Merge 82d7f7b9b3039a39c8b8a55d6f2f553e697e9cc4 into d7eb05b9361febead29a74e71ddffc2ebeff5302

examples/langchain-python-langchain-webui/askGradio.py

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+import random
+import gradio as gr
+from langchain.llms import Ollama
+
+ollama = Ollama(base_url='http://localhost:11434', model="llama2")
+
+
+
+
+def random_response(message, history):
+    return ollama(message)
+
+demo = gr.ChatInterface(random_response, title="Echo Bot")
+
+if __name__ == "__main__":
+    demo.launch()
+
+
+

examples/langchain-python-langchain-webui/main.py

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+#! /usr/bin/python3.10
+
+import gradio as gr
+import sys
+import os
+import subprocess
+from collections.abc import Iterable
+from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader, UnstructuredHTMLLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.llms import Ollama
+from langchain.vectorstores import FAISS, Chroma
+from langchain.embeddings import GPT4AllEmbeddings, CacheBackedEmbeddings
+from langchain.storage import LocalFileStore#, RedisStore, UpstashRedisStore, InMemoryStore
+
+docsUrl = "/home/user/dev/docs"
+ollamaModel="llama2"
+
+def get_ollama_names():
+    output = subprocess.check_output(["ollama", "list"])
+    lines = output.decode("utf-8").splitlines()
+    names = {}
+    for line in lines[1:]:
+        name = line.split()[0].split(':')[0]
+        names[name] = name
+    return names
+
+names = get_ollama_names()
+
+def greet(name):
+    global ollamaModel
+    ollamaModel=name
+    return f"{name}"
+
+dropdown = gr.Dropdown(label="Models available", choices=names, value="llama2")
+textbox = gr.Textbox(label="You chose")
+
+def AI_response(question, history):
+    ollama = Ollama(base_url='http://localhost:11434', model=ollamaModel)
+    print(ollamaModel)
+    documents = []
+    for file in os.listdir(docsUrl):
+        if file.endswith(".pdf"):
+            pdf_path = docsUrl + "/" + file
+            loader = PyPDFLoader(pdf_path)
+            documents.extend(loader.load())
+            print("Found " + pdf_path)
+        elif file.endswith('.docx') or file.endswith('.doc'):
+            doc_path = docsUrl + "/" + file
+            loader = Docx2txtLoader(doc_path)
+            documents.extend(loader.load())
+            print("Found " + doc_path)
+        elif file.endswith('.txt') or file.endswith('.kt') or file.endswith('.json'):
+            text_path = docsUrl + "/" + file
+            loader = TextLoader(text_path)
+            documents.extend(loader.load())
+            print("Found " + text_path)        
+        elif file.endswith('.html') or file.endswith('.htm'):
+            htm_path = docsUrl + "/" + file
+            loader = UnstructuredHTMLLoader(htm_path)
+            documents.extend(loader.load())
+            print("Found " + htm_path)        
+
+    text_splitter = CharacterTextSplitter(chunk_size=32, chunk_overlap=32)
+    all_splits = text_splitter.split_documents(documents)
+    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings(embeddings_chunk_size=1000))
+    docs = vectorstore.similarity_search(question)
+    len(docs)
+    qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
+    reply=str(qachain.run(question))
+    return reply
+
+
+
+with gr.Blocks() as demo:
+    interface = gr.Interface(fn=greet, inputs=[dropdown], outputs=[textbox], title="Choose a LLM model")
+    chat = gr.ChatInterface(AI_response, title="Put your files in folder " + docsUrl)
+
+demo.launch(server_name="0.0.0.0", server_port=7860)
+

examples/langchain-python-langchain-webui/readme.md

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+# LangChain Web UI
+
+A python Gradio web UI using GPT4AllEmbeddings that lets can choose among the installed models
+
+## Screenshot
+
+![plot](https://raw.githubusercontent.com/suoko/ollama/main/examples/langchain-python-langchain-webui/ollama-langchain-gradio-webui.png)
+
+## Setup
+
+```
+pip install -r requirements.txt
+```
+
+## Run
+
+```
+python main.py
+```
+
+Running this example will open a web server with port 7860:
+
+```
+
+```

examples/langchain-python-langchain-webui/requirements.txt

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+langchain==0.0.313
+pypdf==3.16.4
+chromadb==0.4.14
+tiktoken==0.5.1
+docx2txt==0.8
+unstructured==0.10.25
+gradio==3.50.2
+Pillow==10.1.0
+matplotlib==3.8.0
+numpy==1.23.5