Package: promptengineers
Prompt Engineers AI Open Source PackageBuild and Publish## Build Packagebashscripts/build.sh## Publish Package to PyPibashscripts/publish.shDevelopment## In the application directory start your virtual env (this would be the workspace## where your API server that you would like to install the model)source.venv/bin/activate## Then change directory to where your package is, make changes and run the following.pipinstall.## Switch back to the directory of your where your workspace is for you app server.cd<path>/<app>/<server>
pipinstall-rrequirements.txt## Make sure your app server has the packages shown in setup.py and run your server...How to use...Load Documents from Sourcesimportosfrompromptengineers.retrieval.factoriesimportEmbeddingFactory,LoaderFactory,RetrievalFactoryfrompromptengineers.retrieval.strategiesimportVectorstoreContextOPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')PINECONE_KEY=os.environ.get('PINECONE_API_KEY')PINECONE_ENV=os.environ.get('PINECONE_ENV')PINECONE_INDEX=os.environ.get('PINECONE_INDEX')INDEX_PROVIDER='pinecone'INDEX_NAME='default'NAMESPACE='000000000000000000000000::formio'EMBEDDING_LLM='text-embedding-ada-002'LOADERS=[{"type":"copy","text":"In a quaint village, an elderly man discovered a mysterious, ""ancient book in his attic. Each night, he read a chapter, and the ""next day, miracles began happening in the village. Unbeknownst to ""the villagers, their fortunes were being written by the man's dreams, ""guided by the magical book."},{"type":"web_base","urls":['https://adaptive.biz']},{"type":"website","urls":['https://adaptive.biz']# Retrieves subpages},{"type":"youtube","urls":['https://www.youtube.com/watch?v=GyllRd2E6fg']}]# Generate Embeddingsembedding=EmbeddingFactory(EMBEDDING_LLM,OPENAI_API_KEY)# Choose the appropriate vector search provider strategy for Pineconeretrieval_provider=RetrievalFactory(INDEX_PROVIDER,embedding.create_embedding(),{'api_key':PINECONE_KEY,'env':PINECONE_ENV,'index_name':INDEX_NAME,'namespace':NAMESPACE})# Create loaders and process eachall_loaders=[]forloader_configinLOADERS:try:loader=LoaderFactory.create(loader_type=loader_config['type'],loader_config=loader_config)all_loaders.append(loader)exceptValueErrorase:print(f"Error creating loader:{e}")exceptExceptionase:print(f"Unexpected error:{e}")# Assuming PineconeService and EmbeddingFactory have internal error handlingtry:# Create a vector store service contextvectostore_service=VectorstoreContext(retrieval_provider.create_strategy())vectorstore=vectostore_service.add(all_loaders)exceptExceptionase:print(f"Error processing documents with Pinecone:{e}")Retrieval Augemented Generation (RAG) - HTTP Chatimportosimportasynciofrompromptengineers.chatimportlangchain_http_retrieval_chatfrompromptengineers.retrieval.factoriesimportEmbeddingFactory,RetrievalFactoryfrompromptengineers.retrieval.strategiesimportVectorstoreContext# Environment VariablesOPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')PINECONE_KEY=os.environ.get('PINECONE_API_KEY')PINECONE_ENV=os.environ.get('PINECONE_ENV')PINECONE_INDEX=os.environ.get('PINECONE_INDEX')# Retrieval ConstantsINDEX_PROVIDER='pinecone'INDEX_NAME='default'NAMESPACE='000000000000000000000000::formio'EMBEDDING_LLM='text-embedding-ada-002'# Chat ConstantsCHAT_LLM='gpt-3.5-turbo'TEMPERATURE=0.9MESSAGES=[{'role':'system','content':'You are a helpful document retrieval AI, ''use the context to answer the user queries.'