Merge branch 'shared-process-document-common-refactor' of github.com:phact/gendb into shared-process-document-common-refactor
This commit is contained in:
phact
commit
c6e6ecfcdc
3 changed files with 345 additions and 337 deletions
|
|
@ -38,13 +38,9 @@ The file is loaded into your OpenSearch database, and appears in the Knowledge p
|
||||||
To load and process a directory from the mapped location, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**, and then click **Process Folder**.
|
To load and process a directory from the mapped location, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**, and then click **Process Folder**.
|
||||||
The files are loaded into your OpenSearch database, and appear in the Knowledge page.
|
The files are loaded into your OpenSearch database, and appear in the Knowledge page.
|
||||||
|
|
||||||
### Ingest files through OAuth connectors (#oauth-ingestion)
|
### Ingest files through OAuth connectors {#oauth-ingestion}
|
||||||
|
|
||||||
OpenRAG supports the following enterprise-grade OAuth connectors for seamless document synchronization.
|
OpenRAG supports Google Drive, OneDrive, and AWS S3 as OAuth connectors for seamless document synchronization.
|
||||||
|
|
||||||
- **Google Drive**
|
|
||||||
- **OneDrive**
|
|
||||||
- **AWS**
|
|
||||||
|
|
||||||
OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage.
|
OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -79,8 +79,46 @@ For more information on virtual environments, see [uv](https://docs.astral.sh/uv
|
||||||
Command completed successfully
|
Command completed successfully
|
||||||
```
|
```
|
||||||
|
|
||||||
7. To open the OpenRAG application, click **Open App** or press <kbd>6</kbd>.
|
7. To open the OpenRAG application, click **Open App**, press <kbd>6</kbd>, or navigate to `http://localhost:3000`.
|
||||||
8. Continue with the Quickstart.
|
The application opens.
|
||||||
|
8. Select your language model and embedding model provider, and complete the required fields.
|
||||||
|
**Your provider can only be selected once, and you must use the same provider for your language model and embedding model.**
|
||||||
|
The language model can be changed, but the embeddings model cannot be changed.
|
||||||
|
To change your provider selection, you must restart OpenRAG and delete the `config.yml` file.
|
||||||
|
|
||||||
|
<Tabs groupId="Embedding provider">
|
||||||
|
<TabItem value="OpenAI" label="OpenAI" default>
|
||||||
|
9. If you already entered a value for `OPENAI_API_KEY` in the TUI in Step 5, enable **Get API key from environment variable**.
|
||||||
|
10. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
|
||||||
|
11. To load 2 sample PDFs, enable **Sample dataset**.
|
||||||
|
This is recommended, but not required.
|
||||||
|
12. Click **Complete**.
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="IBM watsonx.ai" label="IBM watsonx.ai">
|
||||||
|
9. Complete the fields for **watsonx.ai API Endpoint**, **IBM API key**, and **IBM Project ID**.
|
||||||
|
These values are found in your IBM watsonx deployment.
|
||||||
|
10. Under **Advanced settings**, select your **Embedding Model** and **Language Model**.
|
||||||
|
11. To load 2 sample PDFs, enable **Sample dataset**.
|
||||||
|
This is recommended, but not required.
|
||||||
|
12. Click **Complete**.
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="Ollama" label="Ollama">
|
||||||
|
9. Enter your Ollama server's base URL address.
|
||||||
|
The default Ollama server address is `http://localhost:11434`.
|
||||||
|
Since OpenRAG is running in a container, you may need to change `localhost` to access services outside of the container. For example, change `http://localhost:11434` to `http://host.docker.internal:11434` to connect to Ollama.
|
||||||
|
OpenRAG automatically sends a test connection to your Ollama server to confirm connectivity.
|
||||||
|
10. Select the **Embedding Model** and **Language Model** your Ollama server is running.
|
||||||
|
OpenRAG automatically lists the available models from your Ollama server.
|
||||||
|
11. To load 2 sample PDFs, enable **Sample dataset**.
|
||||||
|
This is recommended, but not required.
