Api overview

In [1]:

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License.

# Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License.

API Overview¶

This notebook provides a demonstration of how to interact with graphrag as a library using the API as opposed to the CLI. Note that graphrag's CLI actually connects to the library through this API for all operations.

In [2]:

from pathlib import Path
from pprint import pprint

import pandas as pd

import graphrag.api as api
from graphrag.config.load_config import load_config
from graphrag.index.typing.pipeline_run_result import PipelineRunResult

from pathlib import Path from pprint import pprint import pandas as pd import graphrag.api as api from graphrag.config.load_config import load_config from graphrag.index.typing.pipeline_run_result import PipelineRunResult

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[2], line 4
      1 from pathlib import Path
      2 from pprint import pprint
----> 4 import pandas as pd
      6 import graphrag.api as api
      7 from graphrag.config.load_config import load_config

ModuleNotFoundError: No module named 'pandas'

In [3]:

PROJECT_DIRECTORY = "<your project directory>"

PROJECT_DIRECTORY = ""

Prerequisite¶

As a prerequisite to all API operations, a GraphRagConfig object is required. It is the primary means to control the behavior of graphrag and can be instantiated from a settings.yaml configuration file.

Please refer to the CLI docs for more detailed information on how to generate the settings.yaml file.

Generate a GraphRagConfig object¶

In [4]:

graphrag_config = load_config(Path(PROJECT_DIRECTORY))

graphrag_config = load_config(Path(PROJECT_DIRECTORY))

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[4], line 1
----> 1 graphrag_config = load_config(Path(PROJECT_DIRECTORY))

NameError: name 'load_config' is not defined

Indexing API¶

Indexing is the process of ingesting raw text data and constructing a knowledge graph. GraphRAG currently supports plaintext (.txt) and .csv file formats.

Build an index¶

In [5]:

index_result: list[PipelineRunResult] = await api.build_index(config=graphrag_config)

# index_result is a list of workflows that make up the indexing pipeline that was run
for workflow_result in index_result:
    status = f"error\n{workflow_result.errors}" if workflow_result.errors else "success"
    print(f"Workflow Name: {workflow_result.workflow}\tStatus: {status}")

index_result: list[PipelineRunResult] = await api.build_index(config=graphrag_config) # index_result is a list of workflows that make up the indexing pipeline that was run for workflow_result in index_result: status = f"error\n{workflow_result.errors}" if workflow_result.errors else "success" print(f"Workflow Name: {workflow_result.workflow}\tStatus: {status}")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 index_result: list[PipelineRunResult] = await api.build_index(config=graphrag_config)
      3 # index_result is a list of workflows that make up the indexing pipeline that was run
      4 for workflow_result in index_result:

NameError: name 'api' is not defined

Query an index¶

To query an index, several index files must first be read into memory and passed to the query API.

In [6]:

entities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/entities.parquet")
communities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/communities.parquet")
community_reports = pd.read_parquet(
    f"{PROJECT_DIRECTORY}/output/community_reports.parquet"
)

response, context = await api.global_search(
    config=graphrag_config,
    entities=entities,
    communities=communities,
    community_reports=community_reports,
    community_level=2,
    dynamic_community_selection=False,
    response_type="Multiple Paragraphs",
    query="Who is Scrooge and what are his main relationships?",
)

entities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/entities.parquet") communities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/communities.parquet") community_reports = pd.read_parquet( f"{PROJECT_DIRECTORY}/output/community_reports.parquet" ) response, context = await api.global_search( config=graphrag_config, entities=entities, communities=communities, community_reports=community_reports, community_level=2, dynamic_community_selection=False, response_type="Multiple Paragraphs", query="Who is Scrooge and what are his main relationships?", )

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[6], line 1
----> 1 entities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/entities.parquet")
      2 communities = pd.read_parquet(f"{PROJECT_DIRECTORY}/output/communities.parquet")
      3 community_reports = pd.read_parquet(
      4     f"{PROJECT_DIRECTORY}/output/community_reports.parquet"
      5 )

NameError: name 'pd' is not defined

The response object is the official reponse from graphrag while the context object holds various metadata regarding the querying process used to obtain the final response.

In [7]:

print(response)

print(response)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[7], line 1
----> 1 print(response)

NameError: name 'response' is not defined

Digging into the context a bit more provides users with extremely granular information such as what sources of data (down to the level of text chunks) were ultimately retrieved and used as part of the context sent to the LLM model).

In [8]:

pprint(context)  # noqa: T203

pprint(context) # noqa: T203

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[8], line 1
----> 1 pprint(context)  # noqa: T203

NameError: name 'context' is not defined