},{'role':'user','content':'Can you summarize the context?'}]# Generate Embeddingsembedding=EmbeddingFactory(EMBEDDING_LLM,OPENAI_API_KEY)# Choose the appropriate vector search provider strategy for Pineconeretrieval_provider=RetrievalFactory(INDEX_PROVIDER,embedding.create_embedding(),{'api_key':PINECONE_KEY,'env':PINECONE_ENV,'index_name':INDEX_NAME,'namespace':NAMESPACE})# Create a vector store service contextvectostore_service=VectorstoreContext(retrieval_provider.create_strategy())# Load the vectorstore using the service contextvectorstore=vectostore_service.load()# Run the chatasyncdefmain():response,cb=awaitlangchain_http_retrieval_chat(messages=MESSAGES,model=CHAT_LLM,temperature=TEMPERATURE,vectorstore=vectorstore,openai_api_key=OPENAI_API_KEY)print(response)print(cb)asyncio.run(main())Retrieval Augemented Generation (RAG) - Stream Chatimportosimportasynciofrompromptengineers.chatimportlangchain_stream_vectorstore_chatfrompromptengineers.retrieval.factoriesimportEmbeddingFactory,RetrievalFactoryfrompromptengineers.retrieval.strategiesimportVectorstoreContext# Environment VariablesOPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')PINECONE_KEY=os.environ.get('PINECONE_API_KEY')PINECONE_ENV=os.environ.get('PINECONE_ENV')PINECONE_INDEX=os.environ.get('PINECONE_INDEX')# Retrieval ConstantsINDEX_PROVIDER='pinecone'INDEX_NAME='default'NAMESPACE='000000000000000000000000::formio'EMBEDDING_LLM='text-embedding-ada-002'# Chat ConstantsCHAT_LLM='gpt-3.5-turbo'TEMPERATURE=0.9MESSAGES=[{'role':'system','content':'You are a helpful document retrieval AI, ''use the context to answer the user queries.'},{'role':'user','content':'Can you summarize the context?'}]# Generate Embeddingsembedding=EmbeddingFactory(EMBEDDING_LLM,OPENAI_API_KEY)# Choose the appropriate vector search provider strategy for Pineconeretrieval_provider=RetrievalFactory(INDEX_PROVIDER,embedding.create_embedding(),{'api_key':PINECONE_KEY,'env':PINECONE_ENV,'index_name':INDEX_NAME,'namespace':NAMESPACE})# Create a vector store service contextvectostore_service=VectorstoreContext(retrieval_provider.create_strategy())# Load the vectorstore using the service contextvectorstore=vectostore_service.load()# Run the chatasyncdefmain():response=langchain_stream_vectorstore_chat(messages=MESSAGES,model=CHAT_LLM,temperature=TEMPERATURE,vectorstore=vectorstore,openai_api_key=OPENAI_API_KEY)asyncfordatainresponse:print(data)asyncio.run(main())Agent Chat equipped w/ tools - HTTP Chatimportosimportasynciofrompromptengineers.chatimportlangchain_http_agent_chatfrompromptengineers.core.config.toolsimportAVAILABLE_TOOLSfrompromptengineers.llms.utilsimportgather_toolsfrompromptengineers.retrieval.factoriesimportEmbeddingFactory,RetrievalFactoryfrompromptengineers.retrieval.strategiesimportVectorstoreContext# Environment VariablesOPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')PINECONE_KEY=os.environ.get('PINECONE_API_KEY')PINECONE_ENV=os.environ.get('PINECONE_ENV')PINECONE_INDEX=os.environ.get('PINECONE_INDEX')# Retrieval ConstantsINDEX_PROVIDER='pinecone'INDEX_NAME='default'NAMESPACE='000000000000000000000000::formio'EMBEDDING_LLM='text-embedding-ada-002'# Chat ConstantsCHAT_LLM='gpt-3.5-turbo-1106'TEMPERATURE=0.1MESSAGES=[{'role':'system','content':'You are a powerful AI assistant, you are equipped with tools ''to help you accomplish your tasks. Query the context when you need ''additional information to complete your task. If the user query is not ''related to the context then you can use the tools complete the task.'