|
||||||
|
12. Click **Complete**.
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
</Tabs>
|
||||||
|
|
||||||
|
13. Continue with the [Quickstart](/quickstart).
|
||||||
|
|
||||||
### Advanced Setup {#advanced-setup}
|
### Advanced Setup {#advanced-setup}
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -11,7 +11,41 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
|
||||||
|
|
||||||
## Prerequisites
|
## Prerequisites
|
||||||
|
|
||||||
- Install and start OpenRAG
|
- [Install and start OpenRAG](/install)
|
||||||
|
- Create a [Langflow API key](https://docs.langflow.org/api-keys-and-authentication)
|
||||||
|
<details>
|
||||||
|
<summary>Create a Langflow API key</summary>
|
||||||
|
|
||||||
|
A Langflow API key is a user-specific token you can use with Langflow.
|
||||||
|
It is **only** used for sending requests to the Langflow server.
|
||||||
|
It does **not** access to OpenRAG.
|
||||||
|
|
||||||
|
To create a Langflow API key, do the following:
|
||||||
|
|
||||||
|
1. In Langflow, click your user icon, and then select **Settings**.
|
||||||
|
2. Click **Langflow API Keys**, and then click <Icon name="Plus" aria-hidden="true"/> **Add New**.
|
||||||
|
3. Name your key, and then click **Create API Key**.
|
||||||
|
4. Copy the API key and store it securely.
|
||||||
|
5. To use your Langflow API key in a request, set a `LANGFLOW_API_KEY` environment variable in your terminal, and then include an `x-api-key` header or query parameter with your request.
|
||||||
|
For example:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Set variable
|
||||||
|
export LANGFLOW_API_KEY="sk..."
|
||||||
|
|
||||||
|
# Send request
|
||||||
|
curl --request POST \
|
||||||
|
--url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
|
||||||
|
--header "Content-Type: application/json" \
|
||||||
|
--header "x-api-key: $LANGFLOW_API_KEY" \
|
||||||
|
--data '{
|
||||||
|
"output_type": "chat",
|
||||||
|
"input_type": "chat",
|
||||||
|
"input_value": "Hello"
|
||||||
|
}'
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
## Find your way around
|
## Find your way around
|
||||||
|
|
||||||
|
|
@ -20,14 +54,18 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
|
||||||
For more information, see [Langflow Agents](/agents).
|
For more information, see [Langflow Agents](/agents).
|
||||||
2. Ask `What documents are available to you?`
|
2. Ask `What documents are available to you?`
|
||||||
The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care.
|
The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care.
|
||||||
|
Knowledge is stored in OpenSearch.
|
||||||
|
For more information, see [Knowledge](/knowledge).
|
||||||
3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
|
3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
|
||||||
The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database. Click on a document to display the chunks derived from splitting the default documents into the vector database.
|
The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database.
|
||||||
|
Click on a document to display the chunks derived from splitting the default documents into the vector database.
|
||||||
|
|
||||||
## Add your own knowledge
|
## Add your own knowledge
|
||||||
|
|
||||||
1. To add documents to your knowledge base, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**.
|
1. To add documents to your knowledge base, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**.
|
||||||
* Select **Add File** to add a single file from your local machine (mapped with the Docker volume mount).
|
* Select **Add File** to add a single file from your local machine (mapped with the Docker volume mount).
|
||||||
* Select **Process Folder** to process an entire folder of documents from your local machine (mapped with the Docker volume mount).
|
* Select **Process Folder** to process an entire folder of documents from your local machine (mapped with the Docker volume mount).
|
||||||
|
* Select your cloud storage provider to add knowledge from an OAuth-connected storage provider. For more information, see [OAuth ingestion](/knowledge#oauth-ingestion).
|
||||||
2. Return to the Chat window and ask a question about your loaded data.
|
2. Return to the Chat window and ask a question about your loaded data.
|
||||||
For example, with a manual about a PC tablet loaded, ask `How do I connect this device to WiFI?`
|
For example, with a manual about a PC tablet loaded, ask `How do I connect this device to WiFI?`
|
||||||
The agent responds with a message indicating it now has your knowledge as context for answering questions.
|
The agent responds with a message indicating it now has your knowledge as context for answering questions.
|
||||||
|
|
@ -40,353 +78,289 @@ If you aren't getting the results you need, you can further tune the knowledge i
|
||||||
To modify the knowledge ingestion or Agent behavior, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
|
To modify the knowledge ingestion or Agent behavior, click <Icon name="Settings2" aria-hidden="true"/> **Settings**.
|
||||||
|
|
||||||
In this example, you'll try a different LLM to demonstrate how the Agent's response changes.
|
In this example, you'll try a different LLM to demonstrate how the Agent's response changes.
