Update langchain docs (DataDog#18977)

* update langchain docs * add space * Fix tabs for docs build * Formatting fixes * Update langchain/README.md * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Update structure * Update LLM Obs description * Update capitalization * Update langchain/README.md * Update langchain/README.md * Update langchain/README.md * Update langchain/README.md Co-authored-by: Heston Hoffman <[email protected]> * Remove tabs * indentation * Update langchain/README.md Co-authored-by: Barry Eom <[email protected]> * Update langchain/README.md Co-authored-by: Barry Eom <[email protected]> --------- Co-authored-by: hestonhoffman <[email protected]> Co-authored-by: Heston Hoffman <[email protected]> Co-authored-by: Barry Eom <[email protected]>
bhargavnariyanicrest · Nov 14, 2024 · f849fdb · f849fdb
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diff --git a/langchain/README.md b/langchain/README.md
@@ -1,52 +1,150 @@
 # LangChain
 
 ## Overview
+Monitor, troubleshoot, and evaluate your LLM-powered applications (e.g. chatbot, data extraction tool, etc) built using LangChain.
+
+Use LLM Observability to investigate the root cause of issues, monitor operational performance, and evaluate the quality, privacy, and safety of your LLM applications.
+
+See the [LLM Observability tracing view video](https://imgix.datadoghq.com/video/products/llm-observability/expedite-troubleshooting.mp4?fm=webm&fit=max) for an example of how you can investigate a trace.
 
 Get cost estimation, prompt and completion sampling, error tracking, performance metrics, and more out of [LangChain][1] Python library requests using Datadog metrics, APM, and logs.
 
 ## Setup
 
-### Installation
+### LLM Observability: Get end-to-end visibility into your LLM application using LangChain
+You can enable LLM Observability in different environments. Follow the appropriate setup based on your scenario:
+
+#### Installation for Python
+
+##### If you do not have the Datadog Agent:
+1. Install the `ddtrace` package:
+
+   ```shell
+   pip install ddtrace
+   ```
+
+2. Start your application with the following command, enabling Agentless mode:
+
+   ```shell
+    DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_AGENTLESS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME> ddtrace-run python <YOUR_APP>.py
+   ```
+
+##### If you already have the Datadog Agent installed:
+1. Make sure the Agent is running and that APM and StatsD are enabled. For example, use the following command with Docker:
+
+   ```shell
+   docker run -d \
+     --cgroupns host \
+     --pid host \
+     -v /var/run/docker.sock:/var/run/docker.sock:ro \
+     -v /proc/:/host/proc/:ro \
+     -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
+     -e DD_API_KEY=<DATADOG_API_KEY> \
+     -p 127.0.0.1:8126:8126/tcp \
+     -p 127.0.0.1:8125:8125/udp \
+     -e DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true \
+     -e DD_APM_ENABLED=true \
+     gcr.io/datadoghq/agent:latest
+   ```
+
+2. Install the `ddtrace` package if it isn't installed yet:
+
+   ```shell
+   pip install ddtrace
+   ```
+
+3. Start your application using the `ddtrace-run` command to automatically enable tracing:
+
+   ```shell
+     DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME> ddtrace-run python <YOUR_APP>.py
+   ```
+
+**Note**: If the Agent is running on a custom host or port, set `DD_AGENT_HOST` and `DD_TRACE_AGENT_PORT` accordingly.
+
+##### If you are running LLM Observability in a serverless environment (AWS Lambda):
+1. Install the **Datadog-Python** and **Datadog-Extension** Lambda layers as part of your AWS Lambda setup.
+2. Enable LLM Observability by setting the following environment variables:
+
+   ```shell
+    DD_SITE=<YOUR_DATADOG_SITE> DD_API_KEY=<YOUR_API_KEY> DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=<YOUR_ML_APP_NAME>
+   ```
+
+**Note**: In serverless environments, Datadog automatically flushes spans when the Lambda function finishes running.
+
+##### Automatic LangChain Tracing
+
+LangChain integration is automatically enabled when LLM Observability is configured. This captures latency, errors, input/output messages, and token usage for LangChain operations.
+
+The following methods are traced for both synchronous and asynchronous LangChain operations:
+
+- LLMs: `llm.invoke()`, `llm.ainvoke()`
+- Chat Models: `chat_model.invoke()`, `chat_model.ainvoke()`
+- Chains/LCEL: `chain.invoke()`, `chain.ainvoke()`, `chain.batch()`, `chain.abatch()`
+- Embeddings: `OpenAIEmbeddings.embed_documents()`, `OpenAIEmbeddings.embed_query()`
+
+No additional setup is required for these methods.
+
+##### Validation
+
+Validate that LLM Observability is properly capturing spans by checking your application logs for successful span creation. You can also run the following command to check the status of the `ddtrace` integration:
+
+   ```shell
+   ddtrace-run --info
+   ```
+
+Look for the following message to confirm the setup:
+
+   ```
+   Agent error: None
+   ```
+
+##### Debugging
+If you encounter issues during setup, enable debug logging by passing the `--debug` flag:
+
+   ```shell
+   ddtrace-run --debug
+   ```
+
+This will display any errors related to data transmission or instrumentation, including issues with LangChain traces.
+
+### APM: Get Usage Metrics for your python Applications
 
