Creating-a-summary-index-with-specific-document-retrieval

from llama_index.core.response_synthesizers import TreeSummarize

synth = TreeSummarize(llm=llm)

nodes = vector_store.get_nodes(filters=filters)

response_str = synth.get_response("query", [node.text for node in nodes])

from llama_index.core.vector_stores import MetadataFilters, MetadataFilter
from pydantic import BaseModel, Field

class Filter(BaseModel):
  """A filter on metadata."""
  key: str = Field(description="The key name to filter on")
  value: str = Field(description="The value to match on.")

class Filters(BaseModel):
  """A list of metadata filters for a query."""
  filters: list[Filter]

sllm = llm.as_structured_llm(Filters)

response = sllm.complete(f"I have an index with metadata like <some examples>. Given a user query, generate some filters (if any) that can be used to help narrow down the search.\n\n{user_query}")

filters = Filters.model_validate_json(str(response))

metadata_filters = []
for filter in filters:
  metadata_filters.append(MetadataFilter(key=filter.key, value=filter.value))

nodes = vector_store.get_nodes(filters=MetadataFilters(filters=filters))