|
||||||
|
You can only change the **Language model**, and not the **Model provider** that you started with in OpenRAG.
|
||||||
|
If you're using Ollama, you can use any installed model.
|
||||||
|
|
||||||
1. To edit the Agent's behavior, click **Edit in Langflow**.
|
1. To edit the Agent's behavior, click **Edit in Langflow**.
|
||||||
|
You can more quickly access the **Language Model** and **Agent Instructions** fields in this page, but for illustration purposes, navigate to the Langflow visual builder.
|
||||||
2. OpenRAG warns you that you're entering Langflow. Click **Proceed**.
|
2. OpenRAG warns you that you're entering Langflow. Click **Proceed**.
|
||||||
|
|
||||||
3. The OpenRAG OpenSearch Agent flow appears.
|
3. The OpenRAG OpenSearch Agent flow appears.
|
||||||
|
|
||||||
|
|
||||||
|
4. In the **Language Model** component, under **Model**, select a different OpenAI model.
|
||||||
|
|
||||||
4. In the **Language Model** component, under **Model Provider**, select **Anthropic**.
|
|
||||||
:::note
|
|
||||||
This guide uses an Anthropic model for demonstration purposes. If you want to use a different provider, change the **Model Provider** and **Model Name** fields, and then provide credentials for your selected provider.
|
|
||||||
:::
|
|
||||||
5. Save your flow with <kbd>Command+S</kbd>.
|
5. Save your flow with <kbd>Command+S</kbd>.
|
||||||
6. In OpenRAG, start a new conversation by clicking the <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab.
|
6. In OpenRAG, start a new conversation by clicking the <Icon name="Plus" aria-hidden="true"/> in the **Conversations** tab.
|
||||||
7. Ask the same question as before to demonstrate how a different language model changes the results.
|
7. Ask the same question as before to demonstrate how a different language model changes the results.
|
||||||
|
|
||||||
## Integrate OpenRAG into your application
|
## Integrate OpenRAG into your application
|
||||||
|
|
||||||
:::tip
|
To integrate OpenRAG into your application, use the [Langflow API](https://docs.langflow.org/api-reference-api-examples).
|
||||||
Ensure the `openrag-backend` container has port 8000 exposed in your `docker-compose.yml`:
|
Make requests with Python, TypeScript, or any HTTP client to run one of OpenRAG's default flows and get a response, and then modify the flow further to improve results.
|
||||||
|
|
||||||
```yaml
|
Langflow provides code snippets to help you get started with the Langflow API.
|
||||||
openrag-backend:
|
|
||||||
ports:
|
|
||||||
- "8000:8000"
|
|
||||||
```
|
|
||||||
:::
|
|
||||||
|
|
||||||
OpenRAG provides a REST API that you can call from Python, TypeScript, or any HTTP client to chat with your documents.
|
1. To navigate to the OpenRAG OpenSearch Agent flow, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Edit in Langflow** in the OpenRAG OpenSearch Agent flow.
|
||||||
|
2. Click **Share**, and then click **API access**.
|
||||||
|
|
||||||
These example requests are run assuming OpenRAG is in "no-auth" mode.
|
The default code in the API access pane constructs a request with the Langflow server `url`, `headers`, and a `payload` of request data. The code snippets automatically include the `LANGFLOW_SERVER_ADDRESS` and `FLOW_ID` values for the flow. Replace these values if you're using the code for a different server or flow. The default Langflow server address is http://localhost:7860.
|
||||||
For complete API documentation, including authentication, request and response parameters, and example requests, see the API documentation.
|
|
||||||
|
|
||||||
### Chat with your documents
|
<Tabs>
|
||||||
|
<TabItem value="python" label="Python">
|
||||||
Prompt OpenRAG at the `/chat` API endpoint.
