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Merge pull request langchain-ai#15 from langchain-ai/wfh/run_collector
Rec run collector
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,87 @@ | ||
| import React from "react"; | ||
| import Tabs from "@theme/Tabs"; | ||
| import TabItem from "@theme/TabItem"; | ||
| import CodeBlock from "@theme/CodeBlock"; | ||
|
|
||
| export const AccessRunIdBlock = ({}) => { | ||
| const callbackPythonBlock = `from langchain import chat_models, prompts, callbacks | ||
| chain = ( | ||
| prompts.ChatPromptTemplate.from_template("Say hi to {name}") | ||
| | chat_models.ChatOpenAI() | ||
| ) | ||
| with callbacks.collect_runs() as cb: | ||
| result = chain.invoke({"name": "Clara"}) | ||
| run_id = id.traced_runs[0].id | ||
| print(run_id) | ||
| `; | ||
|
|
||
| const alternativePythonBlock = `from langchain.chat_models import ChatOpenAI | ||
| from langchain.chains import LLMChain\n | ||
| chain = LLMChain.from_string(ChatOpenAI(), "Say hi to {name}") | ||
| response = chain("Clara", include_run_info=True) | ||
| run_id = response["__run"].run_id | ||
| print(run_id)`; | ||
|
|
||
| const chatModelPythonBlock = `from langchain.chat_models import ChatOpenAI | ||
| from langchain.prompts import ChatPromptTemplate | ||
| chat_model = ChatOpenAI() | ||
| prompt = ChatPromptTemplate.from_messages( | ||
| [ | ||
| ("system", "You are a cat"), | ||
| ("human", "Hi"), | ||
| ] | ||
| ) | ||
| res = chat_model.generate(messages=[prompt.format_messages()]) | ||
| res.run[0].run_id`; | ||
|
|
||
| const llmModelPythonBlock = `python | ||
| from langchain.llms import OpenAI | ||
| openai = OpenAI() | ||
| res = openai.generate(["You are a good bot"]) | ||
| print(res.run[0].run_id)`; | ||
| return ( | ||
| <Tabs groupId="client-language"> | ||
| <TabItem key="python" value="python" label="Python"> | ||
| <CodeBlock className="python" language="python"> | ||
| {callbackPythonBlock} | ||
| </CodeBlock> | ||
| <p> | ||
| For older versions of LangChain ({`<`}0.0.276), you can instruct the | ||
| chain to return the run ID by specifying the `include_run_info=True` | ||
| parameter to the call function: | ||
| </p> | ||
| <CodeBlock className="python" language="python"> | ||
| {alternativePythonBlock} | ||
| </CodeBlock> | ||
| <p> | ||
| For python LLMs/chat models, the run information is returned | ||
| automatically when calling the `generate()` method. Example: | ||
| </p> | ||
| <CodeBlock className="python" language="python"> | ||
| {chatModelPythonBlock} | ||
| </CodeBlock> | ||
| <p>or for LLMs</p> | ||
| <CodeBlock className="python" language="python"> | ||
| {llmModelPythonBlock} | ||
| </CodeBlock> | ||
| </TabItem> | ||
| <TabItem key="typescript" value="typescript" label="TypeScript"> | ||
| <CodeBlock className="typescript" language="typescript"> | ||
| {`import { ChatOpenAI } from "langchain/chat_models/openai"; | ||
| import { LLMChain } from "langchain/chains"; | ||
| import { PromptTemplate } from "langchain/prompts";\n | ||
| const prompt = PromptTemplate.fromTemplate("Say hi to {name}"); | ||
| const chain = new LLMChain({ | ||
| llm: new ChatOpenAI(), | ||
| prompt: prompt, | ||
| });\n | ||
| const response = await chain.invoke({ name: "Clara" }); | ||
| console.log(response.__run);`} | ||
| </CodeBlock> | ||
| </TabItem> | ||
| </Tabs> | ||
| ); | ||
| }; |