},{'role':'user','content':'What is 14125 compounded annually for 5 years at 4 percent for 23 years?'# Math Agent# 'content': 'Can you provide a react code sample to render a form in Form.io?'           # Retrieval Agent}]# Generate Embeddingsembedding=EmbeddingFactory(EMBEDDING_LLM,OPENAI_API_KEY)# Choose the appropriate vector search provider strategy for Pineconeretrieval_provider=RetrievalFactory(INDEX_PROVIDER,embedding.create_embedding(),{'api_key':PINECONE_KEY,'env':PINECONE_ENV,'index_name':INDEX_NAME,'namespace':NAMESPACE})# Create a vector store service contextvectostore_service=VectorstoreContext(retrieval_provider.create_strategy())# Load the vectorstore using the service contextvectorstore=vectostore_service.load()# Gather the toolstools=gather_tools(tools=['math_tool'],available_tools=AVAILABLE_TOOLS,vectorstore=vectorstore,plugins=[])# Run the chatasyncdefmain():response,cb=awaitlangchain_http_agent_chat(messages=MESSAGES,model=CHAT_LLM,tools=tools,temperature=TEMPERATURE,openai_api_key=OPENAI_API_KEY)print(response)print(cb)asyncio.run(main())Agent Chat equipped w/ tools - Stream Chatimportosimportasynciofrompromptengineers.chatimportlangchain_stream_agent_chatfrompromptengineers.core.config.toolsimportAVAILABLE_TOOLSfrompromptengineers.llms.utilsimportgather_toolsfrompromptengineers.retrieval.factoriesimportEmbeddingFactory,RetrievalFactoryfrompromptengineers.retrieval.strategiesimportVectorstoreContext# Environment VariablesOPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')PINECONE_KEY=os.environ.get('PINECONE_API_KEY')PINECONE_ENV=os.environ.get('PINECONE_ENV')PINECONE_INDEX=os.environ.get('PINECONE_INDEX')# Retrieval ConstantsINDEX_PROVIDER='pinecone'INDEX_NAME='default'NAMESPACE='000000000000000000000000::formio'EMBEDDING_LLM='text-embedding-ada-002'# Chat ConstantsCHAT_LLM='gpt-4-0125-preview'TEMPERATURE=0.1MESSAGES=[{'role':'system','content':'You are a powerful AI assistant, you are equipped with tools ''to help you accomplish your tasks. Query the context when you need ''additional information to complete your task. If the user query is not ''related to the context then you can use the tools complete the task.'},{'role':'user',# 'content': 'What is 14125 compounded annually for 5 years at 4 percent for 23 years?' # Math Agent'content':'Can you provide a react code sample to render a form in Form.io?'# Retrieval Agent}]# Generate Embeddingsembedding=EmbeddingFactory(EMBEDDING_LLM,OPENAI_API_KEY)# Choose the appropriate vector search provider strategy for Pineconeretrieval_provider=RetrievalFactory(INDEX_PROVIDER,embedding.create_embedding(),{'api_key':PINECONE_KEY,'env':PINECONE_ENV,'index_name':INDEX_NAME,'namespace':NAMESPACE})# Create a vector store service contextvectostore_service=VectorstoreContext(retrieval_provider.create_strategy())# Load the vectorstore using the service contextvectorstore=vectostore_service.load()# Gather the toolstools=gather_tools(tools=['math_tool'],available_tools=AVAILABLE_TOOLS,vectorstore=vectorstore,plugins=[])# Run the chatasyncdefmain():response=langchain_stream_agent_chat(messages=MESSAGES,model=CHAT_LLM,tools=tools,temperature=TEMPERATURE,openai_api_key=OPENAI_API_KEY)asyncfordatainresponse:print(data)asyncio.run(main())