|
|
||||||
|
|
||||||
<Tabs>
|
|
||||||
<TabItem value="python" label="Python">
|
|
||||||
|
|
||||||
```python
|
|
||||||
import requests
|
|
||||||
|
|
||||||
url = "http://localhost:8000/chat"
|
|
||||||
payload = {
|
|
||||||
"prompt": "What documents are available to you?",
|
|
||||||
"previous_response_id": None
|
|
||||||
}
|
|
||||||
|
|
||||||
response = requests.post(url, json=payload)
|
|
||||||
print("OpenRAG Response:", response.json())
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
<TabItem value="typescript" label="TypeScript">
|
|
||||||
|
|
||||||
```typescript
|
|
||||||
import fetch from 'node-fetch';
|
|
||||||
|
|
||||||
const response = await fetch("http://localhost:8000/chat", {
|
|
||||||
method: "POST",
|
|
||||||
headers: { "Content-Type": "application/json" },
|
|
||||||
body: JSON.stringify({
|
|
||||||
prompt: "What documents are available to you?",
|
|
||||||
previous_response_id: null
|
|
||||||
})
|
|
||||||
});
|
|
||||||
|
|
||||||
const data = await response.json();
|
|
||||||
console.log("OpenRAG Response:", data);
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
<TabItem value="curl" label="curl">
|
|
||||||
|
|
||||||
```bash
|
|
||||||
curl -X POST "http://localhost:8000/chat" \
|
|
||||||
-H "Content-Type: application/json" \
|
|
||||||
-d '{
|
|
||||||
"prompt": "What documents are available to you?",
|
|
||||||
"previous_response_id": null
|
|
||||||
}'
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
</Tabs>
|
|
||||||
|
|
||||||
<details closed>
|
|
||||||
<summary>Response</summary>
|
|
||||||
|
|
||||||
```
|
|
||||||
{
|
|
||||||
"response": "I have access to a wide range of documents depending on the context and the tools enabled in this environment. Specifically, I can search for and retrieve documents related to various topics such as technical papers, articles, manuals, guides, knowledge base entries, and other text-based resources. If you specify a particular subject or type of document you're interested in, I can try to locate relevant materials for you. Let me know what you need!",
|
|
||||||
"response_id": "resp_68d3fdbac93081958b8781b97919fe7007f98bd83932fa1a"
|
|
||||||
}
|
|
||||||
```
|
|
||||||
|
|
||||||
</details>
|
|
||||||
|
|
||||||
### Search your documents
|
|
||||||
|
|
||||||
Search your document knowledge base at the `/search` endpoint.
|
|
||||||
|
|
||||||
<Tabs>
|
|
||||||
<TabItem value="python" label="Python">
|
|
||||||
|
|
||||||
```python
|
|
||||||
import requests
|
|
||||||
|
|
||||||
url = "http://localhost:8000/search"
|
|
||||||
payload = {"query": "healthcare data quality", "limit": 5}
|
|
||||||
|
|
||||||
response = requests.post(url, json=payload)
|
|
||||||
results = response.json()
|
|
||||||
|
|
||||||
print("Search Results:")
|
|
||||||
for result in results.get("results", []):
|
|
||||||
print(f"- {result.get('filename')}: {result.get('text', '')[:100]}...")
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
<TabItem value="typescript" label="TypeScript">
|
|
||||||
|
|
||||||
```typescript
|
|
||||||
const response = await fetch("http://localhost:8000/search", {
|
|
||||||
method: "POST",
|
|
||||||
headers: { "Content-Type": "application/json" },
|
|
||||||
body: JSON.stringify({
|
|
||||||
query: "healthcare data quality",
|
|
||||||
limit: 5
|
|
||||||
})
|
|
||||||
});
|
|
||||||
|
|
||||||
const results = await response.json();
|
|
||||||
console.log("Search Results:");
|
|
||||||
results.results?.forEach((result, index) => {
|
|
||||||
const filename = result.filename || 'Unknown';
|
|
||||||
const text = result.text?.substring(0, 100) || '';
|
|
||||||
console.log(`${index + 1}. ${filename}: ${text}...`);
|
|
||||||
});
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
<TabItem value="curl" label="curl">
|
|
||||||
|
|
||||||
```bash
|
|
||||||
curl -X POST "http://localhost:8000/search" \
|
|
||||||
-H "Content-Type: application/json" \
|
|
||||||
-d '{"query": "healthcare data quality", "limit": 5}'
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
</Tabs>
|
|
||||||
|
|
||||||
|
|
||||||
<details closed>
|
|
||||||
<summary>Example response</summary>
|
|
||||||
|
|
||||||
```
|
|
||||||
Found 5 results
|
|
||||||
1. 2506.08231v1.pdf: variables with high performance metrics. These variables might also require fewer replication analys...
|
|
||||||
2. 2506.08231v1.pdf: on EHR data and may lack the clinical domain knowledge needed to perform well on the tasks where EHR...
|
|
||||||
3. 2506.08231v1.pdf: Abstract Large language models (LLMs) are increasingly used to extract clinical data from electronic...
