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 @@
The following section describes how Arch can help you build faster, smarter and more accurate Retrieval-Augmented Generation (RAG) applications.
-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.
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.
+ 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
+Once the prompt targets are configured as above, handling those parameters is
+ 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)
+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.
x-arch-intent-marker headers to the request before sending it your application servers.
+ 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
-
-
- 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
-
-
- 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)
-
-
-
-
-
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.
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, null]], "Cloud Serving (GPU - Blazing Fast)": [[6, "cloud-serving-gpu-blazing-fast"]], "Concepts": [[13, "concepts"], [21, null]], "Configuration": [[8, 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Fast)": [[6, "local-serving-gpu-fast"]], "Messages": [[7, "messages"]], "Model Serving": [[6, null]], "Monitoring": [[17, null]], "Network topology": [[8, "network-topology"]], "Next Steps": [[14, "next-steps"]], "Observability": [[18, null]], "Overview": [[8, "overview"], [8, "id1"], [13, null], [19, "overview"]], "Parallel & Multiple Function Calling": [[0, "parallel-multiple-function-calling"]], "Parameter Extraction for RAG": [[1, "parameter-extraction-for-rag"]], "Post-request processing": [[8, "post-request-processing"]], "Prerequisites": [[14, "prerequisites"]], "Prompt Guard": [[7, "prompt-guard"], [20, null]], "Prompt Target": [[3, null]], "Prompt Targets": [[7, "prompt-targets"]], "Prompting LLMs": [[7, "prompting-llms"]], "Prompts": [[7, null]], "Quickstart": [[14, null]], "RAG Application": [[1, null]], "Request Flow (Egress)": [[8, "request-flow-egress"]], "Request Flow (Ingress)": [[8, "request-flow-ingress"]], "Request Lifecycle": [[8, null]], "Resources": [[21, null]], 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\ No newline at end of file
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