diff --git a/build_with_arch/rag.html b/build_with_arch/rag.html index edf804df..d15cf1a7 100755 --- a/build_with_arch/rag.html +++ b/build_with_arch/rag.html @@ -155,17 +155,102 @@

RAG Application

The following section describes how Arch can help you build faster, smarter and more accurate Retrieval-Augmented Generation (RAG) applications.

-
-

Intent-drift Detection

-

Developers struggle to handle follow-up or clarification questions. -Specifically, when users ask for changes or additions to previous responses their AI applications often generate entirely new responses instead of adjusting previous ones. -Arch offers intent-drift tracking as a feature so that developers can know when the user has shifted away from a previous intent so that they can dramatically improve retrieval accuracy, lower overall token cost and improve the speed of their responses back to users.

+
+

Parameter Extraction for RAG

+

To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters, +enabling Arch to retrieve critical information in a structured way for processing. This approach improves the +retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull +the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can +streamline data retrieval and processing to build more efficient and precise RAG applications.

+
+

Step 1: Define Prompt Targets

+
+
Prompt Targets
+
 1prompt_targets:
+ 2  - name: get_device_statistics
+ 3    description: Retrieve and present the relevant data based on the specified devices and time range
+ 4
+ 5    path: /agent/device_summary
+ 6    parameters:
+ 7      - name: device_ids
+ 8        type: list
+ 9        description: A list of device identifiers (IDs) to reboot.
+10        required: true
+11      - name: time_range
+12        type: int
+13        description: The number of days in the past over which to retrieve device statistics
+14        required: false
+15        default: 7
+
+
+
+
+
+

Step 2: Process Request Parameters in Flask

+

Once the prompt targets are configured as above, handling those parameters is

+
+
Parameter handling with Flask
+
 1from flask import Flask, request, jsonify
+ 2
+ 3app = Flask(__name__)
+ 4
+ 5
+ 6@app.route("/agent/device_summary", methods=["POST"])
+ 7def get_device_summary():
+ 8    """
+ 9    Endpoint to retrieve device statistics based on device IDs and an optional time range.
+10    """
+11    data = request.get_json()
+12
+13    # Validate 'device_ids' parameter
+14    device_ids = data.get("device_ids")
+15    if not device_ids or not isinstance(device_ids, list):
+16        return jsonify(
+17            {"error": "'device_ids' parameter is required and must be a list"}
+18        ), 400
+19
+20    # Validate 'time_range' parameter (optional, defaults to 7)
+21    time_range = data.get("time_range", 7)
+22    if not isinstance(time_range, int):
+23        return jsonify({"error": "'time_range' must be an integer"}), 400
+24
+25    # Simulate retrieving statistics for the given device IDs and time range
+26    # In a real application, you would query your database or external service here
+27    statistics = []
+28    for device_id in device_ids:
+29        # Placeholder for actual data retrieval
+30        stats = {
+31            "device_id": device_id,
+32            "time_range": f"Last {time_range} days",
+33            "data": f"Statistics data for device {device_id} over the last {time_range} days.",
+34        }
+35        statistics.append(stats)
+36
+37    response = {"statistics": statistics}
+38
+39    return jsonify(response), 200
+40
+41
+42if __name__ == "__main__":
+43    app.run(debug=True)
+
+
+
+
+
+
+

[Coming Soon] Drift Detection via Arch Intent-Markers

+

Developers struggle to efficiently handle follow-up or clarification questions. Specifically, when users ask for +changes or additions to previous responses their AI applications often generate entirely new responses instead of adjusting +previous ones.Arch offers intent tracking as a feature so that developers can know when the user has shifted away from a +previous intent so that they can dramatically improve retrieval accuracy, lower overall token cost and improve the speed of +their responses back to users.

Arch uses its built-in lightweight NLI and embedding models to know if the user has steered away from an active intent. Arch’s intent-drift detection mechanism is based on its’ prompt_targets primtive. Arch tries to match an incoming prompt to one of the prompt_targets configured in the gateway. Once it detects that the user has moved away from an active -active intent, Arch adds the x-arch-intent-drift headers to the request before sending it your application servers.

-
-
Intent Detection Example
+active intent, Arch adds the x-arch-intent-marker headers to the request before sending it your application servers.

+
+
Intent Detection Example
 1@app.route("/process_rag", methods=["POST"])
  2def process_rag():
  3    # Extract JSON data from the request
@@ -328,89 +413,6 @@ improved retrieval, etc. With Arch and a few lines of code, you can improve the
 token cost and dramatically improve the speed of their responses back to users.

-
-

Parameter Extraction for RAG

-

To build RAG (Retrieval-Augmented Generation) applications, you can configure prompt targets with parameters, -enabling Arch to retrieve critical information in a structured way for processing. This approach improves the -retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull -the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can -streamline data retrieval and processing to build more efficient and precise RAG applications.