|
|
||||||
4. 2506.08231v1.pdf: these multidimensional assessments, the framework not only quantifies accuracy, but can also be appl...
|
|
||||||
5. 2506.08231v1.pdf: observed in only the model metrics, but not the abstractor metrics, it indicates that model errors m...
|
|
||||||
```
|
|
||||||
|
|
||||||
</details>
|
|
||||||
|
|
||||||
### Use chat and search together
|
|
||||||
|
|
||||||
Create a complete chat application that combines an interactive terminal chat with session continuity and search functionality.
|
|
||||||
|
|
||||||
<Tabs>
|
|
||||||
<TabItem value="python" label="Python">
|
|
||||||
|
|
||||||
```python
|
|
||||||
import requests
|
|
||||||
|
|
||||||
# Configuration
|
|
||||||
OPENRAG_BASE_URL = "http://localhost:8000"
|
|
||||||
CHAT_URL = f"{OPENRAG_BASE_URL}/chat"
|
|
||||||
SEARCH_URL = f"{OPENRAG_BASE_URL}/search"
|
|
||||||
DEFAULT_SEARCH_LIMIT = 5
|
|
||||||
|
|
||||||
def chat_with_openrag(message, previous_response_id=None):
|
|
||||||
try:
|
|
||||||
response = requests.post(CHAT_URL, json={
|
|
||||||
"prompt": message,
|
|
||||||
"previous_response_id": previous_response_id
|
|
||||||
})
|
|
||||||
response.raise_for_status()
|
|
||||||
data = response.json()
|
|
||||||
return data.get("response"), data.get("response_id")
|
|
||||||
except Exception as e:
|
|
||||||
return f"Error: {str(e)}", None
|
|
||||||
|
|
||||||
def search_documents(query, limit=DEFAULT_SEARCH_LIMIT):
|
|
||||||
try:
|
|
||||||
response = requests.post(SEARCH_URL, json={
|
|
||||||
"query": query,
|
|
||||||
"limit": limit
|
|
||||||
})
|
|
||||||
response.raise_for_status()
|
|
||||||
data = response.json()
|
|
||||||
return data.get("results", [])
|
|
||||||
except Exception as e:
|
|
||||||
return []
|
|
||||||
|
|
||||||
# Interactive chat with session continuity and search
|
|
||||||
previous_response_id = None
|
|
||||||
while True:
|
|
||||||
question = input("Your question (or 'search <query>' to search): ").strip()
|
|
||||||
if question.lower() in ['quit', 'exit', 'q']:
|
|
||||||
break
|
|
||||||
if not question:
|
|
||||||
continue
|
|
||||||
|
|
||||||
if question.lower().startswith('search '):
|
```python
|
||||||
query = question[7:].strip()
|
import requests
|
||||||
print("Searching documents...")
|
import os
|
||||||
results = search_documents(query)
|
import uuid
|
||||||
print(f"\nFound {len(results)} results:")
|
|
||||||
for i, result in enumerate(results, 1):
|
api_key = 'LANGFLOW_API_KEY'
|
||||||
filename = result.get('filename', 'Unknown')
|
url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow
|
||||||
text = result.get('text', '')[:100]
|
|
||||||
print(f"{i}. {filename}: {text}...")
|
# Request payload configuration
|
||||||
print()
|
payload = {
|
||||||
else:
|
"output_type": "chat",
|
||||||
print("OpenRAG is thinking...")
|
"input_type": "chat",
|
||||||
result, response_id = chat_with_openrag(question, previous_response_id)
|
"input_value": "hello world!"
