This is an automated email from the ASF dual-hosted git repository.
jin pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-hugegraph-ai.git
The following commit(s) were added to refs/heads/main by this push:
new 96c721b docs(llm): update preparations and example codes (#103)
96c721b is described below
commit 96c721ba48bf259d0e700f42a40a6969935b64b9
Author: vichayturen <[email protected]>
AuthorDate: Thu Oct 31 14:57:40 2024 +0800
docs(llm): update preparations and example codes (#103)
---
hugegraph-llm/README.md | 61 +++++++++++++++--------
hugegraph-llm/src/hugegraph_llm/enums/__init__.py | 16 ++++++
2 files changed, 56 insertions(+), 21 deletions(-)
diff --git a/hugegraph-llm/README.md b/hugegraph-llm/README.md
index 04b766f..eeb7f43 100644
--- a/hugegraph-llm/README.md
+++ b/hugegraph-llm/README.md
@@ -70,8 +70,6 @@ for guidance. (Hubble is a graph-analysis dashboard include
data loading/schema
### 1.Build a knowledge graph in HugeGraph through LLM
-Run example like `python3 ./hugegraph_llm/examples/build_kg_test.py`
-
The `KgBuilder` class is used to construct a knowledge graph. Here is a brief
usage guide:
1. **Initialization**: The `KgBuilder` class is initialized with an instance
of a language model.
@@ -86,8 +84,8 @@ This can be obtained from the `LLMs` class.
(
builder
.import_schema(from_hugegraph="talent_graph").print_result()
- .extract_triples(TEXT).print_result()
- .disambiguate_word_sense().print_result()
+ .chunk_split(TEXT).print_result()
+ .extract_info(extract_type="property_graph").print_result()
.commit_to_hugegraph()
.run()
)
@@ -97,65 +95,86 @@ This can be obtained from the `LLMs` class.
```python
# Import schema from a HugeGraph instance
- import_schema(from_hugegraph="xxx").print_result()
+ builder.import_schema(from_hugegraph="xxx").print_result()
# Import schema from an extraction result
- import_schema(from_extraction="xxx").print_result()
+ builder.import_schema(from_extraction="xxx").print_result()
# Import schema from user-defined schema
- import_schema(from_user_defined="xxx").print_result()
+ builder.import_schema(from_user_defined="xxx").print_result()
```
-3. **Extract Triples**: The `extract_triples` method is used to extract
triples from a text. The text should be passed as a string argument to the
method.
+3. **Chunk Split**: The `chunk_split` method is used to split the input text
into chunks. The text should be passed as a string argument to the method.
```python
- TEXT = "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom
she's shared a home with since 2010."
- extract_triples(TEXT).print_result()
+ # Split the input text into documents
+ builder.chunk_split(TEXT, split_type="document").print_result()
+ # Split the input text into paragraphs
+ builder.chunk_split(TEXT, split_type="paragraph").print_result()
+ # Split the input text into sentences
+ builder.chunk_split(TEXT, split_type="sentence").print_result()
```
-4. **Disambiguate Word Sense**: The `disambiguate_word_sense` method is used
to disambiguate the sense of words in the extracted triples.
+4. **Extract Info**: The `extract_info` method is used to extract info from a
text. The text should be passed as a string argument to the method.
```python
- disambiguate_word_sense().print_result()
+ TEXT = "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom
she's shared a home with since 2010."
+ # extract property graph from the input text
+ builder.extract_info(extract_type="property_graph").print_result()
+ # extract triples from the input text
+ builder.extract_info(extract_type="property_graph").print_result()
```
5. **Commit to HugeGraph**: The `commit_to_hugegraph` method is used to commit
the constructed knowledge graph to a HugeGraph instance.
```python
- commit_to_hugegraph().print_result()
+ builder.commit_to_hugegraph().print_result()
```
6. **Run**: The `run` method is used to execute the chained operations.
```python
- run()
+ builder.run()
```
The methods of the `KgBuilder` class can be chained together to perform a
sequence of operations.
### 2. Retrieval augmented generation (RAG) based on HugeGraph
-Run example like `python3 ./hugegraph_llm/examples/graph_rag_test.py`
-
The `RAGPipeline` class is used to integrate HugeGraph with large language
models to provide retrieval-augmented generation capabilities.
Here is a brief usage guide:
-1. **Extract Keyword:**: Extract keywords and expand synonyms.
+1. **Extract Keyword**: Extract keywords and expand synonyms.
```python
+ from hugegraph_llm.operators.graph_rag_task import RAGPipeline
+ graph_rag = RAGPipeline()
graph_rag.extract_keywords(text="Tell me about Al Pacino.").print_result()
```
-2. **Query Graph for Rag**: Retrieve the corresponding keywords and their
multi-degree associated relationships from HugeGraph.
+2. **Match Vid from Keywords*: Match the nodes with the keywords in the graph.
+
+ ```python
+ graph_rag.keywords_to_vid().print_result()
+ ```
+
+3. **Query Graph for Rag**: Retrieve the corresponding keywords and their
multi-degree associated relationships from HugeGraph.
```python
graph_rag.query_graphdb(max_deep=2, max_items=30).print_result()
```
-3. **Synthesize Answer**: Summarize the results and organize the language to
answer the question.
+
+4. **Rerank Searched Result**: Rerank the searched results based on the
similarity between the question and the results.
+
+ ```python
+ graph_rag.merge_dedup_rerank().print_result()
+ ```
+
+5. **Synthesize Answer**: Summarize the results and organize the language to
answer the question.
```python
- graph_rag.synthesize_answer().print_result()
+ graph_rag.synthesize_answer(vector_only_answer=False,
graph_only_answer=True).print_result()
```
-4. **Run**: The `run` method is used to execute the above operations.
+6. **Run**: The `run` method is used to execute the above operations.
```python
graph_rag.run(verbose=True)
diff --git a/hugegraph-llm/src/hugegraph_llm/enums/__init__.py
b/hugegraph-llm/src/hugegraph_llm/enums/__init__.py
new file mode 100644
index 0000000..13a8339
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/enums/__init__.py
@@ -0,0 +1,16 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
