3.10-bullseye
+COPY ./langchainpy-localai-example /app
+WORKDIR /app
+RUN pip install --no-cache-dir -r requirements.txt
+ENTRYPOINT [ "python", "./demo.py" ];

examples/langchain/README.md

@@ -1,11 +1,13 @@
 # langchain
 
-Example of using langchain in TypeScript, with the standard OpenAI llm module, and LocalAI.
-
-Example for python langchain to follow at a later date
+Example of using langchain, with the standard OpenAI llm module, and LocalAI.
 
+## Typescript
 Set up to make it easy to modify the `index.mts` file to look like any langchain example file.
 
+## Python
+
+
 **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.
 
 ## Setup

examples/langchain/docker-compose.yaml

@@ -15,11 +15,24 @@ services:
       - ./models:/models:cached
     command: ["/usr/bin/local-ai" ]
 
-  langchainjs:
+  js:
     build:
       context: .
       dockerfile: JS.Dockerfile
+    depends_on:
+    - "api"
     environment:
       - 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
-      - 'OPENAI_API_HOST=http://api:8080/v1'
+      - 'OPENAI_API_BASE=http://api:8080/v1'
+      - 'MODEL_NAME=gpt-3.5-turbo' #gpt-3.5-turbo' # ggml-gpt4all-j' # ggml-koala-13B-4bit-128g'
+
+  py:
+    build:
+      context: .
+      dockerfile: PY.Dockerfile
+    depends_on:
+    - "api"
+    environment:
+      - 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
+      - 'OPENAI_API_BASE=http://api:8080/v1'
       - 'MODEL_NAME=gpt-3.5-turbo' #gpt-3.5-turbo' # ggml-gpt4all-j' # ggml-koala-13B-4bit-128g'

examples/langchain/langchainjs-localai-example/src/index.mts

@@ -4,7 +4,7 @@ import { Document } from "langchain/document";
 import { initializeAgentExecutorWithOptions } from "langchain/agents";
 import {Calculator} from "langchain/tools/calculator";
 
-const pathToLocalAi = process.env['OPENAI_API_HOST'] || 'http://api:8080/v1';
+const pathToLocalAi = process.env['OPENAI_API_BASE'] || 'http://api:8080/v1';
 const fakeApiKey = process.env['OPENAI_API_KEY'] || '-';
 const modelName = process.env['MODEL_NAME'] || 'gpt-3.5-turbo';
 

examples/langchain/langchainpy-localai-example/.vscode/launch.json

@@ -0,0 +1,24 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "redirectOutput": true,
+            "justMyCode": false
+        },
+        {
+            "name": "Python: Attach to Port 5678",
+            "type": "python",
+            "request": "attach",
+            "connect": {
+                "host": "localhost",
+                "port": 5678
+              },
+            "justMyCode": false
+        }
+    ]
+}

examples/langchain/langchainpy-localai-example/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.defaultInterpreterPath": "${workspaceFolder}/.venv/Scripts/python"
+}

examples/langchain/langchainpy-localai-example/demo.py

@@ -0,0 +1,32 @@
+import os
+from langchain.chat_models import ChatOpenAI
+from langchain import PromptTemplate, LLMChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+print('Langchain + LocalAI PYTHON Tests')
+
+base_path = os.environ.get('OPENAI_API_BASE', 'http://192.168.10.131:8080/v1')
+key = os.environ.get('OPENAI_API_KEY', '-')
+
+chat = ChatOpenAI(temperature=0, openai_api_base=base_path, openai_api_key=key, model_name="ggml-gpt4all-j")
+
+template = "You are a helpful assistant that translates {input_language} to {output_language}."
+system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+human_template = "{text}"
+human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+
+chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+# get a chat completion from the formatted messages
+chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
+

examples/langchain/langchainpy-localai-example/requirements.txt

@@ -0,0 +1,32 @@
+aiohttp==3.8.4
+aiosignal==1.3.1
+async-timeout==4.0.2
+attrs==23.1.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+colorama==0.4.6
+dataclasses-json==0.5.7
+debugpy==1.6.7
+frozenlist==1.3.3
+greenlet==2.0.2
+idna==3.4
+langchain==0.0.154
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+multidict==6.0.4
+mypy-extensions==1.0.0
+numexpr==2.8.4
+numpy==1.24.3
+openai==0.27.6
+openapi-schema-pydantic==1.2.4
+packaging==23.1
+pydantic==1.10.7
+PyYAML==6.0
+requests==2.29.0
+SQLAlchemy==2.0.12
+tenacity==8.2.2
+tqdm==4.65.0
+typing-inspect==0.8.0
+typing_extensions==4.5.0
+urllib3==1.26.15
+yarl==1.9.2