|
||||||
print(f"OpenRAG: {result}\n")
|
|
||||||
previous_response_id = response_id
|
|
||||||
```
|
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
<TabItem value="typescript" label="TypeScript">
|
|
||||||
|
|
||||||
```ts
|
|
||||||
import fetch from 'node-fetch';
|
|
||||||
|
|
||||||
// Configuration
|
|
||||||
const OPENRAG_BASE_URL = "http://localhost:8000";
|
|
||||||
const CHAT_URL = `${OPENRAG_BASE_URL}/chat`;
|
|
||||||
const SEARCH_URL = `${OPENRAG_BASE_URL}/search`;
|
|
||||||
const DEFAULT_SEARCH_LIMIT = 5;
|
|
||||||
|
|
||||||
async function chatWithOpenRAG(message: string, previousResponseId?: string | null) {
|
|
||||||
try {
|
|
||||||
const response = await fetch(CHAT_URL, {
|
|
||||||
method: "POST",
|
|
||||||
headers: { "Content-Type": "application/json" },
|
|
||||||
body: JSON.stringify({
|
|
||||||
prompt: message,
|
|
||||||
previous_response_id: previousResponseId
|
|
||||||
})
|
|
||||||
});
|
|
||||||
const data = await response.json();
|
|
||||||
return [data.response || "No response received", data.response_id || null];
|
|
||||||
} catch (error) {
|
|
||||||
return [`Error: ${error}`, null];
|
|
||||||
}
|
}
|
||||||
}
|
payload["session_id"] = str(uuid.uuid4())
|
||||||
|
|
||||||
|
headers = {"x-api-key": api_key}
|
||||||
|
|
||||||
|
try:
|
||||||
|
# Send API request
|
||||||
|
response = requests.request("POST", url, json=payload, headers=headers)
|
||||||
|
response.raise_for_status() # Raise exception for bad status codes
|
||||||
|
|
||||||
|
# Print response
|
||||||
|
print(response.text)
|
||||||
|
|
||||||
|
except requests.exceptions.RequestException as e:
|
||||||
|
print(f"Error making API request: {e}")
|
||||||
|
except ValueError as e:
|
||||||
|
print(f"Error parsing response: {e}")
|
||||||
|
```
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="typescript" label="TypeScript">
|
||||||
|
|
||||||
|
```typescript
|
||||||
|
const crypto = require('crypto');
|
||||||
|
const apiKey = 'LANGFLOW_API_KEY';
|
||||||
|
const payload = {
|
||||||
|
"output_type": "chat",
|
||||||
|
"input_type": "chat",
|
||||||
|
"input_value": "hello world!"
|
||||||
|
};
|
||||||
|
payload.session_id = crypto.randomUUID();
|
||||||
|
|
||||||
|
const options = {
|
||||||
|
method: 'POST',
|
||||||
|
headers: {
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
"x-api-key": apiKey
|
||||||
|
},
|
||||||
|
body: JSON.stringify(payload)
|
||||||
|
};
|
||||||
|
|
||||||
|
fetch('http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', options)
|
||||||
|
.then(response => response.json())
|
||||||
|
.then(response => console.warn(response))
|
||||||
|
.catch(err => console.error(err));
|
||||||
|
```
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
<TabItem value="curl" label="curl">
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl --request POST \
|
||||||
|
--url 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false' \
|
||||||
|
--header 'Content-Type: application/json' \
|
||||||
|
--header "x-api-key: LANGFLOW_API_KEY" \
|
||||||
|
--data '{
|
||||||
|
"output_type": "chat",
|
||||||
|
"input_type": "chat",
|
||||||
|
"input_value": "hello world!",
|
||||||
|
}'
|
||||||
|
```
|
||||||
|
|
||||||
|
</TabItem>
|
||||||
|
</Tabs>
|
||||||
|
|
||||||
async function searchDocuments(query: string, limit: number = DEFAULT_SEARCH_LIMIT) {
|
3. Copy the snippet, paste it in a script file, and then run the script to send the request. If you are using the curl snippet, you can run the command directly in your terminal.
|
||||||
try {
|
|
||||||
const response = await fetch(SEARCH_URL, {
|
If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.
|
||||||
method: "POST",
|
The following is an example of a response from running the **Simple Agent** template flow:
|
||||||
headers: { "Content-Type": "application/json" },
|
|
||||||
body: JSON.stringify({ query, limit })
|
<details>
|
||||||
});
|
<summary>Result</summary>
|
||||||
const data = await response.json();
|
|
||||||
return data.results || [];
|
```json
|
||||||
} catch (error) {
|
{
|
||||||
return [];
|
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"inputs": {
|
||||||
|
"input_value": "hello world!"
