Skip to content

Evaporate

DFEvaporateProgram #

Bases: BaseEvaporateProgram[DataFrameRowsOnly]

Evaporate DF program.

Given a set of fields, extracts a dataframe from a set of nodes. Each node corresponds to a row in the dataframe - each value in the row corresponds to a field value.

Source code in llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py 
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
class DFEvaporateProgram(BaseEvaporateProgram[DataFrameRowsOnly]):
    """Evaporate DF program.

    Given a set of fields, extracts a dataframe from a set of nodes.
    Each node corresponds to a row in the dataframe - each value in the row
    corresponds to a field value.

    """

    def fit(
        self,
        nodes: List[BaseNode],
        field: str,
        field_context: Optional[Any] = None,
        expected_output: Optional[Any] = None,
        inplace: bool = True,
    ) -> str:
        """Given the input Nodes and fields, synthesize the python code."""
        fn = self._extractor.extract_fn_from_nodes(nodes, field)
        logger.debug(f"Extracted function: {fn}")
        if inplace:
            self._field_fns[field] = fn
        return fn

    def _inference(
        self, nodes: List[BaseNode], fn_str: str, field_name: str
    ) -> List[Any]:
        """Given the input, call the python code and return the result."""
        results = self._extractor.run_fn_on_nodes(nodes, fn_str, field_name)
        logger.debug(f"Results: {results}")
        return results

    @property
    def output_cls(self) -> Type[DataFrameRowsOnly]:
        """Output class."""
        return DataFrameRowsOnly

    def __call__(self, *args: Any, **kwds: Any) -> DataFrameRowsOnly:
        """Call evaporate on inference data."""
        # TODO: either specify `nodes` or `texts` in kwds
        if "nodes" in kwds:
            nodes = kwds["nodes"]
        elif "texts" in kwds:
            nodes = [TextNode(text=t) for t in kwds["texts"]]
        else:
            raise ValueError("Must provide either `nodes` or `texts`.")

        col_dict = {}
        for field in self._fields:
            col_dict[field] = self._inference(nodes, self._field_fns[field], field)

        df = pd.DataFrame(col_dict, columns=self._fields)

        # convert pd.DataFrame to DataFrameRowsOnly
        df_row_objs = []
        for row_arr in df.values:
            df_row_objs.append(DataFrameRow(row_values=list(row_arr)))
        return DataFrameRowsOnly(rows=df_row_objs)

output_cls property #

output_cls: Type[DataFrameRowsOnly]

Output class.

fit #

fit(nodes: List[BaseNode], field: str, field_context: Optional[Any] = None, expected_output: Optional[Any] = None, inplace: bool = True) -> str

Given the input Nodes and fields, synthesize the python code.

Source code in llama-index-integrations/program/llama-index-program-evaporate/llama_index/program/evaporate/base.py 
137
138
139
140
141
142
143
144
145
146
147
148
149
150
def fit(
    self,
    nodes: List[BaseNode],
    field: str,
    field_context: Optional[Any] = None,
    expected_output: Optional[Any] = None,
    inplace: bool = True,
) -> str:
    """Given the input Nodes and fields, synthesize the python code."""
    fn = self._extractor.extract_fn_from_nodes(nodes, field)
    logger.debug(f"Extracted function: {fn}")
    if inplace:
        self._field_fns[field] = fn
    return fn