Merge pull request #105 from SylphAI-Inc/main

[Beta Release 0.0.0b1]

.github/workflows/python-test.yml

@@ -11,7 +11,7 @@ jobs:
 
     strategy:
       matrix:
-        python-version: ['3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
 
     steps:
       - uses: actions/checkout@v3 # Updated to the latest version

README.md

@@ -10,7 +10,7 @@
 [![](https://dcbadge.vercel.app/api/server/zt2mTPcu?compact=true&style=flat)](https://discord.gg/zt2mTPcu)
 
 
-### ⚡ The PyTorch Library for Large Language Model Applications ⚡
+### ⚡ The Lightning Library for Large Language Model Applications ⚡
 
 *LightRAG* helps developers with both building and optimizing *Retriever-Agent-Generator* pipelines.
 It is *light*, *modular*, and *robust*, with a 100% readable codebase.
@@ -20,7 +20,7 @@ It is *light*, *modular*, and *robust*, with a 100% readable codebase.
 
 # Why LightRAG?
 
-LLMs are like water; they can almost do anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience.
+LLMs are like water; they can be shaped into anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience.
 
 Because of this, no library can provide out-of-the-box solutions. Users must build toward their own use case. This requires the library to be modular, robust, and have a clean, readable codebase. The only code you should put into production is code you either 100% trust or are 100% clear about how to customize and iterate.
 
@@ -240,7 +240,7 @@ LightRAG full documentation available at [lightrag.sylph.ai](https://lightrag.sy
 ```bibtex
 @software{Yin2024LightRAG,
   author = {Li Yin},
-  title = {{LightRAG: The PyTorch Library for Large Language Model (LLM) Applications}},
+  title = {{LightRAG: The Lightning Library for Large Language Model (LLM) Applications}},
   month = {7},
   year = {2024},
   doi = {10.5281/zenodo.12639531},

developer_notes/react_note.py

@@ -0,0 +1,74 @@
+from lightrag.components.agent import ReActAgent
+from lightrag.core import Generator, ModelClientType, ModelClient
+from lightrag.utils import setup_env
+
+setup_env()
+
+
+# Define tools
+def multiply(a: int, b: int) -> int:
+    """
+    Multiply two numbers.
+    """
+    return a * b
+
+
+def add(a: int, b: int) -> int:
+    """
+    Add two numbers.
+    """
+    return a + b
+
+
+def divide(a: float, b: float) -> float:
+    """
+    Divide two numbers.
+    """
+    return float(a) / b
+
+
+llama3_model_kwargs = {
+    "model": "llama3-70b-8192",  # llama3 70b works better than 8b here.
+    "temperature": 0.0,
+}
+gpt_model_kwargs = {
+    "model": "gpt-3.5-turbo",
+    "temperature": 0.0,
+}
+
+
+def test_react_agent(model_client: ModelClient, model_kwargs: dict):
+    tools = [multiply, add, divide]
+    queries = [
+        "What is the capital of France? and what is 465 times 321 then add 95297 and then divide by 13.2?",
+        "Give me 5 words rhyming with cool, and make a 4-sentence poem using them",
+    ]
+    # define a generator without tools for comparison
+
+    generator = Generator(
+        model_client=model_client,
+        model_kwargs=model_kwargs,
+    )
+
+    react = ReActAgent(
+        max_steps=6,
+        add_llm_as_fallback=True,
+        tools=tools,
+        model_client=model_client,
+        model_kwargs=model_kwargs,
+    )
+    # print(react)
+
+    for query in queries:
+        print(f"Query: {query}")
+        agent_response = react.call(query)
+        llm_response = generator.call(prompt_kwargs={"input_str": query})
+        print(f"Agent response: {agent_response}")
+        print(f"LLM response: {llm_response}")
+        print("")
+
+
+if __name__ == "__main__":
+    test_react_agent(ModelClientType.GROQ(), llama3_model_kwargs)
+    # test_react_agent(ModelClientType.OPENAI(), gpt_model_kwargs)
+    print("Done")

docs/Makefile

@@ -21,7 +21,8 @@ help:
 
 apidoc:
 	@sphinx-apidoc -o $(APIDOCOUTDIR)/core ../lightrag/lightrag/core --separate --force
-	@sphinx-apidoc -o $(APIDOCOUTDIR)/components ../lightrag/lightrag/components --separate --force --templatedir=$(SOURCEDIR)/_templates
+	@sphinx-apidoc -o $(APIDOCOUTDIR)/components ../lightrag/lightrag/components --separate --force
+#--templatedir=$(SOURCEDIR)/_templates
 	@sphinx-apidoc -o $(APIDOCOUTDIR)/eval ../lightrag/lightrag/eval --separate --force
 	@sphinx-apidoc -o $(APIDOCOUTDIR)/optim ../lightrag/lightrag/optim --separate --force
 	@sphinx-apidoc -o $(APIDOCOUTDIR)/utils ../lightrag/lightrag/utils --separate --force

docs/source/apis/components/index.rst

@@ -31,7 +31,6 @@ Retriever
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
    components.retriever.bm25_retriever
    components.retriever.faiss_retriever
@@ -46,23 +45,20 @@ Output Parsers
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
    components.output_parsers.outputs
 
 Agent
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
    components.agent.react
 
 Data Process
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
 
    components.data_process.text_splitter
@@ -73,15 +69,13 @@ Memory
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
    components.memory.memory
 
 Reasoning
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
-   :nosignatures:
 
    components.reasoning.chain_of_thought
 

docs/source/apis/index.rst

@@ -104,10 +104,11 @@ Utils
 .. autosummary::
 
    utils.logger
+   utils.setup_env
+   utils.lazy_import
    utils.serialization
    utils.config
    utils.registry
-   utils.setup_env
 
 
 .. toctree::