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"message": {
|
||||||
|
"text_key": "text",
|
||||||
|
"data": {
|
||||||
|
"timestamp": "2025-06-16 19:58:23 UTC",
|
||||||
|
"sender": "Machine",
|
||||||
|
"sender_name": "AI",
|
||||||
|
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"text": "Hello world! 🌍 How can I assist you today?",
|
||||||
|
"files": [],
|
||||||
|
"error": false,
|
||||||
|
"edit": false,
|
||||||
|
"properties": {
|
||||||
|
"text_color": "",
|
||||||
|
"background_color": "",
|
||||||
|
"edited": false,
|
||||||
|
"source": {
|
||||||
|
"id": "Agent-ZOknz",
|
||||||
|
"display_name": "Agent",
|
||||||
|
"source": "gpt-4o-mini"
|
||||||
|
},
|
||||||
|
"icon": "bot",
|
||||||
|
"allow_markdown": false,
|
||||||
|
"positive_feedback": null,
|
||||||
|
"state": "complete",
|
||||||
|
"targets": []
|
||||||
|
},
|
||||||
|
"category": "message",
|
||||||
|
"content_blocks": [
|
||||||
|
{
|
||||||
|
"title": "Agent Steps",
|
||||||
|
"contents": [
|
||||||
|
{
|
||||||
|
"type": "text",
|
||||||
|
"duration": 2,
|
||||||
|
"header": {
|
||||||
|
"title": "Input",
|
||||||
|
"icon": "MessageSquare"
|
||||||
|
},
|
||||||
|
"text": "**Input**: hello world!"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"type": "text",
|
||||||
|
"duration": 226,
|
||||||
|
"header": {
|
||||||
|
"title": "Output",
|
||||||
|
"icon": "MessageSquare"
|
||||||
|
},
|
||||||
|
"text": "Hello world! 🌍 How can I assist you today?"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"allow_markdown": true,
|
||||||
|
"media_url": null
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",
|
||||||
|
"flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"duration": null
|
||||||
|
},
|
||||||
|
"default_value": "",
|
||||||
|
"text": "Hello world! 🌍 How can I assist you today?",
|
||||||
|
"sender": "Machine",
|
||||||
|
"sender_name": "AI",
|
||||||
|
"files": [],
|
||||||
|
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"timestamp": "2025-06-16T19:58:23+00:00",
|
||||||
|
"flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"error": false,
|
||||||
|
"edit": false,
|
||||||
|
"properties": {
|
||||||
|
"text_color": "",
|
||||||
|
"background_color": "",
|
||||||
|
"edited": false,
|
||||||
|
"source": {
|
||||||
|
"id": "Agent-ZOknz",
|
||||||
|
"display_name": "Agent",
|
||||||
|
"source": "gpt-4o-mini"
|
||||||
|
},
|
||||||
|
"icon": "bot",
|
||||||
|
"allow_markdown": false,
|
||||||
|
"positive_feedback": null,
|
||||||
|
"state": "complete",
|
||||||
|
"targets": []
|
||||||
|
},
|
||||||
|
"category": "message",
|
||||||
|
"content_blocks": [
|
||||||
|
{
|
||||||
|
"title": "Agent Steps",
|
||||||
|
"contents": [
|
||||||
|
{
|
||||||
|
"type": "text",
|
||||||
|
"duration": 2,
|
||||||
|
"header": {
|
||||||
|
"title": "Input",
|
||||||
|
"icon": "MessageSquare"
|
||||||
|
},
|
||||||
|
"text": "**Input**: hello world!"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"type": "text",
|
||||||
|
"duration": 226,
|
||||||
|
"header": {
|
||||||
|
"title": "Output",
|
||||||
|
"icon": "MessageSquare"
|
||||||
|
},
|
||||||
|
"text": "Hello world! 🌍 How can I assist you today?"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"allow_markdown": true,
|
||||||
|
"media_url": null
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"duration": null
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"artifacts": {
|
||||||
|
"message": "Hello world! 🌍 How can I assist you today?",
|
||||||
|
"sender": "Machine",
|
||||||
|
"sender_name": "AI",
|
||||||
|
"files": [],
|
||||||
|
"type": "object"
|
||||||
|
},
|
||||||
|
"outputs": {
|
||||||
|
"message": {
|
||||||
|
"message": "Hello world! 🌍 How can I assist you today?",
|
||||||
|
"type": "text"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"logs": {
|
||||||
|
"message": []
|
||||||
|
},
|
||||||
|
"messages": [
|
||||||
|
{
|
||||||
|
"message": "Hello world! 🌍 How can I assist you today?",
|
||||||
|
"sender": "Machine",
|
||||||
|
"sender_name": "AI",
|
||||||
|
"session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
|
||||||
|