 1. Enable APM and StatsD in your Datadog Agent. For example, in Docker:
 
-```
-docker run -d --cgroupns host \
-              --pid host \
-              -v /var/run/docker.sock:/var/run/docker.sock:ro \
-              -v /proc/:/host/proc/:ro \
-              -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
-              -e DD_API_KEY=<DATADOG_API_KEY> \
-              -p 127.0.0.1:8126:8126/tcp \
-              -p 127.0.0.1:8125:8125/udp \
-              -e DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true \
-              -e DD_APM_ENABLED=true \
-              gcr.io/datadoghq/agent:latest
-```
+   ```shell
+   docker run -d --cgroupns host \
+                 --pid host \
+                 -v /var/run/docker.sock:/var/run/docker.sock:ro \
+                 -v /proc/:/host/proc/:ro \
+                 -v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
+                 -e DD_API_KEY=<DATADOG_API_KEY> \
+                 -p 127.0.0.1:8126:8126/tcp \
+                 -p 127.0.0.1:8125:8125/udp \
+                 -e DD_DOGSTATSD_NON_LOCAL_TRAFFIC=true \
+                 -e DD_APM_ENABLED=true \
+                 gcr.io/datadoghq/agent:latest
+   ```
 
 2. Install the Datadog APM Python library.
 
-```
-pip install ddtrace>=1.17
-```
-
+   ```shell
+   pip install ddtrace>=1.17
+   ```
 
 3. Prefix your LangChain Python application command with `ddtrace-run`.
 
-```
-DD_SERVICE="my-service" DD_ENV="staging" DD_API_KEY=<DATADOG_API_KEY> ddtrace-run python <your-app>.py
-```
+   ```shell
+   DD_SERVICE="my-service" DD_ENV="staging" DD_API_KEY=<DATADOG_API_KEY> ddtrace-run python <your-app>.py
+   ```
 
 **Note**: If the Agent is using a non-default hostname or port, be sure to also set `DD_AGENT_HOST`, `DD_TRACE_AGENT_PORT`, or `DD_DOGSTATSD_PORT`.
 
 See the [APM Python library documentation][2] for more advanced usage.
 
-
-### Configuration
+#### Configuration
 
 See the [APM Python library documentation][3] for all the available configuration options.
 
-
 #### Log Prompt & Completion Sampling
 
 To enable log prompt and completion sampling, set the `DD_LANGCHAIN_LOGS_ENABLED=1` environment variable. By default, 10% of traced requests will emit logs containing the prompts and completions.
@@ -55,36 +153,35 @@ To adjust the log sample rate, see the [APM library documentation][3].
 
 **Note**: Logs submission requires `DD_API_KEY` to be specified when running `ddtrace-run`.
 
-
-### Validation
+#### Validation
 
 Validate that the APM Python library can communicate with your Agent using:
 
-```
-ddtrace-run --info
-```
+   ```shell
+   ddtrace-run --info
+   ```
 
 You should see the following output:
 
-```
-    Agent error: None
-```
+   ```
+       Agent error: None
+   ```
 
 #### Debug Logging
 
 Pass the `--debug` flag to `ddtrace-run` to enable debug logging.
 
-```
-ddtrace-run --debug
-```
+   ```shell
+   ddtrace-run --debug
+   ```
 
 This displays any errors sending data:
 
-```
-ERROR:ddtrace.internal.writer.writer:failed to send, dropping 1 traces to intake at http://localhost:8126/v0.5/traces after 3 retries ([Errno 61] Connection refused)
-WARNING:ddtrace.vendor.dogstatsd:Error submitting packet: [Errno 61] Connection refused, dropping the packet and closing the socket
-DEBUG:ddtrace.contrib._trace_utils_llm.py:sent 2 logs to 'http-intake.logs.datadoghq.com'
-```
+   ```
+   ERROR:ddtrace.internal.writer.writer:failed to send, dropping 1 traces to intake at http://localhost:8126/v0.5/traces after 3 retries ([Errno 61] Connection refused)
+   WARNING:ddtrace.vendor.dogstatsd:Error submitting packet: [Errno 61] Connection refused, dropping the packet and closing the socket
+   DEBUG:ddtrace.contrib._trace_utils_llm.py:sent 2 logs to 'http-intake.logs.datadoghq.com'
+   ```
 
 ## Data Collected
 
@@ -100,7 +197,6 @@ The LangChain integration does not include any events.
 
 The LangChain integration does not include any service checks.
 
-
 ## Troubleshooting
 
 Need help? Contact [Datadog support][5].