-
-

Step 1: Define Prompt Targets

-
-
Prompt Targets
-
 1prompt_targets:
- 2  - name: get_device_statistics
- 3    description: Retrieve and present the relevant data based on the specified devices and time range
- 4
- 5    path: /agent/device_summary
- 6    parameters:
- 7      - name: device_ids
- 8        type: list
- 9        description: A list of device identifiers (IDs) to reboot.
-10        required: true
-11      - name: time_range
-12        type: int
-13        description: The number of days in the past over which to retrieve device statistics
-14        required: false
-15        default: 7
-
-
-
-
-
-

Step 2: Process Request Parameters in Flask

-

Once the prompt targets are configured as above, handling those parameters is

-
-
Parameter handling with Flask
-
 1from flask import Flask, request, jsonify
- 2
- 3app = Flask(__name__)
- 4
- 5
- 6@app.route("/agent/device_summary", methods=["POST"])
- 7def get_device_summary():
- 8    """
- 9    Endpoint to retrieve device statistics based on device IDs and an optional time range.
-10    """
-11    data = request.get_json()
-12
-13    # Validate 'device_ids' parameter
-14    device_ids = data.get("device_ids")
-15    if not device_ids or not isinstance(device_ids, list):
-16        return jsonify(
-17            {"error": "'device_ids' parameter is required and must be a list"}
-18        ), 400
-19
-20    # Validate 'time_range' parameter (optional, defaults to 7)
-21    time_range = data.get("time_range", 7)
-22    if not isinstance(time_range, int):
-23        return jsonify({"error": "'time_range' must be an integer"}), 400
-24
-25    # Simulate retrieving statistics for the given device IDs and time range
-26    # In a real application, you would query your database or external service here
-27    statistics = []
-28    for device_id in device_ids:
-29        # Placeholder for actual data retrieval
-30        stats = {
-31            "device_id": device_id,
-32            "time_range": f"Last {time_range} days",
-33            "data": f"Statistics data for device {device_id} over the last {time_range} days.",
-34        }
-35        statistics.append(stats)
-36
-37    response = {"statistics": statistics}
-38
-39    return jsonify(response), 200
-40
-41
-42if __name__ == "__main__":
-43    app.run(debug=True)
-
-
-
-
-
@@ -432,17 +434,17 @@ streamline data retrieval and processing to build more efficient and precise RAG
diff --git a/concepts/tech_overview/prompt.html b/concepts/tech_overview/prompt.html index 527a4ce6..f5a2f8e1 100755 --- a/concepts/tech_overview/prompt.html +++ b/concepts/tech_overview/prompt.html @@ -343,14 +343,14 @@ traffic, apply rate limits, and utilize a large set of traffic management capabi

Attention

When you start Arch, it automatically creates a listener port for egress calls to upstream LLMs. This is based on the llm_providers configuration section in the prompt_config.yml file. Arch binds itself to a local address such as -127.0.0.1:51001/v1.

+127.0.0.1:12000/v1.

Example: Using OpenAI Client with Arch as an Egress Gateway

import openai
 
 # Set the OpenAI API base URL to the Arch gateway endpoint
-openai.api_base = "http://127.0.0.1:51001/v1"
+openai.api_base = "http://127.0.0.1:12000/v1"
 
 # No need to set openai.api_key since it's configured in Arch's gateway
 
diff --git a/concepts/tech_overview/terminology.html b/concepts/tech_overview/terminology.html
index d45289c3..698fd7dd 100755
--- a/concepts/tech_overview/terminology.html
+++ b/concepts/tech_overview/terminology.html
@@ -166,7 +166,7 @@ before forwarding them to your application server endpoints. rch enables you to
 

Note

When you start Arch, you specify a listener address/port that you want to bind downstream. But, Arch uses are predefined port -that you can use (127.0.0.1:10000) to proxy egress calls originating from your application to LLMs (API-based or hosted). +that you can use (127.0.0.1:12000) to proxy egress calls originating from your application to LLMs (API-based or hosted). For more details, check out LLM provider.

Instance: An instance of the Arch gateway. When you start Arch it creates at most two processes. One to handle Layer 7 diff --git a/searchindex.js b/searchindex.js index 6a5bb59f..105aa2aa 100755 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"AI Agent Tracing Visualization Example": [[19, "ai-agent-tracing-visualization-example"]], "AWS X-Ray": [[19, "aws-x-ray"]], "Access Logging": [[16, null]], "Additional Resources": [[19, "additional-resources"]], "Agentic Apps via Prompt Targets": [[7, "agentic-apps-via-prompt-targets"]], "Agentic Workflow": [[0, null]], "Arch-Function": [[15, "arch-function"]], "Arch-Guard": [[20, "arch-guard"]], "Basic Configuration": [[3, "basic-configuration"]], "Benefits of Using Prompt Guard": [[20, "benefits-of-using-prompt-guard"]], "Benefits of Using Traceparent Headers": [[19, "benefits-of-using-traceparent-headers"]], "Best Practices": [[19, "best-practices"]], "Best Practices and Tips": [[4, "best-practices-and-tips"], [15, "best-practices-and-tips"]], "Build with Arch": [[13, "build-with-arch"], [21, 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