query_engine = index.as_chat_engine(chat_mode='condense_plus_context',
similarity_top_k=similarity_top_k,
llm=llm_engine,
system_prompt=prepared_system_prompt)
query_tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(
name="query_tool",
description=self.query_description,
),
)
tools.append(query_tool)
agent = OpenAIAgent.from_tools(
tools, llm=llm_engine,
verbose=True,
system_prompt=self.system_prompt
)
response = agent.chat(query_text, chat_history=chat_history) # <====== Exception vector_store = storage_service.get_vector_store(collection_name, db_name)
embed_model = OpenAIEmbedding(mode='similarity', embed_batch_size=2000, api_key=api_Key)
service_context = ServiceContext.from_defaults(chunk_size=project_chunk_size, embed_model=embed_model,
llm=None,
callback_manager=token_counter_callback_manager)
node_parser = SimpleNodeParser.from_defaults(chunk_size=project_chunk_size, chunk_overlap=20)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)from llama_index.agent.openai import OpenAIAgentWorker, OpenAIAgent
# Import and initialize our tool spec
from .image_tool_spec import TextToImageToolSpec
text_to_image_spec = TextToImageToolSpec()
tools = text_to_image_spec.to_tool_list()
# Create the Agent with our tools
agent = OpenAIAgent.from_tools(tools, verbose=False)
print(agent.chat("show an of a beautiful beach with a palm tree at sunset"))# My current approach
chatmemory = ChatMemoryBuffer.from_defaults(token_limit=(history_limit + context_limit))
retriever = index.as_retriever(verbose=True, chat_mode="context", similarity_top_k=similarity_top_k)
custom_chat_engine = CustomContext.from_defaults( # Inherits ContextChatEngine and slightly changes it
retriever=retriever,
memory=chatmemory,
context_template=generate_context_template(),
system_prompt=prepared_system_prompt,
node_postprocessors=[CustomPostprocessor(
context_limit, query_text + prepared_system_prompt, project.db_name, include_threshold)],
service_context=service_context)
response = custom_chat_engine.chat(query_text, chat_history=chat_history)OpenAIAgent.from_tools(??)
token_counter = TokenCountingHandler(
tokenizer=tiktoken.encoding_for_model(model_name).encode,
verbose=False
)
callback_manager = CallbackManager([token_counter])
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, chunk_size=project_chunk_size,
callback_manager=callback_manager)
index = VectorStoreIndex.from_vector_store(vector_store, service_context)
retriever = index.as_retriever(verbose=True, chat_mode="context", similarity_top_k=similarity_top_k)
custom_chat_engine = CustomContext.from_defaults(
retriever=retriever,
memory=chatmemory,
context_template=generate_context_template(),
system_prompt=prepared_system_prompt,
node_postprocessors=[CustomPostprocessor(
context_limit, query_text + prepared_system_prompt, project.db_name, None)])
response = custom_chat_engine.chat(query_text, chat_history=chat_history)
tokens_used = token_counter.total_llm_token_count # <----- ALWAYS ZERO vector_store = storage_service.get_vector_store(collection_name, db_name)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
embed_model = OpenAIEmbedding(mode='similarity', embed_batch_size=2000, api_key=user_settings_data.item.get('openai_key'))
service_context = ServiceContext.from_defaults(chunk_size=chunk_size, embed_model=embed_model,
llm=None,
callback_manager=token_counter_callback_manager)
node_parser = SimpleNodeParser.from_defaults(chunk_size=chunk_size, chunk_overlap=20)
VectorStoreIndex(nodes, storage_context=storage_context, service_context=service_context) # <== THISindex = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context) index._add_nodes_to_index(nodes=content_nodes) # <== EXCEPTION: index structure is not provided
retriever = index.as_retriever(verbose=True, chat_mode="context", similarity_top_k=similarity_top_k)
# CustomContext inherits the ContextChatEngine, and just has a small changes for the best results.
custom_chat_engine = CustomContext.from_defaults(
retriever=retriever,
memory=chatmemory,
context_template=generate_context_template(),
system_prompt=prepared_system_prompt,
node_postprocessors=[CustomPostprocessor(
context_limit, query_text + prepared_system_prompt, project.db_name, None)])
response = custom_chat_engine.chat(query_text, chat_history=chat_history) node_parser = SimpleNodeParser.from_defaults(chunk_size=chunk_size, chunk_overlap=20)
content_nodes = node_parser.get_nodes_from_documents([document])
index = VectorStoreIndex(content_nodes, storage_context=storage_context, service_context=service_context)embed_model = OpenAIEmbedding(mode='similarity', embed_batch_size=2000)
service_context = ServiceContext.from_defaults(chunk_size=chunk_size, embed_model=embed_model,
callback_manager=token_counter_callback_manager)
node_parser = SimpleNodeParser.from_defaults(chunk_size=chunk_size, chunk_overlap=20)
index = VectorStoreIndex(nodes, storage_context=storage_context, service_context=service_context)query_engine = index.as_chat_engine(verbose=True,chat_mode="context",
system_prompt=system_prompt)
response = query_engine.chat(query_text, chat_history=chat_history)document = Document(text)
document.doc_id = data_source_id
service_context = ServiceContext.from_defaults(chunk_size=chunk_size)#1024)
vector_store = QdrantVectorStore(client=get_qrant_client(), collection_name=project_id)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
[document], storage_context=storage_context, service_context=service_context
) return cls(
nodes=nodes,
storage_context=storage_context,
service_context=service_context,
**kwargs,
)