"stream_url": null,
|
||||||
|
"component_id": "ChatOutput-aF5lw",
|
||||||
|
"files": [],
|
||||||
|
"type": "text"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"timedelta": null,
|
||||||
|
"duration": null,
|
||||||
|
"component_display_name": "Chat Output",
|
||||||
|
"component_id": "ChatOutput-aF5lw",
|
||||||
|
"used_frozen_result": false
|
||||||
|
}
|
||||||
|
]
|
||||||
}
|
}
|
||||||
|
]
|
||||||
}
|
}
|
||||||
|
|
||||||
// Interactive chat with session continuity and search
|
|
||||||
let previousResponseId = null;
|
|
||||||
const readline = require('readline');
|
|
||||||
const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
|
|
||||||
|
|
||||||
const askQuestion = () => {
|
|
||||||
rl.question("Your question (or 'search <query>' to search): ", async (question) => {
|
|
||||||
if (question.toLowerCase() === 'quit' || question.toLowerCase() === 'exit' || question.toLowerCase() === 'q') {
|
|
||||||
console.log("Goodbye!");
|
|
||||||
rl.close();
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
if (!question.trim()) {
|
|
||||||
askQuestion();
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (question.toLowerCase().startsWith('search ')) {
|
|
||||||
const query = question.substring(7).trim();
|
|
||||||
console.log("Searching documents...");
|
|
||||||
const results = await searchDocuments(query);
|
|
||||||
console.log(`\nFound ${results.length} results:`);
|
|
||||||
results.forEach((result, i) => {
|
|
||||||
const filename = result.filename || 'Unknown';
|
|
||||||
const text = result.text?.substring(0, 100) || '';
|
|
||||||
console.log(`${i + 1}. ${filename}: ${text}...`);
|
|
||||||
});
|
|
||||||
console.log();
|
|
||||||
} else {
|
|
||||||
console.log("OpenRAG is thinking...");
|
|
||||||
const [result, responseId] = await chatWithOpenRAG(question, previousResponseId);
|
|
||||||
console.log(`\nOpenRAG: ${result}\n`);
|
|
||||||
previousResponseId = responseId;
|
|
||||||
}
|
|
||||||
askQuestion();
|
|
||||||
});
|
|
||||||
};
|
|
||||||
|
|
||||||
console.log("OpenRAG Chat Interface");
|
|
||||||
console.log("Ask questions about your documents. Type 'quit' to exit.");
|
|
||||||
console.log("Use 'search <query>' to search documents directly.\n");
|
|
||||||
askQuestion();
|
|
||||||
```
|
```
|
||||||
|
|
||||||
</TabItem>
|
|
||||||
</Tabs>
|
|
||||||
|
|
||||||
<details closed>
|
|
||||||
<summary>Example response</summary>
|
|
||||||
|
|
||||||
```
|
|
||||||
Your question (or 'search <query>' to search): search healthcare
|
|
||||||
Searching documents...
|
|
||||||
|
|
||||||
Found 5 results:
|
|
||||||
1. 2506.08231v1.pdf: variables with high performance metrics. These variables might also require fewer replication analys...
|
|
||||||
2. 2506.08231v1.pdf: on EHR data and may lack the clinical domain knowledge needed to perform well on the tasks where EHR...
|
|
||||||
3. 2506.08231v1.pdf: Abstract Large language models (LLMs) are increasingly used to extract clinical data from electronic...
|
|
||||||
4. 2506.08231v1.pdf: Acknowledgements Darren Johnson for support in publication planning and management. The authors used...
|
|
||||||
5. 2506.08231v1.pdf: Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted In...
|
|
||||||
|
|
||||||
Your question (or 'search <query>' to search): what's the weather today?
|
|
||||||
OpenRAG is thinking...
|
|
||||||
OpenRAG: I don't have access to real-time weather data. Could you please provide me with your location? Then I can help you find the weather information.
|
|
||||||
|
|
||||||
Your question (or 'search <query>' to search): newark nj
|
|
||||||
OpenRAG is thinking...
|
|
||||||
```
|
|
||||||
|
|
||||||
</details>
|
</details>
|
||||||
## Next steps
|
|
||||||
|
|
||||||
TBD
|
To further explore the API, see:
|
||||||
|
|
||||||
|
* The Langflow [Quickstart](https://docs.langflow.org/quickstart#extract-data-from-the-response) extends this example with extracting fields from the response.
|
||||||
|
* [Get started with the Langflow API](https://docs.langflow.org/api-reference-api-examples)
|
||||||
Loading…
Add table
Reference in a new issue