from llama_index.langchain_helpers.agents import LlamaToolkit, create_llama_chat_agent, IndexToolConfig, GraphToolConfig
agent_chain = create_agent(toolkit, llm)
while True:
query = input("What do you want to ask? ")
print('question: ', query)
response = agent_chain.run(input=query)
print(f'Agent: {response}')func for the tool, you can put your own wrapper function instead of a lambda, that calls query and gets the sources nodes from the response objecttoolkit = LlamaToolkit(
index_configs=index_configs,
graph_configs=graph_configs
)
memory = ConversationBufferMemory(memory_key="chat_history")
agent_chain = create_llama_chat_agent(
toolkit,
llm,
memory=memory,
verbose=True
)func property of the Tool object will query your graphgraph = [create your graph] query_configs = [create your query configs] def query_index(q): response = graph.query(q, query_configs=query_configs) source_nodes = response.source_nodes source_texts = [x.node.get_text() for x in source_nodes] source_scores = [x.score for x in source_nodes] # Do something with the texts/scores? ... return str(response) ... func=lambda q: query_index(q), ...
initialize_agent to build an agent_chain, but the it does not give the accurate answer as create_llama_chat_agent does.graphs = []
tools = []
idx = 0
for file in files:
print(file)
graph = get_graph('indexing/' + file+'.json')
graphs.append(graph)
desc = graph_desc[idx] if idx < len(graph_desc) else 'others'
tool = Tool(
name = file + ' Graph',
func= lambda q: str(graph.query(q)), #lambda q: str(query_index(q, graph, query_configs)),
description="useful for when you want to answer questions about the " + desc,
return_direct=True
)
tools.append(tool)
idx += 1
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo", max_tokens=512)
agent_chain = initialize_agent(tools, llm, agent="conversational-react-description", memory=memory)
return agent_chaininitialized_agentmemory = ConversationBufferMemory(memory_key="chat_history")
agent_chain = create_llama_chat_agent(
toolkit,
llm,
memory=memory,
verbose=True
) graph_desc = [' general gurufocus tutorials questions, such as some key page tutorials or stock summary page', ' warren buffet and BERKSHIRE HATHAWAY INC SHAREHOLDER LETTERS', ' 10K sec filing for some popular stocks']
graphs = []
graph_configs = []
idx = 0
for file in files:
print(file)
graph = ComposableGraph.load_from_disk('indexing/' + file+'.json', llm_predictor=llm_predictor, prompt_helper=prompt_helper)
graphs.append(graph)
# graph config
graph_config = GraphToolConfig(
graph=graph,
name=f"Graph Index",
description="useful for when user ask about gurufocus" + graph_desc[idx],
query_configs=query_configs,
tool_kwargs={"return_direct": True}
)
graph_configs.append(graph_config)
idx += 1
# load index
index_list = []
index_configs = []
index_desc = { 'getting-started.json': ', including homepage dashboard, stock summary page, guru pages, insider trades, all-in-one screener, excel add-in, and google sheets add-on', 'stock-summary-page.json': ', including stock summary page, warning signs, gf score, gf value, performance charts, peter lynch chart, segment data charts' }
for file in files:
file_list = os.listdir('indexing/' + file)
for filename in file_list:
file_path = f'indexing/{file}/{filename}'
cur_index = GPTSimpleVectorIndex.load_from_disk(file_path, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
index_list.append(cur_index)
desc = index_desc[filename] if filename in index_desc else ' '
tool_config = IndexToolConfig(
index=cur_index,
name=f"Vector Index {filename}",
description=f"useful for when you want to answer queries about the {filename[:-5]} " + desc,
index_query_kwargs={"similarity_top_k": 3},
tool_kwargs={"return_direct": True}
)
index_configs.append(tool_config)
toolkit = LlamaToolkit(
index_configs=index_configs,
graph_configs=graph_configs
)create_llama_chat_agent, I wonder if it's possible that I can modify this to pass func in the tool . So that I can get the resources using toolkit.lambda q: str(graph.query(q, query_configs=query_configs)) # define LLM
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
decompose_transform = DecomposeQueryTransform(
llm_predictor, verbose=True
)
# define query configs for graph
query_configs = [
{
"index_struct_type": "simple_dict",
"query_mode": "default",
"query_kwargs": {
"similarity_top_k": 1,
"include_summary": True,
"refine_template": CHAT_REFINE_PROMPT
},
"query_transform": decompose_transform
},
{
"index_struct_type": "list",
"query_mode": "default",
"query_kwargs": {
"response_mode": "tree_summarize",
"verbose": True,
"refine_template": CHAT_REFINE_PROMPT
}
},
]
# define query configs for index
index_query_configs = [
{
"index_query_kwargs": {"similarity_top_k": 3},
"tool_kwargs": {"return_direct": True}
}
] Sorry, the provided knowledge source context is not related to the topic # add graph tools
graph_desc = [' general gurufocus tutorials questions, such as some key page tutorials or stock summary page', ' warren buffet and BERKSHIRE HATHAWAY INC SHAREHOLDER LETTERS', ' 10K sec filing for some popular stocks']
graphs = []
tools = []
idx = 0
for file in files:
print(file)
graph = get_graph('indexing/' + file+'.json')
graphs.append(graph)
desc = graph_desc[idx] if idx < len(graph_desc) else 'others'
tool = Tool(
name = file + ' Graph',
func= lambda q: str(query_index(q, graph, query_configs)),
description="useful for when you want to answer questions about the " + desc,
return_direct=True
)
tools.append(tool)
idx += 1
# Add all index tools
index_desc = { 'getting-started.json': ', including homepage dashboard, stock summary page, guru pages, insider trades, all-in-one screener, excel add-in, and google sheets add-on', 'stock-summary-page.json': ', including stock summary page, warning signs, gf score, gf value, performance charts, peter lynch chart, segment data charts' }
for file in files:
file_list = os.listdir('indexing/' + file)
for filename in file_list:
file_path = f'indexing/{file}/{filename}'
cur_index = GPTSimpleVectorIndex.load_from_disk(file_path, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
desc = index_desc[filename] if filename in index_desc else ' '
tool = Tool(
name = file + ' index',
func= lambda q: str(query_index(q, index, index_query_configs)),
description="useful for when you want to answer questions about the " + desc,
return_direct=True
)
tools.append(tool) query_configs = [
{
"index_struct_type": "simple_dict",
"query_mode": "default",
"query_kwargs": {
"similarity_top_k": 3, # updated to match the index query config?
"include_summary": True,
"refine_template": CHAT_REFINE_PROMPT
},
"query_transform": decompose_transform
},
{
"index_struct_type": "list",
"query_mode": "default",
"query_kwargs": {
"response_mode": "tree_summarize",
"verbose": True,
"refine_template": CHAT_REFINE_PROMPT
}
},
]query_configs = [
{
"index_struct_type": "simple_dict",
"query_mode": "default",
"query_kwargs": {
"similarity_top_k": 3, # updated to match the index query config?
"include_summary": True,
"refine_template": CHAT_REFINE_PROMPT
},
"query_transform": decompose_transform
},
{
"index_struct_type": "list",
"query_mode": "default",
"query_kwargs": {
"response_mode": "tree_summarize",
"verbose": True,
"refine_template": CHAT_REFINE_PROMPT
}
},
]
