"'To load a source code as documents for a QA over code, you can use the `CodeLoader` class. This class allows you to load source code files and split them into classes and functions.\\n\\nHere is an example of how to use the `CodeLoader` class:\\n\\n```python\\nfrom langchain.document_loaders.code import CodeLoader\\n\\n# Specify the path to the source code file\\ncode_file_path = \"path/to/code/file.py\"\\n\\n# Create an instance of the CodeLoader class\\ncode_loader = CodeLoader(code_file_path)\\n\\n# Load the code as documents\\ndocuments = code_loader.load()\\n\\n# Iterate over the documents\\nfor document in documents:\\n # Access the class or function name\\n name = document.metadata[\"name\"]\\n \\n # Access the code content\\n code = document.page_content\\n \\n # Process the code as needed\\n # ...\\n```\\n\\nIn the example above, `code_file_path` should be replaced with the actual path to your source code file. The `load()` method of the `CodeLoader` class will return a list of `Document` objects, where each document represents a class or function in the source code. You can access the class or function name using the `metadata[\"name\"]` attribute, and the code content using the `page_content` attribute of each `Document` object.\\n\\nYou can then process the code as needed for your QA task.'"
"'To initialize a ReAct agent, you need to follow these steps:\\n\\n1. Initialize a language model `llm` of type `BaseLanguageModel`.\\n\\n2. Initialize a document store `docstore` of type `Docstore`.\\n\\n3. Create a `DocstoreExplorer` with the initialized `docstore`. The `DocstoreExplorer` is used to search for and look up terms in the document store.\\n\\n4. Create an array of `Tool` objects. The `Tool` objects represent the actions that the agent can perform. In the case of `ReActDocstoreAgent`, the tools must be \"Search\" and \"Lookup\" with their corresponding functions from the `DocstoreExplorer`.\\n\\n5. Initialize the `ReActDocstoreAgent` using the `from_llm_and_tools` method with the `llm` (language model) and `tools` as parameters.\\n\\n6. Initialize the `ReActChain` (which is the `AgentExecutor`) using the `ReActDocstoreAgent` and `tools` as parameters.\\n\\nHere is an example of how to do this:\\n\\n```python\\nfrom langchain import ReActChain, OpenAI\\nfrom langchain.docstore.base import Docstore\\nfrom langchain.docstore.document import Document\\nfrom langchain.tools.base import BaseTool\\n\\n# Initialize the LLM and a docstore\\nllm = OpenAI()\\ndocstore = Docstore()\\n\\ndocstore_explorer = DocstoreExplorer(docstore)\\ntools = [\\n Tool(\\n name=\"Search\",\\n func=docstore_explorer.search,\\n description=\"Search for a term in the docstore.\",\\n ),\\n Tool(\\n name=\"Lookup\",\\n func=docstore_explorer.lookup,\\n description=\"Lookup a term in the docstore.\",\\n ),\\n]\\nagent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)\\nreact = ReActChain(agent=agent, tools=tools)\\n```\\n\\nKeep in mind that this is a simplified example and you might need to adapt it to your specific needs.'"
]
]
},
},
"execution_count": 30,
"execution_count": 43,
"metadata": {},
"metadata": {},
"output_type": "execute_result"
"output_type": "execute_result"
}
}
],
],
"source": [
"source": [
"question = \"How can I load a source code as documents, for a QA over code, spliting the code in classes and functions?\"\n",
"question = \"How can I initialize a ReAct agent?\"\n",
"result = qa(question)\n",
"result = qa(question)\n",
"result['answer']"
"result['answer']"
]
]
@ -328,6 +328,74 @@
"\n",
"\n",
""
""
]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Private chat\n",
"\n",
"We can use [Code LLaMA](https://about.fb.com/news/2023/08/code-llama-ai-for-coding/) via the Ollama integration.\n",
" \"How can I initialize a ReAct agent?\" To initialize a ReAct agent, you can use the `ReActAgent.from_llm_and_tools()` class method. This method takes two arguments: the LLM and a list of tools.\n",
"Here is an example of how to initialize a ReAct agent with the OpenAI language model and the \"Search\" tool:\n",
" The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential."
]
},
{
"data": {
"text/plain": [
"' To initialize a ReAct agent, you can use the `ReActAgent.from_llm_and_tools()` class method. This method takes two arguments: the LLM and a list of tools.\\nHere is an example of how to initialize a ReAct agent with the OpenAI language model and the \"Search\" tool:\\nfrom langchain.agents.mrkl.base import ZeroShotAgent\\n\\nagent = ReActDocstoreAgent.from_llm_and_tools(OpenAIFunctionsAgent(), [Tool(\"Search\")]])\\n\\n'"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"question = \"How can I initialize a ReAct agent?\"\n",
"result = qa_llama(question)\n",
"result['answer']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can view the [LangSmith trace](https://smith.langchain.com/public/fd24c734-e365-4a09-b883-cdbc7dcfa582/r) to sanity check the result relative to what was retrieved."
Lance Martin
1 year ago
committed by
GitHub